Wind-Diesel Systems: A Guide to the Technology and its Implementation

This book provides for the first time in a single volume the collective knowledge of many leading researchers on state-of-the-art wind-diesel technology. It contains the results and advice of numerous experts from many different countries, and has been carefully edited to provide a coherent reference volume. Wind has long been recognised as one of the most promising 'renewable' sources of power, and much development and commercialisation of the technology has taken place in recent years. The first half of the book discusses selection of an appropriate system from the different wind-diesel options available, taking into account the needs of a particular community and the available wind resource. It then goes on to describe in detail how a practical system should be designed. The second half of the book is concerned with getting the best out of a system once it is installed. It starts by presenting case studies to illustrate systems that work excellently, and some that have been disappointing. For complex systems, modelling can be very useful for getting the optimum configuration and these are discussed in a separate chapter. The following chapter discusses the installation, monitoring, and maintenance of winddiesel systems. The final chapter of the book is devoted to the economics of running a wind-diesel system. The book will be useful to all professional engineers and researchers who are interested in wind energy conversion. It is hoped that the collected knowledge of these leading experts will serve to hasten the development and application of winddiesel systems.

Wind-Diesel Systems

Wind-Diesel Systems A Guide to the Technology and its Implementation Prepared under the auspices of the International Energy Agency

Edited by Ray Hunter and George Elliot

CAMBRIDGE UNIVERSITY PRESS

Published by the Press Syndicate of the University of Cambridge The Pitt Building, Trumpington Street, Cambridge CB2 1RP 40 West 20th Street, New York, NY 10011^211, USA 10 Stamford Road, Oakleigh, Melbourne 3166, Australia © Cambridge University Press 1994 First published 1994 A catalogue record for this book is available from the British Library Library of Congress cataloguing in publication data available ISBN 0 521 43440 8 hardback Transferred to digital printing 2004

Contents

Editors' note

ix

Foreword

1

How to use this book

3

Chapter 1

5

Wind-diesel system options Remote power, Simple wind-diesel combinations, Energy storage - the advantages and disadvantages, Load management, Electrical generator options, System architecture options, Nature and size of market

Chapter 2

Matching the wind-diesel system to the community

27

Assessing the consumer load demand, Siting considerations, Climatic conditions, Legal and statutory considerations, Annoyance, Other environmental factors, Community impact Chapter 3

Assessing the wind resource

54

Introduction, Survey of available meteorological information, Inspection and selection of candidate sites, Simple 'Guidelines' terrain model, More sophisticated numerical terrain models, Other evaluation techniques, Wind measurement programme, Decision tree, Glossary Chapter 4

Designing a system System operation, Quality of power, Choice of generators, Choosing a diesel generator set, Wind turbine selection, Dump or auxiliary heat loads, Storage selection, System control, Electrical safety, Load management, Appendix Sample calculations for rating the diesel's generator

95

Contents

vm Chapter 5

Wind-diesel case studies

140

The Froeya wind-diesel demonstration, The Foula electricity scheme, The RAL/ICST wind-diesel research facility Chapter 6

Modelling techniques and model validation

165

Application of models, General description of time series modelling, Statistical modelling techniques, Modelling package summary, Trial validation of models considered by the authors, Guidelines for future validation exercises, Error propagation, Appendix - Simple example of use of statistical modelling, Appendix - Detailed treatment of start/stop cycling in statistical models, Appendix - Uncertainty analysis Chapter 7

Installation and monitoring of wind-diesel systems

209

The importance of verification tests, The pre-installation phase, The assembly and erection phase, Commissioning, Monitoring, Operation and maintenance Chapter 8

Assessing the economics

221

The economic incentives, Cost parameters, Indirect financial considerations of total environmental and social cost, Direct financial considerations, Economic appraisal methodology, Economic assessment methods, Economic assessment example Index

245

Foreword

The International Energy Agency (IEA) was formed as a result of the 1973 oil crisis to provide a means whereby member countries could co-ordinate their energy policies to ensure long term mutual safeguards to continuity of supply. Wind has been recognised for some time as one of the most promising 'renewable' sources of energy, and considerable development and commercialisation of the technology has taken place in recent years. There is in addition growing concern about carbon dioxide emissions and the detrimental effects which result from burning fossil fuels on our planet. The IEA has been active in encouraging interaction within wind technology, and has sponsored two major programmes, one (IEA LS WECS) has supported development of large machines, whilst the other (IEA R&D WECS) has encouraged more generic research. As part of this latter programme, a project was set in motion in 1985 to support research into the problems of integrating wind into small decentralised systems. In June 1985, a group of experts, gathering under the auspices of the IEA, met at the National Engineering Laboratory, UK, to discuss their common interest in the development of wind-diesel technology. A programme of work was set in motion, the formal technical aims of which were: a T o define cost effective models and techniques suitable for obtaining wind and electrical load data necessary for planning and specifying decentralised wind energy conversion system installations', and b T o apply and further develop models suitable for analysing the performance of wind-diesel systems, and to obtain a sound analytical basis for planning and designing wind-diesel systems'. At subsequent meetings, the desire was widely expressed to produce a work of reference which would convey to the wider engineering community the potential difficulties, and stage of development that wind-diesel technology had reached. This book is the result.

2

Foreword

The authors comprise the foremost experts from the ten participating countries, who by discussion and information exchange have agreed upon the contents. The main concentration of wind research in many countries has been to provide supplementary power to main electricity networks. However, many millions of people in the world have no access to grid electricity, and it is on them that wind-diesel systems could have the greatest impact. This book aims to facilitate the development and application of wind-diesel systems by providing in a single volume the collective knowledge and advice of the leading researchers in the field, a group of people with whom it has been our pleasure to work over the past five years.

Ray Hunter and George Elliot (UK) Editors and Operating Agents for the IEA R&D WECS Annex VIII Project, National Wind Turbine Centre, NEL, East Kilbride, Glasgow, G75 OQU, Scotland. May 1992

How to use this book

Wind-diesel technology is at an exciting stage of development. Much research work on sophisticated wind-diesel systems is under way and encouraging performance characteristics have been demonstrated in the laboratory. A number of wind-diesel systems have been installed to serve real loads, and although the earlier ones did not save appreciable quantities of fuel, more recent installations are proving increasingly attractive, particularly those which include load control. The wind-diesel option is now an economic alternative to straightforward diesel at good locations The purpose of this book is twofold. For the interested researcher it sets out in simple terms the state-of-the-art, but secondly, and more importantly, it forms a first attempt at dissemination of knowledge to the wider non-expert community who may wish to consider wind-diesel for remote power applications. In the real world, wind-diesel must justify itself economically, and this book is designed to provide an indication of the tools necessary for an assessment to be made of wind-diesel potential at any given site. In Chapter 1, the reader is introduced to wind-diesel technology and shown by way of example the various system options that the installer may wish to consider. The various system building blocks are specified and possible system architectures are outlined. As for any power supply network, before installing generating plant, it is important to know the nature of the load in terms of its peak and mean values and also any daily or seasonal trends. The quality of power required must also be appraised. Chapter 2 indicates what to take into account. Also in Chapter 2 other community and local considerations are outlined. For instance, how easy would it be to install a turbine? Does the road and craneage infrastructure exist? Is there any likelihood of special grants being available? Are there any areas where the turbine could not be sited due to presence of transmitter stations? What impact will the system have on the community? For a wind power based system, the economic viability is dependent more than anything else upon the host site's wind climate and in particular its mean wind speed. Accurate wind appraisal is seldom simple and Chapter 3 gives a comprehensive overview of the techniques which can be used. Measurement methods are described, as

4

How to use this book

are a number of modelling techniques of varying complexity. A methodical approach to wind appraisal is proposed. Chapter 4 deals with the large number of technical design considerations which are relevant to wind-diesel. The object here is to help the reader decide what his or her particular system should include and what the approximate ratings should be for each element. To help show how these considerations fit together in a real system, a number of practical case studies are presented in Chapter 5. However, it is a somewhat complex matter to optimise the configuration and control of a wind-diesel network. Some degree of modelling is nearly always required, and Chapter 6 gives details of several types of simple model. Chapter 7 deals with the practical issues of installation, operation and reliability, and gives advice on testing, commissioning and monitoring system performance and behaviour. These chapters will therefore be of interest to the researcher, the system designer, the installation engineer, and the end user or operator. Chapter 8 indicates how the economics of a particular system can be assessed using standard economic appraisal techniques. Taken together with Chapters 4 and 6, an iterative optimisation loop can be adopted. Having decided at the outset on a 'sensible' system (Chapter 4), the performance can be assessed (Chapter 6), and the resultant power production estimates used as input to the economic calculations (Chapter 8). Thereafter, the designer can vary the input parameters one by one to see the effect on overall system economics.

1 Wind-diesel system options

REMOTE POWER Historically, until the advent of electricity, all power systems were decentralised, ie power was produced at the location where it was required. In the case of wind power, milling, pumping, and irrigation were popular applications. However, electricity brought about the development of grid networks with centralised generating capacity, and the demise of many decentralised power systems. Today, we tend to forget that there are still many locations in the world which do not have an electrical connection to a central utility network. Furthermore, in many places, due to remoteness and cost, it is unlikely that a main grid connection will ever be established. However the need for power still exists. Power systems which can generate and supply electricity to such remote locations are variously termed 'remote, decentralised, autonomous, or stand-alone'. The purpose of this book is to show that in many locations wind power can usefully be incorporated into, or form the basis of, such systems. Broadly speaking, there are three types of application for remote electrical power, these being: *

Power for specialised applications in remote areas, eg, communications, irrigation.

*

Power to remote communities in industrialised countries, and on islands.

*

Community power generation in developing countries.

Each has its own particular requirements and design constraints, for example system reliability can be more important than cost of power in unmanned locations, whilst communities in industrialised countries quickly develop high expectations of power quality and availability as well as competitiveness, whilst for communities in developing countries simplicity of maintenance is a prime consideration. Diesel networks At present, the most common way to supply electricity to remote loads, whether communities or special applications, is with a diesel engine driving a generator set.

6

Wind-diesel system options

For small loads a single diesel set might be appropriate, whereas for larger communities multiple diesels are commonly employed. In the latter case, one or more diesels, typically the most efficient, supplies the base load. Frequently, enough reserve capacity is provided so that at least one machine can be taken out for overhaul at any given time. Multiple diesel grids can run at high efficiency since it is possible to ensure that all running plant is highly loaded. Diesel manufacturers strongly advise operating above a minimum load, typically 40 per cent, in order to maintain high efficiency since fuel consumption can be significant and therefore wasteful, and to minimise engine wear. However, due to continually changing load patterns and the desire to always meet the demands of the load, many locations have diesels which are incorrectly sized or inefficiently controlled. This quite often results in the diesels not complying with the operational constraints. Nevertheless diesel plants offer reliable long term power supply. The main disadvantage with diesel electric grids is that the cost of power tends to be high, often many times greater than for larger capacity networks. Poor economics are traceable to the cost of diesel fuel, including the cost of transportation which is often the dominant factor and the cost of operation and maintenance in what is usually a remote location. Although diesel networks are simple, they need to be well maintained and like all plant, they need to be periodically replaced either due to unavailability of spare parts, to maintain reliability, or to take advantage of improved efficiency resulting from technological developments. However such factors are often neglected, and there are many ageing, unreliable systems in existence at present which are nearing the end of their useful lives. The major advantage of diesel systems is that they are extremely well proven, and if maintained correctly, highly dependable. The key point is that although diesel electric power is often reliable, it is also expensive. This situation is not likely to change in the future, indeed it is likely that costs will rise. Wind power Although wind power continues to be used for water pumping and irrigation, developments brought about by the oil crises of the 1970's have led to the creation of a new market for wind power, for electricity production. Modern wind turbine generators have been developed using highly complex sophisticated technology and differ from the traditional wind mill or wind pump in that they have few blades, typically two or three. This new generation of wind turbine also has much improved efficiency. Most recent work has concentrated either on developing very large prototype machines with rotor diameters of up to 100 metres and rated up to 3 MW for large utility grids, or on evolving competitive medium sized machines, typically rated at 200-500 kW with rotor diameters of between 25 and 50 m, for use in arrays or wind parks. Such

Wind-diesel system options

7

machines are designed to be integrated into large networks where the wind capacity represents a small proportion of the overall capacity. In grid connected mode wind power has proven itself to be extremely cost effective at good windy sites. Wind parks feeding large grids are now capable of supplying energy at a cost which can compete with more conventional forms of electrical supply such as coal or nuclear. Many places which require remote power are in regions of high wind energy potential, and it might seem strange that initial wind turbine generator developments have not taken place at such locations. The reason for this is primarily due to the great variation in available wind power which occurs from season to season, hour to hour, minute to minute, and indeed second to second. The power in the wind is proportional to the cube of the speed and hence the presence of short term wind speed fluctuations (turbulence) and the frequent passage of weather systems can lead to an extremely variable power availability. This would not be a problem if the load was well correlated to the energy availability, but unfortunately this is not often the case, and to supply all the load from wind would involve either vast excess capacity or alternatively expensive energy storage systems. Electrical requirements at decentralised sites The nature of the electrical load at a decentralised site is of primary importance and depends strongly on the type of electrical apparatus in the system. Two considerations are: *

The variability of the electrical load.

*

The required power quality or 'firmness' of the grid.

In the first case, a community load tends to vary in a more or less regular way over the day, often reaching a maximum sometime during normal waking hours and falling to a low in the very early hours of the morning. As well as the gradual changes, there are also fluctuations of much shorter duration caused by switching in or out large electrical equipment. There may also be substantial changes in the load with the time of week, or with the season of the year. From an economic point of view it is quite important to know if the annual variation in wind energy potential in any way corresponds to that of the load or if there is a substantial mismatch. In the first instance if a good match exists it would be possible to take full advantage of most of the wind energy potential, even from a relatively large machine. However, this might well be at the expense of having to run a diesel inefficiently at a low load. A poor match can give better advantage for certain systems since the diesel can be better loaded when demand is high although with the penalty of using more diesel fuel. The second consideration, the 'firmness' of the supply, is a little less obvious, but quite important nonetheless. In order to properly supply most electrical loads, the voltage

8

Wind-diesel system options

and frequency of the power should stay within appropriate limits. Accomplishing this is usually fairly straightforward when only diesel generators are used. When other sources of power, such as wind machines are added, the problem is more difficult. This is due primarily to the short term fluctuations (in the order of seconds to minutes) in wind speed which cause corresponding variations in wind power generation. Being able to accommodate these fluctuations is a necessary requirement of any wind-diesel system. The potential of wind diesel The obvious solution to the problem of using wind power capacity at a remote location would be to compensate for the variability of the wind by using a diesel electric generator to make up any shortfall. Such a system would have the benefit of using the free wind resource, of saving on existing levels of fuel consumption, and of providing power on demand to meet the consumer load. Ideally the diesel could be used to provide firm power during periods of insufficient wind, and the wind turbine could be used to save diesel fuel when wind energy is plentiful. Such basic systems exist and supply firm power while fuel is being saved. However, as will be shown later some combination of dump load, short term storage, and load control will often improve power quality and fuel savings, but at present not necessarily economics. For example, the use of basic load management techniques can allow excess wind energy to be well used for low quality, high capacity loads such as heating, so helping to enhance the standard and quality of living of the community. Different definitions of 'wind-diesel system' are possible, many of them depending upon total rating of the system. However, for the purposes of this book it will be assumed that a wind-diesel system is one in which the wind energy penetration is sufficiently high to require special control strategies to be adopted to maintain the continuity and quality of the supply. It should always be borne in mind however that wind-diesel systems can only be economic and workable if an adequate wind resource exists. In many applications solar-diesel or hydro-diesel or charge-cycle battery systems might be more viable options.

SIMPLE WIND-DIESEL COMBINATIONS The simplest approach to incorporating wind derived power into a diesel system is to connect a wind turbine to the network in the same way as would be done in a conventional large electric grid. Ideally, operation should be straightforward. When the wind is blowing, the effective load on the diesel(s) is reduced and, if the wind machine is large enough, the diesel(s) may be shut off altogether. The result will be significant but not necessarily very large fuel savings relative to what there would be with no wind turbine in the system. While this approach might seem like a good idea, in reality the situation is not that easy. When large fuel savings are desired, the system must generally contain

Wind-diesel system options

9

a variety of additional components and control systems. Conversely, it must be borne in mind that one of the main concerns is nearly always the reduction of total costs for generating the electrical power. Thus the value of the fuel savings must not be overshadowed by the capital cost of the system. The pattern of wind power availability The pattern of wind power availability has a dramatic impact on the overall economics as well as the design details of any wind-diesel system. The most significant aspect of this pattern is the variability of wind, which occurs on various time scales, ranging from 4 long term' changes (hourly to seasonally) to 'short term' turbulent fluctuations (seconds to minutes). In considering long term variability, it can frequently be desirable from an economic point of view if the long term wind speed patterns are fairly similar to those of the load. For example, a site which has high winds as well as a high load in the winter may be a better candidate for a wind-diesel system (all other things being equal) than one in which the winds are highest in seasons of lowest load, such as on some resort islands. While the correlation between long term wind power and load may significantly affect the economics of a system, it is the short term fluctuations which have the greatest impact on the system design. Most of the problems with the simple wind-diesel combination are traceable to this level of variability in the wind. The rapidly varying wind speed results in correspondingly drastic changes in wind power. This means that the amount of power which can be depended upon over a moderate time interval (an hour, for example) can be much less than the average over that interval. Because of the random nature of wind fluctuations, absolute minimum dependable power levels cannot be guaranteed, but probabilities of exceedence and confidence levels are useful concepts in this regard. Power variability is highly site dependent. It is affected primarily by the local turbulence, which in turn is influenced by the terrain (eg, hills and mountains), the surface conditions (eg, presence of trees), and the climatic conditions. Power variability may also be affected by the type of wind turbine used. Regardless of the cause or the magnitude, the basic point remains the same: the wind power available from a single machine may vary drastically from one minute to the next, even while the average remains relatively constant. The severity of the problem will decrease if a number of wind turbines are used, but it will not disappear. The pattern of diesel loading Because the wind power varies so much, the diesel power must also vary in a complementary way in order to meet a relatively steady load. As long as the maximum instantaneous wind power is less than the load, operational problems should be minimal. The wind turbine will act as a negative load. Thus what the diesel 'sees' is the load less the available wind power. In addition to the variability of the wind, the load itself may

10

Wind-diesel system options

also change dramatically over a short time interval. With progressively smaller systems, the switching on or off of any single item will have an ever more noticeable effect. When the available wind power can exceed the load, the control situation becomes significantly more difficult, as is discussed below. Fuel consumption The impact of the wind turbine on diesel fuel consumption is also not as obvious as it might at first seem. The simplest way to estimate fuel savings would be to determine first how much fuel is required to produce a kWh of electrical energy under rated conditions of the diesel. One might then assume that for every kWh produced by the wind turbine there should be a pro rata drop in diesel fuel consumption. This is not the case, however, because the efficiency of the diesel generator decreases at decreased loadings, causing more fuel to be consumed for a given amount of energy produced. To a first approximation, fuel consumption at no load is 15-30% of the full load value, and the relationship for intervening loads is linear. Note that the no load fuel consumption to rated fuel consumption ratio is engine dependent, with the larger values corresponding to smaller engines. A decrease in power generation will indeed result in a reduction in fuel use, but it is typically closer to 2/3rd of the amount that would be calculated in the simple way described above. The actual fuel saving at decreased loadings depends on such factors as the size, type, and age of the diesel engine. An additional complication is that some engine manufacturers recommend that diesels should not normally be allowed to operate for long periods below some minimum acceptable power level (typically 40 per cent of rated power). This may mean that at times not as much of the available wind power could be used as might be expected. Improvements can be expected in systems with multiple diesels where the ability to switch diesels on and off allows for greater flexibility and possibly greater fuel savings. However, the operational control strategy must be carefully designed to reap the benefits of this option. Furthermore, such strategies must take into account the warm-up time that may be required for the engines. Diesel engine starts and stops If the instantaneous wind power is greater than the load it might be thought desirable to turn off the diesel, thereby saving more fuel, although this would require some modifications to the system architecture. However, unless the load is always less than that supplied by the wind turbine, the diesel would not be able to stay off for long, because the wind power would on occasion drop to less than the load. The effect of trying to stop the diesel whenever the wind power is above a certain level is to create an operating strategy in which the diesel cycles on and off very frequently, possibly hundreds of times in an hour. Such a strategy has little to recommend it. It would put excessive strain on the engine and the starter motor, possibly resulting in much shorter component lifetimes. The rate of cycling could be

Wind-diesel system options

11

reduced by an appropriate start-stop strategy, but this would be at the cost of increased fuel use. In practice, the desirability of maintaining a minimum run time also complicates the issue. It should also be noted that rapid load changes, especially in small grids, may have a similar, and at times more serious, effect than that of the fluctuating wind power. Use of dump loads When the maximum instantaneous wind power is greater than the load (less the minimum acceptable diesel power if the diesel is on), some operational problems may appear. In this case it is at least necessary to provide a method of dissipating the excess power. A number of methods have been advanced to deal with this excess power (in a system with no storage). They are basically of two types: power control and machine control. The first type includes so-called 'dump loads'. These usually comprise resistors which may be controlled by frequency or power sensitive relays or some other form of power converter. When possible, the dumped power is put to some secondary use, such as space or domestic hot water heating. The second method involves power regulation of the wind machine itself. Two of the techniques that have been considered are blade pitch control and generator and hence speed control. The latter can for instance be achieved on machines with synchronous generators by adjusting the field excitation. Use of mechanical systems such as pitch control which is only possible with some wind turbines may also have to be accompanied by a control system capable of a response rapid enough to accommodate drastic power changes, ie a dump load.

ENERGY STORAGE - THE ADVANTAGES AND DISADVANTAGES To minimise the frequent start-stop cycles which would be associated with turning the diesel on and off it may be advantageous to use some kind of short-term energy storage. In addition such storage may be used to ensure continuity of supply or to improve frequency stability. There are four types of short-term storage which show promise for this purpose. They are: batteries, hydraulic accumulators, flywheels, and hydrostatic (pumped) storage. These are described in more detail below. Batteries This type of storage system includes storage batteries, usually lead-acid traction units, together with a rectifier and inverter for power conditioning. So far, this has been the most common type of storage system for wind-diesel applications. (Other types of batteries are discussed in Chapter 4.) Battery technology has the advantage of being well proven, although not in this context where they are being tried out. Batteries are readily available in most parts of the world. The disadvantage is that they are best suited to longer term energy storage, of the order of hours or more. What is really needed to minimise start-stops is a store which contains relatively little energy (a few minutes duration of the rated load), but which has a high power transfer rate. Batteries are not ideally suited to this task. They can be used for short term storage, but in order to keep

12

Wind-diesel system options

the power transfer rates to and from each battery down to acceptable levels, and to maximise battery bank lifetime, a relatively large number of batteries will generally be needed. This can result in high cost. The associated cost of the power converters (for ac systems) will also significantly increase the price of the complete system. Factors which must be considered when batteries are to be used include lifetime, methods for monitoring the state of charge, in-out efficiency, equalisation charging, and maximum charging rates. Lifetime is to a large extent a function of the design and construction of the battery, but in any case it is also strongly dependent on how deeply the battery is regularly discharged. Ensuring a reasonably long life requires a method of assessing the state of charge. This cannot be measured directly. Rather it must be inferred, usually on the basis of measurements of voltage and currents, and the battery's charge/discharge history. The battery must also be charged periodically to higher than a normal level ('equalising charge') to enhance its longevity. One important restriction on batteries is the maximum charging current they can accept. This amount decreases as the state-of charge increases. A 'rule of thumb' for lead-acid batteries is that the charging current in amps should never exceed the ampere hours that are left to be filled. This is important for wind-diesel systems because it means that as the battery level becomes full it can take progressively less of the excess wind power that may be available. Thus some other mechanism must be provided to reduce or dissipate that energy, such as a dump load. Hydraulic/pneumatic accumulators Hydraulic/pneumatic accumulators are devices which allow energy to be stored as compressed gas in a pressure tank and recovered later when needed. The process is facilitated by use of a hydraulic pump/motor which is used to transfer the energy to and from the gas. These are described in more detail in Chapter 4. A prototype system has been installed in Machynlleth, Wales and is described by Slack and Musgrove (1987, 1988). Flywheels Flywheels are storage devices which are particularly well suited to high power applications, but not for holding large amounts of energy. Conceptually, then, they should be a good match to the requirements of wind-diesel systems for smoothing out the turbulence induced power fluctuations. Two types of flywheel storage have been applied to wind-diesel systems and are discussed by Infield, et al. (1988) and Davies, et al. (1987). In the first, and simplest, case the flywheel is attached to the shaft of the diesel's synchronous generator. When a clutch is used, the flywheel must be on the generator side. A power dissipator, such as a dump load, is also generally included for power fluctuations which are too great for the storage to absorb. The system works in the following way: when the wind power exceeds the load by some specified amount, the diesel's generator is disconnected from the engine, which

Wind-diesel system options

13

is assumed to then stop. The synchronous generator and flywheel continue to spin. In order to use the rotating inertia for short term storage, the rotational speed of the dieseFs generator (and hence the grid frequency) is allowed to change within a certain range. The synchronous generator and flywheel accelerate as energy is absorbed and decelerate when energy is delivered back to the system. Generally in this arrangement the flywheel can store the energy equivalent of up to a minute's operation of the wind turbine at rated power. Note that in this scheme one function of the synchronous generator is to supply reactive power to the network (when a wind turbine fitted with an induction generator is used), whether or not the diesel is operating. Thus it must continue spinning at all times if the wind turbine is to continue operating. An advantage of this configuration is that the flywheel can be readily incorporated as a clutch needs to be installed in any case to uncouple the diesel engine from the generator. The disadvantage is that the frequency must be allowed to vary and this may not always be acceptable to the electrical equipment making up the load. Furthermore, space limitations may prevent a flywheel being installed between the diesel engine and its generator. An additional consideration is that special provisions may also be needed to start up a diesel which is coupled to a relatively high inertia flywheel. A second type of flywheel storage which is still at the experimental stage uses a separate flywheel driven by an a.c. motor which operates asynchronously. By allowing large speed variations the amount of energy which can be removed from a given size of flywheel increases dramatically. This is the main advantage. This configuration consists of an ordinary induction generator, the flywheel, and a variable speed regenerative a.c. motor drive. In this system the grid frequency does not have to vary for the stored energy to be used. Disadvantages include the introduction of added complexity and increased cost associated with the power electronics of the motor drive. Pumped storage With this type of storage excess power from the wind turbine(s) is used to pump water into an elevated storage reservoir. When power is required by the load, the stored water passes back from the reservoir through a water turbine, which in turn drives an electric generator. This system has the advantage of including a very controllable storage medium from which energy can be extracted at a high rate. The capital costs are quite high and there are not many locations where such systems could be built. A disadvantage is the overall inefficiency since energy is lost when the water is pumped and again when it passes back through the turbine. Additional losses occur in the piping through which the water must pass. Such a system is in operation on the Island of Foula, in the Shetland Islands of Scotland (Somerville and Stevenson (1987)).

14

Wind-diesel system options

LOAD MANAGEMENT Load management involves adjusting the load rather than the supply, to take best advantage of the available wind power. The possibilities for load management are very dependent on characteristics of the demand at the specific site and these must be examined thoroughly. To be applicable load management requires that some loads be deferrable in time. Heating loads, which incorporate thermal storage are ideal for this. Other applications which are not time critical include water pumping, purification, ice making etc. Load management is discussed in more detail in Chapter 4. The main advantage of load management is that it provides an attractive alternative to regenerative energy storage. The disadvantages are that it cannot always be used and additional capital equipment may be involved. The system will invariably require load control electronics which introduce additional complexity.

ELECTRICAL GENERATOR OPTIONS The types of generators that may be considered for wind-diesel systems include: a) synchronous generators, b) induction generators and c) direct current (d.c.) generators. In some cases they may be combined with power electronics to allow variable speed operation. These different generators and their applications are described in more detail in Chapter 4. As a very broad generalisation most, but by no means all, simple, standalone diesels are fitted with synchronous generators whereas most wind turbines, which are designed primarily to integrate with existing grids, have induction generators.

SYSTEM ARCHITECTURE OPTIONS As shown in Figure 1.1a generalised wind-diesel system consists of the following major components: *

One or more wind turbines.

*

One or more diesel generator sets.

*

A consumer load.

*

An additional controllable or dump load.

*

A storage system.

*

A control unit (including load management).

Actual systems depend on circumstances. One way to describe a scheme is shown in Table 1.1, and is based on a four part classification adapted from Manwell and McGowan (1988). A number of wind-diesel systems from throughout the world which are listed in Table 1.2 have been categorised according to this classification in Table 1.3.

Wind-diesel system options

Wind energy

15

Control Unit

Diesel fuel

Diesel Generator (S)

Wind Turbine Generator (S)

L

Storage Unit

r* -i I Additional i I Controllable I L ad

""__J L__ ° __JJ

Consumer Loads

Fig 1.1 Schematic of generalised wind-diesel systems

Table 1.1 System classification method Wind turbine generator type I II III

Induction Synchronous Induction or synchronous with a.c/d.c./a.c. power conversion

System power control1 A B C D E

None Dump load Storage Load management Turbine rotor

1 2 3 4 5 6

No storage Battery Flywheel Hydraulic/pneumatic Pumped storage End use

a b c d

One WTG/one diesel One WTG/multiple diesels Multiple WTGs/one diesel Multiple WTGs/multiple diesels

Storage

Configuration

Table! .2 List of various real wind-diesel systems Country Australia Brazil Canada ••

Location

Oper. Dates

Rottnest Island 985Fernand de Noronha 19861987AWTS 1986Calvert Island 1987Cambridge Bay Ft. Severn 1985Cape Verde Sal Island 1985•• 1987Sta. Catarina 1987Tarrafal Denmark 1984Riso France Domaine de Las Tours 1987Germany 1987Helogoland Schnittlingen 19831984Greece Kythnos Island Cape Clear Ireland 1985Inis Oirr 1981 Italy Calbria 1986Netherlands ECN 1982Norway Froeya 1989Spain Bujaraloz 1986Sweden Askeskar 1984" Chalmers University 1982Switzerland Martingy 1985UK Fair Isle 19821987Falkland Islands •• 1982Lundy Island " Machynileth 1986•* RAL 1983•i Shetland Islands 1988U.S. Block Islands 1979-1982 Clayton, N. M. 1977-1982

WTG Size (kW) 20, 50, 55 2-5 37.5 2-3 4-25 60 55 55 30 55 10-12 1200 11 5-22 2-30 1 -63 20 2-30 55 25 18.5 22 160 55 10 55 15 16 750kW 150 200

Diesel Size (kW) 100 50 2-50 2 4:380 - 760 85,125,195

Load Range (kW) 90 - 460 200 max 0-100 0.5-3.5 2375 max 50-150 30-90 11-45

Storage

Yes-batteries Yes-batteries Yes-batt./fly Yes-batteries No No Yes-batteries Yes-batteries 125 Yes-water 70 Yes-batt./fly 35 100 max 152 No Yes-water 1000-3000 2-1200 1 -15 No 25 31.4 Yes-batteries Yes-batteries 15-100 65 1-12,1-26,1-44 No Yes-batteries 2-20 Yes-batteries 50 50 15-50 Yes-batteries 50 Yes-water 16 Yes-batteries 8.1 Yes-batt./fly 20 60-80 No 130 No 1-20,1-50 Yes-batteries 10 3-6,1-27 No Yes-hydraulic 10 7 Yes-flywheel 30MW No 1800 max No 1-225,400,500 No 1-400,1700;2-1000; 3-1250 1000-3500

Load Control No No Yes Yes No No Yes Yes Yes Yes No No Yes Yes No No No Yes No No No No Yes Yes Yes Yes Yes No No No

Notes

w/PV

Biogas fuel input

Table 1.3 Architecture classification of various real wind diesel systems Country Australia Brazil Canada •• ••

Location

Rottnest Island Fernand de Noronha AWTS Calvert Island Cambridge Bay Ft. Severn Cape Verde Sal Island Sta. Catarina •• Tarrafal Denmark Riso France Domaine de Las Tours Germany Helogoland •• Schnittlingen Greece Kythnos Island Ireland Cape Clear Inis Oirr Italy Calbria Netherlands ECN Norway Froeya Spain Bujaraloz Sweden Askeskar Chalmers University Switzerland Martingy UK Fair Isle n Falkland Islands Lundy Island Machynileth RAL M Shetland Islands U.S. Block Islands Clayton, N. M. M

Wind Turbine Generator Type 1 1 III 1 II III III 1 1 III II 1 1 II III III 1 II III III 1 II III 1 II II II II II

System Power Control C C B C A A B C D,B B E E B.D B,E C,E D B,C B B,C B B,C B,C B B.D C B,D C B A A A

Storage

Configuration

2 2 2,3 2 1 1 6 2 6 2,3 1 6 1 2 2 1 2 2 2 6 2,6 2,3 1 6 2 6 4 3 1 1 1

d c b c d b

a a a a c b a c c b b c

a a a a a b a b

a a b b b

Wind-diesel system options

18

However, as summarised by McGowan, Manwell, and Connors (1988), another approach to categorising systems is by their basic architecture or configuration. This approach is being adopted by the American Wind Energy Association (1989). Examples of common wind-diesel system types according to this classification are as follows:

Battery storage/cycle charge This type of system is predominately used for smaller applications. As shown in Figure 1.2, it incorporates a diesel generator set and a wind turbine, both fitted with synchronous generators. Most of the power is fed through a rectifier to charge a bank of storage batteries. The load uses either direct current directly or is supplied with alternating current through an inverter. Normally the diesel gen set is only used to charge the batteries and not to supply the load directly. When it operates, it runs near rated power and for a substantial period of time. Its fuel efficiency and wear rate are therefore kept near to the optimum. Other advantages of this type of system include simplicity, reliability and the use of proven components. A major disadvantage is the added cost of having batteries and the reduction in efficiency caused by cycling the energy through the batteries and the power electronics. Such systems are most commonly applied where the chief concern is reliability of the power supply and not where the cost of energy is the primary consideration. A substantial number of systems of this type have been installed in remote telecommunications stations.

Voltage Regulator

Wind turbine

Dump Load

H

DC Load Dist.

Synchronous Generator

System Controller

Diesel Control Voltage Regulator

DC/AC Inverter Synchronous Generator Diesel

Battery Bank

AC Dist

i

Rectifier

Aux. AC loads

Fig 12 A cycle charge wind-diesel system

Auto Transfer Switch

AC Load Dist.

Wind-diesel system options

19

Basic system with no storage This system uses a conventional diesel engine with synchronous generator to supply the consumer demand. It is augmented by a wind turbine, which essentially acts as a negative load. If there is excess power from the wind turbine it may be shunted to a resistive secondary or dump load or otherwise dissipated. The diesel engine is not allowed to stop. When the wind turbine is small in comparison with the load this type of system has the advantage of being relatively simple. On the other hand, when the wind turbine is large, the fraction of useful energy, relative to the total generated energy, may be relatively small. However, total fuel savings might still be significant. Examples of this type of system have been installed at a hotel in Sal, Cape Verde (Hansen, Madsen, and Lundsager, 1986) and at a sewage plant at Martigny, Switzerland which uses biogas to operate the diesel generator. Basic system with flywheel This type is similar to the basic system with no storage, except that the diesel engine is separated from the synchronous generator by a clutch, and a flywheel is connected to the generator. When there is surplus wind power the diesel is stopped and disconnected from its generator. The latter continues to run as a synchronous condenser, providing reactive power to the wind turbine's induction generator. The flywheel/generator's rotating inertia compensates for the wind turbine's power fluctuations and can be used to start-up the diesel when it is needed. In this system the flywheel stores the energy equivalent of between a few seconds to a few minutes of operation at rated power. Examples of this approach are the experimental systems at the Netherlands Energy Research Foundation, ECN (De Bonte, et al.y 1985) and the RIS0 National Laboratory in Denmark (Madsen and Greisen, 1987, Lundsager and Norgaard, 1988). Basic system with batteries This system is the same as the basic system with no storage except that storage batteries and associated power converters are also included. A rectifier is used to charge the batteries when there is excess a.c. power from the wind turbine. A conventional line commutated inverter supplies alternating current back to the grid from the batteries when there is a drop in wind power. The addition of the batteries allows some power to be used which would otherwise be dumped. Basic system with flywheel and batteries A system installed on Cape Verde as part of a United Nations/DANIDA project has been configured to include both mechanical and electrical storage (Hansen, Madsen and Lundsager, 1986).

Wind-diesel system options

20

Dump load control

Consumer Load

Wind turbine

Dump Load

Induction Generator

Synchronous Generator

Diesel Diesel control

Frequency control Synchronous Generator

MicroProcessor

I

Accumulator Buffer Cylinder

Pump/ Motor

Pressure control Displacement control

Fig 13

. J

An hydraulic accumulator wind-diesel system

Basic system with hydraulic accumulator As mentioned earlier and described in more detail in Chapter 4 and by Slack and Musgrove (1987) this type of system includes an hydraulic accumulator to provide short term, high-power energy storage. A schematic diagram of this type of system is shown in Figure 1.3.

Wind Turbine

Voltage Regulator

Generator

•*• Dump Load

Inverter

Rectifier

1

I Battery Voltage Regulator

Diesel

Synchronous Generator

Fig 1.4 An integrated wind-diesel system

Consumer

Wind-diesel system options

21

Integrated wind-diesel system In such integrated systems, various components mentioned in the above systems are amalgamated, so that they can no longer be regarded as discrete units. An example of such a system is the integration of a battery bank into the power electronic conversion system of the wind turbine. As illustrated in Figure 1.4, a system can be designed in which the power from the wind turbine can feed the load directly or alternatively the current can be temporarily diverted and stored in batteries. Although the batteries are fed from an a.c. machine and in turn feed an a.c. load, they do not require their own dedicated rectifier/inverter power electronics as they can make use of those of the wind turbine conversion system. Development of systems of this type has been carried out by the Chalmers University in Sweden (Linders et al.9 1987) and by the Netherlands Energy Research Foundation, ECN(DeBonte,
22

Wind-diesel system options

scheme into a multiple diesel system on the Shetland Islands in Northern Scotland is described by Twidell, et al (1987). Load managed system In a load managed system the capacity of the wind turbine is normally comparable to that of the diesel. The diesel can therefore be shut off for long periods of time, which requires the load demand on the wind turbine to be manipulated to match its fluctuating output. Rather than dump the excess free power, consumer loads which are deferrable such as heating appliances can be brought on or off line by, for instance, frequency sensing switches. Such systems have been pioneered by Somerville (Stevenson & Somerville, 1984) and have been extremely successful. Degree of sophistication As can be seen from the foregoing examples a wide variety of wind-diesel systems are possible. They can also have very different levels of complexity in their design and operation. It is vital when selecting a system for a particular application to consider whether it is appropriate. A system is likely to be most successful where it does not involve a major departure from systems with which the operators are already familiar. For example, a small island in a remote location may already have a diesel generator and someone able to run it. It is suggested however that it may be better in some circumstances when installing a wind diesel system to replace the existing diesel. The wind turbine and associated controls should also be such that they can be kept operating by someone of roughly the same skill level. Special purpose components should be as modular as possible to simplify repair. Larger isolated grids, on the other hand, may have multiple diesels, trained operators and utility personnel with a variety of relatively specialized knowledge available. In such an application a more sophisticated system may be appropriate. It is more important for a system to work satisfactorily than for it to have the latest and most efficient design. The concept should first be proven and accepted. Improvements can always be added later.

NATURE AND SIZE OF MARKET To be economically attractive the fuel savings brought about by installing wind capacity into a diesel system must, over the lifetime of the system, be adequate to more than pay for the additional capital equipment and maintenance that is required. Thus sites which are particularly windy and particularly remote are likely to be ideal hosts to wind-diesel systems. Due to the great diversity of possible system types and end uses, no definitive economic/market analysis for international applications of remote power systems has yet been carried out. However, several sources have cited evidence which indicates that

Wind-diesel system options

23

the potential for such systems is very large, and is likely to grow rapidly in the next few years. Specific examples include the following: a A Canadian paper by Chappell and Templin (1984) observed that more than 800 diesel-generator sets, with a combined installed rating of over 500 MW, currently provide power to about 300 communities and industries in Canada which are not connected to the main electricity utility networks. Rated capacities of these isolated power stations range from less than 100 kW to over 50 MW. b For the continent of Australia, Musgrove (1987) notes that over 12 000 homesteads do not have access to a centralized electricity grid. Even when connection is physically possible, the high costs of transmission lines present a formidable barrier and most of these consumers are dependent on diesel generators. c As reported in Brown (1987), an estimated 580 MW of stand alone diesel systems are now in use in the Third World. They note that diesel generators of 10 kW or less are used to power individual homes and communication systems, while larger generators of several hundred kilowatts or more can run a whole village. d Frost and Sullivan (1983) concluded that remote power sources throughout the world totalled 10.6 gigawatts. Furthermore, they estimated that by 1991 the world market for these sources, including wind, would expand to 37.0 gigawatts. e As noted by Schultz (1988), sales of diesel generator sets rated at up to 500 kW and produced in the United States, totalled 25 906 units in 1986 and increased to almost 27 000 in 1987. Other sources show that there is immense potential for decentralised power systems world-wide. In India, for example, where the government has shown a firm commitment to wind energy, there are around 80 000 villages for which no plans exist to provide a link to the main utility networks. However the rate at which wind-diesel developments are likely to be realised may be limited in many countries due to the high subsidies which diesel fuel currently enjoys. The true cost of electricity must be seen by the owners of the system before wind-diesel is likely to become attractive. For reasons of decentralisation, of limited population, and of wind regime, islands provide ideal potential for wind-diesel systems. There are indications that this market sector is significant. For example, of the 146 inhabited Greek islands, 85 per cent have populations in the range 100 to 100 000 persons with peak load demand being approximately 300 W per capita. The Caribbean Islands also have great potential. In the Bahamas alone, there are 700 inhabited islands, and initial indications show the need for a generating capacity of 200 W per capita. The conclusions that may be drawn from all evidence available is that there is a substantial need for remote power generation sources throughout the world. At the present time, the most common method of supplying these loads is with diesel gener-

24

Wind-diesel system options

ators, but the inclusion of wind energy is potentially a very attractive proposition. The number of situations in which wind energy can be used is still an open question, but it is significant to note that according to Jaras (1987) for the first time in 1986 more than a megawatt of wind turbines for wind-diesel systems were shipped, although in fairness most were installed in large diesel grids. The need and applications for remote power are vast and varied. In the developing world, and in parts of the industrialised world, there are many rural communities still without electricity. In the developing world over 2 billion people are still without power. It is possible that electricity could be provided to a significant proportion of them via decentralised wind-diesel systems. There are many farms and ranches world-wide that currently derive their electricity needs from diesel gen-sets and could introduce wind turbines with little difficulty. Markets also exist throughout the world for high reliability systems in which cost is not the only concern, such as to supply power to telecommunications, navigational beacons or pipeline cathodic protection equipment. Other market opportunities associated with food production that exist in remote locations include pumping, water purification or desalination, and refrigeration systems. Thus, although the market for wind-diesel is difficult to define quantitatively, there is clearly significant scope and opportunity.

ACKNOWLEDGEMENTS This chapter was prepared by Jim Manwell (USA), and Ray Hunter (UK). REFERENCES American Wind Energy Association Wind Diesel Systems Architecture Guidebook (Draft 1989). Brown, L.R., Ed., State of the World 1987, W.W. Norton and Co (1987). Chappell, M.S., and Templin, R.J., 'Canadian Wind Energy Projects', Proc. Sixth BWEA Wind Energy Conference (1984). Davies, T. S., et al., 'The Application of Energy Storage in Wind Energy Conversion', Proc. Fifth Int. Conf. on Energy Options, IEE Conference Publication No. 276 (1987). De Bonte, J. A. N., Klerks, W. M. S., Kraayvanger, A. W., Pierik, J. T. G., and Warmenhoven, A., 'Two Dutch Autonomous Wind Diesel Systems', Windpower 85, AWEA, San Francisco (1985). Frost and Sullivan, 'The World-wide Market for Remote Site Power Systems' (1983).

Wind-diesel system options

25

Hansen, J. C, Madsen, P. H., and Lundsager, P., 'Wind Energy for Electrification in Developing Countries', Proc. European Wind Energy Association Conference (EWEC), Rome (1986). Infield, D. G., et al.., 'A Wind/Diesel System Operating with Flywheel Storage', Proc. 1988 EWEC, Denmark (1988). Jaras, T.F., Wind Energy 1987: Wind Turbine Shipments and Applications, Stadia, Inc., Grezt Falls, Va (1987). Linders, J., Holmblad, L., and Anderson, B., 'Current Progress with the Autonomous Wind-Diesel System at Chalmers University', Proc. BWEA Wind/Diesel Workshop (1987). Lipman, N. H., De Bonte, J. A. N., Lundsager, P., 'An Overview of Wind/Diesel Integration: Operating Strategies and Economic Prospects', Proc. European Wind Energy Association Conf. (EWEC), Rome (1986). Lundsager, P., Norgaard, P., 'The 55/30 kW Experimental Wind-Diesel System at RIS0 National Laboratory', Paper F6, EWEC '88 Conference, Herning, Denmark, June 1988 (Also available as RIS0 report RIS0-M-2717) McGowan, J. G., Manwell, J. F., Connors, S. R., 'Wind/Diesel Systems: Review of Design Options and Recent Developments', Solar Energy, 44, No. 4, (1988). Madsen, H. A., and Greisen, H. 'Results from the experimental wind diesel system at RIS0'. Jan 1986-April 1987. Report RIS0-M-2567, RIS0 National Laboratory, Denmark (1987). Manwell, J. F. and McGowan, J. G., 'Wind/Diesel Energy Systems: Review of System Architectures', CANWEA Wind/Diesel Workshop, PEI, Canada, May (1988). Musgrove, A.R., 'The Optimization of Hybrid Wind Energy Conversion Systems for Remote Area Power Supply', Proc. of Fifth Int. Conf. on Energy Options - The Role of Alternatives in the World Energy Scene, Reading, England, April 1987. Schultz, R., 'The United States Engine Markets in Perspective', Diesel Progress North American, July 1988. Slack, G., and Musgrove, P. J., 'A Wind-Diesel System with Hydraulic Accumulator Energy Buffer', Proc. 1987 BWEA Wind/Diesel Workshop (1987). Slack G., and Musgrove, P.J., 'Field trials of a stand-alone wind system with hydraulic accumulator energy storage'. Proc. 1988 BWEA Wind Energy Conference (1988). Somerville, W. M., and Stevenson, W. G., 'Wind power and microhydro congeneration for isolated communities. Proc. 1987 BWEA Wind Energy Conference (1987). Stevenson, W. G., and Somerville, W. M., 'Optimal use of wind and diesel generation on a remote Scottish island'. Proc. 1984 European Wind Energy Conference, Hamburg (1984). Twidell, J. W., Gardner, P., Halliday, J. A., Anderson, G. A., Holding, N. L., and Lipman, N., 'Wind Generated Power for Shetland: Tactical Planning for the 300

26

Wind-diesel system options MW Peak Autonomous Grid and Diesel/Thermal Plant', Proc. Ninth BWEA Wind Energy Conference (1987). Wilreker, V. R, Stiller, P. H., Scott, G. W., Kruse, V. J., and Smith, R. R, 'Wind Turbine Generator Interaction with Conventional Diesel Generators on Block Island, Rhode Island', Volume 1- Executive Summary, Volume II- Data Analysis, DOE/NASA/0354-1,2, NTIS (1984).

Matching the wind-diesel system to the community

To assess the viability of a proposed decentralised wind energy system, it is essential to obtain knowledge both about the present and anticipated pattern of power and energy use, and also about the meteorological regime and other environmental and community aspects that may be specific to the area and need to be considered.

ASSESSING THE CONSUMER LOAD DEMAND

What data are needed? Consumer demand can best be described on two timescales - long-term (up to 1 year) and short-term (less than 1 minute). Both can have a major impact on the design, viability and operation of decentralised wind-diesel systems. The long-term data will show if the demand changes on a daily, weekly or seasonal basis. This is most important as it is vital to match the available wind energy to the consumer demand requirements. A simple comparison of annual totals will fail to identify this. Often it may be possible to delay particular consumer demands (for example heating and refrigeration loads) by a few hours to obtain a better match between supply and demand. Sometimes there may be a correlation between available wind energy and electricity use. This is particularly true where there is a high heating component to the demand. Long-term data are also important when deciding on the relative ratings of the components of the wind-diesel system. Ideally, the information described in Table 2.1 should be obtained when assessing a site. However it should be stressed that Table 2.1 lists the information which will enable a full and thorough assessment and that in practice it may only be possible to obtain some of the information listed.

Matching the wind-diesel system to the community

28

Table 2.1 Load data to be obtained if possible 1

Hourly values for the day of maximum demand

2

Hourly values for the day of minimum demand

3

Hourly values of a typical weekday (Monday-Friday) for each season (winter, spring, summer and autumn)

4

Hourly values of a typical weekend day (Saturday or Sunday) for each season (winter, spring, summer and autumn)

5

One second data values for the hour of maximum demand for each calendar month (ideally to be split into weekday and weekends if possible)

6

One second data values for the hour of minimum demand for each calendar month (ideally to be split into weekday and weekends, if possible)

Examples of the long term data listed in Table 2.1 are illustrated in Figures 2.1 to 2.5 which relate to a small residential area in Norway for which actual measurements were made in 1989. If some of the consumers use electric space heating, then the load pattern curves ought

160--

10 HOUR OF DAY

Fig 2.1 Hourly mean loads for days of maximum and minimum demand (load data types 1 &2)

Matching the wind-diesel system to the community

29

Winter

120

80

cr LU

o

40-

-4-

20

10 HOUR OF DAY

Fig 2.2 Hourly mean loads for typical weekdays by season (load data type 3)

120-

80-LU Q

cr LU

o

40-.

Q.

-f-

-H

10 HOUR OF DAY

1

1—

20

Fig 23 Hourly mean loads for typical weekend days by season (load data type 4)

Matching the wind-diesel system to the community

30

60.0-• Active power (kW) ^40.0-a

20.0Reactive power

o

(kVAr)

Q_

0.0 18.0

H-

18.2

-f-

18.4 18.6 TIME OF DAY

18.8

19.0

Fig 2.4 Load data (1 sec samples) for 1 hour at peak demand period (load data type 5)

60.0 A c t i v e power

(kW):

2, 40.0

LU

a 20.0o

Reactive power

CL

0.0

(kVArJ

•4-

4.0

4.2

A.A 4.6 TIME OF DAY

4.8

5.0

Fig 2.5 Load data (1 sec) for 1 hour at minimum demand periods (load data type 6)

Matching the wind-diesel system to the community

31

to be well correlated with meteorological measurements, for example temperature and wind speed. This analysis is necessary to estimate peak demand under extreme climatic conditions. Short-term data, on the other hand, usually of timescales less than a minute, are important as they indicate the change in frequency and voltage that is acceptable to the consumer, which in turn can affect the control strategy of the wind-diesel system. Finally, it should be pointed out that industrial and commercial consumers usually have very specific load patterns with specific characteristics and must be treated separately from domestic consumers. Obtaining the required data The first step is to classify all consumers into the following categories: a

Domestic (residential).

b

Schools.

c

Shops.

d

Health and Social.

e

Farms/Fisheries.

/

Small industry.

At the same time customers who will need to be treated individually should be identified. The second step is to obtain information about each category of customer. Such information will include details of: a

The number and geographical distribution of the customers.

b The nature of their demand, for example, does it contain a large reactive power element? Is it liable to change suddenly? Is it dependent upon the prevailing climate in any way? Is any part of it subject to load management? Is there scope for further load management? Can the load be reduced by conservation measures? c The standard of supply currently experienced and expected in the future, as determined by the allowable frequency and voltage variations and number of power outages. All this information can often be obtained by use of a simple questionnaire followed up by selective visits. The third step is to obtain detailed information about the load pattern. If long term measurements are available relevant details can be extracted, but often limited measurements have to be extrapolated as described below: Several countries have investigated the load characteristics and load patterns associated with different kinds of consumers. Such analyses are publicly available from the International Union of Producers and Distributors of Electrical Energy (UNIPEDE),

32

Matching the wind-diesel system to the community

37 Avenue de Friedland, Paris, F75008, France, or from the relevant national authority. The need to measure the detailed load pattern may therefore be reduced if such results are available. However, in such instances it will be necessary to analyse the mix of consumers. If this can be done with precision, and the area consists of a relatively homogeneous group of consumers, separate load measurements will not be needed. If however industrial consumption forms a large share of the total load in an area, the prediction of load profiles based on statistical data and general load measurements will be unreliable. The reason is that almost every consumer in the industrial sector has its own unique load structure. When estimating the need for specific load structure information, it is important to assess the availability of data and also the objectives of the project. Three different levels of load assessment are recommended, depending on the information available. The methods and their outputs are described in Table 2.2. Table 2.2 Levels of available load information Method No.

Available Information

Result (output)

1

Load measurements for the whole area (ie all consumption amalgamated) for a period of one year with a maximum sampling interval of 1 hour

Load structure known in detail

2

Load measurements with a maximum 1 hour sampling interval for the whole area in a recording period of at least 1 month

Load structure known in detail for the recording period. This result by extrapolation will form the basis of an estimate of the load structure for a full year

3

Infrastructural information about consumers (consumer category annual energy consumption and peak load demand) for one or more years

Basis for estimating load structure in the area

The three methods are applicable to all kinds of consumer groups. They give results that provide a basis for estimating annual, seasonal and daily load patterns for each consumer group. The type of heating system used by each, whether electric or nonelectric, will affect the energy demand and peak load. If there is no electrical heating, the demand is almost certain to be constant throughout the year. This phenomenon simplifies the task of basing estimates on the type of data required by Method 2. However, the results from all three methods specify the electrical load structure information from which wind energy system planning can be carried out. The various approaches to the problem of load demand estimation are illustrated in Fig. 2.6.

Matching the wind-diesel system to the community

(0

33

Method 1 Full year of recording

Q

1 year

*

CO

v...

Method 2 Extrapolated short term recording and total annual consumption, combined with peak demand information

1 year Method 3 Total annual consumption plus peak demand information and knowledge of distribution and type of consummer

(0

Recorded Estimated 1 year Fig 2.6 Qualitative illustration of three methods of assessing the load structure of a number of consumers

Extrapolating load data For Methods 2 and 3 in Table 2.2, special techniques for estimating the load structure are necessary. Depending on the customer's heating system, the most commonly used techniques are as follows: Method 2 Where there is electrical space heating, load recordings for a minimum of one month should be compared with simultaneous outdoor temperatures and extrapolated through 12 months by using the consumer category coefficients given in Table 2.3. The coefficients are valid when the monthly outdoor average temperature is below 12°C.

Matching the wind-diesel system to the community

34

Table 23

Temperature related peak energy demand coefficients

Peak demand as a function of changes in the outdoor temperature (typical for buildings in Norway with electric space heating) from report EFI-TR-3411 by Livik (1986). Other countries may have their own coefficients. Consumer

Temperature Dependency W/m2oC

Domestic Schools Shops Health/social Farms/fisheries Small industry

1.4 1.8 0.8 2.0 0.0 0.0.

However if there is no electrical space heating, Table 2.4 and/or Fig. 2.7 should be used in place of the coefficients given in Table 2.3. Each month in this case will have the same load structure. Table 2.4 shows the individual annual demands for electrical energy of the most frequently used domestic appliances in some European countries. For other groups of consumers, reports are being prepared by the UNIPEDE countries.

20

40

60

30

100

120

140

160

NUMBER OF SINGLE HOUSES [n]

Fig 2.7 Peak load demand as a function of number of single houses (Assoc. of Swedish Electric Utilities 1979)

Table 2.4 Average annual domestic consumption per appliance in kWh Some information about consumption of electrical energy in the domestic sector in 10 European countries (UNIPEDE Congress 1988, household Load Curve Studies' ) Country Appliance Refrigeration Freezing Hot water Washing Drying Dishwashing Cooking TV Average number of persons per household Average size of dwelling unit (m ) Average consumption of small appliances Average consumption of lighting

B

CH

D

DK

E

F

NL

N

S

UK

365 550 2000 300 300 470 800 110

350 370 2080 480 590 400 1080 120

470 600 1300 250 270 315 440 160

330 700 2500 490 420 460 750 100

318 1200 127 220 246 292 276

540 700 2100 300 430 600 160

370 500 1800 300 400 475 700 100

450 550 3500 500 500 500 850 200

385 745 375 190 280 575 140

300 755 952 200 300 500 855 254

2.7

2.4

2.2

2.3

3.5

3.2

2.7

2.7

2.2

2.5

82

100

80

107

95

105

85

105

105

94

500

500

368

308

-

160

600

600

660

560

300

472

264

525

321

275

500

1300

635

355

* Includes dual-energy appliances (gas + electricity) B - Belgium, CH - Switzerland, D - Germany, DK - Denmark, E - Spain, F - France, NL - Netherlands, N - Norway, S - Sweden, UK - United Kingdom

36

Matching the wind-dieseI system to the community

Method 3 Where electrical space heating is present, the annual energy consumption should be distributed over the 12 months using Table 2.3. Peak demand should be estimated by using consumer information and equations 2.1 and 2.2 or 2.3 given later. For commercial buildings typical values for peak demand (W/m ) exist for most countries. The total peak load will then be a weighted sum for each commercial building (Table 2.5, Fig. 2.7). If no electric space heating is present Table 2.4 and/or Fig. 2.8 replace the coefficients previously given in Table 2.3. The typical load curve for a day is multiplied by the number of buildings. Peak demand should be estimated from Fig. 2.7 which takes the number of domestic buildings into account. Table 2.5 Peak load demand coefficients for use in equation 2.3 Examples of constants K\ and Ki in Norway (EFI report EFI-TR-3355 by Livik{ 1986}). Country

Consumer

Heating System

K\ (h"1 )

Ki [V(kW/h)J

Norway Sweden Sweden Sweden Sweden

domestic domestic domestic rural commercial

80% electric no electric 100% electric no electric no electric

0.00022 0.00016 0.00027 0.00019 0.00026

0.020 0.117 0.045 0.063 0.095

Figure 2.8 shows typical daily curves which do not include electric space heating for a selection of European countries. Predicting the trends When attempting to predict the trends in load structure, it is important to have typical planning information about, for example, numbers of new buildings, old building clearances and changes in population. All these factors influence directly the prospective demand for energy. It may also be convenient to identify any plans which exist for community space heating systems.

Modelling the electrical load demand The following simple model can be used to predict annual energy demand at time T in the future with reference to known data at a time To (ie, the present time) and predicted changes in the planning parameters over the interval T-To. E(T) = E(T0) + - ^

AA(T-T0) - 7 ^ MC(T-T0)

+ ^ ^ AI(T-T0) + AE (2.1)

Matching the wind-diesel system to the community

[kW] 0.8-

37

Average load curve pr household

Average load curve pr household [kW]

0.6-

1.6

0.4-

0.8-

0.2 n

C

8

16 Time [hours]

24

8

°C

8

Average load curve pr household

16 Time [hours]

16 Time [hours]

Germany (D)

Spain (E)

Average load curve pr household

Average load curve pr household

1.00

8

16 Time [hours] Sweden (S)

24

24

Norway (N)

England & Wales (UK)

Average load curve pr household

16 Time [hours]

8

16 Time [hours] Denmark (DK)

Fig 2.8 Typical load demand day curves (excluding space heating) for selected European countries (UNIPEDE congress 1988)

38

Matching the wind-diesel system to the community

Energy for a year: Cleared Areas: Changes over time: Planning year:

E AC A T

Population: Developed Area: The present time:

/ A To

AE represents other steps in energy demand, arising from, for instance, construction of new commercial buildings or permanent disconnection of important customers. The future peak load demand mainly depends on the development of energy demand. If there are no differences in trends for energy and peak load demand (P), then

Alternatively to estimate the peak load demand, the following formula may be used: P(T) = K\ E(T) + K2 <E(X)

(2.3)

where K\ and K2 in equation 2.3 are constants which have been defined for most countries. Table 2.5 gives examples of coefficients K\ and K2. Each group of consumers is associated with different constants. Domestic consumers show the best fit to equation 2.3. In general, the following restrictions must be observed: *

The consumer group must be homogeneous, implying the formula is also valid for one consumer

*

The formula is valid only for mean annual values

Figure 2.7 shows how the dependency presented as curves in a diagram. The values of K\ and K2 are based on results from Sweden, but note these are not the same as those presented in table 2.5. It should be emphasised that often local factors associated with consumer demand will have a profound effect on the decentralised wind energy system installed. For example on the UK island of Fair Isle two priorities of demand have been set up: high priority for loads such as lights and lower priority for loads which are interruptable, e.g. heating and refrigeration.lt has also been noted from several islands that the presence of a wind turbine has been known to cause expectations of cheap electricity and thus created a massive increase in the projected demand. In some instances the assessment of the consumer demand might reveal that other measures, such as enhanced conservation or replacement of an older diesel generator with a more efficient and better sized set, should be implemented either in conjunction with, or even instead of, wind energy. Or it might be possible to utilise the unused heat from a diesel generator in a mini CHP (Combined Heat and Power) system. Where a wind-diesel system incorporates a dump load then this may be used to provide useful heat to consumers and so reduce the electricity requirement.

Matching the wind-diesel system to the community

39

Inevitably the consumer distribution and load pattern will change during the lifetime of a wind turbine (10-20 years) and provision should be made for this wherever possible when designing the system. The assessment of techniques outlined above will not be applicable in developing countries, where a wind-diesel system is proposed for a region without an existing electricity supply. These must be considered quite differently.

SITING CONSIDERATIONS The erection of a wind turbine will require the developer to consider a number of infrastructure, civil engineering and planning issues.

Access If the proposed wind turbine site is not located next to a road which can support heavy vehicles, improvement of the existing road or even construction of a new road may be necessary for the access of trailers, concrete-trucks and cranes. On narrow or difficult roads (heavy traffic, bad surface, etc.), inspection by an experienced crane driver for possible problem areas is recommended. Access restrictions may require additional or special wind turbine features like tilt down towers, man handleable components etc. Access for movement of equipment at the site during installation is also an important aspect, and the space needed for assembly and erection should be planned in consultation with the wind turbine manufacturer. Transportation of personnel and equipment to remote sites by plane or helicopter may be necessary in some instances. This may also require a special tower design to allow all components to be broken down to a manageable size. Safety of both personnel and equipment during transportation to site, and during site operations must always be considered as early as possible.

Soil types for wind turbine foundations The structural loading on the wind turbine and the type of soil at the site, influence the design of the foundation. An assessment of subsoil (and earthquake) conditions is recommended in order to design the foundation for the particular site. To determine subsoil conditions, a soil test boring should be performed. A soil test can sometimes be performed by hand, and will often be the only option available at a remote site. In every case, the design of the foundation should be carried out by a qualified company overseen by a qualified civil engineer.

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Matching the wind-diesel system to the community

While preparing the foundation, it is prudent to install a lightning protection system and suitable power cable ducting.

Land use and purchase Before selecting and purchasing a site for the wind turbine the developer should find out whether there are any alternative competing or conflicting uses for the land. Wildlife, livestock or soil conservation interests may preclude the development of certain sites. A complete land use plan should include evaluation of the environmental and community impact. If several turbines are to be deployed, the effect of spacing on energy yield should be included. Site inspection and selection of candidate sites with respect to wind resource is covered in Chapter 3. Environmental and community impact is covered later in this chapter. When deciding how close a turbine should be to habitation, it may be important to define a zone which would be safe from blade throw in event of catastrophic failure. Longest possible trajectory of a failed wind turbine blade depends on the hub height and the rotational speed of the blades. At certain sites where icing could be a problem, the safety zone may be defined by ice shedding rather than blade throw. The need for a safety zone should always be considered when siting wind turbines around built-up areas, or near places of work. Further consideration of this is given by Berkhuizen et al. (1986) and AWEA (1987).

CLIMATIC CONDITIONS For a wind turbine, it is clearly important to assess a potential site's wind regime for the purpose of predicting energy yield (this is covered in full in Chapter 3). It is also important however to look at the wind regime from the point of view of structural loading. Other climatic conditions should also not be overlooked since they too can affect the turbine's life or performance.

Extreme winds The wind turbine must be strong enough to withstand the strongest winds to be expected within its design life. Climatic records for the general area can sometimes be consulted to find the strongest wind reported within the period of data collection, and statistical methods can be used to estimate the highest wind to be expected within a 50 year or 100 year period. From this, a corresponding extreme wind load pressure can be computed. The engineer must be satisfied that the wind turbine manufacturer has designed the

Matching the wind-diesel system to the community

41

system to withstand these extreme winds. Local, regional or national building codes may specify how the calculation must be made and what wind speeds are to be assumed. In some countries they often do not require the designer to account for possible topographic enhancement of the extreme values at the site selected. It is becoming more common, when building codes are updated, to include this factor. The methods given in Chapter 3 are recommended for estimating the 'speed-up' factor.

Extreme temperatures Extremely low temperatures may cause materials to become more brittle and less ductile and lubricants to become more viscous, which could result in damage to parts. Such temperatures may also cause electronics or hydraulic systems to cease working. A check of climatic records will indicate whether special materials, lubricants and equipment are necessary. A further factor to be considered is the difficulty of access, installation or maintenance during such adverse conditions. It may be that a finite 'weather window' will have to be identified during which all construction will have to take place. Extremely high temperatures, especially if accompanied by high humidity, can make working conditions very severe. Again, this may be an important factor affecting installation or maintenance. High temperatures also reduce viscosity of lubricants and can increase rates of corrosion. Electronics too may be adversely affected by such temperatures. Electrical apparatus and gear boxes may require to be derated under high temperature operating conditions to avoid overheating.

Solar radiation Over a prolonged period, solar radiation may cause damage to plastic components and paint. Ultraviolet rays can cause embrittlement of plastic materials, but sometimes this can be greatly ameliorated by injecting a dark pigment into the material. On the other hand thermal effects are more of a problem with darker blades.

Relative humidity If the relative humidity is often high, additional sealing of electrical equipment may be necessary to avoid corrosion (or, in winter, damage due to icing). Auxiliary heaters for the drying the generator enclosure and control panels may be advised.

Rain Rain may be beneficial in that it cleans the blades of bugs and other debris, resulting in improved performance. While the rain is falling, however, aerodynamic performance

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may be altered. Electrical equipment needs to be properly waterproofed and fully enclosed. IEC Publication 529 (1977) provides a system for specifying the enclosures of electrical equipment on the basis of the degree of protection provided. Auxiliary heaters may be required (see Relative Humidity, above).

Snow Aerodynamic performance can be degraded by wet snow adhering to the blades. Access may also be particularly difficult during periods of snow. Drifting can restrict access to control panels and block diesel exhausts, air intakes etc. Prudent design can minimise such problems.

Freezing rain or freezing spray When the exposed surfaces of the turbine are below freezing, rain or freezing rain may adhere to the blades causing degradation of performance, damage to parts or a safety hazard due to the shedding of ice or malfunction of aerodynamic control devices. At sites near open water, similar problems may be caused by spray. It is important to be aware that even when the air temperature is above freezing, components of the turbine may be below freezing since they may be slow to warm up after a period of sub-zero temperatures. On moving parts (e.g. the blades), aerodynamic and pressure effects may cause the temperature of the surfaces to fall to below freezing even when the air temperature is slightly above freezing.

Rime icing Rime icing accrues when supercooled water droplets come into contact with a surface. The rate of accretion depends on the liquid water content and the wind speed, both of which may be high at an otherwise ideal windy site. Rime ice tends to accumulate through the cold season, unless a thaw occurs. Results are a degradation in performance, damage, or a safety hazard due to the shedding of ice. Seifert (1989) gives an overview of the mechanisms and the effects of icing.

Thunderstorms These are always accompanied by lightning and often by hail. The former can cause damage to the turbine and/or the electrical components. The latter can cause damage to any exposed surfaces. In areas where thunderstorms are frequent, special care has to be taken to protect the wind turbine against damage by lightning. A copper-ring, or mat connected to the tower, all around the foundation can help considerably, but it has to be kept in mind that 100%

Matching the wind-diesel system to the community

43

protection against a direct hit is not practicable. It may be possible to take out insurance against such events.

Windborne contaminants Dust, sand and salt carried by the wind can cause damage to the wind turbine, the diesel and the control system. Electrical equipment needs to be properly enclosed. Protection levels are set out in IEC publication 529 (1977). LEGAL AND STATUTORY CONSIDERATIONS As for any building, structure, or machine, a wind turbine or a wind-diesel system must adhere to certain legal requirements. Standards Wind-diesel projects may be subject to the following types of standards, both national and international: *

Electrical

*

Construction

*

Diesel equipment

*

Wind energy equipment

Although no standards are specifically directed at wind-diesel equipment, many of the component parts will be covered individually. Safety Safety of wind-diesel installations may be the subject of several types of regulation including: *

National administrative rules on for example, construction, electrical design and installation, and hazards to aviation

*

Regional or local government administrative rules

*

International or national electrical or construction standards

*

National wind energy industry standards

*

Host utility regulations

*

National, regional or local standards on noise and pollution emissions (both of which may be hazardous to health)

In many countries, wind-diesel projects will fall within the jurisdiction of more than one of the above and information should be sought from each prior to construction.

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Matching the wind-diesel system to the community

Reliability and quality of power When a wind turbine for electricity generation is integrated into an existing distribution system, alterations or reinforcements of that grid may be necessary. Analysis of the existing distribution system and planning of possible improvements should be carried out by a qualified consultant. Reliability issues are of paramount importance to the host utility or other purchasing agency and should therefore normally be dealt with in the purchase contract for the wind-diesel system. Currently, there are no minimum performance requirements imposed on the manufacturers of wind-diesel equipment. Wind-diesel systems planned to supply domestic loads should as a minimum provide a power quality matching that found in a modern diesel-only supply. In general, quality of power can be defined by the electricity supply's degree of *

availability

*

continuity

General topics of interest concerning power quality in this context normally include voltage variations during generation, harmonics and transients. For isolated systems, frequency variations are also relevant. The final impact of power quality on the electrical system depends not only on the network itself or for example on the amount of wind generation compared to the local system load, but also on the appliances used by the consumers. It is important for the system to match the current and envisaged local needs. In some situations it may be economically damaging to the project to make provision for unnecessarily high power quality. A more detailed description of power quality in low voltage electricity supplies, which also quantifies acceptable limits, is given in Chapter 4, as are the frequency limits used to determine the accuracy and the quality of control required of the dump load, for different operational modes. For further details, EPRI (1986), Frandsen (1990), and Ballard et al. (1984) should be consulted.

Insurance Insurance requirements may be imposed on wind-diesel projects by amongst others: *

National laws or administrative rules

*

Regional or local government laws or administrative rules

*

Host utility regulations

*

Purchase contract provisions

Matching the wind-diesel system to the community

45

In most countries, wind-diesel projects will fall within the jurisdiction of more than one of the above authorities, and information should be sought from each prior to starting construction. Although insurance for wind turbines can be rather high, especially in the early working years, it should be regarded as essential. As operational experience with wind-diesel systems is limited, insurance coverage for personnel and equipment during installation, operation and maintenance is particularly recommended.

Planning permits National, regional, and local governments may impose planning or zoning requirements under which a formal permit is required before any construction-related activity is begun. Because of the impact of wind turbines on the landscape, it is recommended that any opposition or concern from the public is resolved before installation starts.

Security As for any power generation or distribution plant, wind-diesel systems should be fitted with security equipment to prevent burglary or unauthorised tampering. As a matter of good practice, provision should always be made for first-aid and fire extinguishing. National, regional and local governments may impose specific security requirements, as too may the host utility or other purchasing agency.

Training of personnel Training of personnel engaged in either construction or operation of the wind-diesel plant may be required by: *

National, regional, or local jurisdiction administrative rules

*

International or national electrical or construction standards

*

Host utility regulations

As stated previously, in most countries wind-diesel projects will fall within the remit of more than one the above, and information should be sought from each prior to construction. As for other aspects of installation and operation, while no rules specifically directed at training of wind-diesel operating personnel are as yet under consideration by standards organizations, it is likely that they will be developed in the future. Only qualified personnel should be employed for installing wind turbines.

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Matching the wind-diesel system to the community

Taxes, fees Normally the electricity companies will charge connection and metering fees for grid connected wind turbines. For autonomous wind-diesel systems there is insufficient experience to know what local taxes may apply and this will be governed by the local situation.

Grant aid The possibility of receiving grant aid, and the ease with which it might be obtained is worth investigating at an early stage. If the system is likely to be innovative either in terms of the system, its control, or the novelty of the load being served, then demonstration funds may sometimes be available via for instance the Commission of the European Communities, United Nations, World Bank etc. At a local level, regional development grants or subsidies might be available, but this is clearly highly dependent on region and country.

ANNOYANCE Wind turbines are large pieces of rotating aerodynamic machinery. They can therefore be highly visible and potentially noisy. They can also interfere with electromagnetic transmissions. It is possible however to minimise disturbance.

Visual impact Interference with nature and the landscape in this context depends upon aesthetic factors (design, siting and scenic character) and upon visual amenity. Visual impact is often mentioned as the most negative effect of wind power on the environment. The visual impact of wind turbines is affected by the elevation and openness of the site, the tower height and type, the number and spacing of the machines, the number and rotational speed of the blades, and the colour and finish of the surface coatings. During planning, prior to the beginning of construction, it may be wise to consult a landscape architect. Studies focusing upon aesthetic factors, such as the design and siting of wind turbines, have been carried out in many countries in recent years. A survey carried out by Carlman (1986) in two areas of Sweden, each with a large wind turbine, states that aesthetic factors are considered to be important by the public. Much more important, however, seems to be whether the technology is useful and

Matching the wind-diesel system to the community

47

whether it is generally harmless to the environment and beneficial to society. Dissemination of information to the public from the utilities and authorities, and the active encouragement of community involvement, seem to be important means by which the public's attitudes towards wind turbines in the landscape can be made more favourable. Thayer and Robert (1988) propose procedures for increasing public acceptance of wind farms. Such procedures are also thought to be useful when only a single or a few wind turbines are to be deployed. Further reading on this matter includes Thayer et al. (1989) and Wolsink (1986).

Shadows Nuisance caused by shadow flicker of the wind turbine blades can occur in an area close to the wind turbine on sunny days. Different zones can be computed from the maximum height of the wind turbine, giving different average periods of time per day when nuisance of shadows on houses can occur. It is recommended to position wind turbines in such a way that shadows on houses are avoided. Further details can be found in Berkhuizen et al. (1986). Reflection of sunlight can also give rise to annoying flicker.

Noise/acoustics Wind turbines may be noisy and consideration of this should be made when siting machines close to houses. To avoid noise nuisance to the community from wind turbines and wind-diesel plants, special attention should be paid to international, national, regional or local noise emissions standards. To compute a zone for noise, information on the sound power level of the wind turbine or the wind-diesel plant is needed. Using the requirements given in the standards, the distance between the wind turbine and the nearest habitation necessary to avoid problems can be determined. However further care should be exercised since noise generated by wind turbines has a high degree of impulsivity and tonality. Thus although the noise power of the acoustic emissions may fall within prescribed limits, the nature of the noise may still cause unacceptable intrusion. With the dimensions usually found in medium or large scale machines, it is only when operating that a wind turbine generates noise sources. The noise levels are generally low with well designed wind turbines and cannot

48

Matching the wind-diesel system to the community

usually be heard above the background noise at higher wind speeds. Wind turbine noise can be caused by aerodynamic or mechanical means. Aerodynamic noise is affected by the turbulence spectra and turbulence intensity, by the wind speed and wind shear, and by the number and the design of the blades. In general the fewer the blades a turbine has, the faster it rotates, therefore the noisier it becomes. Also because of tower shadow, downwind horizontal axis machines tend to be noisier than those with upwind rotors. Accordingly, noise measurements under one set of conditions may not be representative of noise levels during other conditions. Nevertheless standard measurement methods have been developed by various testing and standards authorities to enable comparisons to be made. Mechanical noise can be caused by the bearings, blade imbalance, structural resonance, or generator and gears, and depends on the quality of these components. These noise sources are usually of secondary importance in comparison to the aerodynamic sources as they can be reduced by noise insulation methods around the offending component. Integration of a wind turbine into an existing diesel plant may lead to less overall acoustic noise from the system due to reduced running time of the diesel engine which is probably a greater source of noise than the wind turbine. Further details can be found in EPRI (1986), Berkhuizen et al (1986), ISO (1971), Ljunggren (1988), AWEA (1988) and Demichelis et al (1986).

Electromagnetic interference (EMI) Wind turbines can potentially cause electromagnetic interference. Investigations should always be carried out to ascertain whether the wind turbine site is close to transmitters or receivers of electromagnetic signals. In addition to television and radio, investigations into the existence of specialised communication or navigation beacons or other equipment should be undertaken. The most commononly reported electromagnetic interference effect from wind installations concerns conventional TV broadcasting. Normally EMI can be distinguished in two interference zones/regions, each having different scatter and reflection characteristics: *

forward interference region/shadow region/forward scatter zone (beyond the turbine, seen from the transmitter)

*

backward interference region/back scatter zone

A detailed understanding of wind turbines and EMI has still to evolve, but it is clear

Matching the wind-diesel system to the community

49

that by careful design of the wind turbine and by careful siting, problems can be minimised. On behalf of the IEA, Chignell (1986) has published preparatory information on avoiding problems. The information includes details of the mechanisms whereby interference is generated, the relevant wind turbine parameters and of possible effects on radio services. A zone for electromagnetic interference should always be determined for the selected candidate sites, as part of the pre-installation studies. Measurements may be necessary both prior to initial siting of the wind turbines, and after installation. In extreme cases problems can alternatively be alleviated by resiting receivers or by laying a local cable TV network. Further details can be found in EPRI (1986), Berkhuizen et al (1986), Demichelis et al (1986) and Overbeek (1986).

OTHER ENVIRONMENTAL FACTORS Care should always be taken to respect the environment in which the wind-diesel system is to be sited. Careful assessment of potential impacts on nature should be made.

Flora and fauna A number of surveys have been carried out to investigate possible effects of single wind turbines and wind farms on flora and fauna. Here particular attention will be be paid to fauna. In the literature, bird surveys seem to be emphasized. Investigations have been carried out both for migratory birds and birds with their habitat close to wind turbines. The number of recorded cases of injured and dead birds caused by wind turbines is relatively small and does not seem to be higher than what is found for other tall towers, buildings, transmission lines, etc. In the Californian wind parks, particular concern has been expressed about the vulnerability of birds of prey to blade strike who when concentrating on small animals or rodents seem to be oblivious to the presence of the wind turbine. Reports of harmful effects of wind turbines on livestock or wild animals has not been found in the literature, and indeed domestic livestock have often shared the same areas as wind turbines without obvious effect. It must be expected that the flora to some extent will be disturbed or damaged during transportation and installation on a site. Tourist traffic attracted to such areas may also

50

Matching the wind-diesel system to the community

cause disturbance to the flora. It is possible to minimise such disturbances if care is taken in the development and siting process.

Agriculture With careful planning, it should not be necessary to disturb greatly local agricultural activities. Any new access road should wherever possible be routed to avoid destroying arable land. It may be necessary to fence off the installation to prevent cattle or sheep interfering with electrical cables and wind turbine guy wires.

Site restoration In some cases it will be a necessary requirement of local planning to dismantle the wind turbine or wind-diesel system at the end of its operational life. This issue should form part of the complete land use plan.

COMMUNITY IMPACT Although the provision of electricity is the most obvious benefit of a wind-diesel system, it is worthwhile assessing various other effects.

Local socio-economic habits It should be ascertained whether any local socio-economic habits might affect either erection, maintenance or operation of the system. Due to religious customs, it may be difficult to arrange for work to be carried out on a Saturday or Sunday for instance. Alternatively, for a small community with highly seasonal work (e.g. lobster fishing) it may be difficult to recruit local labour at certain times of year.

Employment The presence of a wind-diesel system can have both a direct and indirect effect on local employment. It may often be necessary and advisable to hire and train local labour to look after maintenance and operation of the system. Additionally labour might be required for a shorter period for construction and provision of the civil and electrical infrastructure for the system. Indirectly the improved availability and cost of power that might be expected to result

Matching the wind-diesel system to the community

51

from deployment of a wind-diesel system can encourage the creation and/or expansion of local power dependent industries, and therefore employment. Quality of life At a remote location which might previously have relied upon intermittently available and expensive electricity derived from diesel fuel, the improvements in quality of life that can result from the deployment of a wind-diesel system cannot be underestimated. If the location is in a zone subject to persistent rainfall for instance, a wind-diesel system incorporating load-managed domestic heating can greatly enhance the quality of life by allowing dampness to be eradicated. An excellent illustration of the dramatic effect which a wind-diesel system can have on quality of life is given by Sinclair et al. (1983).

Tourism With the improved quality of life that can result from having a reliable continuous power supply, it becomes easier to provide hotel and other visitor accommodation of a standard which might not normally be expected in remote areas. Particularly where wind power is a relatively new phenomenon, visitors can be attracted to the location specifically to see the installation.

ACKNOWLEDGEMENTS This chapter has been prepared from material supplied by: Malcolm Lodge, John L. Walmsley, Canada 0yvin Skarstein, Trond Toftevaag, Kjetil Uhlen, Norway Francisco Martin, Spain Leo Dubai, Robert Horbaty, Markus Real, Switzerland Jim Halliday, George Elliot, UK Thomas Gray, USA The assistance of Mr Klaus Livik in collating source material for the consumer demand assessment part of this chapter is gratefully acknowledged. REFERENCES American Wind Energy Association (AWEA). Recommended Practice for the Installation of Wind Energy Conversion Systems. Preliminary Draft Document prepared by Installation Sub-committee of AWEA Standards Program, July 1987. American Wind Energy Association (AWEA). Procedure for Measurement of Acoustic Emissions from Wind Energy Conversion Systems. Volume 1 First Tier, Draft 6, 1988

52

Matching the wind-diesel system to the community Association of Swedish Electric Utilities 'Studies of load curves and diversified demand'. Stockholm, Sweden, 1979 (In Swedish). Ballard, L. J., in Swansborough, R. H. (ed) Recommended Practices for Wind Turbine Testing and Evaluation. Part 7, Quality of Power. Single Grid-Connected WECS, IEA. 1st. Edition 1984. Berkhuizen, J.C., Van Den Doel, J. C, Slob, A. F. L., and de Vries, E.T., Estimation of the Wind Energy Potential in the Netherlands taking into Account Environmental Aspects. Research Department, Energie Anders, The Netherlands. Paper no. II. European Wind Energy Association Conference and Exhibition, 7-9 October 1986. Rome, Italy. Carlman, I., Public Opinion on the Use of Wind Power in Sweden. Institute for the Management of Natural Resources. Stockholm University, Sweden. Paper no. 12. European Wind Energy Association Conference and Exhibition. 7-9 October 1986. Rome, Italy. Chignell, R. J., Recommended Practices for Wind Turbine Testing. Part 5, Electromagnetic Interference. Preliminary information. IEA Issue 1 February 1986. Demichelis, R, Fiorina, M, Marini, R., and Truzzi, G., Acoustic Noise and Electromagnetic Interference produced by Wind Generators: Experimental Investigation at an ENEL Wind Power Station in Sardinia. ENEL-centro di Ricerca di Automatica, Milano (Italy). Paper no. 13. European Wind Energy Association Conference and Exhibition, 7-9 October 1986. Rome, Italy. Electric Power Research Institute (EPRI). Guidelines for Testing Wind Turbines EPRI AP-4682. Research Project 1996-25. Final Report, August 1986. Prepared by Southern California Edison Company. Frandsen, S., in Pedersen, B. M. (ed), Recommended Practices for Wind Turbine Testing and Evaluation. Part 2, Power Performance Testing. IEA. 2nd Edition 1990. International Electrotechnical Commission. Publication 529: Specification for classification of degrees of protection provided by enclosures (1977). ISO. Assessment of Noise with Respect to Community Response. ISO Recommendation R-1996, 1971 Livik, K. 'Main Findings of Load Research in Norway between 1981-1985'. Technical Report EFI-TR-3411. The Norwegian Electric Power Research, Trondheim, Norway, 1986. Livik, K. 'Diversified demand in distribution network'. Technical Report EFI-TR3355. The Norwegian Electric Power Research, Trondheim, Norway, 1986. Ljunggren, S., in Gustafsson, A. (ed), Recommended Practices for Wind Turbine Testing and Evaluation. Part 4, Acoustics. Measurement of Noise Emission from Wind Turbines. IEA. 2nd Edition (1988). Overbeek, H.H., Exploitation of a 1 MW Windturbine. Provinciaal Electriciteitsbedrijf van Noord-Holland. Paper no. G3. European Wind Energy Association Conference and Exhibition. 7-9 October 1986. Rome, Italy.

Matching the wind-diesel system to the community Seifert, H., WEC under icing conditions - what has to be considered? Deutsche Forschungs Paper for International Meeting of Test Stations, Brussels, October 1989. Sinclair, B.A., Stevenson, W.G., and Somerville, W. M, Wind Power Generation on Fair Isle. Proc. Energy for Rural and Island Communities III, Inverness, UK (1983). Thayer, R. L., The aesthetics of wind energy in The United States: Case studies in public perception. European Community Wind Energy Conference, Herning, Denmark 6-10 June 1988. Proceedings, pp. 470-476. Thayer, R. L., and Freeman, C. M., Altamont: Public perceptions of a wind energy landscape. Landscape and Urban Planning, 14 (1989) 379-398. UNIPEDE, 'Household Load Curve Studies', UNIPEDE Congress, Sorrento, 1988. Wolsink, M., Public Acceptance of Large WECS in the Netherlands. Department of Environmental Sciences, University of Amsterdam, The Netherlands. Paper no. 15. European Wind Energy Association Conference and Exhibition, 7-9 October 1986. Rome, Italy.

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Assessing the wind resource

INTRODUCTION The main purpose of this chapter is to provide advice on choosing a decentralised wind energy site on the basis of an assessment of its wind resource alone. (Other factors are considered elsewhere in this book.) A secondary purpose is to provide guidance on estimating the wind resource. The end product, which may apply for various time periods (e.g., month, season, year), may be in one or more of the following forms: a b c d e / g

Mean wind speed. Wind speed as a function of wind direction. Wind speed frequency distribution (a function of wind speed class). Joint wind speed frequency distribution (function of direction and class). Weibull parameters of wind speed frequency distribution. Weibull parameters as a function of wind direction. Wind speed time series (sampled typically at 1 Hz) for periods of hours to days.

Items a to/may be derived by a combination of modelling and measurement whilst itemg can only be obtained by measurement. Wind speed information may be converted to a similar description of available wind power which can then be used to select a suitable Wind Turbine Generator (WTG) or evaluate the economics of one that has been previously selected, say to fit the needs of the community, as discussed in Chapter 2. An attempt will be made to take account of factors peculiar to such sites (e.g., limited funding and local turbulence effects). The emphasis, therefore, will be on small WTG's. At times, however, techniques that are perhaps more appropriate for larger systems will be mentioned, in order not to omit ideas of possible interest in decentralised installations. Terms which are described in the Glossary (later in this Chapter) are indicated in bold print. The recommended steps involve several different assessment techniques. These include examination of published data and topographic maps, inspection of sites to verify and update the topographic maps and possibly to screen out the poorer sites, various numerical modelling techniques and wind measurement programmes. As a minimum requirement, some model calculations and/or measurements should

Assessing the wind resource

55

be made before a site is selected. There is often a debate between modellers and experimentalists regarding the most effective way to gain an understanding of the wind flow in a given region, each group tending to argue in favour of its own speciality. In fact, both approaches have advantages and disadvantages. Experience has shown that model calculations have assisted in the design of a field measurement programme, the results of which have in turn been used to verify the model. Each approach has served to enhance and increase confidence in the other. The disadvantages of models are that, of necessity, they are a simplification of the real atmosphere. In each case it has to be ensured that the conditions for which the model is valid are satisfied. The primary advantages of models are that they are quite inexpensive to run, in comparison with field or wind-tunnel measurements. Using models, controlled experiments can be run which are not possible in the field. The fact that models simplify the real situation means that results can usually be readily understood in terms of the physics and dynamics of the atmosphere and comparison of results from different runs can be related to changes in the external parameters or the model formulation itself. Wind-tunnel studies have the advantages that assumptions do not have to be made about the flow and that experiments can be controlled. They can also deal with steep terrain slopes or obstacles to the flow that are difficult to simulate with models. Problems are related to those of scaling the model and its roughness and being able to measure as close to the surface as would be wished. It is also very difficult to simulate thermal effects. Field programmes have the advantage that measurements are being made in the real environment at the actual site. Difficulties arise in interpretation of the data because the external conditions cannot be controlled and are not always adequately known. A measurement programme may have to be carried out for a long time in order to obtain sufficient data for reliable statistics under the desired conditions. There are also questions of spatial variability. It may be difficult to be sure that measurements at one point are representative of a larger area. Finally, a field measurement programme tends to be costly in terms of capital expenditure, operating, maintenance and overtime expenditure and personnel time before, during and after the actual measurement period.

Recommended steps in site selection It is recommended that the following steps be followed when assessing the meteorological factors involved in choosing a site for a small WTG. a b c d

Survey of Available Meteorological Information Inspection and Selection of Candidate Sites Simple 'Guidelines' Terrain Model More Sophisticated Numerical Terrain Models

56 e /

Assessing the wind resource Other Evaluation Techniques Wind Measurement Programme

Each of the above steps is described in more detail below. A summary in the form of a decision tree is presented later in the chapter. A list of references to cited work and a glossary can be found at the end of this chapter. It should be noted that the above are listed in approximately ascending order of complexity and cost. They also progress from general region(s) to specific site(s). In general, one would begin with a number of potential sites as a result of Step (a), and then rank the sites and gradually eliminate the poorer ones by the time Step (d) is completed. Step (e) offers some alternatives to the previous steps that are difficult to rank in terms of complexity or cost. If a final decision has not been reached on the basis of the steps that have so far been completed, then a final selection of the best site (or sites, if more than one is needed) may be made after completion of Step (f). In a given situation, it may not be necessary or possible to invoke all six of the above-mentioned steps. A site may have been selected, for example, by elimination due to other factors. Therefore, one could proceed to Step (0 to obtain wind data needed for an economic evaluation directly after Step (a) or (b). In another example, perhaps a few candidate sites are reduced to one after Step (c). In a third case, possibly two or three potential sites remain after Step (c), but further modelling efforts appear to be more difficult or costly than a measurement programme, Step (f), at the locations still in contention. In still another, enough information is obtained from models to ensure economic viability without a measurement programme. As a final example, a measurecorrelate-predict (MCP) model, Step (e), may be applied following a wind measurement programme, Step (0, at the site(s) of interest. Ideally, however, as many of the steps as possible should be employed, as a good decision on siting a WTG will tend to maximize the socio-economic benefit, minimize the environmental impact and reduce the amount of time needed to recover the capital investment. Before carrying out any assessment programme, it is recommended that a knowledge of the fundamentals of meteorology is gained. There are a number of text books that provide basic background meteorological information. Both Munn (1966) and Panofsky and Dutton (1984), for example, include sections on air flow and turbulence in homogeneous and complex terrain.

SURVEY OF AVAILABLE METEOROLOGICAL INFORMATION Choosing an area that generally has high winds will clearly improve the chances of finding a suitable specific site with high winds. If a general geographic region has not yet been chosen, it may be worthwhile investigating whether any maps exist of wind energy potential. These may already have been prepared by the national weather service or another government department (e.g., Energy). Maps of this type should only be used

Assessing the wind resource

57

to identify broad regions that may be favourable for wind energy. The accuracy of the maps will depend on how typical the available data are which, in turn will depend on how representative the terrain is in which the anemometers have been placed. For this reason, maps that depend on only one data point in a particular region may be suspect. Climatic data for the region(s) under consideration should be consulted. Generally, these will be available from the national weather service and may be biased towards airport sites (ie, relatively flat terrain without major obstructions from trees or buildings); on the other hand the climatic records are likely to extend over all seasons and (in many cases) all hours of the day. If appropriate data are not available from the weather service, then other sources of climatic data should be consulted. These include research institutes, utilities, water boards, forestry commissions, universities, local municipalities and embassies or consulates. Up-to-date anemometer site information should include the height of the anemometer, its position (relative to buildings, trees and other obstructions) and photographs, maps or written information on obstructions to the airflow, small-scale features (such as boulders and depressions) and surface cover. In describing a site, a 'logarithmic' procedure is recommended: photographs to show obstructions out to a range of, say, 200 m, a detailed topographic map (e.g., scale 1:5000 or 1:10,000) or three dimensional diagram for the 200 m - 2 km range and a general map (e.g., a road map or topographic map of scale 1:50,000 or 1:100,000) to show large-scale features such as coastlines and valleys in the 2-20 km range. Information on instrumentation should include the manufacturer's name, model and model number (and, hence, the anemometer accuracy, starting speed and response distance), the recording method and its response time, calibration procedures and maintenance intervals. In the case of wind direction measurements, information on accuracy and response should be available. Other important details are the averaging period and recording frequency (e.g., ten-minute averages every hour), the recording of magnitudes of maximum gusts, whether observations are taken 24 hours per day and whether the data are quality-controlled. The averaging period should preferably be about one hour or less. The recording frequency should be such that both diurnal and annual cycles are adequately resolved. The standard height for wind data is at 10 m above (preferably, flat) terrain, although some winds are measured at non-standard heights (e.g., on tops of buildings, preferably on a mast that is high enough to be above the effect of the building itself on the wind flow). Details of measurement height, nature of terrain and obstacles need to be known in order that the data may be properly evaluated. Climatic wind data should not, by themselves, be used to eliminate possible regions for WTG siting. They may be used, however, to rank areas for further consideration. Climatic wind data may be considered ideal if there are no flow obstructions at the measurement location, if the anemometer is properly maintained, if the data are quality-controlled, if the measurement height is known, if changes to the anemometer

58

Assessing the wind resource

type or location are logged, if there are no serious biases in season or time of day and if the period of record is sufficiently long. We will now consider some characteristics of the climate and attempt to determine what length of record and type of statistics are required. Wind profiles in ideal conditions The variation of wind speed with height in flat terrain in neutrally stratified, steady flow over flat, horizontally homogeneous terrain is described in the Glossary (see logarithmic wind speed profile). In the real world, such conditions are difficult to achieve and profiles that are based on, for example, one 10-min average are often not in agreement with surface-layer theory, even when the topographic constraints are satisfied. Averaging must, therefore, be done over periods of near-steady, near-neutral conditions. Furthermore, the logarithmic relationship must be modified when the atmospheric surface layer is stably or unstably stratified. Conditions that are of main interest for wind energy purposes, however, are the strong-wind situations and these tend to be nearly neutrally stratified in the lowest 100 m over common terrain types, particularly in overcast conditions. It has been common practice to use a power law representation of the vertical variation of wind speed. This formulation is not supported by theory and, therefore, is not recommended.

Seasonal variations At most climatic stations around the world, especially at mid and high latitudes, there is a marked variation in the wind speed and direction regime throughout the year. Wind speeds along the tracks of mid latitude storms, for example, are frequently higher in winter than summer. Some sub-tropical locations may be under the influence of Trade Winds throughout the year. These winds have the reputation of being the most reliable and constant winds of the globe but are subject to interruption by passing hurricanes (called typhoons in the western North Pacific) or tropical storms. The monsoon climates of Africa, Asia and Australasia, on the other hand, exert a strong seasonal influence on the wind regimes of those tropical (and, in some areas, sub-tropical) latitudes. In short, variability is the norm over much of the globe and such variability suggests that the annual mean wind speed may not be very meaningful. In order to gain an impression of the variations in the wind climate during the year, minimum requirements are means and standard deviations (or parameters of the Weibull distribution) preferably on a seasonal basis, but, ideally, monthly. It has been suggested that 5-12 years of data are needed to reduce undue influence of unusual months in the statistics.

Assessing the wind resource

59

Interannual variations For any given month, significant variations in the mean monthly wind speed can occur from one year to another. Even the mean annual wind speed can vary interannually. Probably a minimum of two to five years of data is needed to establish a sufficiently accurate wind climate. Even five years is considered somewhat short by climatologists, but if consideration is given to statistics such as confidence limits on the mean, it is probably sufficient for use in formulae and models that estimate wind speeds at potential WTG sites. Two years of data should be sufficient to give a sample annual mean wind speed within 10 per cent of the long-term mean. This is supported by results that are reproduced in Table 3.1. To reduce the uncertainty to 5 per cent would require about 4-5 years of data. Series shorter than two years should only be used in combination with simultaneous observations at a nearby station for which a long-term series of data is available (the measure-correlate-predict approach). It should be noted that for wind energy purposes the available power, which is proportional to the cube of the wind speed, is the quantity that is of interest. Assuming that its variability (e.g., standard deviation) is proportional to the cube of the variability in wind speed, then a 5 per cent uncertainty in wind speed would give a 16 per cent uncertainty in available power. If this degree of uncertainty in long-term prospects for wind power is unacceptable, then an even longer period of data is required (e.g., from Table 3.1, about 10 years to reduce the uncertainty to 10 per cent). Wind data requirements Generally, a data record sufficiently long to establish reliable seasonal (or, preferably, monthly) statistics is required. Climatic data are sometimes not published until such a requirement is met, but reliability of the information should always be verified before using the data. Ideally, one would like to have a statistic such as 'Mean monthly (scalar) wind speed' stratified into 8,12 or 16 wind direction classes, together with the standard deviation about the mean and frequency of occurrence of different wind speed classes, including calms. Data on frequency of occurrence by wind direction sectors and wind speed classes is needed for evaluation of the two parameters of a Weibull distribution. Information on peak wind speeds and gusts may also be important for assessing stresses on turbine blades. The demand for a large number of wind speed and wind direction classes must be tempered by a requirement to have enough data points in each class for a proper statistical analysis. The results of Table 3.1 indicate that the uncertainty in estimates of long-term mean wind speed is inversely proportional to the square root of the number of observations. Thus, if the data are stratified into, say, five classes with approximately the same number of observations in each class, then one can expect the uncertainty to more than double. Uncertainties that are at the 5 per cent level, approximately, with five years of unstratified data become 11 per cent when stratified into

60

Assessing the wind resource

five classes and 16 per cent when 10 classes are selected. In general, one needs only enough wind speed classes for a given wind direction sector to compute reliable estimates of the two Weibull-distribution parameters. Table 3.1 Uncertainties (%) in estimates of long-term mean wind speed Confidence Interval % 80 90 95 95

Averaging Period

Source

WR83 Cherry 1980 WR83 Cherry 1980

1 mo 24 37 37 44

3 mo 14 21 22 26

6 mo 10 15 15 18

1 yr 7.0 11 11 13

2yr 5.0 7.6 7.6 9.1

5yr 3.1 4.8 4.8 5.7

10 yr 2.2* 3.4 3.4* 4.0

WR83 = Wieringa and Rijkoort (1983), p. 128, reproduced with permission. * = Estimated Cherry's results are computed with mean wind speed of 6 m/s. The following sections will discuss methods of evaluating wind speeds at specific sites. It should be emphasized that the climatic wind data should mainly be used to rank potential sites on the basis of mean speeds, turbulence and frequency of occurrence of the various speed and direction classes. Unless there are physical reasons to suspect strong local winds, the low wind-speed sites could be eliminated at this stage. One must be aware, however, that specific sites can experience speeds more than double those measured at standard weather or climatic stations in the vicinity. The prime use of climatological data will, in fact, be to aid in evaluating (with techniques to be described in following sections) the wind speed regime at specific sites within the same region. Local knowledge The knowledge of local residents should always be considered, as windy sites are often well known and this information can be obtained at low cost simply by spending a few hours interviewing. Local knowledge is often reflected in geographical place names (e.g., 'Windy Point'), which can be gleaned by examination of topographic maps. Information derived by these means, although qualitative in nature, can form a starting point for evaluation of specific candidate sites for WTGs. There is, however, a natural tendency to emphasize freak situations. One should regard quantitative details in the light of possible exaggeration of the magnitude of the wind speed, the frequency of occurrence, the duration, or all three! Turbulence data Detailed turbulence data are normally not routinely available, at least not in a form from which turbulence intensity can be computed. Turbulence intensity, /M, is defined as the ratio of the wind speed's standard deviation to its mean over a period of 10 minutes.

Assessing the wind resource

61

The reason for this lack of data is that standard deviations of wind speed from the mean are usually not recorded. Some information about turbulence may be deduced, nevertheless, if information on gusts is available. Gust data may be obtained from a chart record of the wind speed or from a number of digital samples of wind speed over a given averaging period. For most anemometers in operational use, the sampling interval should be at least 2-3 seconds and much smaller than the averaging period, which should be of the order of 10 min. From the maximum gust of a given duration, a gust factor may be computed. Provided that gusts of meteorological origin (e.g., frontal passages, thunderstorm downdrafts) can be eliminated, the gust factor can be related to an effective surface roughness length. At least for homogeneous terrain, turbulence intensity can be related to surface roughness length, z0 , indicative values of which are listed in table 3.2. Table 32 Surface roughness lengths and turbulence intensities for typical surfaces Surface Ice Water Snow Sand, desert Bare soil Grass Crops Typical rural Orchards Forests Suburban/Towns City centres

Comments Smooth Wind-speed dependent Dependent on underlying surface Dependent on grain size and presence of dunes or ripples Higher values if ploughed 0.02-0.1 mhigh 0.25- 1.0 mhigh Can be wind-speed dependent Farmland with isolated trees and buildings May be seasonally dependent Low housing, trees Buildings 10 - 50 m high

zo(m)

Iu (10 m)

10"4 10"5 - 10"3

0.08 0.07-0.10

0.0001 - 0.02

0.08-0.15

3 x 10"4 10"3 - 10"2 0.003 - 0.01 0.04 - 0.10 0.04 - 0.20

0.09 0.10-0.14 0.12-0.14 0.17-0.21 0.17-0.25

0.02 0.5 1.0 0.40 1

0.15-0.21 0.32 - 0.42 0.42-1.9 0.30-0.60

-

0.10 1.0 6.0 2.0 10

0.42- oo

Sources: Oke (1987), Taylor and Lee (1984), Wieringa (personal communication) In qualitative terms, the rougher the terrain, the higher the terrain induced gusts and the higher the turbulence intensity. The higher the turbulence, the greater the stress on turbine blades and the greater the risk of failure due to fatigue of the blade material.

INSPECTION AND SELECTION OF CANDIDATE SITES Prior to inspection of specific sites, an examination of detailed topographic maps of the area would be time well spent. Generally, one should make a preliminary selection based on exposure (e.g., lack of trees and upwind hills) from the wind directions that are of

62

Assessing the wind resource

main concern (ie, the directions for which wind speeds are high and that occur frequently). It is suggested that a minimum of five sites should be considered for one proposed turbine. If more than one turbine is proposed, it may be more practical to consider fewer sites per turbine. Regarding types of terrain features, the following are listed approximately in decreasing order of potential for WTG siting: a b c d e

Top of a ridge, escarpment or hill. Col or pass. Flat coastal site. Flat inland site. Valley or base of a ridge, escarpment or hill.

These categories should only be used for ranking potential sites, not for eliminating them. In some situations, col or pass regions may be ranked higher than nearby ridge or hilltop sites due to channelling of the flow. Rat coastal sites may be better than wooded sites on topographic features due to surface roughness effects (see below in this section). Occasionally, valley sites may be better than flat inland sites for the same reason. At this stage, other considerations that can be estimated subjectively from topographic maps are the height, A, of a terrain feature and its extent, or horizontal scale, L. Within certain limits, the larger the ratio, h/L, the higher will be the wind speed compared with flat-terrain sites. In addition, the larger the value of L, the higher above ground will the enhancement of wind speed penetrate, a factor that will be important when estimating hub-height speeds. A later section in this chapter will describe how to quantify h and L in order that speeds at various heights may be estimated. Having obtained a list of potential sites based on climate, local knowledge and examination of topographic maps, an inspection of the sites is recommended, preferably on a day with moderate and steady winds so that the site-to-site variation will be most noticeable. The use of a handheld anemometer would help to quantify these variations at a height of approximately 2 m above ground, but one should beware of placing too much significance on data so obtained as they will not be representative of wind speeds at 10 or 20 m above ground, nor of the long-term average speed. Furthermore, they will probably not be representative of wind speeds that occur when the flow is from a different direction. Finally, they will only enable site-to-site comparison if the background conditions are the same for all measurements. One of the main purposes of a site visit is to verify and update the information obtained from topographic maps as the maps will be a prime source of data for computations to be described later. During the inspection of potential sites, the exposure of the site to all wind directions should be noted. Sites which are sheltered by other features and trees should be avoided, if possible. The degree of afforestation, in particular, may have changed from the time the latest topographic maps were prepared. Horizontal Axis Wind

Assessing the wind resource

63

Turbines (HAWTs) with low hub-heights, or Vertical Axis Wind Turbines (VAWTs) with low equator heights, are likely to be significantly influenced by the surface cover type. Typical surfaces listed in order of increasing roughness (ie, decreasing wind resource potential for a WTG site) are: a b c d e

Snow, ice or water. Sand. Short grass or low crops. Long grass or tall crops. Trees and houses.

See Table 3.2 for more details. These categories should be used for ranking, not eliminating, potential sites. Water is included in the above list because, at coastal sites, a WTG erected on a beach in onshore wind flow, may, in fact, experience the same wind as over water if the distance from the shoreline is sufficiently short, ie, if the new wind regime associated with the beach surface has not yet penetrated to hub-height. Snow and ice are included as reminders that the surface roughness may be reduced in winter. While visiting the site, the wind speed should be estimated (or, preferably, measured) and compared with speeds in the immediate neighbourhood (e.g., wind speed on the beach versus further inland; hilltop wind speed versus flat-terrain site). As mentioned above, a hand-held anemometer may be used, but caution should be exercised in reaching firm conclusions or eliminating sites on this evidence alone. One means of measuring hub-height winds at this stage would be a calibrated kite system. Some practical problems inherent in the use of kites or balloons are discussed later. For the moment it should simply be noted that such measurements are most effective when related to longer-term data at a fixed height and location. Small-scale terrain features such as boulders, depressions and sharp changes in slope (e.g., ravines, gullies, escarpments) can all contribute to turbulence intensity at the site, increasing the likelihood of blade vibration and fatigue. Such sites should be avoided, if possible, or at least given a lower priority. Following inspection of all potential sites, some may possibly be eliminated on the basis of meteorological considerations and some for other reasons. The experience of the evaluator(s) will determine how many locations can be eliminated from contention at this stage. The remaining sites, especially if there are still many of them, should be ranked in order of potential, or at least grouped into categories (e.g., low, medium and high potential). If time and/or resources are limited, then the techniques of the next section should be applied only to those sites (e.g., 2-4 per turbine) thought to have the highest potential for a WTG after the site-inspection phase.

64

Assessing the wind resource

'GUIDELINES' TERRAIN MODEL The Guidelines method of Taylor and Lee (1984) has been found to give reliable estimates of wind speed both in flat terrain in which a change of surface roughness is involved and on tops of certain of the terrain features listed in Table 3.3. Table 3.3 Parameters A and B for various terrain types Terrain type 3D hills 2D hills (ridges) 2D escarpments 2D rolling terrain 3D rolling terrain Flat terrain

A

B

4.0 3.0 2.5 3.5 4.4 0.0

1.6 2.0 0.8 1.55 1.1 0.0

Alternative methods are available (e.g., the BZ polar co-ordinate model of Troen et al. (1988); the LSD method of Lemelin et al. (1988) but it is the simple Guidelines that will be described here. It should be pointed out that LSD, although it allows for more general estimates of topographic elevation effects, does not include variations in surface roughness, the effects of which can be very significant, particularly at low heights above terrain. Recently, the original Taylor and Lee Guidelines have been updated to include rolling terrain (ie, a situation in which the upstream terrain is not flat) and to account for cases where the reference wind speed is not located in the upstream area. The changes are documented in Walmsley et al. (1989). A calculation of turbulence intensity has been incorporated in the Guidelines. Details are given later. Information regarding software availability appears in Table 3.5. The Guidelines model is implemented using detailed topographic maps of the candidate sites. Such maps should be at a scale of 1:5000 to 1:50,000. The former is preferred as, generally, the larger the scale, the better. Ideally, the contour interval (of topographic elevation) should be of the order of 1/10 or 1/20 of the height of the terrain feature under consideration. The information on the maps should be augmented by details of the surface cover type distribution observed during the site inspection. The surface cover types should then be converted to surface roughness lengths, expressed in metres. The result will probably be a 'patchwork quilt' of surface roughness areas superimposed on a detailed topographic contour map. Input data requirements Input data to be used in the Guidelines formulae should be prepared for each of the wind directions of concern. Generally, the wind directions may be either those with the highest

Assessing the wind resource

65

frequency of occurrence or, better, for 4, 8, 12 or 16 compass points. The terrain type should be determined and parameters A and B should then be extracted from Table 3.3. In some cases, the terrain may appear to have characteristics of more than one type. Subjective interpolation in Table 3.3 may be done in such instances. The chosen values of A and B will be used in equation 3.1. The height, h, of the ridge, escarpment or hill above relatively flat terrain in the upwind direction should be extracted from the topographic contour map. This will generally mean subtracting two heights obtained from the map (ie, h = z\ - zf, where z\ and zf are the heights above sea level of the terrain feature and the flat terrain, respectively). For valleys and other depressions, h is negative, although these situations will be of little interest for wind energy applications. For flat terrain, h - 0. The horizontal scale of the terrain feature, L, is determined as the distance from the top of the terrain feature in the upwind direction to the point at which the elevation is z\ - h/2. In the case of flat terrain, a nominal value of L - 1000 m may be used. An example of the determination of h and L is shown in Fig. 3.1. For illustration purposes, the wind direction is assumed to be parallel to the A and AA lines, from left to right. The upstream terrain height, zf = 8 m. At point HT, zt = 126 m, so A = 126 - 8 = 118 m and zt - h/2 = 126 - 59 = 67 m. The distance upwind from HT to the point

Fig 3.1 Determination ofh and L for Askervein. (Base map reproduced from Beljaars et al. (1987). Contour interval is 10m)

66

Assessing the wind resource

where the elevation is 67 m is L = 220 m. Similar calculations for point CP give h = 108 m and L = 260 m. Table 3.2 may be used in estimating surface roughness lengths. Also shown in the table is the first component of turbulence intensity for a height of 10 m and assuming neutral stratification. This has been computed as Iu(z) = Ou/u(z), where cu = 2.4 u* and u = (M*/K) ln(z/z0) with K = 0.4 and z = 10 m. (Although this logarithmic relation for the wind speed profile is valid for z > z0, in practice it is only applied for z » z0 , thus avoiding unrealistically large values of Iu - see Table 3.2.) Here u* is the friction velocity. The other two components of turbulence intensity are computed from the following relations: o v = 1.92M* and GW = 1.25w* . In rolling, but non-mountainous, terrain, au and o v are as much as 50% larger, but cw remains about the same. For the simple case of a single step-change in surface roughness in the upwind direction, the choice of input parameters for the Guidelines estimates is quite straightforward. The surface roughness at both the candidate site and an upwind site, z 0 and zOu, respectively, may be estimated from the topographic map. These estimates should be improved, if possible, by examining air photographs and/or by site inspection. The distance, r, is the distance, in the upwind direction, from the candidate site to the point at which the surface roughness changes. An estimate may be obtained from a topographic map and/or air photographs or from a site survey. The choice of input parameters for the more complicated situation where multiple changes of roughness occur, is discussed later. Calculation of the terrain elevation effect According to the Guidelines the speed-up at height, Az, above a terrain feature is given by: AS = B (h/L) exp [- A Az/L],

(3.1)

where exp is the exponential function and AS is the fractional speed-up ratio. Equation (3.1) is stated to be valid for h/L < 0.5, 100 m < L < 2000 m and u0 >3 m/s, although the first two limitations can probably be relaxed slightly to - 0.4 < h/L < 0.6 and 10 m < L < 2000 m. It is assumed that any effects of roughness changes can be computed separately. To indicate how the * Guidelines' method is applied, let us deal with the isolated terrain feature shown in Fig. 3.1. For a wind direction from left to right parallel to the A and AA lines, it has already been determined that h = 118 m and L = 220 m at point HT. Assuming a situation intermediate between a 3D hill and a 2D ridge, Table 3.3 gives A - 3.0 and B = 1.8, so for Az = 10 m, (3.1) gives AS = 0.84. A similar calculation for point CP (h = 108 m, L = 260 m) gives AS = 0.66. For comparison with field measurements, calculations were also done for a wind

Assessing the wind resource

67

direction of 210 degrees (ie, from the left in Fig. 3.1). At HT and CP, the values of L were 215 m and 270 m and the calculated results were AS = 0.86 and 0.64, respectively. The corresponding field measurements were AS = 0.88 and 0.62 (Salmon et al. (1988). Vertical profiles of fractional speed-up ratio, AS, above idealized hills are shown in Fig. 3.2a. Cross-sections of the hills appear in Fig. 3.2b. The Guidelines results are shown as dots and are compared with output from the MS3DJH/3R model (see later).

1000 Az - L Cosine—squared hill Bell-shaped hill • • • • • Guidelines

x

2D Ridges

2.0

1.0 AS/(h/L)

a) Vertical profiles of fractional speed-up ratio, AS, normalized by (h/L). (MS3DJHI3R model results are shown by solid and dashed lines).

Bell-shaped hill Cosine—squared hill

2.5

-1.5

-0.5

0.5

1.5

2.5

b) Hill cross-sections. (See glossary) Fig 3.2 Flow above the crest of two- and three-dimensional idealized hills (calculated using the MS3DJHI3R model and the Guidelines)

68

Assessing the wind resource

Near the surface, the Guidelines calculations represent a compromise between results for cosine-squared and bell-shaped hills from the MS3DJH/3R model. At heights of interest for installation of a decentralised WTG (e.g. 10 - 30 m) the Guidelines results are roughly 10% larger than those of the model. In general, the 'Guidelines' represent a reasonable estimate of speed-up at the top of an isolated terrain feature, at least in comparison with results from a well validated model. For particular applications, they may give slightly different results, but they are certainly adequate at least for preliminary evaluations. The 'Guidelines' approach can be extended to 'rolling terrain' where there is no appropriate level upstream site. This allows estimates of wind speed to be made at summit or valley locations. For rolling terrain, the height, h> is taken as the hilltop to valley bottom elevation difference (positive for calculations at the hilltop, negative for a valley bottom). The horizontal scale, L, is taken as half the hilltop to valley bottom distance in the upwind direction. Upstream speeds are estimated as spatial averages over similar topography with roughness length, zou • Table 3.3 shows values of A and B for two and three dimensional rolling terrain. For intermediate cases, subjective interpolation between the two 'rolling terrain' options in Table 3.3 is recommended. Calculation of the surface roughness effect The 'Guidelines' approach also takes account of changes in terrain roughness upstream of the proposed wind turbine site. The speed-up or 'slow-down' due to roughness changes is computed as follows: First, the height 8 of the internal boundary layer generated by the roughness change must be calculated iteratively from:

/

-l)+l]

(3.2a)

where 5' = 5/z0, / = r/z0 , and a = 2. A suitable initial estimate for 5' is given by 5' = 0.75 ( O 0 8 + 1

(3.2b)

Equations (3.2a) and (3.2b) are derived from Panofsky and Dutton (1984) and Elliott (1958), respectively. The surface roughness, z 0, can be selected from Table 3.2. Alternative range values of z0 and of horizontal turbulence intensity, suggested in the European Wind Atlas by Troen and Petersen (1989), are reproduced in Table 3.4.

69

Assessing the wind resource

Table 3.4 Estimates of surface roughness length and turbulence intensity for roughness classes of the European Wind Atlas Description

Class

Water, smooth sand or snow Grass, farmland with few buildings or trees Other farmland Bushes, trees, shelter belts suburbs, small towns

0 1 2 3

zo(m)

Iu0 10 m)

0.0001 - 0.001

0.08 -0.10

0.01 - 0.03 0.05-0.10

0.14 -0.17 0.18 -0.21

0.20 - 0.40

0.25 -0.30

Source: Troen and Petersen (1989), p58.

Elliott (1958) Panofsky & Dutton (1984)

300

200

100Zou - 0.03 m

-10a 1000

w

0 1000 2000 Distance from roughness change, r (m)

Fig 3.3 Internal boundary-layers due to roughness change as computed from equations 3.2a and 3.2b

The internal boundary-layer concept is illustrated schematically in Fig. 3.3. Having computed 8 from (3.2a), the change in speed due to a change in roughness is obtained as: M = [ln(Az/zo)ln(5/zOM)]/[ln(5/zo)ln(Az/zow)] - 1, when Az <5

(3.3a)

AR = 0, when Az >5

(3.3b)

Assessing the wind resource

70

10

10

8 10 6 Wind Speed (m/s)

12

a) 'Slow-down' over smooth-to-rough change in roughness

10

10 Wind Speed (m/s)

12

b) 'Speed-up' over rough-to-smooth change in roughness Fig 3.4 Fractional change in wind speed due to flow over a roughness change 1) Upstream profile, 2) Profile within internal boundary layer.

Assessing the wind resource

71

Here In indicates the natural logarithm and AR is a fractional change in speed, defined similarly to AS, but this time assuming no topographic elevation effects. Two illustrations of equation 3.3 are given in Fig. 3.4. In Fig. 3.4a, the upstream flow is indicated by line 1, a logarithmic profile over a surface of roughness length 0.0001 m. At a measurement height Azm = 3 m, the wind speed in the upstream area is indicated as Wo • The flow encounters a rougher surface (0.003 m) and an internal boundary layer is generated. At a distance of r = 550 m downwind of the roughness change, the boundary layer is computed by equation 3.2 and shown at height 8 = 33.1 m. A new logarithmic profile, line 2, is established below that height and extends down to the new roughness length. At the height of interest, 3 m in this example, the decrease in wind speed, computed by equation 3.3a, is shown as AR in the diagram. Fig. 3.4b shows an example in which the flow is from a rough (0.003 m) to a smooth (0.0001 m) surface, giving an internal boundary layer height of 24.1 m and an increase in wind speed, indicated by AR, at a downwind distance of 550 m. Otherwise, the notation is the same as in Fig. 3.4a. In cases with multiple changes of roughness, the choice of the values of z 0, zou and r are not quite so straightforward. In Fig. 3.5, an example of wind estimation with multiple roughness changes is illustrated. It should be noted in Fig. 3.5a that the internal boundary layer (IBL) due to the change in roughness at the beach-grass interface grows with downwind distance more rapidly than either of the other two. It quickly eliminates the IBL generated at the surf-beach interface. With a further increase in downwind distance, it will also overtake the IBL generated at the ocean-surf interface.

•

80 60 (m)

— Ocean—Surf Surf-Beach Beach-Grass

40 20 Beach

Grass

200

400

600

Surf

800

Ocean

1000

1200

Distance upwind from site, r (m)

Fig 3.5 a Wind flow with multiple roughness changes. Internal boundary layers. (Wind flow is from the right.)

1400

Assessing the wind resource

72

10'

5 (Beach - Grass) 5 (Ocean - Surf) 5 (Surf - Beach)

10 Grass 3 13 O O) 0

1 Fig 3.5b Wind flow with multiple roughness changes. Windspeed estimation at location r-0 of Fig 3.5 a.

10"

5 (0

f 10"2 Surf

10"

Ocean 10"

0

2

4 6 8 10 Wind speed (m/s)

12

14

Fig. 3.5b shows the upstream (oceanic) wind speed profile and the profiles that would theoretically exist at location r - 0 of Fig. 3.5a if each of the three changes in roughness occurred in the absence of the other two. A composite wind profile, given all three roughness changes, is made up of the grass profile up to the height of the IBL generated at the beach-grass interface, the surf profile in a thin layer above that and the ocean profile above the ocean-surf IBL height. For proper application of the Guidelines, the choices of z0, zOu and r depend on the height for which calculations are required. At the location of the Fig. 3.5b profiles, there are two regimes separated by the IBL generated at the beach-grass interface (the middle curve at r = 0 in Fig. 3.5a). Above that height, the flow has not been influenced by the underlying grass, nor by the beach upwind. Hence zo and zOu are values appropriate for surf and ocean, respectively, and r is the distance in the upwind direction to the ocean-surf interface (ie, where the z0 roughness began). Below that height, on the other hand, the flow has no 'memory' of either the ocean or the beach. Hence zo and zou are values appropriate for grass and surf, respectively, whereas r is the distance in the upwind direction to the beach-grass interface that marks the start of the grass surface.

Assessing the wind resource

73

Once the roughness changes become complicated, it is more appropriate to use a more sophisticated model. Use of AS and AR in screening sites The elevation and roughness-change effects are assumed to be linearly combined as AS + AR and the normalized wind speed, u/u0 = 1 + AS + AR, typical values of which may range from about 0.7 to about 2.0 (i.e., AS + AR = - 0.3 to 1.0, approximately). Application of the Guidelines method at a number of heights, Az, above terrain, will enable a vertical profile of normalized wind speed to be derived. These results may be used in two ways. If the WTG hub-height is known, the wind speed profile will give valuable information on wind shear near that height. If the hub-height still has not been decided, the profile will indicate heights at which speed-up should be maximum and wind shear minimum. If the normalized wind speed, «(Az, <|);)/wo(Az, <|>/) = 1 + AS + AR, at height Az, is computed for a series of wind directions, (()/, then estimated wind speeds, w(Az, (()/), may be obtained by taking the normalized wind speed and multiplying by the mean wind speed, Wo(Az, ty). A wind speed climate can then be calculated by taking a weighted mean of the results from each wind direction (using as weights the frequencies of occurrence of the wind directions). Similarly, if either the mean speed by direction or the frequency of occurrence of each direction varies significantly with the season, then seasonal or monthly calculations would be preferred. If information on the spectrum of available potential energy for the WTG is required, then the climatic distribution of wind speeds into various speed classes (or, alternatively, the parameters of a Weibull distribution) at the flat-terrain site should be used to generate the site-specific spectrum climate. After this stage, the number of candidate sites per turbine to be installed should have been reduced to a small number (e.g. 1 to 3) that may be examined in more detail, as described in the following sections. Depending on circumstances, the next stage in site selection may be one of the techniques yet to be discussed. Turbulence intensity estimates Turbulence intensity, / u (z) = Ou/u(z)f is computed both upstream and at the prediction site. Here, cu is the standard deviation of the wind speed in the downwind direction and u(z) is the mean wind speed at height z above ground. Typically, cu and u are based on sampling at intervals of 1 second over periods of the order of 10 minutes. At the upstream location, oUo = 2.4 «* where u* is the friction velocity computed from the logarithmic wind speed profile. Note that aUo is height independent. At the prediction site the Gu perturbation due to topographic effects is assumed to be zero, the ou perturbation due to roughness effects is assumed to be:

74 a

Assessing the wind resource zero above the internal boundary layer height (5),

b [2.4(w*e) - CJwo] within the equilibrium layer (z < \x. - 0.18), where w*e is the friction velocity computed from the logarithmic wind speed profile for z < |i, c

logarithmically interpolated between 5 and \i.

The two Gu perturbations are assumed to be added linearly. Non-ideal reference sites Let us look now at the situation in which the reference site for which data are available is not located directly upwind of the prediction site. The reference site may be characterized by a surface roughness length, zOr, which is different from zou. In such circumstances, a procedure can be adopted which uses the 'Resistance Laws' for the neutrally stratified PBL (also known as the 'Geostrophic Drag Law'). An equilibrium relationship between the surface friction velocity and the scalar magnitude of the (spatially-independent) geostrophic wind is assumed to exist at both upstream and reference sites. The Resistance Law is given by: Ug/u* = [(ln(K*//z0) - b)2 + a2}1/2 K ~ \

(3.4)

| is the Coriolis parameter (Q = where Ug is the geostrophic wind speed,/= 2Q sin (> 7.292 x 10" s~ is the angular velocity of rotation of the earth and ty is the latitude, assumed positive), K is Von Karman's constant, a = 4 and b = 2. Equation (3.4) is assumed to be valid with u* (friction velocity) and z 0 (roughness length) appropriate for either the upstream or the reference site. Given a wind speed at the reference site and knowing zOr, the friction velocity at the reference site, M*r, is computed from the logarithmic wind speed profile; then the geostrophic speed is computed from (3.4). Equation (3.4) is used a second time, inserting Ug and zOu to determine w*M, the friction velocity at the upstream site, by means of an iterative technique (Newton's Method). Finally, the upstream wind speed is determined from the logarithmic wind speed profile. There are some difficulties with this approach to handling non-ideal reference sites. First, the reference site itself, in addition to not being upwind of the desired prediction site, may have a roughness change or an obstacle to the flow upwind. Second, there is some disagreement in the literature as to the correct scaling height for the Planetary Boundary Layer. Nevertheless for present purposes, it is probably best to use (3.4), but to be aware that it will not always give satisfactory results.

MORE SOPHISTICATED NUMERICAL TERRAIN MODELS If more detail is required on the wind flow in complex terrain near proposed wind turbine sites, then the use of more sophisticated models may be appropriate. A moderate effort is generally required for familiarization, preparation of input data and initial implemen-

75

Assessing the wind resource

tation. Furthermore, running of such models may be rather time-consuming. On the other hand, preparation of data for the simple model described previously could become quite time-consuming if, for example, results are required for 16 wind directions at say 10 sites within a proposed wind farm. If calculations need to be done monthly for a number of levels above terrain, then the computational effort involved in applying even the simple models becomes significant. Table 3.5 Availability of software discussed in this chapter Model Name(s)

Contact

Address

Guidelines MS3DJH, MS-Micro

Dr. John L. Walmsley Phone: 1-416-739-4861 Fax: 1-416-739-4288 Telex: 06-36700611

Atmospheric Environment Service 4905 Dufferin Street Downsview, Ontario M3H 5T4 Canada

WASP

Dr. Ib. Troen Phone: 45-2-371212 Fax: 45-2-360609 Telex: 46113

RIS0 National Laboratory Meteorology Section PO Box 49 DK-4000 Roskilde, Denmark

FLOWSTAR

Dr. Julian Hunt Phone: 44-(0) 223-35773 Fax: 44-(0) 223-357492

CERCLtd 3d King's Parade Cambridge CB2 1SJ England

Table 3.5 indicates the availability of some of the model software discussed below. Information on other models appears in Table 3.6. Table 3.6 Survey of other site assessment models Model

Description and Reference

Contact Person and Address

Simple Terrain Models EWECSO (YYY)

Predicts energy output of WTGs on short- and long-term basis. Applies direction-dependent factors to meteorological station statistics Macmillan and Saluja (1988)

Gurudeo Saluja c/o Dept of Building University of Ulster Newtonabbey, Co Antrim N. Ireland BT37 0QB, UK

LSD (YYY)

Wind speed-up over hills of regular shapes for any wind direction, including predictions away from the summit Lemelin etal. (1988)

Prof. David Surry University of Western Ontario Faculty of Engineering Science London, Ontario, N6A 5B9 Canada

Assessing the wind resource

76 Table 3.6 (continued)

More Sophisticated Numerical Terrain Models Mason-King Model D (YYU)

Similar to MS3DJH, but with different formulation for height dependence of pressure and selection of velocity scales. Mason and King (1985)

Dr. Paul J. Mason Meteorological Office London Road Bracknell, Berkshire RG12 2SZ, England, UK

MSFD (YYN)

Normalized predictions of wind speed and turbulent stress. Essentially a research model. Beljaars etal. (1987)

Dr John L. Walmsley, Table 3.5

NOABL (YYY)

Mass-consistent model incorporating height-dependent stratification and variable surface roughness. Predicts three-dimensional wind fields. Traci et al. (1977), Lalas (1985)

Dr. Demetrius P. Lalas Lamda Technical Ltd. Al. Papanastasiou 27 Neo Psychiko, Athens 154 51 Greece. Also Dr Larry Wendell Pacific Northwest Lab., PO Box 999, Richland, WA 99352, USA

Measure-Correlate-Predict (MCP) Methods Synthetic Climatology Program (YNN)

Compares hourly site wind measurements with simultaneous recordings at meteorological stations. Generates long-term climatology for the site.

Steven J. Reid New Zealand Meteorological Service Ministry of Transport PO Box 722 Wellington, 1, New Zealand

Wind Data Correlation Program (NYY)

Generation of long-term climatology (frequencies of occurrence by wind direction, wind speed class and atmospheric stability) from correlations of simultaneous shortterm data. Walmsley and Bagg(1978)

Dr. John L. Walmsley [see Table 3.5]

COREXTRA/ SEDV (UYU)

Long-term wind characteristics at a candidate site from short period of simultaneous measurements at site and at meteorological station.

Francisco Martin CIEMAT Instituto de Energias Renovables Avda Complutense 22 DP 28040 Madrid, Spain

MT-MCP (YYU)

Parameterization of wind speed distributions at candidate site and reference site. Extrapolation of short-term measurements at candidate sites. Kunz and Baumgartner(1986)

Dr. Stefan Kunz Meteotest Fabrikstrasse 29a CH-3012Bern Switzerland

Assessing the wind resource

77

Table 3.6 (continued) MCPNEL (YYY)

Generation of long term climatologies and energy predictions, with statistical assessment of uncertainty.

Alan Derrick NEL East Kilbride Glasgow, G75 OQU Scotland

Siting Handbooks and Wind Atlases Dutch Handbook (YYY)

Estimates local long-term regime from corrected meteorological station data. Vermeulen et al (1986)

Ir. L. Van der Snoek T. N. G. Laan Van Westenenk501, 7334 DT Apeldoorn, The Netherlands

Danish Wind Atlas (YYY)

Petersen er 0/. (1981)

Ris0 National Laboratory P. 0. Box 49 DK-4000 Roskilde, Denmark

European Wind Atlas (YYY)

Troen and Petersen (1989)

Ris0 National Laboratory PO Box 49, DK-4000 Roskilde, Denmark.

Wind Energy Resource Atlas of the United States (UYU)

Elliott er al (1987)

US Govt Printing Office

Siting Handbook Wegley era/. (1980) for Small WECS (UYU)

Pacific North-West Lab Richland WA, USA.

Note: The three characters in parentheses below the model name indicate, respectively, whether or not the model has been validated, whether documentation exists and whether the software is routinely available. The characters may be Y (yes), N (no) or U (unknown). This information, based partly on responses to a questionnaire, is believed to be accurate. Details should be confirmed with the contact person.

Linearized Models A number of linearized models are available that are suitable for obtaining detailed estimates of wind speeds in complex terrain (e.g., the MS3DJH/3R model of Walmsley et al. (1986); the MSFD model of Beljaars et al (1987); the FLOWSTAR model based on Hunt et al (1988a, 1988b) and Belcher et al (1990); the BZ model of Troen et al (1988); Troen and De Baas (1986). It is beyond the scope of this chapter to discuss any of these in detail. Instead, an attempt will be made to outline the characteristics of the MS3DJH/3R model and the

78

Assessing the wind resource

input data it requires and to describe briefly the features incorporated in the other three models. The MS3DJH/3R model is designed to simulate neutrally stratified surface layer flow in complex terrain. It includes effects of variable surface roughness as well as the effects of changes in topographic elevation. The use of a Fourier transformation in the two horizontal co-ordinates enables analytical solutions in Fourier (or wave number) space in the vertical co-ordinate. Once the Fourier coefficients have been computed, an inverse transform yields gridpoint values of wind speed perturbation, from which a field of fractional speed-up ratio or normalized wind speed may easily be derived. The horizontal transformation makes possible high resolution output (e.g., horizontal grid spacings of order 10-100 m and any number of levels in the vertical) at relatively low computing cost when compared with a 3D finite difference model at comparable resolution. Typical runs consume only about 20-100 seconds CPU time on a powerful CRAY XMP computer, about 2 hours on an IBM XT personal computer and about 30 minutes on a 386 personal computer. The microcomputer version ('MS-Micro') was introduced in Salmon and Morris (1987) and has recently been made more 'userfriendly'. MS-Micro is particularly well suited to low cost appraisals and may thus be of particular interest to developers of small decentralised wind systems.

To provide input fields for MS3DJH/3R, detailed topographic contour maps and accompanying surface roughness maps are needed. The specific requirements are similar to those outlined previously. The topographic and roughness maps are first digitized and then preprocessed to derive grid point values of the two fields that are then read by the model. Fig. 3.6 illustrates wind profiles at the crest of idealized hills with smooth-to-rough surface roughness transitions, as computed by the MS3DJH/3R model. Above about 20 m, the difference between the two cases ('Coastal HilF and 'Island') are quite small. At lower levels, the contribution to a reduction in speed due to increased roughness differs by about 10 per cent between the two cases. Both examples, however, exhibit profiles of similar shape with a maximum at about 30 m and a gradual return to upstream conditions by about 500 m. The variation of wind speed with height above ground is very relevant in the siting of a WTG and in deciding on the hub (HAWT) or equator height (VAWT). It is recommended that computed wind profiles be plotted in a manner similar to that of Fig. 3.6 so as to gain a good visual impression of the relationship between wind speed and height. Input requirements of the MSFD model are identical to those of MS3DJH/3R. MSFD is a more sophisticated model, incorporating a higher-order turbulence-closure scheme and avoiding some of the approximations and assumptions (e.g., advection velocity

79

Assessing the wind resource

1000

+ •

UPSTREAM PROFILE "COASTAL HILL" "ISLAND" ROUGHNESS EFFECTS ONLY ELEVATION AND ROUGHNESS EFFECTS

10

15

20

25

30

35

40

Fig 3.6 Wind speed profiles at the crest of idealized hills with smooth-to-rough surface roughness changes calculated using the MS3DJHI3R model-source: Walmsley et al (1986). independent of height; vertical advection neglected in the terrain-following co-ordinate) that made an analytical solution possible in MS3DJH/3R. MSFD uses a Fourier transformation of the horizontal co-ordinates, but has to rely on a finite-difference scheme in the vertical to obtain a solution. This scheme has recently been improved so that the model is more accurate and robust than originally reported. The FLOWSTAR model described in CERC (1988) is based on a general analytical method for calculating the mean flow and shear stress near the surface for two and three dimensional turbulent shear flows over hills with low slopes. The analysis can handle a wider range of conditions than the MS3DJH/3R model. In particular, the atmosphere may have weak stable or unstable stratification and corrections have been derived for weak nonlinear effects.

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Assessing the wind resource

The WASP package of Troen et al. (1988) incorporates the BZ complex terrain model, a roughness change module that is capable of handling multiple roughness changes and a shelter module that accounts for effects of obstacles to the flow. The BZ model features a polar co-ordinate transformation and produces a potential-flow solution at the pole (origin) of the co-ordinate system. The spacing of the co-ordinate circles increases with increasing radius, thus giving high resolution of the terrain features close to the location of interest and lower resolution farther away. Solutions for an arbitrary wind direction are obtained by superposition of solutions computed for two directions 90 degrees apart. The WASP package was used for deriving the statistics in the European Wind Atlas (Troen and Petersen (1989)) with the BZ model calculating the influence of topography on the many stations located in complex terrain. At present, the MS-Micro, WASP and FLOWSTAR models are the most easily adaptable of those mentioned. MS3DJH/3R (the mainframe forerunner of MS-Micro) is also available in source code accompanied by a user's manual (Salmon and Walmsley (1986)). MSFD offers attractive improvements that make it more widely applicable but, at the time of writing was still in the research mode. With time, it too may achieve 'operational' status. Limitations of linearized models All the models discussed in this section have limitations due to linearization of the model equations that restrict their applicability to low terrain slopes (e.g. <0.3). (In some regions, this limitation may invalidate application at most sites, or at least give somewhat inaccurate results in the immediate vicinity of cliffs, ravines and other features with a steep slope.) Aerodynamic practice has been to limit linearized theory to slopes <0.1 or 0.15, but the models still seem to compare quite well with observations when those theoretical limits are exceeded. In any case, for steeper topography, a nonlinear finitedifference model or wind-tunnel simulation may be more appropriate, although both require a greater investment in time and resources. Another difficulty that is often encountered in practice with both simple and sophisticated models is the lack of flat, horizontally homogeneous upstream terrain, as assumed by the models. This means that the incoming flow is also not in agreement with model assumptions. Results in such circumstances are suspect, although there may still be some validity in the relative values of normalized wind speed calculated for different sites.

Validation of models Ideally, when selecting a model, the user should ensure that it has been validated against data from real terrain (or failing that, from wind-tunnel studies). This is not always the case, however, and before a model is used it is desirable to verify its results with high quality data.

Assessing the wind resource

81

Use of numerical models It should perhaps be emphasized that use of a sophisticated model may not be necessary in every application. Modelling involves a certain investment of time (especially at the familiarization stage) and computer resources. It may, on the other hand, be a viable alternative to repeated application of a simpler model of the type previously described. Further more, the use of a linearized model is a much less costly and time-consuming undertaking than measurements in the field or in a wind tunnel. In recent years several of these models have become more user-friendly for use on personal computers. Provided that the terrain slopes are not too steep, a linearized model should be given serious consideration.

OTHER EVALUATION TECHNIQUES Measure-correlate-predict (MCP) methods The basic idea behind these methods is first to obtain simultaneous measurements at two locations, one being the potential WTG site, the other being a site for which long-term data are available such as an airport. Second, the two data sets are analysed to quantify their correlation. Third, the long-term data are used to predict a long-term wind regime at the potential WTG site. AWEA (1986) gives a formula for estimating long-term mean wind speeds at a site, based on short-term measurements and long-term data from a reference station located in the vicinity. The relationship is given by: = Vc - p (VT - ) Cc/Or,

(3.5)

where is the estimated long-term mean wind speed at the site, is the observed mean wind speed at the reference station, Vc is the observed mean wind speed at the measurement station, VT is the observed mean wind speed at the reference station during the period of measurements at the site, p is the spatial cross-correlation of daily wind speeds between the reference station and the site, Gc and <7r are the standard deviations of daily average wind speeds at the site and the reference station, respectively. Another way of writing Equation (3.5) is:

= b0 + bi,

(3.6)

where the parameters bo and b\ can be found by simple linear regression of daily mean wind speeds. This equation has been successfully applied at the regional scale (5-20 km) in complex terrain to predict mean power output and wind speed using weekly, rather than daily, values.

82

Assessing the wind resource Another possibility is to use: = [VC/VT] ,

(3.7)

but this method has been known to give poor results. It is often useful to adopt an alternative formulation that allows for stratification of the data into a number of wind-speed and wind-direction classes and accounts for the occurrence of calms. A variety of models are listed in Table 3.6. MCP methods may be time-consuming and costly because short-term field measurements are required, but it is sometimes possible to obtain good results even in mountainous terrain. Wind tunnel studies At times, the terrain around a potential WTG site is too complex for application of simple terrain models. Depending on the available facilities, a wind tunnel study may, in some cases, be preferable to numerical computations with one of the more sophisticated terrain models. Much but by no means all of the wind tunnel work that has been reported concerns flow over idealized, usually two-dimensional, obstacles. A notable exception is reported by Teunissen et al. (1987) who conducted extensive wind tunnel measurements on flow over Askervein Hill, the location of full-scale experiments in 1982 and 1983 co-ordinated under Annex VI of the International Energy Agency - WECS R & D Agreement. Nonlinear effects As mentioned above, linearized models do not give accurate results when terrain slopes are high (e.g. >0.3). The perturbations in the flow field are no longer * small' with respect to the mean flow, meaning that second-order terms are not negligible in nonlinear expressions. The problem is nonlinear and cannot be handled properly with a linearized model. One modelling approach to the nonlinear problem is to use a finite difference model. Such models may be two-dimensional or three-dimensional. It is also possible to apply a linearized model in an iterative manner. Siting handbooks and wind atlases Early work on this topic was done by Duchene-Marullaz (1977) in France, followed by the siting handbook for small WECSs developed by Wegley et al (1980). The work of Hiester and Pennell (1981), although intended for the siting of large WTGs, contains useful ideas that may be applicable to small WTGs. An important step forward was

Assessing the wind resource

83

achieved with the publication of the Danish Wind Atlas by Petersen et al. (1981). The emphasis was on flat-terrain situations and the work seems to have inspired development of a handbook for wind energy production estimates in the Netherlands. Following successful completion of the Danish Wind Atlas, the Ris0 Laboratory in collaboration with colleagues in most of the countries of the European Economic Community, headed a project to produce the European Wind Atlas (Troen and Petersen (1989).

WIND MEASUREMENT PROGRAMME Short-term study A short-term (1-4 weeks) measurement programme may be conducted for the purpose of verifying model estimates made as outlined previously. The objective would be to obtain data from periods of 1-3 h of relatively constant wind direction (maximum range 10-20 degrees in a series of, for example, 10-min means) for as many directions as are of interest. AWEA (1986) describes three classes of wind measurement systems. In addition to a measurement system at each site, an identical system should be installed at an appropriate nearby flat-terrain climatic site. This will enable direct comparison with routinely measured climatic data and with the data obtained at the candidate sites. Another identical measurement system should be installed at an upwind site for, say, the predominant wind direction. Assuming that the model estimates agree well with the data, one may then proceed with some confidence to derive a site climate based on flat-terrain climatic data and the model estimates for the fractional speed-up ratio at the candidate site relative to the flat-terrain site. Otherwise, either a longer measurement programme, measure-correlate-predict (MCP) methods or wind-tunnel studies would be advisable. Choice of measurement platform and anemometer The most common types of horizontal wind speed sensors are cup or propeller anemometers. A wind vane is used to measure wind direction. As an illustrative example one such wind measurement system will be described. As a contribution to the Canadian Atlantic Storms Program (CASP), two mesoscale meteorological networks were installed. It was decided to use 10 m masts consisting of three 3 m sections and one 1 m section, each made of aluminium pipe with connectors inserted between each section. The three levels of connectors were each fitted with four screw-eyes for guy ropes that were attached to screw anchors at distances of 10 m from the base of the mast. A 3 m gin pole was used to raise the mast after it had been fully assembled and instrumented (including data transmission cables) in the horizontal position. Complete installation

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Assessing the wind resource

could be done in 1-2 hours comfortably by three people, or with a little more experience by two. Winds were measured by Gill-type propeller-and-vane anemometers mounted on top of the 10 m masts. Wind directions were calibrated, taking account of magnetic declination, after the mast was raised. Data were stored in a micrologger. An external 5 amp hr, 12 V battery was used to supplement the logger's internal battery. A small solar panel kept both batteries charged, even in winter at 45°N. In addition to the 10 m winds, some masts were instrumented with Gill cup anemometers at three levels: 2 m, 4 m and 8 m. Included in the measurement programme were two easily erected, non-climbable 26 m masts, each instrumented at six levels with the same type of Gill cup anemometer. These data were useful in comparing with vertical profiles of wind speed estimated by the models. Kite measurements An alternative * platform' for measuring winds is a kite which may be used either indirectly by carrying aloft an instrument package or directly by recording the kite's elevation angle, the length of string and the strain. In the former case, the operation is similar to that of a tethered balloon system. In the latter case, the observed parameters are used with calibration data or tables supplied by the manufacturer to give the height of the kite above ground and the wind speed at that height. For systems without automatic data recording, fluctuations in wind speed must be subjectively averaged by the observer to obtain, for example, 10 min means. Systems with automatic recording systems are considerably more expensive but more accurate. Interpretation and use of data from kite-based measurements present some problems which need to be discussed. Firstly, the measurements are generally taken for a limited time and will therefore only include a small number of incident wind directions and a limited range of thermal stratification. Secondly, the data need to be related to groundbased measurements at a site for which long-term data are available. Otherwise, they are only representative of the specific period during which they were taken. Thirdly, it is normal procedure to make measurements at several levels, perhaps holding the kite at each level for 10 min. This gives a mix of spatial and temporal changes in the data. The temporal changes must be eliminated by normalizing with simultaneous surfacebased data recorded in the same way as the winds aloft (e.g., the same sampling interval and averaging period). Balloon measurements Pilot balloons are sometimes used for wind measurements. These balloons are filled with hydrogen or helium, released and tracked by means of a single or double theodolite system. In a single theodolite operation, the balloon is assumed to rise at a constant rate,

Assessing the wind resource

85

which will not always be the case if there is significant vertical motion or if the degree of thermal stratification varies with height. A double theodolite system which requires two observers and a person to release the balloon, is more accurate but needs a fairly sophisticated analysis programme to deal with missing data, observer error and ambiguities associated with the balloon's passage over or near the baseline between the two observing sites. Either system will give results of doubtful accuracy in the bottom 100 m due to rapidly changing azimuth and elevation angles. Pilot balloons, therefore, are not recommended for evaluating potential WTG sites. Tethered balloons are typically used to carry aloft an instrument package that includes an anemometer, wind-direction vane, barometer (for height measurements) and radio transmitter. Optional sensors are for temperature and humidity measurements. Tethered balloons may be operated in stationary or profiling modes. The latter will give information on wind shear and, if temperature is measured, thermal stratification. In either mode, the data should be calibrated with surface-based measurements at a location for which longer-term data are available so that estimates of the climatology of winds aloft can be made for a few wind directions. A fairly sophisticated analysis programme is required to remove the effects of balloon motion, calibrate measurements, plot results and archive the data. A tethered balloon system is generally quite expensive to acquire and operate. It is not recommended for site evaluation for small, decentralised WTGs. The problems associated with interpreting and using tethered-balloon based data for site evaluation are the same as those for kite systems. Data recording and averaging intervals AWEA (1986) describes four types of recorders: electromechanical, electronic storage data loggers, chart recorders, magnetic tape or solid state. In the CASP experiment, wind speed and wind direction were typically recorded at intervals of 1 s. The recorded data were averaged over 10 min periods and stored along with their standard deviations. The microloggers had enough storage capacity to hold the means and standard deviations for a period of 7-10 days. Every few days the data were dumped via a portable microcomputer to a microcassette tape that was later 'uploaded' to a flexible disk for processing and plotting by a personal-computer system. Long-term study In some cases a longer-term study may be required. The objective would be to estimate the wind climatology more directly by obtaining, for example, hourly measurements over a period of 6 months to 2 years. The shorter period may be sufficient when there are no significant seasonal variations, which will generally not be the case at middle and high latitudes.

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Assessing the wind resource

The same sort of instrumentation as described in the previous section may be used for these purposes. Propeller-and-vane and cup anemometers have been used for long-term studies and are considered sufficiently robust, even in winter conditions. An alternative would be a commercially available automatic wind station. In choosing an anemometer, emphasis should be on continuous reliability of measurement, and any locally available brand can be used if calibration, waterproofness, repair service and availability of spare parts can be guaranteed. Whichever anemometer is chosen, it should be mounted at the proposed hub-height or equator-height. Data averaging intervals would be typically of the order of one hour and the logged data would be analysed to produce monthly statistics. A more complete review is contained in Cherry (1980). Guidelines have been issued by Hunter (1989) on the calibration and use of cup anemometers.

DECISION TREE Figure 3.7 shows a decision tree which may help to summarize the recommended procedures. Users are cautioned not to use this chart without first reading the relevant sections above. In general, the chart follows the outline given in Section 'Recommended Steps' although two decision boxes interrupt the linear arrangement. These boxes involve three branches which are numbered in Fig. 3.7. Other routes through the decision tree are possible, but it is rather difficult to show all these variations in one diagram. Therefore, just the recommended paths are displayed, but the user should be aware of alternative strategies. First decision: choice of simple terrain model At the first decision box of Fig. 3.7, 'Special Case' (Branch number 3) is used if the restrictions of the Guidelines and WASP are not satisfied. The methods of one of the other evaluation techniques may be applicable. Otherwise, the choice between the Guidelines or LSD (Branch 1) and WASP (Branch 2) is mainly up to the user. WASP is perhaps better at handling multiple roughness changes and can also produce energy and power results directly. LSD, although it allows for more general estimates of topographic elevation effects than the Guidelines, does not include variations in surface roughness. Simple applications (isolated hill, maximum of one roughness change, only a few wind directions) are easier and quicker with the Guidelines or LSD than with WASP as no digitisation of terrain is required. On the other hand, the digitised terrain could be used at the second decision box if MS-Micro or FLOWSTAR are selected. It is also possible to select the Guidelines or LSD at the first decision box and WASP at the second.

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87

Survey of Available Information (Selection of Best Areas) 4.2 Site Inspection (Selection of Candidate Sites) 4.3 Choose Simple Terrain Model 4.8 Guidlines or LSD 4.4

Special Case 4.6

WASP 4.5.2 Site Selected

Yes

-»]STOPJ

No

Fig 37 Decision tree for site evaluation and selection. (Paths 1,2 and 3 refer to the choices made in decision box 'choose simple terrain model')

Choose Next Step 4.8

-.

1,2 WASP 4.5.2

MS-Micro 4/5/2

FLOWSTAR 4.5.2

Site No Selected

r-

3 • • Field Measurements 4.7

FINAL SELECTION |

Second decision: sophisticated model or field measurements? At the second decision box of Fig. 3.7, it is assumed that the Branch 3 cases cannot be applied to either of the three sophisticated terrain models. One of the 'other' techniques may have already been performed. If a site has not been selected on that basis, the only remaining option would be a field experiment. In the case of Branches 1 and 2, all of the modelling options are open (except WASP would already have been applied earlier in Branch 2). If thermal stratification effects are significant, then FLOWSTAR should be selected. Otherwise MS-Micro and WASP would generally both be viable options. If only a few sites are of interest within a given (digitised) area, then WASP may be preferred. If, on the other hand, wind speeds throughout the area are required, then MS-Micro should be chosen. WASP gives energy and power output, whereas MS-Micro computes normalised wind speeds.

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Assessing the wind resource

Whichever of the three models is selected, the option to conduct a field study still exists, assuming that a final selection of a site or sites has not already been made. Finally, alternative models are listed in Table 3.6. Part of the information in the table was derived from responses to a questionnaire.

ACKNOWLEDGEMENTS The principal author of this chapter was John L. Walmsley (Can). Drs Jim Halliday (UK), Frans Van Hulle (NL) and Peter Taylor (Can), provided valuable comments at all stages of this work. Frans Van Hulle (NL) collected the questionnaires and tabulated responses that form the basis of Table 3.6. Professors Dimitri Lalas (Greece) and James Manwell (USA), Drs David Infield (UK), Steve Reid (NZ), Stefan Kunz (CH) and Jon Wieringa (NL), and Mr Robert Horbaty (CH), all contributed ideas that have been incorporated. Robert H. Hirchoff (USA) contributed ideas on the use of kites and balloons. Drs Erik Lundtang Petersen (DK) and Ib. Troen (DK) have provided detailed comments.

REFERENCES AWEA, 1986: Standard Procedures for Meteorological Measurements at a Potential Wind Turbine Site . Publ. AWEA 8.1-1986. Amer. Wind Energy Assoc, Alexandria, Virginia, 18 pp. Belcher, S. E., Xu, D. P., and Hunt, J. C. R., 1990: The response of a turbulent boundary layer to arbitrarily distributed two-dimensional roughness changes. Quart. J. Roy. Meteorol. Soc. 116, 611-635. Beljaars, A. C. M., Walmsley, J. L., and Taylor, P. A., 1987: A Mixed Spectral Finite-Difference Model for Neutrally Stratified Boundary-Layer Flow Over Roughness Changes and Topography. Boundary-Layer Meteorol. 38, 273-303. CERC, 1988: FLOWSTARI - A Computational Model for Airflow Over Hills: Model Description. Cambridge Environmental Research Consultants Ltd., Cambridge, 20 pp. Cherry, N. J., 1980: Wind Energy Resource Survey Methodology. J. Indust. Aerodyn. 5, 247-280. Duchene-Marullaz, P., 1977: Distributions statistiques et cartographie des vitesses moyennes de vent en France - applications a l'energie eolienne. Rep. EN-CLI-771, Centre Sci. Tech. Batiment, Nantes. Elliot, D. L., Holladay, C. G., et al., 1987, Wind Energy Resource Atlas of the United States. DOE/CHI0093-4. Richland, WA, Pacific Northwest Laboratory. Elliot, W. P., 1958: The Growth of the Atmospheric Internal Boundary Layer. Trans. Amer. Geophys Union, 39, 1048-1054. Hiester, T. R., and Pennell, W. T., 1981: The Siting Handbook for Large Wind

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Energy Systems. Windbooks, New York, 501 pp. Hunt, J. C. R., Leibovich, S., and Richards, K. J., 1988a: Turbulent Shear Flow Over Low Hills. Quart J. Roy. Meteorol. Soc., 114, 1435-1470. Hunt, J. C. R., Richards, K. J., and Brighton, P. W. M., 1988b: Stably Stratified Shear Flow Over Low Hills. Quart J. Roy. Meteorol. Soc. 114, 859-886. Hunter, R.S., 1989: Recommendations on the Use of Cup Anemometry at the European Community Wind Turbine Test Stations. Report 225/89, National Engineering Laboratory, East Kilbride, Glasgow. Kunz, S., and Baumgartner, M., 1986: Evaluation von Extrapolationsmodellen der Windgeschwindigkeit. OPEN, Bern. Lalas, D. P., 1985: Wind Energy Estimation and Siting in Complex Terrain. Int. J. Solar Energy, 3, 43-71. Lemelin, D. R., Surry, D., and Davenport, A. G., 1988: Simple Approximations for Wind Speed-Up Over Hills. J. WindEngin. Indust. Aerodyn., 28, 111-121. Macmillan, S., and Saluja, G. S., 1988: A Discrete Methodology for the Prediction of Wind Speeds at Potential Wind Generator Sites. Proc. European Community Wind Energy Assoc. Conf. and Exhibition, Herning , Denmark, 6-10 June 1988, 135-140. Mason, P. J., and King, J. C, 1985: Measurements and Predictions of Flow and Turbulence Over an Isolated Hill of Moderate Slope. Quart. J. Roy. Meteorol. Soc. 111,617-640. Munn, R. E., 1966: Descriptive Micrometeorology. Academic Press, New York, 245 pp. Oke, T., 1987: Boundary-Layer Climates, Second Ed. Methuen, London, 435 pp. Panofsky, H. A., and Dutton, J. A., 1984: Atmospheric Turbulence: Models and Methods for Engineering Applications. John Wiley and Sons, New York, 397 pp. Petersen, E. L., Troen, I., Frandsen, S., and Hedegaard, K., 1981: Wind Atlas for Denmark. Publ. No. R-428, Ris0 National Lab., Roskilde, Denmark. Salmon, J. R., Bowen, A. J., Hoff, A. M., Johnson, R., Mickle, R. E., Taylor, P. A., Tetzlaff, G., and Walmsley, J. L., 1988: The Askervein Hill Experiment; Mean Wind Variations at Fixed Heights Above Ground. Boundary Layer Meteorol., 43, 247-271. Salmon, J. R., and Morris, R.J., 1987: MS-Micro: A Microcomputer Version of a Three-dimensional Wind Flow Model for Use in Complex Terrain, pp 253-266 in: V. Lacey (Ed), Proc. Second National Conf., Canadian Wind Energy Assoc, Quebec, 26-28 October 1986. CanWEA, Ottawa. Salmon, J. R., and Walmsley, J. L., 1986: User's Guide to the MS3DJH/3R Model. Rep. 86-1-2, Atmospheric Environ. Service, Downsview, Ontario, 46 pp. Taylor, P. A., and Lee, R. J., 1984: Simple Guidelines for Estimating Wind Speed Variations Due to Small Scale Topographic Features. Climatol. Bull. 18, 2, 3-32. Teunissen, H. W., Shokr, M. E., Bowen, A. J., Wood, C. J., and Green, D. W. R.,

90

Assessing the wind resource 1987: The Askervein Hill Project: Wind-Tunnel Simulations at Three Length Scales. Boundary-Layer MeteoroL, 40,1-29. Traci, R. M., Phillips, G. T., Patnaik, P. C, and Freeman, B. E., 1977: Development of a Wind Energy Methodology. U.S. Dept. Energy Rep. RLO/2440-11, 205 pp. Troen, L, and De Baas, A., 1986: A Spectral Diagnostic Model for Wind Flow Simulation in Complex Terrain. Proc. European Wind Energy Assoc. Conf. and Exhibition, Rome, Italy, 7-9 October 1986, 1,243-249. Troen, I., Mortensen, N. G., and Petersen, E. L., 1988: WASP: Wind Analysis and Application Program User's Guide, Release 2.0, Ris0 National Lab., Roskilde, Denmark, 37 pp. Troen, I., and Petersen, E. L., 1989: European Wind Atlas. Publ. for the Commission of the European Communities by Ris0 National Laboratory, 656 pp. Vermeulen, P., Curvers, A., Van den Haspel, B., Leene, J., Van der Velden, D., Wieringa, J., and Van Wijk, A., 1986: Further Development of A Dutch Handbook for Wind Energy Production Estimates. Proc. European Wind Energy Assoc. Conf. and Exhibition, Rome, Italy, 7-9 October 1986, 1, 219-223. Walmsley, J. L., and Bagg, D. L., 1978: A method of correlating wind data between two stations with application to the Alberta Oil Sands. Atmosphere-Ocean, 16, 333347. Walmsley, J. L., Taylor, P. A., and Keith, T., 1986: A Simple Model of Neutrally Stratified Boundary-Layer Flow Over Complex Terrain with Surface Roughness Modulations (MS3DJH/3R). Boundary-Layer MeteoroL, 36, 157-186. Walmsley, J. L., Taylor, P. A., and Salmon, J. R., 1989: Simple Guidelines for Estimating Wind Speed Variations due to Small-Scale Topographic Features - an Update. Climatol. Bull, 23, 1, 3-14. Wegley, H. L., Ramsdell, J. V., Orgill, M. M., and Drake, R. L., 1980: Siting Handbook for Small Wind Energy Conversion Systems. Windbooks, New York, 96 pp. Wieringa, J., 1986: Roughness-dependent Geographical Interpolation of Surface Wind Speed Averages. Quart. J Roy. MeteoroL Soc, 112, 867-889. Wieringa, J., and Rijkoort, P. J., 1983: Windklimaat van Nederland. Staatsuitgeverij, Den Haag, Netherlands (in Dutch; for background review in English, see Wieringa, 1986).

GLOSSARY Atmospheric Boundary Layer: see Planetary Boundary Layer. Averaging Period: The period of time over which a number of measurements of a quantity are averaged (see Sampling Interval). For wind speed, this interval typically ranges from about 1 to 10 min. (In the case of routine meteorological observations,

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91

reporting of the averaged values is normally only at intervals of 1 -12 h.) Bell-shaped Hill: An analytically specified hill such that the height above surrounding terrain is given by zs = hflj)y where r - (x/L) + (y/L) , and J{r) = 1/(1 + r ). Here L is the distance from the hilltop to the point where/= 1/2 and (x, y) are the horizontal co-ordinates. Cosine-squared Hill: An analytically specified hill such that the height above surrounding terrain is given by zs = hf(r)t where r = (x/L) + (y/L) , and/(r) = cos (nr/4) when r < 2 and/(r) = 0 when r > 2. Here L is the distance from the hilltop to the point where/= 1/2 and (x, y) are the horizontal co-ordinates. Equilibrium Layer: A layer at the bottom of an internal boundary layer in which the turbulence is assumed to have adjusted to a state of equilibrium with the wind flow. Typically, the thickness of the layer is about 10% of the thickness of the internal boundary layer. Fractional Speed-up Ratio: The ratio, A«(Az)/wo(Az), where AM(AZ) = M(AZ) Wo(Az). The ratio is denoted as AS. (See also Normalized Wind Speed.) Friction Velocity: Denoted as u* and related to the magnitude of the surface shear stress, x, by the definition: u* = (t/p) , where p is the density of the air. Geostrophic Wind: The geostrophic wind results from a balance between the horizontal pressure gradient and Coriolis forces. It is a good approximation to the actual wind in smooth flow aloft in the atmosphere when friction and accelerations are not important (Panofsky and Dutton (1984)). Gust: A temporary increase in wind speed from the mean wind speed. A gust is of shorter duration than a squall, which may last for some minutes. The duration of a gust is usually less than 20s. It is followed by a lull or slackening in the wind speed. Gusts are characteristic of winds near the surface of the earth and are the result of mechanical interference by the surface of the air flow. Gust Factor: The ratio of the maximum gust of a specified duration to the mean wind speed over a specified sampling period that is much larger than the gust duration. Internal Boundary Layer: A boundary layer that develops inside another boundary layer when the flow that has approached over uniform terrain encounters a step change in surface conditions.

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Logarithmic Wind Speed Profile: Mathematical idealization of a vertical profile of wind speed defined by u(z) = (U*/K) ln(z/z0), where u is the wind speed, z is the height above ground, u* is the friction velocity, K = 0.4 is Von Karman's constant and z 0 is the roughness length. This equation is valid for surface-layer flow over a horizontally uniform and flat surface under steady-state and neutral stratification conditions. Stratification effects are often accounted for by the addition of a term that is linear in z. Since u* and K are independent of z, the ratio of wind speeds at two heights is simply u2/u\ = In(z2/zo)/lnfzi/zo) whenever the logarithmic law applies. The requirement for horizontal homogeneity is typically fulfilled for height to fetch ratios of 1/100 and the requirement for steady state is satisfied if temporal changes in wind speed (e.g. between 10 min averages) are much smaller than the mean wind speed (e.g. of order 10%). Neutral Stratification (see also Stratification): If a parcel of air that, after vertical displacement within an atmospheric layer without mixing with the surrounding air, experiences a net vertical force of zero, then the atmospheric layer is neutrally stratified. Under such conditions, the parcel will neither tend to return to its initial position (stable stratification) nor accelerate away from it (unstable stratification). Normalized Wind Speed: The ratio, w(Az)/wo(Az), where u(Az) is the wind speed at height, Az, above terrain and u0 (Az) is the wind speed in flat terrain of uniform surface roughness at the same height, Az, above terrain. The normalized wind speed is equal to AS + 1 (see Fractional Speed-up Ratio). Planetary Boundary Layer (PBL): That region of the atmosphere, lying below the free atmosphere, that is directly affected by friction at the earth's surface. Also known as the Atmospheric Boundary Layer. Roughness Length: The height above ground at which, in surface-layer theory, the wind speed is zero. Denoted as zo, the roughness length is the constant of integration in the surface-layer equation for wind shear in neutrally stratified flow: dw/dz = W*/KZ, where u is wind speed, z is height above ground, u* is the friction velocity (assumed constant) and K = 0.4 is Von Karman's constant. The above equation is integrated to give the equation for the neutrally stratified vertical wind profile: w(z) = (M*/K) ln(z/z0). Sampling Interval: The period of time between measurements of a given quantity. (For present purposes, the term 'measurement' is taken to mean both the measurement and the recording, even temporarily, of the measurement. The individual records must be retained or accumulated during one averaging period in order to derive means and other statistics.) For wind speed measurements, the sampling interval is usually quite

Assessing the wind resource

93

short (e.g. 1 min or less). This interval may range down to seconds or even shorter in the case of some research programmes. In order to obtain information on gusts, the sampling interval of wind speed should be about one second. For proper definition of the spectrum of turbulence and quantification of the turbulence intensity, the sampling interval should be of order 0.1s. In practice, the minimum sampling interval is determined by the combined response of the instrument (e.g. anemometer) and recording system. Sampling Period: see Averaging Period. Steady State: A state during which conditions do not change with time (i.e., partial derivatives with respect to time are zero). Stratification: A description of the vertical stability of the atmosphere or of a layer within the atmosphere. An atmospheric layer may be stably, neutrally or unstably stratified. (See also Neutral Stratification.) Surface Layer: The shallow layer, within the planetary boundary layer, immediately adjacent to the earth's surface, in which the frictional drag force is dominant. The depth of the surface layer may be defined as the height at which AT/T = 0.1, where x is the magnitude of the eddy stress at the top of the layer and AT is the magnitude of the vector difference in eddy stress between the top and bottom of the layer. Typically, this depth is of the order of a few tens of metres (e.g. 20 -100 m). The surface layer is characterized by approximately constant eddy stress and wind direction with height. Surface-layer Theory: A theory applied in the surface layer to steady-state flow over horizontally homogeneous terrain. Turbulence: A state of fluid flow in which the instantaneous velocities exhibit irregular and apparently random fluctuations. Turbulence Closure: A method of closing the system of equations governing turbulent flow by expressing higher-order moments or statistics in terms of those of lower order. In first-order closure, for example, the momentum flux (or Reynolds stress) is parameterized using the vertical gradient of the mean flow, thus reducing the number of dependent variables to the number of governing equations. (See Panofsky and Dutton (1984).) Turbulence Intensity: A three-component (downwind, cross wind, vertical) vector, each component of which is expressed as the ratio of the standard deviation of wind

94

Assessing the wind resource

speed to the mean wind speed component in the same direction. The averaging period used to obtain the mean wind speed components is normally of the order of 10 min, whereas the sampling interval is much shorter. Turbulence Level: see Turbulence Intensity. Weibull Distribution: A probability distribution which has been found to provide a good fit to windspeed data collected over an extended period of time. Can be expressed generally as:

PW =

where u = short term (10 minute or hourly) mean wind speed c = Weibull scale parameter k = Weibull shape parameter, related to long term (annual mean wind speed U by:

U = cT(l + (1/k)) where T is the Gamma function. Typically k is 2, whereupon the expression reduces to the Rayleigh function n u - ^

Designing a system

In Chapter 1 a description was given of what constitutes a typical wind-diesel system, and sample configurations were outlined. The purpose of this chapter is to expand upon this basic information by describing in detail the design constraints and considerations which apply to a wind-diesel system and to its various components. To be effective and economically viable, wind-diesel systems must be optimised for each individual application. In particular both the characteristics of the host wind regime dealt with in Chapter 3 and the consumer load (Chapter 2) must be considered since both affect performance and method of operation.

SYSTEM OPERATION Two major methods of system operation are possible, these involving running the diesel either continuously or intermittently. There are advantages and disadvantages to both methods. Continuous diesel operation This primary case has the advantage of technical simplicity and reliability since there is little difficulty in maintaining the continuity of supply, although care must be taken not to overload other generator sets in multiple diesel systems when some of the units are switched off. In general the operating diesel set(s), together with a dump load where required, maintain system voltage and frequency. The main limitations of this approach are low utilisation of wind energy, and correspondingly moderate diesel fuel savings. The former is especially noticeable for systems with a large wind component. It is poor part-load performance of diesel engines, especially of smaller sets, which limits potential fuel savings. The role of wind energy is to reduce the load on the diesel, but even at zero load a small engine might typically consume fuel at 20-30% of the rate at full load. Moreover, to avoid potentially damaging effects of low load operation on the diesel engine, manufacturers usually recommend a minimum diesel loading. If this is say 40% of rated output, then the minimum diesel fuel consumption will be about

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Designing a system

60% of that at full load. Diesel fuel savings under these circumstances will therefore not be very significant. It is concluded that to make best use of the wind contribution and to achieve maximum diesel fuel savings, the diesel(s) must be switched off when not required. Alternatively, the operational constraints on the diesels may have to be rethought (Lundsager and Sherwin, 1990). Intermittent diesel operation To allow large fuel savings, it may be decided to have the diesel running only when the wind is low or the demand is high. However, to guarantee continuity of supply under these circumstances, added complexity in the architecture or control strategies is required. For systems without energy storage this entails switching on the diesel whenever the surplus of wind power over the load drops below some safety margin (usually a significant fraction of the available wind power) to take account of fluctuations caused by wind turbulence. In practice this approach results in the diesel running for extended periods when in fact its output is not needed. Only when energy storage or load management is incorporated into the system can the diesel be left off until actually required to make up an energy shortfall. Moving away from continuous diesel operation introduces the question of how much on/off cycling of the diesel engine can be tolerated. No definitive statements can be made as the effects vary with engine type and the method of starting. Increasing the size of the energy store reduces the rate of cycling and, depending on the losses associated with the larger storage, can be expected to marginally reduce diesel fuel consumption as well. In the design process the additional cost of energy storage must be traded off against the fuel savings and economic benefits of reduced cycling of the diesel. It is also possible to decrease the diesel cycling rate by the use of controls but this tends to result in an increase in fuel consumption. Control strategies are considered further under 'System Control'. To define an optimum system, some degree of analysis must usually be carried out. This is dealt with in Chapter 6. However, before any optimization analysis can be undertaken, the system designer must define what is likely to be a good starting point for the iteration. In particular, decisions must be made regarding what components are to be incorporated: e.g. whether storage is required, how the system is to be operated, and roughly what rating each element should have. Thus the aim of this chapter is to present enough information to allow the designer to identify what basic characteristics are appropriate to the requirement, and to estimate what capacity and performance are required of each component. The information presented here is predominantly technical, therefore decisions taken by the designer at this stage will not be governed greatly by cost. Such economic considerations must wait.

Designing a system

97

QUALITY OF POWER Where an existing utility system exists, for example on a remote island using multiple diesels, then the quality of power required of a wind-diesel system will be determined by the utility itself. In instances where the wind-diesel system is to be the first or only supply of electrical energy to end users, then quality of power and reliability issues need to be addressed through careful assessment of the load. Reliability of electrical systems is often described in terms of the availability of each of the component generators on the system. Availability is the percentage of time that the generation equipment is available to the consumer at its full or specified output. There is obviously a substantial difference in the way in which the availability of wind turbines and diesel electric generators can be assessed. Wind turbine output is solely dependent upon the stochastic nature of the wind energy resource, whilst diesel availability is dependent upon the presence of diesel fuel in the tank. An allowance for the maintenance requirements of each type of generator must of course also be made. Quality of power is expressed in terms of the physical characteristics and properties of the electricity generated and is most often described by: *

Voltage stability

*

Frequency stability

*

Harmonic content

*

Telephone interference factors

*

Electromagnetic interference effects

*

Phase balance

*

Power factor

The power supply must have a quality of power such that no components or consumers' appliances should develop faults or experience deteriorated function during operation. Perfect power quality means that the voltage is continuous and virtually purely sinusoidal, with a constant amplitude and frequency. In practice, it is physically impossible to maintain perfect 'stability' of the voltage and its frequency at the user's terminals. Therefore, the degree of deviation from the ideal (nominal) values can be used as a measure of quality of power. Work is being undertaken by the International Electrotechnical Commission (IEC), the International Union of Producers and Distributors of Electrical Energy (UNIPEDE), and others to specify limit values as a basis for assessing quality of power.

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Designing a system

Voltage irregularities The main irregularities that may affect the voltage wave are as follows: *

Slow voltage variations

*

Sudden changes in the RMS value of the voltage

*

Voltage dips

*

Rapid fluctuations in voltage

*

Unbalance of the three-phase voltages

*

Harmonic voltage distortion

*

Frequency variations

Definitions and information regarding the characteristics of these irregularities are normally given in national or international standards. Some of these irregularities will now be discussed. Emphasis will be placed on those which are likely to be most common in decentralised wind-diesel systems. Slow voltage variations Slow voltage variations can be defined as changes in the RMS value of the voltage occuring in a time span of minutes or more. National standards often state allowable variations in nominal voltage over an extended period, for instance 24 hours. IEC Publication 38 recommends 230/400 V to be the only normal/standard voltage for 50 Hz systems. The voltage at the consumer's terminals under these conditions must not differ from the nominal voltage by more than ±10%. Voltage level control within a permissible range is sometimes a useful means of controlling load. Rapid voltage fluctuations The terms 'voltage change' and 'voltage fluctuation' are defined in IEC Publication 555-3 (1982). According to this standard, which concerns household appliances, voltage fluctuation is 'a series of voltage changes or a cyclical variation of the voltage envelope'. Rapid voltage fluctuations are a series of changes with intervals (intervals of time which elapse from the beginning of one voltage change to the beginning of the next) shorter than approximately one or two minutes. A dip can be defined as a short voltage drop greater than a minimum value, with a change interval of 10 ms to 60 seconds. Light fittings are particularly sensitive to rapid voltage fluctuations and tend to flicker under their influence. Maximum permitted voltage changes as a function of the possible fluctuation rate are

Designing a system

99

normally given in national and international standards (see for example IEC Publication 555-3 (1982) or UNIPEDE experts (1981)). The former reference also classifies four different types of voltage fluctuation waveform, requiring the use of different assessment methods (e.g., periodic rectangular voltage changes of equal magnitudes (type a), a series of random or continuous voltage fluctuations (type d) etc.). Imbalance of three-phase voltages The voltage imbalance of a three-phase system can be defined by the ratio of the negative phase sequence component to the positive phase sequence component. It is difficult to quote general limits for the allowable imbalance of three-phase voltages. IEC Publication 34-1 permits an imbalance factor <1% over a long period, or 1.5% for a short period not exceeding a few minutes. Frequency variation Normally the frequency of large power systems is very stable (<1% variation). Limits for permissible frequency deviations are not stated in current international publications. Electrical components and appliances should normally be designed to withstand a frequency deviation of at least ±3%. Small diesel grids and wind-diesel systems may benefit from the ability to operate over wider frequency ranges. The system developer should ensure that this is acceptable. Harmonic voltage distortion Distortions of the voltage waveform can be caused by the flow of harmonic currents in the system. Static power inverters and converters, and magnetic saturation of transformers, are the most common sources of this distortion. Arc furnaces can also produce such effects. Maximum permitted values of, for instance, total harmonic distortion are found in national and international standards, such as in UNIPEDE (1981), and should be used as guidance towards preserving a certain degree of voltage quality. Power quality in autonomous systems Although the irregularities described above, together with their referenced allowable bounds, actually concern * normal' low voltage electricity supplies, they also form a basis for assessing power quality in autonomous grids. The IE A Recommended Practice by Ballard & Swansborough (1984) concerning Quality of Power from WECS and EPRI Report AP-4682 do not however cover wind-diesel systems. IEEE Standard 1001 (1988) covering interfacing of Dispersed Storage and Generation Facilities provides useful background, but again does not address small decentralised autonomous systems. It is to be expected that deviations occurring in isolated grids may be greater than

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those in a 'normal' supply. The power fluctuations caused by the wind turbine and the possible use of static power converters/inverters in such grids are specific reasons for this. When developing and evaluating autonomous systems, special concern should be given to rapid voltage fluctuations (flicker) and frequency variations. Wind turbines designed for interconnection to a large utility grid may not be able to tolerate significant changes of frequency, and it is always wise to ascertain what changes to the structure and control systems might be necessary. The complexity and cost of the control unit for such isolated systems are strongly dependent on the acceptable or specified limit values governing power quality.

CHOICE OF GENERATORS In general, for an electrically, rather than a mechanically coupled scheme, there will be more than one electrical generator. In such circumstances it is normal though not essential for one generator, usually the one on the diesel, to be synchronous, and for the others to be asynchronous. The wind turbines therefore generally drive asynchronous (induction) generators. These take their reactive power excitation from the synchronous machine which in consequence must be kept spinning and excited. Despite this there is no need for the synchronous generator to remain coupled to the diesel if active diesel power is not required. It is common therefore for small diesel units to have a mechanical clutch between the synchronous generator and the diesel, thus allowing 'wind only' operation. There are three main classes of generator which can be considered for the various system elements, these being d.c, a.c. synchronous, and a.c. induction. In principle each can be run at variable speed, which in the case of the wind turbine can be advantageous in terms of improving system aerodynamic efficiency, torque transient behaviour and power variability, but it is more normal in the case of the a.c. generators to run at fixed frequency and hence nominally fixed speed. The basic attribute of each type of generator, when used in conjunction with the local a.c. network, will now be outlined, from which design considerations and constraints will emerge. D.C. generators Direct Current generators are relatively unusual in wind turbine applications except in low power demand situations where the load is physically close to the turbine. The stator consists of a number of poles, the field windings of which are excited by d.c. The rotor consists of conductors wound on an iron armature which are connected to

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a split slip ring commutator. Power is extracted via brushes which ride on the commutator. Direct current machines generally require regular maintenance and are expensive. Wind turbines having d.c. generators tend to be used primarily for battery charging and resistive heating applications, and to operate at variable speed. Nowadays for most d.c. applications, for example battery charging, it is more common to employ an a.c. generator (alternator), frequently permanent-magnet excited, to generate a.c. which is then converted to d.c. with simple solid-state rectifiers. Permanent-magnet machines operating at variable speed, and producing variable frequency voltage, offer the opportunity of controlling maximum power output at higher wind speeds by utilising the change in inductive reactance of the stator windings to limit current output. A.C. synchronous generators In a synchronous generator, if the rotor is excited with a d.c. current and is caused to rotate, a 3-phase voltage is generated in the stator at a frequency determined by the speed of rotation. When connected to a grid, an important feature of the synchronous generator is that the rotor speed must match exactly the synchronous speed. Loss of synchronisation will occur if rotor torque becomes too high, and it is therefore important to know the generator's 'pull-out' torque to avoid such eventualities. Synchronous machines when fitted to a wind turbine must be controlled carefully to prevent the rotor speed rapidly accelerating through synchronous speed, especially during start-up sequences or in turbulent winds. Synchronous generators are closely coupled devices, ie they have very low damping and therefore do not allow drive train transients to be absorbed electrically. When they are used in a wind turbine which is subject to turbulence it is advisable to install an additional damping element such as a flexible coupling in the drive train, or to mount the gearbox assembly on springs and dampers. The reactive power characteristics of synchronous generators can be controlled and therefore such machines are often used to supply reactive power to other items of generating or motoring plant on the grid which may require it. It is normal for a stand alone wind-diesel system to have a synchronous generator, usually connected to the diesel, which keeps rotating whether or not it is generating active power. Although a synchronous generator does not require an external source of reactive power and may operate on its own, its connection with the local grid is often significantly more complex than that for an induction generator. In particular, synchronizing the

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generator's frequency to that of the grid is a delicate operation and must always be carried out correctly, or serious damage can be done to the machinery. A related problem is that of voltage control. Whenever multiple synchronous generators are operated on the same network, special control equipment must be installed to ensure proper voltage regulation and the minimisation of circulating currents. Another disadvantage of synchronous generators is that they are more costly than induction machines, particularly in the smaller size ranges. It is also sometimes argued that they are more prone to failure. For wind driven machines, electrical stability problems may also arise. When a synchronous generator is being used to supply reactive power only, for example in a wind-diesel system where the synchronous generator has been uncoupled from its diesel by a clutch but is kept spinning, the quality of the waveform can be of particularly poor quality and may require conditioning. A.C. induction generators The induction (asynchronous) generator differs from the synchronous generator in several regards. A rotating magnetic field is induced by the application of an a.c. voltage to the stator. Power is generated due to a difference in the frequency of the rotating magnetic field and the speed of the rotor. This characteristic is termed slip and differences of only one or two percent form the normal design point. Thus when the machine is generating, the rotor does not rotate at synchronous speed, but at some slightly higher rate. Induction generators are consumers of reactive power, and it is not common for them to operate in isolation from other plant, although this is possible with special power electronics. It is recommended, especially in a small grid where the capacity of the other items of plant to generate reactive power may be limited, that power correction capacitors are added to the generator to compensate for its demand on the system. For certain designs of wind turbine, and to increase operating hours, the induction generator is called upon to motor the rotor up to generating speed. Current demand during this phase can be significantly higher than the maximum generating current. In a small decentralised network this current surge can cause problems which would not be so serious in a larger grid. Special control is thus required for motor start applications to prevent excessive power surges. Induction generators are very popular in wind turbine applications. They are reliable, well developed and resilient. Additionally, induction generators are loosely coupled devices, ie they are heavily damped and therefore have the ability to absorb slight changes in rotor speed whilst remaining connected to the grid. Drive train transients to some extent can therefore be absorbed.

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Power electronics Power converters can have a number of useful roles to play in wind diesel systems. By using an a.c.-d.c.-a.c. conversion in the case of a synchronous or asynchronous generator, or a d.c.-a.c. conversion in the case of a d.c. generator, the wind turbine can be allowed to operate at variable speed (see later). Power electronics can also be used to condition the power coming from storage devices of significant capacity such as wide speed range flywheels or battery banks. In effect power electronics provide an interface between d.c. or variable frequency power sources and the * fixed' frequency grid. Variable speed drives can be designed in a variety of ways, four common examples being indicated below: Synchronous, generator/line commutated converter Here the a.c.-d.c.-a.c. link consists of a rectifier, a d.c. choke, and a line commutated inverter. Line commutated inverters are well developed and relatively cheap. However, they cannot function independently of the grid they serve, and demand reactive power. Additionally, they induce harmonics which may have to be filtered to improve power quality. Induction generator/force commutated converter Here a force commutated rectifier supplies the generator with reactive power and the d.c. line with active power. The inverter connected to the grid has stand-alone capability since it does not require the network to have an independent source of power. At present force commutated converters are not as well developed as their line commutated counterparts and are therefore relatively expensive. Induction generator with slip recovery Here the recovery system consists of a rectifier, a line commutated inverter and a transformer. Induction generator with a cyclaconverter The cycloconverter comprises three line commutated inverters. In general, power electronics will impart harmonics to the grid voltage and current. The extent of the distortion depends upon the type and quality of the converter. Such harmonics can be reduced to an acceptable level by filtering. Variable speed systems If the wind turbine is allowed to operate at variable speed, it is possible to achieve the following advantages:

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The wind turbine can be used as a regenerative storage unit (flywheel) to smooth out the wind induced power fluctuations.

*

Aerodynamic efficiency of the wind turbine can be improved since constant tip speed ratio operation becomes possible.

*

The stresses in the drive train can be minimised since the torque is more controllable.

*

Critical speeds corresponding to structural resonances can be avoided.

*

Acoustic noise emissions from the wind turbine at low wind speeds can be reduced by lowering the speed of operation.

The major drawback is that more components are required and that the system becomes more complex. This can be a problem in remote locations where it may be difficult to get spare parts and well trained service personnel.

CHOOSING A DIESEL GENERATOR SET A diesel engine generator is a device which converts fuel (diesel oil) into mechanical energy in an engine and subsequently converts mechanical energy to electrical energy in a generator or alternator. Speed regulation and controls are necessary to maintain useful power. In the following sections an overview of diesel engine attributes is given, and this is followed by guidance on diesel engine rating and on defining the requirements of the generator. An Appendix (p. 133) is provided which gives sample calculations for rating a diesel driven synchonous generator. Diesel engines Diesel engines can be categorised according to several different criteria, including type of fuel, engine speed, form of aspiration and operating cycle. Type of fuel Diesel cycle engines can be fuelled by oil products ranging from No 2 diesel oil (light) to crude or No 6 residual oil (heavy). The choice of fuel depends upon cost, availability, calorific value, temperature conditions and specific engine features. Smaller diesel engines generally utilise No 2 fuel oil although some are designed to be multi-fuelled. Fuel generally accounts for about 80% of the diesel generator operating costs. At a remote location the system designer should ensure that the fuel system of the diesel contains adequate provision for filtering. This is particularly pertinent if the fuel supply is likely to come from a local fuel store which might only be replenished infrequently.

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Aspiration Engines are either naturally aspirated or turbo charged. In naturally aspirated engines, air is taken in at atmospheric pressure. Turbo charged engines inject the air/fuel mixture into the engine cylinders at pressures higher than atmospheric. Turbo charging significantly increases engine capacity (up to 40%) and efficiency by allowing an increased quantity of air to enter the combustion chamber, so ensuring a greater and more efficient burning of the fuel. Operating cycle Engines are classified either as two-stroke or four-stroke. In a four-stroke engine the induction, compression, power (expansion) and exhaust phases of the combustion cycle all occur on separate strokes of the piston, whereas in the two-stroke engine the exhaust and induction phases occur when the piston is almost stationary at the end of its expansion stroke. Two-stroke engines tend to be either very large or very small. Method of cooling This can be by air, forced air, or liquid. Engine speed The speed of diesel engines is generally related to size, large engines tending to operate at lower speeds - usually under 900 r/min. Higher speed engines (over 900 r/min) are more common in the small to medium size range - common speeds are 1200,1500,1800, 2400,3000 and 3600 r/min which are suited to the normal operating speeds of generators which in turn are determined by the grid frequency, typically 50 or 60 Hz. Engine speed is generally a trade off between size (capacity), cost, and engine operating life. Diesel engine controls The engine governor controls the engine speed which regulates the frequency of the generator. Governors occur in two basic configurations, these being mechanical or electronic. The mechanical governor is most often utilised on installations under 500 kW and where shared loads may fluctuate by ±5-10%. The electronic governor is used where frequency stability is very important or in automatic parallel operation. Loads are generally managed within 5%. On large grids, multi-megawatt diesel generators, which are used for peak loads, are generally operated in a droop mode. Droop mode operation allows the speed and/or voltage of the generator to change slightly with load. However, current practice for smaller generators makes it undesirable to operate in the droop mode but rather in the isochronous mode in which frequency and voltage are both invariant, although this

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practice may lead to occasional dynamic instability of the wind-diesel system if limits are set too close. Operational considerations In deciding upon the control and configuration of a wind-diesel system, there is always a trade-off to be made in deciding upon the size of the dump load and/or storage, the tolerable on/off cycling rate and the loading of the diesel. On/off cycling can give rise to engine wear and to poor fuel consumption, although some measurements made by Infield et al. (1985) indicated that for a conventionally started set, a rate of ten starts per hour did not give rise to excessively enhanced wear rates. Care should be taken, however, in extending this result to other situations, and as a general guide, if cycling rates are to be high then the maintenance of fluid temperatures within the diesel during shut-down will reduce wear and fuel use when the set is restarted. Engine wear and temperature are directly related. If a diesel does not reach a high operating temperature, then contaminants in the lubricant are not burned off, which in the long term can lead to damage. Many manufacturers recommend that a diesel engine should not operate below a certain part load for extended periods of time. 40% is a common threshold, although 20-30% might be more common for newer sets. One of the reasons is that even when idling a diesel will consume appreciable amounts of fuel, say 20-30% of that at full load. However, this is not to imply that low loading cannot be tolerated for short periods of time, indeed it has been shown that a diesel set can accommodate negative loads of up to 30% of rated output (Lundsager and Sherwin, 1990). In this mode the diesel acts as a 'compressor' dissipating excess system power. Fuel consumption in the negative region continues falling as more negative load is applied. System frequency is generally stable as long as the negative load does not exceed 30% of rating whereupon a rapid rise may be expected. It is stressed that negative loads should only be applied for short periods. Heat recovery from the diesel engine Only 30 to 40% of the calorific value of fuel burned in a diesel engine is converted to mechanical power. The balance of energy is converted to heat, most of which is removed either by water based coolants in a radiator system or by air. The remainder of the heat is either exhausted in the high temperature combustion gases, or is radiated from the engine surface. Turbo charged diesels also lose heat from their intercoolers. Typical energy (work and heat) balances for turbo and non-turbo charged diesels are shown below in Table 4.1.

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Table 4.1 Typical diesel engine heat balance (%) With turbocharger Usable power Water jacket ^j Lube oil I Intercooler I Exhaust Radiation

Without turbocharger

33.0

35

39.5

30

20.0 7.5

28 7

Figures vary from engine to engine and depend on load, the type of cooling and engine speed. Recovery of heat from the system to provide air conditioning or heat for residential and industrial processes greatly enhances the economics of operating diesel engine generating sets. Use of this heat may also help with load balance in a wind-diesel system. However the economics of heat recovery during prolonged diesel shutdown have to be taken into consideration. Generally most of the heat in the water jacket and about half of the heat available in the exhaust system may be recovered. This more than doubles the overall fuel efficiency to about 75%. Depending on frequency of stop/start cycles, it is recommended that the diesel should be kept warm, perhaps via system waste energy to improve start-up performance. For air cooled diesels, oil sump heater options should be investigated, whilst for water cooled sets the possibility exists of keeping the set warm via a water jacket heater. It must be remembered that in dry heat recovery schemes the engine must still be properly cooled when heat demand is low. Methods for recovering heat from a diesel can be divided into various groups as follows: Normal temperature water [93°C (200°F)]. High temperature water [121°C (250°F), 137 kN/m 2 (20 psi)]. High temperature ebullient steam [121°C (250°F), 103 kN/m 2 (15 psi)]. Normal temperature water Water jacket temperatures of 93 °C (200°F) can normally be utilised by incorporating a liquid-to-liquid heat exchanger to transfer engine heat to a secondary fluid, usually water, circuit. Additional efficiency may be gained by using an external heat recovery muffler. However, it is important to maintain fluid flow to avoid thermal shock to the exhaust system. Care should be taken to avoid lowering the exhaust temperature below

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150-175°C (3OO-35O°F) as this may cause condensation to build up, leading to corrosion and possible engine damage. High temperature water High water jacket temperatures 99-121 °C (210-250°F) require a higher operating system pressure to keep the water from flashing to steam. These pressures are normally 30-140 kN/m 2 (4-20 psi) above the steam-point pressure. Pressure is usually controlled by an elevated or pressure controlled expansion tank. These systems utilise heat exchangers as described above, but require specialised fluid pumps. Higher temperature operation also requires special oil cooling systems. High temperature ebullient steam This type of system removes the heat from the diesel by a phase drainage of the coolant within the engine. The steam and water mixture is not as dense as water alone and rises through the diesel engine to enter a steam separator. This steam can be drained off and used as another energy source. The residual water is then re-circulated through the engine. This system has the advantage of providing low pressure steam for individual processes, and also eliminates the need for pumps and heat exchangers whilst minimising thermal stresses in the engine. Sizing the diesel engine To a great extent the diesel can be sized independently of the rest of the system. This is because it is usually assumed, in the worst case, when the wind power is zero and the storage is exhausted, that the diesel alone must be able to meet the consumer load requirements. The major exceptions to this are systems based on a cycle charge approach in which the diesel(s) run periodically to charge an energy store. It is implicit with these systems that the diesel rating is considerably larger than the average or sustainable load demand. For non-cycle charge systems the diesel should be sized so as not only to meet the maximum expected demand, but also any losses inherent in the system which need to be covered. A safety margin may be added and provision must be made for the expansion of demand. In this last respect the effect on demand of installing a wind-diesel system may be much greater than for conventional generation plant in that the consumers may not have had previous access to electricity or may have had seriously limited access due to availability or cost. It is common for a new wind-diesel system to give rise to a significant, often dramatic, increase in electricity consumption. In principle though, the sizing of the diesel can proceed in a conventional manner with due regard to the nature of the load which should include factors such as motor starting loads, required frequency and voltage characteristics etc. However, the diesel's generator has to meet some additional demands in wind-diesel applications, therefore attention has to be paid to the choice of this component as discussed below.

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Care should be taken in rating the diesel and its generator in locations of low air density. Both power development and cooling capabilities can be affected by low atmospheric pressure or high ambient temperatures. Choosing a synchronous generator for the diesel General guidance on generator options has already been given. Here, by way of example, a specific case is examined. The configuration is indicated in Fig. 4.1 and consists of a synchronous machine connected via a clutch to the diesel. The wind turbine fitted with an induction generator takes reactive power from the synchronous machine, and a dump load is used to absorb excess power. No storage is provided.

Voltage control unit Clutch

wind turbine

Electrical conversion system

Diesel

Dumpload

Fig 4.1 General block diagram of an autonomous wind-dies el system

Generator functions The functions which the synchronous generator may perform are: a Conversion of the mechanical power generated by the diesel into electrical power. b Supply of the reactive power demand of the system (for the load, the dump load, the wind turbine generator, etc). c Control of the voltage level of the grid. d Supply of the sinusoidal shape of the grid voltage. e Supply of short circuit current for blowing fuses. /

Commutation of thyristors in the case of line commutated convertors being connected to the grid.

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D esigning a system Supply of inrush currents for induction machines on the system.

Modes of operation This simple autonomous wind diesel system without storage, has two modes of operation. i If the wind power is less than the load demand then the synchronous generator is driven by the diesel and converts mechanical diesel shaft power into electrical power to make up the shortfall in supply. If the generator has an efficiency, n (0
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WIND TURBINE SELECTION Most of the background information required for the assessment of the wind-diesel potential of a given site pertains to the wind turbine element. Chapters 2 and 3 described the wind, load, environmental and other factors which must be assessed before a turbine can be selected and a system configured. As for the other system components, the primary question here concerns the relative size of the wind turbine. In general the proportion of energy which the turbine can contribute to the system will increase with rotor size, but at the expense of complexity of the system control. At a good wind site it must be remembered that a wind turbine's long term average power output will only equate to perhaps 30% of the generator's nameplate rating. Nevertheless at remote sites load factors lower than 20% can still be economic; however this is very site dependent, and therefore it is essential that the system designer should make some estimate of the energy production potential. The IEA has published (Frandsen and Pedersen, 1990) a recommended practice on power performance testing of grid connected wind turbines in which a method of predicting energy productivity is given. This involves multiplying the power performance characteristic of the wind turbine, a sample of which is shown in Fig. 4.2, by the long term probability distribution of wind speed. The former is usually available from 150 Cut-out or 20 m/s whichever is less

Maximum power or rated power Typical pitch regulated

100

Typically stall regulated

GL

^ - 0 . 5 m/s bins-4*— 2 m/s bins-*

50

Each bin shall contain a minimum of 3 datapoints

Cut-in 10

15

20

Wind speed at hub height, V - (m/s)

Fig 4.2 Wind turbine power curve (IEA recommended practice No 1)

25

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the turbine supplier, and it is recommended that the purchaser satisfies him/herself that the curve has been derived independently. It should be noted that energy production is a function of air density, and therefore adjustments should be made to the estimates if the turbine is to be sited at a very cold, a very warm, or a very high location. It is vital in the case of a decentralised system to realise that not all of the power produced by the wind turbine can be utilised unless a large energy store is included and even here large losses in regeneration are likely to be experienced. A certain proportion of the energy has to be dumped during times of inadequate consumer demand, and attempts to improve long term wind power penetration by increasing the turbine rating and/or size are often counterproductive since this serves on many occasions merely to increase the power surplus. The system designer should therefore look at the nature of the load and the nature of the power availability to enable some optimum rating to be identified. Often in a wind-diesel system it is helpful to reduce the rated windspeed to enhance energy production at lower speeds. It is pertinent to note however for a typical turbine, which cuts in at 4 m/s and reaches rated power at 14 m/s, on a good site having a mean wind speed of 7.9 m/s, that 90% of the energy is produced in wind speeds below 14 m/s but only 10% is produced in wind speeds below 7 m/s, ie energy production covers a fairly narrow band of windspeeds and therefore time. It can be assumed for early sizing, that there is a certain mean wind speed above which consumer demand will be fully met by the turbine, although the concept of guaranteed power levels is somewhat misleading due to the random nature of wind turbulence. The greater the turbine size the lower is this windspeed. By using more than one turbine, the variability of the total wind produced power can be reduced (by up to the reciprocal of the square root of the number of turbines), so allowing the 'guaranteed' power level to rise proportionately. When choosing a turbine there are a number of important general considerations regarding siting and these are dealt with in Chapters 2 and 3. However it is worth emphasising that it is vital at an early stage to consider the logistics of shipping the turbine to site. Local craneage, transport and roads will normally only be able to handle parts up to a certain weight and/or size, and this may have a bearing on the maximum rotor diameter. Whereas parts for a 100 kW turbine might require expensive helicopter transport, two 50 kW units, despite their extra capital cost, might have little associated marginal transportation costs and has the advantage of still producing half the rated capacity in event of one machine being shutdown for servicing or maintenance. Wind turbine options Anyone considering the installation of a wind turbine should be aware of the various types of machine on the market and their relative merits. Although most wind turbines in the range 25 kW upwards are of horizontal axis, upwind design, other types of machine such as horizontal axis downwind, or vertical axis are available. In a horizontal axis wind turbine (HAWT), the axis of rotation is substantially horizontal and parallel to the

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wind velocity, whilst for a vertical axis wind turbine (VAWT) the axis of rotation is vertical and perpendicular to the wind. An apparent shortcoming of a horizontal axis machine where the rotor is upwind of the tower is that it requires a yaw system to keep the rotor correctly aligned to the wind. For very small wind turbines this can take the form of a tail vane whilst on larger machines a geared yaw ring at the top of the tower in which a cog is driven either by 'fan-tail' side rotors or by a motor driven system is more common. The yaw mechanism obviously adds complexity and therefore cost to the turbine. An alternative approach which avoids the need for the yaw system is to place the rotor downwind of the tower. This however can cause dynamic instabilities and certainly produces added machine noise and blade loading as a result of the blades having to pass through the wake of the tower. Downwind rotors can be of free or damped yaw design. Wind turbine rotors are generally either of fixed or variable pitch. A variable pitch machine allows the angle between each blade's chord line and rotor plane to change. This is usually utilised to regulate power and to control rotor overspeed and shutdown of the wind turbine. On a fixed pitch wind turbine the blades maintain the same angle between the chord line and the rotor plane. Rotor control is generally accomplished by aerodynamic stall of the blades with a brake to stop the rotor. There is a considerable variation of design approaches in existing horizontal axis wind turbines. Primary differences and their advantages and disadvantages are outlined in Table 4.2 below.

Table 4.2 Relative merits of different wind turbine types Configuration

Advantages

Disadvantages

Upwind rotor (vs downwind)

Avoids tower shadow turbulence Lower noise

Requires method of maintaining wind turbine rotor in upwind position. (Added complexity and cost.) Added loads on tower

Fixed Pitch rotor (vs variable pitch)

Eliminates need for pitch control mechanisms, fewer parts - less costly

Requires self stalling blade. More subject to decreased performance if the blades get dirty or collect insects. Requires tip brakes or other means of stopping rotor overspeed. Lower operating efficiency

Rigid rotor (vs teetered rotor)

Eliminates need for teeter stops and teeter bearings. Less costly

Wind turbine must absorb higher loads

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The reversing gravitational force experienced at the rotational speed by a rotating blade can also cause problems for larger horizontal axis wind turbines, since this force increases non-linearly with size. Vertical axis machines require no yaw system, take wind from all directions and have the potential to have the power take off placed at ground level. The blades do not suffer from cyclic stressing due to gravity, but aerodynamic loading on the blades does vary as a function of azimuth. Various designs are available, the most popular being either of straight bladed or troposkein ('egg-whisk') design sometimes called Darrieus rotor. For a decentralised site, it is probably more important to choose a proven turbine with low maintenance requirements, than to consider one which might at first appear to be more technically elegant. Care should be exercised when talking to suppliers to emphasise that the turbine will be decentralised. For example the service life of the mechanical braking system is likely to be reduced due to the enhanced frequency of grid losses. Additionally, if the turbine generator is to be allowed wide frequency excursions, then mechanical loads on the turbine will increase. The manufacturer should be made aware of such special circumstances and requirements and should supply evidence that the system and structure offered will perform properly and safely. There are several types of generator configurations utilised in wind turbines as indicated in Table 4.3.

Table 4.3 Wind turbine generator options Type Permanent magnet Alternator D.C. generator Induction Synchronous Variable speed

Application Small battery chargers Small battery chargers Small battery chargers Small to medium sized wind turbines 1 kW + Medium to large wind turbines 30 kW + Medium to large wind turbines

The use of different types of generator necessitates different control and interface strategies for the diesel generator. The first three types generally work in parallel with the diesel generator set to charge a battery bank which then supplies power to the load. Generator type, rotor configuration, and wind turbine control all affect the variability of the power being produced, and therefore must be considered when devising a system control strategy.

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Wind turbine sizing This is a critical issue as the wind turbine(s) is likely to be the most expensive item in the system. It is important to distinguish between the electrical rating of the wind turbine and its rotor swept area. Although for a grid connected wind turbine, the rated wind speed determines the relationship between these two factors and is roughly constant in relation to the mean wind speed for the site, this is not in general true for wind-diesel systems. For isolated systems, continuity of wind supply is more important than in grid connected applications where it is only the total energy yield and not its distribution in time which is important. For any particular situation the wind turbine(s) can only really be properly sized by the application of appropriate wind-diesel models in combination with economic analyses. The only general guidance that can be given regarding initial selection is that it is undesirable for the wind turbine rating to be significantly greater than the maximum consumer load plus the system losses, except where a large energy store is included. The rated wind speed for the turbine will ideally be less than it would be for grid connected applications. This will ideally be reflected in a larger swept area for a given electrical rating. In practice, however, the choice of available commercial machines may demand a higher electrical rating than is ideally required. In the final analysis it is the cost which is important.

DUMP OR AUXILIARY HEAT LOADS The purpose of incorporating a dump load into the system is to enable excess energy from the wind turbine, which cannot be stored, to be dissipated preferably usefully, to preserve the stability of the system frequency and voltage. The dump load must be rated to accommodate the maximum instantaneous power surplus expected from the system. This requires a knowledge of the minimum consumer load, the maximum turbine power, and any minimum diesel loading. The interdependence of load and frequency in many wind-diesel systems must also be taken into account when establishing these power levels. Control parameters for the dump load are normally frequency and power balance. System set parameters determine the maximum system frequency variations, and the dump load's function is to preserve these. The frequency limits, and the nature of the system operation determine the accuracy and the control quality required of the dump load. These are described in Table 4.4 below.

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Table 4.4 Accuracy and control quality required by the dump load for various applications Relative Accuracy: Operational Modes diesel large load operation variations continuous

yes no start/stop yes no Response Time of Control System: Operational Modes diesel large load operation variations continuous start/stop

yes no yes no

narrow <2%

Frequency Limits medium 2-10%

wide >10% medium medium high high

high high high high

medium medium high high

narrow <2%

Frequency Limits medium 2-10%

wide >10%

medium slow fast high

slow slow slow slow

fast medium fast fast

In the table fast can be taken as better than 0.1 seconds, slow to be longer than 1 second, and medium to lie between 0.1 and 1.0 seconds. Various dump load types and control options are available and the characteristics of each are listed in Table 4.5. Figure 4.3 shows the electrical configuration of each. Dump load types / Rectifier controlled resistor Here, power is dumped by applying a d.c. voltage across a resistor. The d.c. voltage is controlled by means of the firing angle of a controllable, three phase, full or half wave rectification thyristor bridge. // Diode rectifier plus chopper controlled resistor In this type of dump load, the three phase a.c. voltage is rectified to a constant d.c. voltage by means of a three phase diode bridge. This d.c. voltage is converted to another d.c. voltage by means of a controllable d.c./d.c. chopper converter. The chopper provides the means whereby the voltage across a d.c. dump load resistor can be controlled. Chopper controlled resistors are not widely available and cannot be obtained as standard items.

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Hi Phase cutting controlled resistors The dump loads this time experience a.c. voltage, the rms values of which are controlled by cutting the three phase voltages. iv Binary resistor bank The resistive load on the grid is here made up of a number of three-phase resistors. Variations in loading are achieved by firing solid-state switches which change the net load seen by the grid. To achieve a smooth variation of this load with a relatively small number of components, the resistor bank is built-up in a binary fashion. It is important that the accuracy of the individual resistances is sufficiently high to ensure that the advancing binary pattern guarantees a monotonically increasing load. v Servo-controlled water resistor The immersion of three conducting plates, one for each phase, is varied by means of a servo motor, thus changing their wetted area and therefore the resistance between them.

Table 4.5 Characteristics of the various types of dump load Type

Accuracy

Rectifier controlled Chopper controlled Phase cutting Binary resistor bank Water resistor

high high high high poor

Response Time

Reactive Power Demand

Voltage Distortion

yes no yes no no

yes slight slight no no

fast fast fast fast slow

STORAGE SELECTION For grid-independent wind-diesel applications the use of an energy buffer is often a necessity. The storage unit can serve various purposes which are mainly to: a Smooth short term fluctuations in the WTG power and/or consumer load for improvement of grid quality (short term storage). b Reduce start/stop cycles of the diesel generator set (short to medium term storage). c Reduce fuel consumption (short to medium term storage). d Balance medium and long term WTG power or consumer load surplus (long term storage).

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Designing a system

Thyrister control

Servo-controlled water resistors

Phase cutting controlled resistors

P^

Pi:

Firing angle control Rectifier controlled resistor

Copper control

SSR control

Diode rectifier and chopper controlled resistor

Binary controlled resistor bank

Fig 4.3 Dump load options

e Minimise diesel start up time and therefore prolong stand-still times of the diesel engine (short term storage). Various technically different types of storage unit exist each of which has scope to achieve only some of the listed objectives. The different devices can be characterised by the following parameters:

Designing a system *

Overall energy content.

*

Maximum energy flow during charge and discharge.

*

Efficiency at high/low cycling rates.

*

Energy density per mass.

*

Average service life time.

*

Cost.

119

At the time of writing, four technically different types of storage unit have been used in wind-diesel applications. These are based on batteries, flywheels, hydraulic pressure vessels, and hydro. A description of each follows, together with a discussion of end use storage. Battery storage systems Batteries, the basic elements of a Battery Storage System (BSS) are used world-wide in many energy storage applications. They are very reliable as long as technical restrictions are observed covering for example the maximum discharge level, maximum charge current, etc. As all batteries operate with d.c. current, a BSS for use in a wind diesel application needs an interface to the a.c. part of the system. The interface includes elements such as rectifiers, transformers and/or inverters. The control of the charge/discharge of the battery elements is a function of the load and power input. Used correctly, batteries have a moderately good life expectancy and are very flexible in use because of their modularity. The inclusion of batteries is a proven way of adding storage to an Autonomous Wind Diesel System (A WDS). They can be used for all of the purposes a to e mentioned above. A schematic of a system using battery storage can be found in Fig. 1.2. Sexon (1985) and Traa (1985) describe typical applications. Generally it can be noted that batteries, independent of type, can only be used for medium to long term storage in the range of minutes to hours. Three different types of batteries are relevant for integration into AWDSs: the conventional lead acid battery, the nickel-cadmium or more generally termed steel battery, and the sodium-sulphur battery, which is less commonly available. The characteristics of battery types, their advantages and disadvantages are now outlined. Lead acid battery The lead acid battery is available in a wide range of sizes and capacities. It has the advantage of being relatively inexpensive in terms of its energy storage capacity per unit cost. As a consequence of the great fluctuations in the supply and the need for high cycle-rate and lifetime, only lead acid batteries of higher quality should be used.

120

Designing a system

Good quality units can be charged and discharged from 20% capacity to nominal capacity within about 10 hours without reduction of lifetime. The life of a lead acid battery is typically limited to between 400 and 1200 cycles, depending on the level of discharge, the battery quality and the power transfer rate. The efficiency of the unit is also very strongly dependent on these parameters, but typically is between 60% and 70%. Higher minimum discharge levels can prolong the battery life. It is essential in wind-diesel applications to use deep cycle (traction) batteries. Car starting batteries are not appropriate. Expert advice in this area should always be sought. Nickel-cadmium battery This type of battery is able to withstand much lower discharge levels (down to 10%), and faster charge and discharge rates without any harm. Additionally, the number of cycles which the battery can sustain is more than 2000 cycles (up to 10 times higher than for the lead acid battery). On the other hand the units are roughly two to four times more expensive. The in-out efficiency for the battery in a wind-diesel system can be up to 80%, although power electronics can lower this appreciably. Other types of battery exist, similar to the nickel-cadmium type, which go under the generic title of * steel batteries'. In the near future, it might be possible that another material combination with the same technical concept will exceed the performance and the economy of the Ni-Cd battery. Sodium-sulphur battery This type, although not widely available, might in the very near future become the most favoured option for wind-diesel applications. The efficiency depends upon the frequency of use of the battery. The unit is heated either by the losses experienced during normal charge or discharge, or by the self-losses during non-operation. There is no reliable information available about the number of charge-discharge cycles that can be accommodated nor about the loading current. Flywheel storage systems Flywheels store energy as rotational inertia. They offer a fairly high energy density and a high lifetime even under the fast discharge conditions which are typical of AWDS applications. In existing wind-diesel schemes the storage capacity ranges from seconds to some minutes. Fluctuations in wind power also vary on a timescale of seconds to minutes. Flywheel technology is still at an early stage of development, but plans for systems having an energy storage capacity of up to 1.7 MWh have been reported. Sacks (1989) provides an overview.

Designing a system

121

Flywheel systems developed to date for wind-diesel use have concentrated on medium scale applications. Two main types of flywheel exist, which differ in the nature of their coupling to the system and in their rotational masses and speeds. The simpler one is the low speed type which is coupled directly to the diesel-generator, normally by a freewheel clutch which enables the diesel to stop or to run free during periods of surplus wind power. These flywheels have rather big masses, because the change in the energy stored in a flywheel is directly proportional to its moment of inertia and to the square of the change in its rotational speed. If the flywheel is coupled directly, the energy density is low, because the rotational speed and the acceptable speed range, which is limited by the tolerable bandwidth of the system frequency, is also low. This type of flywheel can be used to improve the grid quality in the sense that the fluctuations in the system frequency can be slowed down although not eliminated. Therefore, during times of wind surplus the wind turbine can store a certain amount of energy in the flywheel, before the system exceeds the upper frequency limit and has to be shut down. Also, the number of diesel start/stop cycles and the diesel run time can be reduced drastically. An example of this type of system is given by De Bonte et al. (1988) and Infield et al. (1988). If the frequency of the grid has to be very stable, a storage with adjustable, very high power transfer rate has to be used. For this purpose one of the most attractive storage types is a variable speed electronically coupled flywheel system. It consists of a variable speed driving motor, a gearbox, a high speed flywheel, and control and interfacing electronics. By adopting asynchronous operation the full energy content of the flywheel can be used, since its speed range can be much wider than the frequency range of the grid. The rotor can be built either of steel, steel compounds, or fibre reinforced plastics. Because of the high rotational speed (up to 30,000 r/min for GFRP rotors) the rotor is enclosed in an evacuated container to reduce aerodynamic losses and to prevent accidents in the event of the rotor bursting. The GFRP rotor system can be reduced in weight drastically, because it can accommodate very high rotational speeds and the strength of its protective container can be minimised as a consequence of the very advantageous destruction mechanisms (no heavy parts can separate from the rotor). There are very few standard products of this type available on the market. They can be fitted to the system by simply changing some parameters in the integrated control. An additional advantage compared to the directly coupled flywheel is that the storage unit can be separated locally from the rest of the system. A major disadvantage of the variable speed system is its high price, caused by additional power electronics, by the increased loadings on the bearings, and by the need for containment and evacuation.

122

D esigning a system

The efficiency of both types of flywheel is high. There are some bearing, air-friction and gearing losses, but these can be minimised by special design and evacuation. If the flywheels are asynchronously coupled to the diesel engine, or if there is a clutch for separating it mechanically, it can also be used for 'crash-starting' the diesel. In a crash-start, the diesel engine is accelerated from standstill to its nominal speed within a very short period. This allows the system control to decide at the very last moment whether the diesel has to be started to supply additional power, so allowing diesel stop times to be maximised and the start/stop actions to be kept to a minimum. Several prototype systems have proved that if the coupling is dimensioned correctly, neither the diesel engine nor the coupling is exposed to excessive wear. Crash-starts are used by the systems described by Infield et al (1988) and Lundsager and Norgaard (1988). Hydraulic storage systems Hydraulic storage is a way of providing short term and high power transfer rate energy. The energy is stored in a simple, commercially available pressure vessel of the type commonly used in many other industrial applications. The vessel contains a rubber separator bladder whose volume is filled with compressed nitrogen gas. During charging, usually with high pressure hydraulic oil, the gas is compressed and energy is stored. When there is a wind energy surplus, the excess electrical energy is used to drive a hydraulic pump/motor in the pumping mode, so increasing the pressure in the pressure vessel. Inversely, if the consumer demand exceeds power production, the pump/motor is operated in the motor mode to drive a generator set. The system is regulated by an electro-hydraulic valve, which controls the speed of the pump/motor. There is also the possibility of coupling the pump/motor directly to the generator shaft of the wind turbine by mechanical gearing. System efficiencies are dependent on power transfer rates, since there are different efficiencies for adiabatic and isothermal processes. The depth of discharge and the system size also affect efficiency. Already smaller systems (with a storage capacity of approximately 6 kW/minute) achieve an efficiency of greater than 65% at higher loads as reported by Slack and Musgrove. It is believed that larger systems will perform even better. The range of nominal storage sizes is compatible with AWDS having a rating of from only a few kilowatts to more than 100 kW. Tests have shown that a significant reduction in diesel starts and fuel consumption can be achieved by using hydraulic storage. It is very important that the storage size fits the wind turbine, because efficiency is very strongly dependent on the power transfer rates and the standing losses caused by leakage.

Designing a system

123

The cost effectiveness of hydraulic storage units has not yet been proven. It is clear that storage costs per energy unit will be relatively high, so that only short time storage will be appropriate, even though specific costs drop slightly with increasing size. Safety precautions must always be observed for hydraulic storage systems because of the high pressure levels. A schematic of a system using hydraulic storage can be found in Fig. 1.3. Hydro storage systems This type of storage uses the principle of converting kinetic energy into hydrostatic potential energy and back again. A pump/turbine is driven by the surplus wind energy to raise water to a higher level, where it can then be stored without losses and in almost unlimited amounts. When additional power is needed, the flow can be inverted. In this case potential energy is transformed into kinetic (rotational) energy by the hydro-turbine which is coupled to a generator to produce electrical power. The efficiency of such a system is less than 70% and decreases with smaller nominal ratings. The costs of hydro storage systems are high compared with the cost of other storage units. Use of natural dams can help to reduce costs. For larger ratings, which are above the typical sizes of actual wind diesel systems, this storage method could be really cost effective. On the island of Foula, Shetland, Somerville and Stevenson (1987) have reported that a prototype system has been installed to supply the 50 inhabitants. This prototype has a storage capacity of 1800 kWh and a maximum transfer rate of 25 kW. A fuller description of this system is given in a case study in Chapter 5. End use storage All of the storage systems discussed so far are designed to feed power back into the supply system when required. An alternative approach, which is very akin to load control (see later in the chapter) is to store excess energy in some useful form which will not be recovered via the main supply system. Typical examples are hot water storage, which is effectively a usable dump load, desalination, hydrogen production, or water pumping. The last can be designed to create a head useful for subsequent irrigation. It is possible that in an end use storage scheme the storage could form the sole load on the system. Rating guidance In general, energy storage can be considered to have two main functions. Firstly, it enables the system to maintain a continuous supply by covering the load while a back-up diesel is started. The worst case occurs when the wind turbine trips out while providing

124

D esigning a system

rated output. Knowing the expected start-up time for the diesel generator, the energy to be covered from the store can be calculated. This can then be converted to storage size depending on the type of energy storage technology being applied. If, as can be the case with flywheel energy storage, energy to accelerate the diesel is also provided by the store, then this must also be taken into account in the calculation. Secondly, energy storage reduces the rate at which the back-up diesel(s) is cycled on and off. It is the system designer's task here to trade off the economic benefits of reduced engine cycling, in terms of wear and maintenance, against the increased cost of the store. One further aspect of energy storage is the possibility of making fuller use of the wind energy and so saving diesel fuel. The magnitude of the losses associated with increasing the storage must be taken into account when assessing this. The effect and efficiency of all of the storage systems discussed above is strongly dependent on their selected rating relative to the overall system size, and can vary with the nature of the supply and/or the load. Therefore it is not possible to give general rating guidance for storage units. If the capacity of the store is not selected correctly, as can easily happen, the benefits to be expected can disappear and can in fact be reversed. The optimization of the rating is a typical problem for a computer model. Such models require either real or synthesised wind and load data, time constants, and a description of the technical limitations of the storage device. The computer model calculates for a certain time step (seconds to minutes) the response of the system. The time step has to be different for different storage devices and applications. For example the analysis of a flywheel used for smoothing the system frequency will require a very short time step for the dynamics to be represented correctly. On the other hand, the design of a battery storage device could be accurate even when significantly longer time steps are chosen. Generally, storage is a very expensive part of the system, and therefore one should not try to save money by just connecting a storage device without previously carrying out a careful examination of the problems and benefits. Examples of simulation models which include storage will be described later in Chapter 6 which deals with modelling techniques. Slack and Musgrove (1986) describe a suitable model for hydraulic storage.

SYSTEM CONTROL As far as possible the standard controllers on the individual system components should be utilised. For example a diesel generator set will already have engine speed governing, a start sequence controller, protection equipment and voltage regulation. Unless there is good reason, the designer should not interfere unnecessarily with these. It may be that

Designing a system

125

the standard voltage controls on the diesel will be inappropriate, and attention should be paid to defining the voltage and reactive power requirements of the other generators on the system. The wind turbine generator(s) will likewise generally come supplied with its own controls to deal with connection and disconnection to a network, and in some cases power regulation. These should enable it to operate in a wind-diesel system, although some modification may be required to the frequency and voltage excursion limits. There are however two specific areas of control with which the system designer will have to cope. First is the control of the frequency by means of power surplus, and where required a minimum loading on the diesel. This is usually achieved by a dump load or storage element which is controlled to provide a power balance. Control is based upon measured power or frequency or both, and it has a critical role with regard to system stability but as such is beyond the scope of this handbook. The second area concerns the switching in and out of the diesel(s). Crudely the requirement can be viewed as 'switching on' a diesel when required and 'off when not required. In practice there is also the constraint to avoid excessive on/off cycling. Conditions will vary from site to site (especially relevant here is turbulence intensity) and the control strategy should be designed to take account of this. For systems with some storage, ie those that can at least cover the load while a diesel is started, the decision to start the diesel can be based on the minimum acceptable system frequency. More complicated is the decision to 'switch off the diesel. A number of different approaches have been used, these being based on: /

Diesel minimum run time, ie once the diesel has been started it is run for at least a pre-set time before being stopped.

//

Required power surplus (hysterisis).

Hi Averaging or filtering of wind power surplus. These of course can be used in combination. Research at Rutherford Appleton Laboratory (RAL) has indicated that a minimum diesel run time is the least effective strategy for reducing cycling. For a given reduction in on/off cycling it causes the greatest fuel penalty, which is not altogether surprising since as a strategy it is in no way responsive to changing wind conditions. Averaging and power surplus criteria are both more effective although care must be taken with averaging in order not to let the effective time constant get too long. In this event switching will occur which will not reflect the current wind conditions and excessive cycling could result. It should also be noted that when selecting a power surplus level, account should be taken of system losses. Failure to do this can also result in excessive

Designing a system

126

1-5 <1 DAY

Increasing exponential averaging Increasing minimum run time

100

0-8

Increasing storage

s

07 1500S

06

,

t

I

,

,

t

.

\

,

.

,

5 10 15 AVERAGE DIESEL CYCLING RATE (STARTS/HR.)

Fig 4.4 Effect of various diesel stop criteria on rates of cycling and fuel consumption

on/off cycling. Figure 4.4 is taken from Infield etal. (1988) and shows the effect of these different strategies on cycling and fuel consumption as calculated by simulation modelling.

ELECTRICAL SAFETY It must be confirmed that the wind-diesel system meets the requirements of established standards and codes for the safety of consumers, the public and operators. Codes and standards may be both those adopted and legislated by government and regulatory groups, and those adopted by utilities and power generation authorities for their specific use. It is the responsibility of the system designer to design and select safety and protection

Designing a system

127

systems, devices and components and their application to ensure the safety of the system. In many areas advice given in standards relating to dispersed storage and generation facilities on a utility network will be applicable. For instance ANSI/IEEE Standard 1021 (1988) sets forth requirements for the interconnection of a small wind energy conversion system to a grid. ANSI/IEEE Standard 1001 (1988) is more extensive and deals with a much wider range of devices. Rizy et al. (1984) give useful advice on operational and design considerations for electrical interconnection of dispersed storage and generation systems. Specific requirements for safety vary from place to place and depend upon the nature and scope of the application. Specific examples of wind diesel applications with varying safety requirements are: a Integration of a wind turbine into an existing small diesel grid or utility by an independent developer The developer will be required to demonstrate that the wind turbine meets national wind turbine safety codes and that the electrical interconnection is safe and meets the electrical safety requirements of the utility and state, province or country. b Integration of a wind turbine into an existing small diesel grid or utility by the utility The utility will apply internal codes and practices to ensure that the equipment conforms. Other agencies or groups may require that the wind turbine meets safety standards if there is an indication that safety of the public may be affected by installation and operation of the wind turbine. c Construction of a new wind diesel plant in a previously unserviced area The designer and developer should be able to demonstrate that each component of the system is suitably designed and applied in the system and that the components meet accepted safety codes for operation under the conditions intended. In each case the successful completion of commissioning tests (see Chapter 7), under real and simulated fault conditions is a mandatory condition of acceptance and conformance to safety standards.

LOAD MANAGEMENT The problem of wind power variability can be tackled by load control. Rather than attempting to match the power generation to the consumer demand by incorporating storage, as was discussed previously, the philosophy here involves an inversion of approach and taking action to vary the load to make it match the power available.

128

Designing a system

If it is required that the behaviour of the power supply system should be totally transparent to the end consumers, as is the expectation on a large grid, then load control on its own is likely to be inappropriate, and the installation of an energy buffer or store as described previously in this chapter would be virtually essential. However, without large energy storage, and for significant wind penetration, excess energy would have to be dumped, which would generally result in poor economic viability. In many applications however it is not always necessary to meet apparent demand, and an alternative less wasteful technique involving dumping the excess power usefully, when available, can be adopted. Additionally, many consumers if given economic incentives are prepared either to reschedule their demand to suit the system or to tolerate limited availability. This gives the designer greater flexibility. The key to successful load control lies in being able to sub-divide the total system load into essential and non-essential elements. Thereafter, by switching in and out, in an appropriate controlled manner, the latter low priority loads, an effective system can be realised. Load control of this nature obviously requires the co-operation of the system users, who cannot be allowed the expectation of total system availability enjoyed by consumers on large grids. A conducive tariff structure can encourage user co-operation, but in certain circumstances legal hurdles can prevent the users seeing the true cost of the energy they consume, thus jeopardising the viability of the scheme. Therefore, a perfectly sound technical solution can fail if certain institutional barriers are not overcome. Load management strategies can be classified as either short or long term. In short term schemes, switching may occur very rapidly, perhaps on a timescale of milliseconds. Short term control Successful short term load control schemes have been designed by Somerville and are described in Stevenson and Somerville (1984) and Somerville (1986). It is worthwhile studying one of these schemes to appreciate the type of network and network control that is required of such a load controlled system. Figure 4.5 shows a schematic layout of the power system on the remote Scottish island of Fair Isle. A 50 kW wind turbine with four pole brushless alternator and automatic voltage regulation is combined with a 50kW 'winter' or a 20 kW 'summer' diesel. The load is split up into three elements, viz essential service load (lighting etc), heating load, and (true) dump load. However only two of the system contactor switches, labelled D, E and F can be closed at any one time, making it impossible for a diesel and the wind turbine to serve the same load simultaneously. Stability of the wind turbine load is

Designing a system

129

Heating Load

Reserve

Fig 4 .5 Schematic layout ofpower supply system (Fair Isle, Scotland)

maintained by frequency sensing load switches located in each user's premises. When the system is lightly loaded, rather than directing excess power to dump, the switches, each of which has a different frequency threshold, add extra low priority consumer loads until the system frequency falls to an acceptable level. Similarly, when the system is heavily loaded and system frequency is falling, the switches drop loads until the system reaches design frequency. When the full heating load is being served and the wind turbine system frequency is still rising, excess power is directed to a dedicated bank of dump load resistors, although in practice, if the system components are correctly rated, only a small proportion of the energy should ever be dumped. Systems of this type can be made more equitable to individual users by the installation of time switches in each property, which act to vary priorities and to stagger guaranteed availability of the system. Control of loads does not necessarily have to be distributed. Central control is possible. Instructions to switches, which have to be located at individual premises can be sent as ripple signals over the power lines or alternatively via the telephone network. Normally, the increase in availability and reduction in tariffs, experienced when a wind turbine is integrated into a small diesel network, encourages demand, and this can

130

Designing a system

be used to justify the laying of a parallel network. This makes it possible to adopt a tariff structure conducive to efficient system use, by allowing separate metering of wind and diesel produced power. Due to changing patterns of energy use and to possible growth in demand, it is important when designing such a system to introduce flexibility. This relates particularly to the ease with which the switching sequence for the controlled loads can be reset. For instance, it may be felt appropriate to allow certain consumers on the network who have young children or elderly persons in their care, a higher priority than others without such an acute need for space or water heating. Long term load management Long, rather than short term, load management can also be effective, as indeed can a combination of the two. Long term management often includes specification of priorities for different loads, perhaps associated with a graduated pricing structure. For example a hospital might have the highest priority whilst water pumping to a large storage tank the lowest. Control of the load in these situations may be undertaken manually or automatically. For severely undersized generating systems, load management may entail scheduled or unscheduled outages for portions of the service area. When assessing the possible use of load control, the system designer should satisfy himself that the consumers are prepared to tolerate a complex tariff structure and are prepared to co-operate in staggering their loads. Experience has shown that with the correct customers, a load controlled system can provide extremely cheap electricity. Conversely, without consumer co-operation, a load controlled system is likely to be fraught with operational problems. A load controlled system is capable of being robust and simple, of requiring no storage, of using a very high proportion of the generated energy, and of significantly reducing diesel running times. The disadvantages relate to the fact that systems must be individually designed for each application, and that low priority loads must be identifiable.

ACKNOWLEDGEMENTS The authors of this chapter were: Ray Hunter (UK), David Infield (UK), Stefan Kessler (CH), Jan de Bonte (NL), Trond Toftevaag (N), Bob Sherwin (USA), Malcolm Lodge (Can) who wish to thank Per Lundsager (DK), Kjetil Uhlen (N), Ola Carlson (SW), and Jim Manwell (USA), for their useful suggestions relating to the text of this chapter.

D esigning a system

131

REFERENCES ANSI/IEEE Standard 1001. Guide for Interfacing Dispersed Storage and Generation Facilities with Electric Utility Systems. IEEE 1988, USA. ANSI/IEEE Standard 1021. Recommended Practice for Utility Interconnection of Small Wind Energy Conversion Systems. IEEE 1988, USA. Ballard, L. J., Swansborough, R. H., Recommended Practices for Wind Turbine Testing. Quality of Power Single Grid-connected WECs, IEA Programme for Research and Development on Wind Energy Conversion Systems. First Edition, 1984. De Bonte, J., Klerks, W.M.A., Kraayvanger, A.W., Recent Results of the Wind Diesel Project of ECN. ECN-Report-88-088, Petten, 1988. Electric Power Research Institute (EPRI). Guidelines for Testing Wind Turbines. EPRIAP-4682. Research Project 1996-25. Final Report, August 1986. Prepared by Southern California Edison Company. Frandsen S., Pedersen, B. M, (Editors) Recommended Practices for Wind Turbine Testing - 1. Power Performance Testing. Second Edition. International Energy Agency (1990). Infield, D.G., et al. Further Progress with Wind/Diesel Integration. Proc. 7th BWEA Workshop, Oxford, 1985, MEP 1985. Infield, D.G., et aL A Wind/Diesel System Operating with Flywheel Storage. Proc. EWEC 88, Herning, Denmark, 1988. International Electrotechnical Commission. DEC Standard Publication 555-3. Disturbances in Supply Systems Caused by Household Appliances and Similar Electrical Equipment. Part 3: Voltage Fluctuations, 1982. Lipman, N. Overview of Wind-Diesel Systems. Proceedings of Mykonos workshop on wind-diesel. 1987. Lundsager, P., and Norgaard, P. The 55/30 kW Experimental W/D System at RIS0 National Laboratory, Paper F6, presented ECWEC 88, Herning, Denmark (1988). (Also available as RIS0 report RIS0-M-2717.) Lundsager, P., Sherwin, R. W. Using Simple Wind-Diesel Systems Without Energy Storage to Obtain High Penetration and Market Acceptance in the Near Future. Proceedings American Wind Energy Association Windpower '90 Conference, Washington DC, USA (1990). Rizy, D. T., Jewell, W. T., and Stovall, J. P., Operational and Design Considerations for Electric Distribution Systems with Dispersed Storage and Generation (DSG). Oak Ridge National Laboratory Report ORNL/CON-134 for US Department of Energy. September 1984, Oak Ridge, Tennessee, USA. Sacks, T., Has the Flywheel Lost its Momentum? Electrical Review, vol 222, No 24, December 1989, UK. Sexon, B., Theoretical and Experimental Analysis of a Wind Turbine/Battery System for Use in Isolated Locations, PhD Thesis, Reading University, 1985. Slack, G., and Musgrove, P.J., Hydraulic Accumulator Storage for Use in Wind-

132

Designing a system Diesel Generating Systems. Proc. 7th BWEA Wind Energy Conference, 185-192, MEP 1985. Slack, G., and Musgrove, PJ. Long Term Performance Modelling of a Wind-Diesel System with Hydraulic Accumulator Storage. Proc. 8th BWEA Wind Energy Conference, 43-50, MEP 1986. Somerville, W.M. Applied wind generation in small isolated electricity systems. Proceedings of the 1986 Eighth BWEA Wind Energy Conference. Cambridge, UK. Somerville, W.M., Stevenson, W.G., Wind power and microhydro cogeneration for isolated communities. Wind Energy conversion. Proceedings of 1987 Ninth British Wind Energy Association Conference, Edinburgh. Mechanical Engineering Publications, London, UK, 1987. Stevenson, W.G., Somerville, W.M. Optimal use of wind and diesel generation on a remote Scottish Island. Proceedings of European Wind Energy Conference 1984. Hamburg, Federal Republic of Germany. Traa, W.G. Feasibility Study of the incorporation of a Battery Bank in an AWDS, TUE-report, EMV 85-01, Eindhoven, 1985. UNIPEDE Group of Experts. UNIPEDE Group of Experts for Determination of the Characteristics of Usual Distortions of the Voltage Waveform. Electricity Supply, 54th Year, No 92, May 1981 (reprint), UNIPEDE, Paris.

APPENDIX 4A SAMPLE CALCULATIONS FOR RATING THE DIESEL'S GENERATOR Here the demands on the diesel's synchronous generator are considered. With reference to the system shown in Fig. 4.1, the maximum apparent power which has to be supplied by the diesel's generator can be determined by the active (P) and the reactive (Q) electrical power balances.

QD = QL + QDL + QW

(4A.2)

where the indices stand for D

= diesel-generator set,

L

=load,

DL = dump load, and w

= wind.

Assuming the system's dump load has solid state relay control, it will switch on and off at zero crossings of the voltage so the reactive power demand is g D L = 0. If the wind turbine conversion system has a line-commutated convertor which is operated at a constant firing angle, a = 150°, then (4A.3) If the minimum power factor, cos 0, of the load is assumed to be 0.8 then the maximum reactive power demand of the load is given by QL = PL tan 0 = 0.75P L .

(4A.4)

The maximum apparent power, S D , which has to be supplied by the diesel's generator corresponds to SD = ^(PD2 + QD2) and the power factor is

p cos 0rj = — .

(4A.5) (4A.6)

With equations (4A.3) and (4A.4), equation (A4.5) can be written as SD = Vd.56.PL2 + 1.34PW2 + P D 2 L - 1.13PW PL + 2PL PDL - 2P W P D L ) .

(4A.7)

If P w > PL then P D L = P w - PL and PD = 0, so SD = 0.75P L + 0.58F w

(4A.8)

Appendix 4A

134

and cos 0 D = — = 0.

(4A.9)

If P w < PL then PDL = 0, so SD =

1.34Pw2 - 1.13PW PL)

(4A.10)

and COS0D = ^ - =

—

(4A.11)

The maximum required apparent power, S^, and the power factor demanded of the diesel's synchronous generator, cos 0 D , are calculated as a function of P w , with P L as a parameter, according to equations (4A.8), (4A.9), (4A.10) and (4A.11). The results are depicted in Fig 4A. 1.

Fig 4A. 1 Maximum required apparent power and power factor demanded of the diesel generator as a function of wind power for various loads

135

Appendix 4A

The apparent power which can be supplied by a synchronous generator depends on the power factor because the maximum power supply at a constant terminal voltage is mainly determined by the maximum value of the open voltage. At the maximum value of the open voltage, the maximum active power at cos 0 = 1 will be larger than the maximum reactive power at cos 0 = 0 at a constant terminal voltage. This is depicted by the active and the reactive current in Figs 4A.2a and 4A.2b respectively for a synchronous machine with a cylindrical rotor and a system frequency of co rad/s. L a is the stator self inductance (La = L d = L q ).

up

a)

jcoUT

b)

Up

coLa?

Fig 4A2

Active (a) and reactive (b) currents at the same value of open voltage (tip) and terminal voltage (u)

For a salient pole synchronous generator the maximum value of the open voltage can be derived from the machine specifications according to V

=

^

L

afd !f,r = ar C 0 S 5 r + °V Ld lr

sin

(4A.12)

Appendix 4A

136

8 r = arctan

corLqcos0rtr

(4A.13)

sin 0r .

=

lr

2 *> 3 u cos(

(4A.14)

The symbol A signifies the maximum value or amplitude of a sinusoidal quantity. The suffix r indicates rated values. Additional terms are defined as follows: Up

= open circuit voltage

Lafd = mutual inductance of stator and field winding If

= field current

u

= stator voltage

8

= load angle

i

= stator current

co

= angular velocity

p

- power

cos 0= power factor Ld and Lq = stator self inductances. At given values of cor, Pr, ur cos 0r, Ld and Lq, the value of i r , 8 r and u p r can be calculated. The maximum current can be calculated as a function of the power factor in accordance with the vector diagram of Fig. 4A.3.

Up

-coLd id

J'd

Fig 4A3

Vector diagram for a synchronous machine

Appendix 4A

137

Starting with the load angle, 5, as a parameter, ($Lq iq and (£>Ld id can be calculated &Lqiq = usmb coLd id = up-u

cos 5.

(4A.15) (4A. 16)

For given values of co, Ld and Lq, id and L can be calculated. From this we can further obtain t and 0.

0 = arccos 4 - 8 .

(4A.18)

Using f, the maximum apparent power can be calculated as a function of the power factor for a constant phase voltage (u = 220 V2~ V) and several values of the load angle, 8

s

=1« max

(4A 19)

-

2

cos 0 = cos arccos — - 8 .

(4A.20)

In Figs A4.4a and A4.4b the results of this calculation are shown for two generators of different rating - 60 and 80 kVA. These figures also indicate the power factor required of the generator. Comparing the demands with the capabilities of both machines, it can be seen that the 80 kVA machine just fulfils the demands.

ACKNOWLEDGEMENTS This appendix was written by J. A. N. De Bonte (NL) and Jan Pierik (NL).

Pw (kW)

Fig 4 A.4a The apparent power demand and the deliverable apparent power for a Stamford Generator C3A, 60 kVA as a function of the power factor To determine the suitability of this generator: at 25 kW wind power and 60 kW consumer demand, the apparent power demand is 72 kVA at a power factor of 0.52. At this power factor the generator can only supply 52 kVA so is unsuitable.

Wind-diesel case studies

In this chapter we will look at a few real wind-diesel schemes. Three systems have been chosen for examination. The first is an experimental/demonstration system which has been developed in Norway. The system is a very good example of a self-contained, * go-any where' package. The storage device is coupled to the system electrically. The system is now commercially available. The second scheme is entirely different from most architectures considered in the book insofar as it relies upon consumer load management principles. It is a real commercial, working system and has been installed on the Scottish island of Foula by Windharvester Ltd of the UK. A comprehensive description is given to illustrate to the reader the level of sophistication and complexity which may be required to make best use of cheap renewable energy inputs. The third system is the wind-diesel research facility at the UK Rutherford Appleton Laboratory. It is a good example of a laboratory scale experimental facility. The scheme was the first to use flywheel energy storage coupled mechanically to the diesel gen-set.

CASE STUDY 1 - THE FROEYA WIND-DIESEL DEMONSTRATION Design aims and background Although Norway has abundant, cheap hydro power, interest in wind-diesel systems is growing, particularly since they could form attractive alternatives for island communities currently served either by decentralised diesels, or by ageing submarine cables. In recognition of this, EB-Energy has developed a commercial wind-diesel package, partly funded by the Norwegian Water Resources and Energy Administration (NVE). The work has been carried out in co-operation with the Norwegian Electric Power Research Institute (EFI) in Trondheim. The target has been to develop control concepts for commercial wind-diesel systems

Pw (kW)

Fig 4 A.4 b The apparent power demand and the deliverable apparent power for a Stamford Generator C3B, 80 kVA as a function of the power factor To determine the suitability of this generator: at 25 kW wind power and 60 kW consumer demand, the apparent power demand is 72 kVA at a power factor of 0.52. At this power factor the generator can just meet the demand.

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141

which will give acceptable power quality in terms of availability, voltage, and frequency. The work has had many elements, including economic and dynamic computer modelling, and the development of a sophisticated wind-diesel laboratory, incorporating a wind turbine simulator, rather than a real wind turbine. As an important part of the programme, the laboratory system was moved to the island of Froeya off the Trondelag coast where the wind turbine simulator was replaced by an actual wind turbine. For a short period of time the system was used to supply parts of the Froeya consumer grid. Elements of the scheme The system is shown schematically in Fig. 5.1. A key feature is the common a.c. busbar. The various components connected to the bus are the: wind turbine (or wind turbine simulator) diesel gen-set dump load battery storage consumer load (or load simulator) control equipment. Wind turbine sim. Wind data Consumer load Wind turbine

•* Load data

Dumpload Diesel gen. set

Battery storage

Fig 5.1 Schematic arrangement of the Froeya System

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Wind-diesel case studies

The wind turbine, known affectionately by the technical team as "Astrid", is a 55kW 'Wincon' stall regulated machine of conventional fixed speed, 3-blade, upwind design and fitted with an induction (asynchronous) generator. Under normal circumstances, the turbine would have been grid connected, but for the purposes of the demonstration, it was coupled into the laboratory package, replacing the dc machine normally used to simulate the turbine. No additional control equipment was installed in the wind turbine for combined operation.

Fig 5.2 'Astrid' and the system components

The wind turbine is shown in Fig. 5.2. Other elements of the system which are containerised are clearly visible.

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143

The diesel is rated at 50 kW, the generator being a 65 kVA synchronous machine. It was decided that the diesel generator set should retain its standard automatic voltage regulator and its mechanical governing equipment. The only special feature incorporated into the gen-set is an electromagnetic clutch between the generator and the diesel which allows the synchronous generator to meet the reactive power demands of the wind turbine and the system load even when the diesel is dormant. A 55 kW dump load is provided, having eight resistors on each phase controlled by static relays. The dump load is used solely for dissipating excess power, not for phase balancing. The storage system comprises a set of 170 nickel-cadmium (Ni-Cd) batteries with a nominal capacity of 27 kWh. The batteries are connected to the a.c. busbar via a line commutated 55 kW rectifier-inverter. The harmonic distortion introduced by the electronics is removed by filters. Ni-Cd batteries were chosen, despite their cost, because of their ability to handle high charge rates and a high number of discharges. During laboratory experimentation, and indeed for much of the time on Froeya, the system supplied power to a load simulator consisting of banks of resistors and inductors rated at 40 kW and 20 kVA respectively. However, for a one day period, a special experiment was conducted involving isolating part of the Froeya consumer grid, and supplying it directly using the hybrid system. No operational problems were encountered. For the purposes of these tests, the system voltage had to be transformed to 22 kV to feed into the main network at a suitable point.

Control and operating modes The system control was developed using computer modelling techniques, and was instituted on the real hardware using an industrial process control computer. Wherever possible it was decided not to replace or over-ride existing control equipment on individual components of the system. The key duties of the controller are to handle the logic of system operation, and also to maintain frequency using the battery storage or dump load when the diesel is stopped. During development and design of the control algorithms, various operating modes were identified. These are shown in Table 5.1. The decision of when to switch between modes is handled by logic control, the most important decision being when to switch the diesel on or off.

Wind-diesel case studies

144 Table 5.1 Operating modes of the Froeya system Active Plant

System Condition

Diesel

Turbine Stopped High Demand or Battery Store Fully Charged

Diesel Rectifier

Turbine Stopped Low Consumer Load Battery Store not Fully Charged

Diesel Turbine Rectifier

Low Wind Power and High Consumer Load Battery Store Fully Charged

Diesel Turbine Dump

Low Load on Diesel Generator

Turbine Rectifier

Wind Power Surplus Battery Storage not Fully Charged

Turbine Inverter

Wind Power Deficit Battery Storage not Empty

Turbine Dump

Wind Power Surplus Battery Store Fully Charged

When the diesel is off, frequency is maintained by switching between the inverter, the rectifier, and the dump load. The decision to stop the diesel is very important, and is a function of the mean load demand and the mean wind power. Clearly, if the wind power is very low, the diesel is kept running, but if a wind power surplus exists, then the diesel is shut off. Additionally, the diesel is allowed to stop even if a small wind power deficit exists provided the battery bank is fully charged. This philosophy is shown graphically in Fig. 5.3. Experience and economics Connecting the system, albeit for a very short period of time, to a real consumer grid,

Wind-diesel case studies

145

Diesel is running

load, mean

Diesel is stopped when the battery storage is full

Diesel is stopped

wind, mean

Fig 53 The operating philosophy has given the developers confidence in the design philosophy as a whole, and in their experimental and analytic predictions in particular. During extended testing on Froeya, with the system feeding the load simulator, a 37% saving in fuel consumption was noted. During this period the wind speed was well below the long term mean of 6.9 m/s. These results have allowed various models to be validated and calibrated, and projections show that the long term fuel saving on Froeya would be 52%. Furthermore, for another nearby location on the island of Sula, where the long term wind speed is 8.2 m/s, a fuel saving of 66% is predicted. The Froeya project has highlighted the benefits to be gained from an experimental point of view of using a wind turbine simulator. Extensive modelling and laboratory experimentation prior to system deployment allowed the full scale field demonstration to go ahead very successfully, the value of extensive research and optimization being shown. Systems based upon the Froeya configuration are now commercially available from EB-Energy. A new system incorporating a voltage source convenor is being developed, which is better suited to fit into networks fed by existing diesel plant.

CASE STUDY 2 - THE FOULA ELECTRICITY SCHEME Design aims and background The island of Foula, part of the Shetland Islands group, lies in the Atlantic Ocean some 25 km west of the mainland of Shetland which itself is 200 km north of the Scottish main-

146

Wind-diesel case studies

2 land. Foula is 20 km in area with extremely high cliffs and mountains up to 400 m. The island has a population of approximately 40 people which can double in summer with day and overnight visitors. The crofts (houses and small-holding farms) are spread up the east side of the island. Prior to installation of the system described here, there was no communal electricity supply on the island. Most of the power came from small diesel gensets at each croft, or from small battery charging wind turbines, whilst some crofts had no electric power at all. Heating on the island comes mainly from abundant natural peat, cut, dried and stored by the islanders. At current rates of usage, peat can be regarded as a renewable commodity. Both the Shetland Islands Council and the Foula islanders wanted a communal electricity supply, since it was the only major island in the Shetland group not to have one. However, the area utility would not consider providing a supply either from a local diesel station or from an undersea cable due to capital and running costs. Conscious of the success of the innovative load managed wind-diesel system on the nearby island of Fair Isle which had been running since 1981, it was decided to develop a similar scheme for Foula. Foula has a good high head hydro site but there is insufficient flow from the small catchment area to provide anything but a very short term supply. Nevertheless, it was identified that the incorporation of pumped hydro storage into the Foula system would be worthwhile. The scheme was thought to be sufficiently novel to warrant financial backing from the European Community, and because of its ability to provide an economic electricity supply to the island, additional funding was attracted from the Shetlands Isles Council and the Highlands and Islands Development Board. The aims were to supply the islanders with electricity at a price comparable with mainland supplies, to give the scheme long term financial viability and to develop the wind-hydro-diesel system for commercial replication by the installing company, Windharvester Ltd. There was no historic record of the pattern of electricity use on Foula, and it was not expected that there would be any correlation between historic consumption and use following the installation of the system. Experience from Fair Isle and other locations had shown that when power availability increases and cost is reduced, consumption of electricity increases considerably. The primary aim of the scheme was to supply enough power for lighting, TV, radio etc, to the island during dark periods in the morning and evening, to supply sufficient

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147

power for freezers at least once per day and to provide power when available for heating, washing machines and other uses. The final installation required connections for: 23 domestic single phase consumers 3 single phase non domestic users (chapel, airstrip, telephone exchange) 4 three phase consumers (harbour, drinking water pump house, 2 workshops) 1 special three phase supply to the new school and community hall. Elements of the system The scheme has the following elements: *

wind turbine

*

pelton water turbine

*

various hydro pumps

*

diesel generator set

*

switchgear and load management equipment

Fig 5.4 The wind turbine on Foula

The wind turbine, shown on-site in Fig. 5.4, is a 17 m diameter, upwind, 3-bladed, stall regulated machine, manufactured by Windharvester Ltd, UK, and has a nominal rating of 60 kW. It is equipped with a synchronous generator for 'stand alone' operation which enables it to supply all the island loads without the need for any other generating

148

Wind-diesel case studies

plant or voltage regulation. The turbine's generator is rated at 97 kVA to allow it to handle high shaft power transients caused by gusts and higher than nominal frequencies, and also to cater for unbalanced loads. The wind turbine has a fantail rotor yaw system and does not require the island grid to be energised to operate. The turbine was delivered complete with a local, stand alone control system and dump load which enables the machine to operate off the grid for start up and testing. When connected to the grid, these elements integrate with and form part of the total load management system. Otherwise the WTG is identical to a grid connected wind turbine in terms of structure, design and safety systems. The island itself experiences a very favourable wind regime estimated at over 8 m/s at 10 m elevation. However the island topography made siting the wind turbine extremely difficult. The north and west of the island is dominated by vertical cliffs up to 350 m in height, leaving the southern tip of the island as the only sensible place available to site the turbine. However the southern site has its own smaller local cliffs, of various slopes and heights, which cause severe local turbulence for certain wind speeds and directions. To avoid undue wear and fatigue damage, the wind turbine is shut down during these wind conditions. The diesel generator is a standard * off-the-shelf gen-set having a 3-phase synchronous generator rated at 32 kVA, and an engine rated at 25.5 kW for continuous, and 28.0 kW for intermittent operation. It is fitted with a mechanical governor and has no synchronising equipment, since it was designed as a backup only to be used when both wind and hydro power sources fail. The hydro turbine comprises a pelton wheel coupled directly to a synchronous generator, and also via flexible couplings to an induction generator. Power is controlled by a spear valve which limits water flow. The 3-phase brushless synchronous generator is rated at 48.6 kVA, and operates at 415/240 V at 50Hz/1500 r/min. It is fitted with an automatic voltage regulator. It is used both when the hydro power is feeding the grid on its own and also when the hydro has been paralleled with the wind turbine. The induction generator, rated at 11 kW for 1500 r/min operation is used solely as a speed limiting control device during synchronisation to the wind turbine. When connected to the grid it holds itself, and hence also the alternator, within limited 'slip' (approx ±2%) of grid frequency and so enables the synchronising equipment to operate onto the relatively fast moving wind turbine frequency. To provide the pumped storage facility there are two high pressure pumps (pumps 1 and 2) which pump water from a tank at the hydro station through non return valves into

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149

a penstock and up to the upper reservoir, formed from a ioch'(small lake). The tank is replenished by a submersible pump (pump 3) situated in a small pool in the burn (stream) at the turbine outfall. Pumps 1 and 2 are centrifugal multi-stage pumps each capable of pumping 2.6 litres per second through a 150 mm diameter pipe against a static head of 116.5 m over a distance of 390 m. Each pump motor is rated at 5.5 kW. Pump 3 is a borehole submersible pump capable of pumping 6 litres per second through a 75 mm diameter pipe against a static head of 5.5 m over a distance of 90 m. The pump motor is rated at 2.25 kW. The loch is located some 100 m above the hydro station and is linked by a single pipeline (penstock). It has a capacity of 7500 m , which is equivalent to approximately 1400 kWh of useful electrical energy. Water leaves the loch via a trash rack and passes through a manual valve. There is a vent pipe to prevent collapse of the penstock in the event of a sudden blockage. Surplus water in the loch escapes through a concrete tower overspill and into a pipe under the dam. The dam also has a spillway for severe floods. The loch can be drained by removing stop logs on one side of the overspill tower. The load management system is distributed, having elements located in each consumer's supply board, and also at the hydro and wind turbine stations. The aim of the load management system is to utilise the maximum amount of energy produced by the generators in general, and the wind turbine in particular. A secondary aim is to maximise the time during which consumers are supplied by renewable resources alone. The system thus tries to avoid starting the diesel generator or disconnecting the supply. It does this in two ways, firstly by automatically turning on more loads (heating load) in times of surplus power, and secondly maintaining only priority supplies in times of power shortage. At each consumer's supply board, there are two consumption meters, one corresponding to high tariff, and one to low tariff. When the system is over loaded, and the frequency falls below 50 Hz, a sensing element activates the high tariff meter. This action encourages consumers to lower their demand. A 100 kW dump load is installed at the wind turbine site and 25 kW is adjacent to the hydro generator. Up to 3 kW of controlled heating load can be installed at each single phase consumer outlet. 1 kW can be installed at each 3-phase consumer outlet. 15 kW is installed in the school building. At the time of writing 45 kW of controllable heating loads, in the form of immersion heaters and storage heaters, have been installed. All Foula's generation and distribution is at 415/240 V and main transmission is via a 3.3 kV 3-phase grid. The high voltage system consists of a 3-phase cable some 5 km in length with 9 step-down transformers to provide the normal 415/240 V service.

150

Wind-diesel case studies

Control and operating modes System voltage is controlled by the Automatic Voltage Regulators (AVR) which are situated on the wind turbine and hydro synchronous generators. The hydro AVR is designed to be operated in parallel with the wind turbine, so has droop control. The diesel generator has a static built-in exciter which is complemented by a current transformer with adjustable airgap and a variable resistor. Frequency control depends on the load management system when the wind and/or hydro turbine is on the grid, as well as the hydro spear valve when the hydro is on the grid. When the diesel generator supplies power, frequency is controlled solely by the diesel generator's mechanical (droop) governor. Load control uses frequency as the sensing element to determine whether the system is over or under loaded. When the frequency is high, the system adds load in a prescribed manner until the frequency falls to a target value. After the load management system has made all the 'on demand' (lighting and ring main/power sockets) circuits available, its next priority is to switch on the domestic heating loads. This is done incrementally according to the switch settings on each frequency sensing, load management element. If there is still surplus power when all these circuits are switched on, the dump loads at the wind and/or hydro stations are connected to absorb surplus power. The frequency settings used to disconnect loads can be adjusted to suit different social requirements and in the light of operating experience. In the event of a power shortfall, manifested by falling frequency, the load management system first disconnects any dump load and then any domestic heating load. As the frequency drops below 50 Hz the high tariff kWh meters at each consumer's installation are activated, thus encouraging consumers to economise. The system also disconnects the 3 phase supplies. If the frequency falls further to 47.6 Hz then the system will disconnect domestic single phase power sockets. This will normally restore the frequency and alert consumers to their excessive power use and encourage them to switch off high demand appliances. However, if frequency falls further the load management system has no further options and what happens is dictated both by the central and distributed controllers and also by which generator is supplying the grid. If the diesel is supplying the grid then power shortage indicates an overload. This would normally cause the diesel to trip off the grid, but is unlikely to happen as the combined maximum load of all circuits not controlled by load management is 45kW. If the wind turbine is supplying the grid, alone or in parallel with the hydro, the wind turbine plant controller disconnects it from the grid when the frequency drops below 47 Hz. If the hydro turbine is supplying the grid alone the frequency can fall further. As it falls the automatic voltage regulator reduces the voltage, so reducing the power output,

Wind-diesel case studies

151

and serves as a warning to consumers of an overloaded system. This could be caused by too large a load or by reduced power output caused by a partially blocked trash rack. If the situation deteriorates the hydro plant controller will disconnect the generator from the grid. Overall central control of the system makes safety a prime consideration. A safety circuit employing underground cable, connects all three generating plants and the network's high voltage earth leakage detection system. An emergency shutdown of the system is initiated when the circuit is broken at any point. Each generator has its own 'local' or 'plant' controller which interfaces the signals from the central control system and the safety circuit with local sensors, lockouts and fault detection. The scheduling of generators is controlled by a central computer, an 'Analog Devices uMAC 6000'. The role of this computer is to make strategic rather than second by second decisions, although it does act on a sub-second timescale when required; for example to call up another generator if a fault occurs in the one supplying power. The human operators of the system can adjust two primary parameters in the control program, these being the water level in the loch and the timeclock. The timeclock sets three daily periods: 'Day', 'Night' and 'Priority'. Priority periods are normally set from dusk to midnight and from 7 am till daylight. School hours can also be added. This ensures that in the event of no wind the hydro will operate and, if the hydro runs out of water, that the diesel will come on line. The diesel will only operate during 'Priority' periods, not during 'Day' or 'Night' periods. The hydro will operate during any period depending on loch water depth. The loch water depth is monitored by a sensor placed in the loch and the signal is transmitted to the central control system. From this reading and from experience of the season, weather forecast, and the state of the ground the operators can set the loch water level to 'Low', 'Medium' or 'High'. Table 5.2 shows the allowable operating parameters for the hydro generator.

Table 5.2 Operating restrictions on hydro turbine

Timeclock

Low

Loch Water Depth Medium

High

Priority Day Night

Yes No No

Yes Yes No

Yes Yes Yes

Yes - hydro may be called on to operate. No - hydro will not be called on to operate.

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Wind-diesel case studies

The control program is written in a Pascal type language and is divided up into 6 sections, one per 'state'. A brief description follows: State 0 The computer program is running but the operator has not selected Automatic Control. The program continues to monitor certain parameters to be ready to bring a generator on line if called for. State 1 Automatic control is selected but no generators are on line. The program is monitoring events ready to bring on a generator if required. From State 1 it may move to: State 2 - if the wind turbine is generating to dump for a period. State 3 - if the timeclock or loch water level changes. State 4 - if the timeclock changes and no hydro is available. State 2 The wind turbine is connected to the grid. The pumps may be operated depending on grid frequency and loch depth. From State 2 it may move to: State 1 - if the wind turbine goes off the grid. State 5 - if the frequency drops and the hydro can come on. State 3 The hydro turbine is connected to the grid. The spear valve is operated according to grid frequency. From State 3 it may move to: State 1 - if the hydro goes off the grid or it can no longer be operated (eg loch water level or timeclock change). State 2 via State 1 - if the wind turbine has been generating to dump for a sufficient time. State 4 The diesel is connected to the grid. The pumps (1 and 3 only) may be operated. From State 4 it may move to: State 1 - if the diesel can no longer be operated (e.g. timeclock ends the Priority period). State 2 via state 1 - if the wind has been generating to dump for sufficient time.

Wind-dies el case studies

153

State 5 The wind turbine and the hydro (synchronous generator) are both connected to the grid. The spear valve is controlled according to grid frequency. The pumps may be operated if the spear valve is fully closed. From State 5 it may move to: State 1 - if both the wind and hydro go off the grid (e.g. because the Safety Circuit is broken). State 2 - if there is a restriction on operating the hydro or if the wind turbine can meet demand on its own. State 3 - if the wind turbine goes off the grid (e.g. due to a lack of wind). The pump may be operated during Control States 2,4 and 5, and can be powered by the wind turbine or the diesel generator. When fed by the diesel, only two of the three pumps can operate. They are only run if the loch water level is 'low' and only if the diesel is lightly loaded. When powering the pumps from the wind turbine, the control algorithm takes account of the loch water level and the timeclock setting. It works the pumps very hard when the loch is 'low' and at 'night', and lightly or not at all when the loch is 'high' and the timeclock is on 'priority'. Table 5.3 summarises the frequency of pump operations. Table 5.3 Number of pump operations in different conditions Timeclock Priority Day Night

Low Few Several Many

Loch Water Depth Medium Very few Few Several

High None None None

The spear valve controls the water flow through the hydro turbine and hence its power output. It is operated by the central control system in control States 3 and 5. Speed of response in State 5 is less crucial as the inertia of the wind turbine provides a buffer against power fluctuations. In State 3 a very small load change or spear adjustment can cause a rapid change in the grid frequency. However the spear valve movements in State 5 are larger due to the larger power variations caused by increasing and decreasing wind power. When there is more water available the need to control the spear valve is reduced. When the loch is full the turbine can be allowed to run at full power and surplus power can be used by the load management system. To conserve water when the reservoir is low, it is important to operate the spear valve more frequently. Spear valve control is

154

Wind-diesel case studies

thus responsive to the loch water depth. Also to maintain quality of supply in peak periods spear valve control must also consider the timeclock settings. Table 5.4 gives details. Table 5.4 Number of spear valve operations in different conditions

Timeclock

Low

Priority Day Night

Many N/A N/A

Loch Water Depth Medium Few Several N/A

High None None None

N/A - no operation of hydro in these conditions. None - hydro runs at full power.

To change from control State 2 to control State 5 the hydro must synchronise onto the wind turbine powered grid. When the hydro is signalled by the central control unit, that it should come on-line, the hydro goes through a synchronisation sequence governed by the its own local controller. The time taken to synchronise depends on various conditions, especially the wind. If the hydro does not synchronise within a certain time limit, it is disabled by the control computer. If the grid frequency recovers sufficiently, attempts to bring the hydro on-line are abandoned, otherwise the program tries again to synchronise the hydro. The system's control program software automatically stops and starts the wind turbine according to the prevailing wind conditions. The aim is to shut down the wind turbine if the wind strength and direction are such as to cause large power output fluctuations and severe structural loads, due to wind turbulence caused by the local topography. Whenever the wind turbine is operating the central control system monitors the wind speed and direction at the wind turbine site. It takes one minute averages and shuts down the turbine if the wind speed exceeds given limits within given directional sectors based on a lookup table. To restart the turbine the wind speed and direction are averaged over 10 min. Following a restart of the WTG the machine can be brought onto the grid when it has been generating to dump continuously for 10 s. In control State 2, following a reading of unsuitable winds, the program tries to synchronise the hydro or to get the diesel running before stopping the WTG. In State 5, following detection of unsuitable winds, the program opens the spear valve fully (100%) before stopping the WTG.

Wind-diesel case studies

155

All these actions are designed to maximise supply quality. Experience and economics A three tier tariff system forms an integral part of the load management system. Electricity supplied through the load management system to space and water heaters is charged at a low tariff of 1.5 p/kWh (US$0,026) at 1991 prices. Energy supplied at lower frequencies (below 50 Hz) is charged at a high tariff of 7 p/kWh, as the diesel genset supplies power below this frequency. The hydro and wind turbines can also supply some power at these low frequencies when overloaded. All other electricity supplied is at the 'normal' tariff of 5 p/kWh. This three tier structure encourages optimal use of the system's renewable energy sources. It ensures that heating appliances are connected to the load management system, enabling them to be turned on and off rapidly to suit power availability. It charges more for electricity when the diesel genset is operating and also when the other generators are overloaded, discouraging use during these periods. Following installation of the scheme with cheap heating available from the load management system, it was expected that a considerable heating load would be served. Indeed many people have now supplemented their existing solid fuel peat heating with electric storage and immersion heaters. This has reduced peat consumption, especially in the summer when it is primarily needed for water heating, provided additional room heating, very useful for older people, and improved the island's holiday accommodation and extended its season of use. Although based on technology and strategies already proven at other sites, such as Fair Isle, the added complexity of this new system together with the remoteness and inaccessibility of the island have made for certain difficulties. In March 1990 a severe electrical storm and attendant lightning strikes caused damage to some of the signalling and communication systems including proprietary equipment with built-in lightning protection. As a measure of the severity of the strikes half the island telephones were destroyed and several cables damaged. Primarily due to good earthing practice, there was no damage to power equipment or the communication cables themselves. As part of the refurbishment, more stringent lightning protection was installed. The lightning resulted in disruption of supply for several days until a semi-manual form of control was re-established. Full computer control was not fully available until November 1990. The restrictions placed on operating the wind turbine during certain combinations of wind strength and direction have been found to result in considerably reduced operating hours. However, when the machine is allowed to run, its output is generally large compared to consumer demand and the excess allows replenishment of the hydro storage

Wind-diesel case studies

156

TIME SUPPLYING GRID December 1990

IThv. /-(30%) 6% )

[]JJ Wind turbine \

L

£

flljl| Parallel

V \

-u.

\

MUjj /

| —(6%)

1 Hydro

fUffl Diesel

1 /

/ /

(48%)-^ (48%)-^ , \—(13%)

ENERGY USES December 1990 Pump

Cheap

Expensive

Losses

—(8%)

Normal

(67%)-

ENERGY SOURCES December 1990

Wind turbine Hydro Diesel

-(30%)

Fig 5.5 Operational statistics of the Foula system for a typical month

Wind-diesel case studies

157

system, which in turn can be used for regeneration when the turbine is prevented from running. Control of the load, the pumped hydro, and the wind turbine, has been successful in reducing diesel running to a tiny fraction of total time. The scheme has shown the viability of allowing a system to operate from a relatively small hydro turbine and still utilise power from a much larger wind turbine. The results shown in Fig. 5.5 of a typical month's operation demonstrate this point admirably. The hydro turbine was on the grid for 64% of the time yet supplied only 30% of the energy. The wind turbine, on the grid for 46% of the time, supplied 67% of the energy. The load management and pricing structure ensures that wind energy is highly utilised and that the hydro can meet the demands from natural rainfall and pumped storage avoiding all but occasional use of the diesel genset. The Foula scheme demonstrates the importance of looking very critically at operating strategies and in matching them to local factors.

CASE STUDY 3 - THE RAL/ICST WIND-DIESEL RESEARCH FACILITY Design aims and background The UK's Rutherford Appleton Laboratory (RAL) and Imperial College of Science and Technology (ICST) have both been involved in wind-diesel research for many years. Both centres are academic institutions and have specific interests in developing and optimising control strategies for wind-diesel systems. In 1986, following on from earlier work, a small experimental facility incorporating flywheel energy storage was assembled at the RAL site. The aims were to provide experimental results to validate various control concepts and dynamic and logistic computer models, to gain experience of real hardware, and to develop a mid-range technology suitable for commercial exploitation. The experimental facility was based upon two fundamental assumptions. The first was that to make a wind-diesel system economically viable, significant amounts of fuel must be saved, the only way of achieving this being to shut the diesel off when it is not required. The second assumption was that for intermittent diesel operation, a short term energy store must be provided to guarantee a continuous supply. In addition to the main funding which came from the UK's Science and Engineering Research Council, a number of commercial organisations sponsored the work, notably John Laing pic, who supplied an energy storage device, and Hawker Siddely Power Plant (HSPP) who provided both the wind turbine and the diesel engine.

158

Wind-diesel case studies

Due to limited finance, the scheme is somewhat idiosyncratic, but nevertheless it has provided over the years much fundamental understanding of the behaviour and performance of wind-diesel systems. The system was designed specifically to provide robust, low cost hardware, and is best suited to relatively small ratings, probably less than 50 kW. In these circumstances, the scheme's power quality is considered acceptable. Elements of the scheme The experimental facility consists of a wind turbine, a diesel, a flywheel energy store, a dump load, a consumer load simulator, and various control devices and ancillaries. The wind turbine, which is shown in Fig. 5.6, is somewhat unusual in design. The turbine is a Mistral MP-9, de-rated to 8 kW. The synchronous generator is directly linked to the rotor via a flexible coupling and gives a 50 Hz output at 125 r/min. This unusually slow rotational speed is made possible by the inclusion of 48 poles in the generator.

Fig 5.6 The RAL test wind turbine (courtesy RAL photographic services)

159

Wind-diesel case studies

The generator's high reactance makes it possible for it to be linked to the grid out of phase. This obviates the need for the expensive synchronisation equipment normally associated with such generators. The turbine rotor itself is of two bladed downwind design with speed and power regulation coming from passive blade pitching. The generator set is a Nova II 6.4 kW unit with a Lister TS2 air-cooled diesel engine which drives an 8 kVA synchronous generator. The diesel has a mechanical governor with a 4% droop rather than isochronous characteristics. The diesel rotates at 1500 r/min, hence does not need a step-up gearbox. Energy storage is provided by a medium-tech flywheel which is connected to the diesel's synchronous generator by a toothed belt. The flywheel consists of a carefully balanced disc made up of a number of laminated steel plates, housed in an evacuated container. The inertia of the disc is 20 kgm . For experimental purposes the effective energy storage can be varied by changing the drive ratio.

1 2 3 4a 4b

Diesel engine Synchronous alternator Flywheel store Electomagnetic clutch Asymmetric gearbox

a)

b) Fig 5.7 Schematic layout ofRAL engine to flywheel/generator inter-connections

160

Wind-diesel case studies

The interconnection between the diesel engine and the generator (and flywheel) can take two forms. In the first, an electromagnetic clutch is provided which allows the diesel to be disconnected totally from the system. In the second the clutch is replaced by an asymmetric gearbox, which allows the diesel to rotate at reduced speed when its fuel supply is shut-off. Figure 5.7 shows the two schemes diagrammatically. A computer controlled consumer load simulator, rated at 7.5 kW and consisting of a bank of resistors and solid state relays is provided. Additionally 24 kW of dump load is also available. Control and data logging facilities make up the rest of the system. A photograph of the hardware, mounted on a frame, is given as Fig. 5.8. Control and operating modes Two basic modes of operation have been studied, one involving the electromagnetic clutch, and the other the asymmetric gearbox. In both modes, diesel power can be removed from the system.

Fig 5.8 The RAL diesel and flywheel test bed (courtesy RAL photograph services)

Wind-diesel case studies

161

In clutch mode, when the decision is taken to shut off the diesel, it can be totally isolated and allowed to come to rest. The flywheel whose primary purpose is to smooth power fluctuations in wind only mode, can be used for the secondary purpose of rapidly crash starting the diesel when the decision is taken to bring it back on line. This causes high transient loads, but these are reported to be acceptable. During start-up of the system from cold, the clutch by a pulsating action allows the flywheel to be accelerated without over-stressing or stalling the diesel. The diesel and flywheel speed up and slow down in anti-phase as energy is transferred in * spurts' between the two. In gearbox mode, the diesel is not allowed to come to rest, but to decelerate to a lower speed, when its fuel supply is turned off. The setting of this speed is adjustable. Lower speed motoring is desirable since mechanical losses are reduced. The RAL wind-diesel system is only nominally of fixed speed. Electrical output can vary from 45 to 55 Hz. This wide speed range makes it possible for the flywheel storage to be mechanically linked to the system - without speed variation there could be no energy flux between the flywheel and the rest of the system. Maximum power delivery from the flywheel is well above the system rating. The effective maximum storage time of the flywheel is 300 seconds. Experimentally, however, it is easy to change storage times by changing pulley sizes or by changing frequency thresholds. Two types of control are instituted, these being dynamic and logistic. Dynamic control is required in the system due to the presence of two synchronous generators, which potentially could be unstable. Control is provided by an analogue dump load which uses binary load steps to maintain frequency within preset limits. An automatic voltage regulator is also provided. Of greater interest is the logistic controller, whose function is to make the decision of when to stop or start the diesel. Control variables available are system frequency and wind turbine power. Outputs depend upon the hardware, and consist of relay solenoids actuating either the electromagnetic clutch or in the case of the asymmetric gearbox, the diesel's fuel supply. The experimental nature of the system allows for control parameters to be changed. The key parameters are minimum frequency, surplus power, weighting factor, and wait time. These require some explanation. 'Minimum frequency' refers to the system frequency at which the decision will be made to restart the diesel when the system is running in wind-flywheel mode. 'Surplus power' applies when the diesel is running and refers to the difference between consumer load demand and wind turbine power output. If the average of this figure over a period of time indicates that wind power is exceeding demand, then the

162

Wind-diesel case studies

decision is taken to shut down the diesel. 'Weighting factor* is a parameter which is used in obtaining 'average' values of wind power and load demand. An exponential weighting is applied so that recent samples are given highest importance. 'Wait time' is effectively a minimum diesel run time. Should the decision be taken to start the diesel, then any decision to shut it down again is inhibited for this period of time. Dump load control is instituted by solid state relays. The function of dump load control is to absorb excess wind generated power, and to keep the diesel, when running, loaded above a certain level. The dump load is also used when the flywheel energy store is 'full', ie when the frequency approaches 55 Hz. Experience and economics Despite its small size, the RAL/ICST experimental rig has proven the technical viability of small scale storage devices. Generally, the smaller the storage, the lower the cost, and

Fuel consumption I/hour

4

8

12

Mean wind speed m/s

Fig 5.9 Fuel consumption as a function of wind speed

163

Wind-diesel case studies

thus in the long run, this work is likely to result in cost effective commercial wind-diesel systems. The research team have found the rig very useful in helping to optimise and understand the effect on fuel consumption and diesel stop-start frequency of storage size, and control philosophy. Typical results produced by thorough experimentation are shown in Figs 5.9 and 5.10. These relate to tests in which the load has been kept fixed at 4 kW and the diesel has been kept loaded to at least 2.5 kW. A diesel minimum run time of 10 s has been selected and the maximum storage time has been set at 14 s. Figure 5.9 shows fuel consumption as a function of mean wind speed, whilst Fig. 5.10 indicates the number of diesel starts every 10 min, also as a function of wind speed. Below 6 m/s the diesel runs continuously, due to lack of wind, whereas, above 10 m/s,

Number of starts

0

4

8

12

Mean wind speed m/s

Fig 5.10 Number of diesel starts as a function of wind speed

164

Wind-diesel case studies

the diesel is permanently off due to the ability of the wind and the store to meet demand unaided. Stop-start cycling thus only occurs between 6 m/s and 10 m/s, with peak activity being at around 8 m/s. Figure 5.9 indicates that this is related to periods of minimum diesel loading, indicated as a departure from the otherwise monotonically decreasing fuel consumption rate as a function of wind speed. Measurements such as those shown have been used to validate model results, which in turn have been used to optimise storage capacities and control strategies for various operating conditions. Although modelling can be very useful for gaining an insight into wind-diesel technology, certain experience can only be gained by practical experimentation; for example, the RAL team have also used the rig to study the effect of intermittent diesel operation on engine wear. The RAL/ICST group have shown that a great deal of knowledge can be developed using fairly limited capital resources, and are widely regarded within the wind-diesel community as having contributed much to the basic understanding of the technology.

ACKNOWLEDGEMENTS This chapter was written by Ray Hunter and Guy Nicholson (UK), to whom helpful advice was given by Kjetil Uhlen (N) and David Infield (UK).

Modelling techniques and model validation

The optimum configuration and operating strategy for a wind-diesel system is highly dependent upon the peculiarities of the wind regime and load pattern at the host location. Thus it is highly desirable before deciding upon a specific system to obtain an insight into performance by using a mathematical model. In this chapter various methods are outlined whereby wind-diesel systems can be simulated and the aim and state of development of each approach is given. Originally it had been the intention of the authors to identify the most accurate and cost effective models to emerge from the International Energy Agency programme on Decentralised Applications of Wind Energy. This has not proven to be possible due to both the specific nature of most of the current models being developed and to the difficulty and cost of obtaining a comprehensive data set with which to validate them. Full details have therefore only been included on the simpler approaches. To guide future researchers an overview is given of the IEA model validation exercise, and recommended common reporting formats are suggested for future intercomparison of the models. The purpose of developing models of wind-diesel systems is primarily to fulfil the need for cost effective planning and design tools. Depending upon the level of decision being taken on the planning of the system, different types and complexity of model are required. There are two main approaches to modelling: Time Series and Statistical.

APPLICATION OF MODELS Even though the models described here are mainly needed for technical and economic design purposes, there are other important applications: a Models can be used as tools to help interpretation of all kinds of experimental data, both in a development laboratory and in field operation. b Models play a major role in system optimization. Technical improvements can easily be tested out with simulation models before the actual hardware is decided upon. This greatly reduces the hardware costs of trial and error strategies. c A comprehensive set of simulation models can in a short time give valuable insight and experience of the behaviour of wind-diesel systems. For instance, it is easy

166

Modelling techniques and model validation

to simulate the response of various wind-diesel configurations to extreme combinations of wind and load which would rarely be seen in real life. d Using a simulation model, it is easier to transfer experience obtained with a winddiesel system at one site to a different site having different wind and load patterns. The necessary tailoring of wind-diesel systems to the new site is then easier to execute. e The effect which scaling of components or the system has on overall behaviour and performance can easily be studied with simulation models.

GENERAL DESCRIPTION OF TIME SERIES MODELLING Introduction The time series approach can be used to study short-term dynamic behaviour, short term performance, or long-term performance, but in each case a different length of time step is appropriate. The purpose of time series modelling is to study the target system's response to time dependent disturbances in the input data. Generally, such a model can be described by a set of ordinary differential equations with time, t, as the independent variable:

t)J)

(6.2)

where x(t)

is the set of state variables. In a wind-diesel system the state variables could be generator speed, grid frequency, fuel consumption, accumulated stored energy etc.

u(t)

is the vector describing the input data. Windspeed and consumer load measurements are the natural input data to a wind-diesel system.

v(t)

is a disturbance vector which also could be regarded as input to the system, but this is a stochastic variable. It is usually described by its statistical parameters (probability distribution, mean value and variance). Wind turbulence and wind gusts are sometimes modelled as stochastic variables, but in this chapter we consider all input variables to be deterministic and thus: v(t) = O.

y(t)

is the measurement vector which includes all variables that are derived from the state variables. In a wind-diesel model these are e.g. power produced from the different units of the system.

Modelling techniques and model validation w(t)

167

is measurement noise. In this text we shall assume that the measurement noise can be neglected.

/ a n d g are nonlinear vector functions. There are several approaches to modelling wind-diesel systems and these depend on what is required from the simulation. The complexity of the model (the model order) is strongly dependent on the desired time resolution. It is natural to group the time-series models into three levels: dynamic, short-term performance, and long-term performance. Dynamic model A dynamic model is normally used to study power quality, component sizing, and system stability, in terms of voltage and frequency. It requires the wind-diesel power plant to be well defined technically. If sub-transient phenomena (harmonics) are to be studied, the model must operate on a millisecond to millisecond (or even microsecond) timescale. If only the transients of RMS-values (phasor values) are of interest, then the modelling details typically need only be known with a time resolution of 20 to 50 milliseconds. These types of model, describing fast (electrical) dynamics are not covered in this book. If a description of the fast dynamics, which would include for instance properties of the voltage controller, is left out, it is still possible to study frequency fluctuations and the slow (mechanical) dynamics. This type of model is considered here, but only in a simple way. Dynamic models describe both electrical and mechanical transients and represent the highest level of detail since they must be capable of simulating the various transient phenomena in the electric equipment. The shortest time constants found in a wind-diesel system are in the power electronics of the dump load controller, in the rectifier/inverter if included, and also in the subtransient reactances of the generators. These time constants range from a few microseconds upwards. Such sub-transient models are required as development tools for the power electronics industry. The applications are analysis of harmonics, voltage control and power quality. For wind-diesel plant it is usually adequate to use standard power electronic components and the effect such components have on power quality in a wind-diesel system can often be estimated using manufacturers' specifications. A more interesting modelling level for wind-diesel applications is that involving the voltage and frequency control system, i.e, the reactive power/voltage control and the active power/frequency control. Such models involve differential equations that describe the dynamic behaviour of phasor values, i.e. the root mean square values of the actual voltages and currents. The averaging is determined by the system frequency, typically 50 Hz or 60 Hz. Sub-transient and transient models of the types mentioned above will not be treated in further detail here.

168

Modelling techniques and model validation

Short-term performance model This level of complexity is used to optimise component rating and to study how the power match between the wind turbine, the diesel and the load should be handled in the short-term. This co-ordination is usually managed by matching any momentary power excess/deficit with the input/output from a short-term energy storage. It is assumed that the technical problems at the dynamic and control level are solved. If the wind-diesel system to be studied has a long-term storage, say 20 minutes of stand-alone supply capacity or more, the rate of power exchange between the storage and the rest of the system can also be described in the long-term performance model described later. Short-term performance models have typical time resolutions of up to one second and describe transients in the mechanical systems, but short-time constants in the electrical system are neglected. Typical input data comprise various, detailed time series of wind and load which should be representative of both normal and extreme operating conditions for the power plant. Short-term performance models are typically used to study system response over periods of up to a few minutes. Prime applications are to find an effective strategy for the switching in and out of the diesel and for power control. Losses and efficiency of the short-term energy storage can also be studied in some detail. This type of model is ideal for systems in which wind and load, by their stochastic nature, steadily change the charge level of the short-term storage. Theoretically short-term performance models might very well be used to simulate a system over long periods of time, and thus be used to provide input to an economic assessment. The problem with this is usually that wind and load data are almost never available with the necessary time resolution. Long-term performance model This level of model is used to analyse the long-term system performance in terms of fuel savings and number of diesel start/stops etc, and in addition, to provide input to economic assessments to allow payback rates etc to be determined. All technical problems are assumed to be solved at this level which of course is only valid provided that a sound technical scheme is chosen, ie, that the wind-diesel concept has acceptable operational reliability. Time resolution is preferably of the order of minutes or more. Long-term performance models are used primarily for calculating the overall performance of the system. The main purpose is to estimate the fuel savings compared to a pure diesel system. Typical input data to such models are one year's time series of wind-speeds and load powers averaged over 10-60 minute periods. With these long time steps, very little dynamics needs to be included. The power flow and start/stop criteria

Modelling techniques and model validation

169

for the diesel engine are the most important aspects of the system which are modelled. With the time series approach to economic performance modelling, simulations should be done over at least 1 year, preferably 5 years. The main results of such simulations describe the proportion of the total annual consumption likely to be supplied by the wind turbine, the diesel saving and the operational costs of incorporating wind power. Other results like total wind generated energy, losses and dumped energy are also valuable. Modelling the components In this section short descriptions are given of how the different units in a wind-diesel system can be incorporated into performance models. The discussion will look at requirements for the input data, and at typical results. The choice of numerical solution methods in computer simulations is an important issue, but here we only give references to the wide range of literature covering this topic. Model validation, and how simulation results could be used in further analysis will also be discussed. Wind turbine A wind turbine consists, from the modeller's point of view, of a rotor and a generator connected via a gearbox to a mechanical shaft. The simplest way of modelling this system is to use an input/output (also called a black box) model. The input is wind speed, and the output is electric power from the generator as indicated in Fig. 6.1.

Fig 6.1 Simple 'black box' model of a wind turbine: wind speed as input, electrical power as output

170

Modelling techniques and model validation

The relationship connecting power and wind speed is the power vs. wind characteristic which is usually available from the wind turbine manufacturer. The advantage of this model is its simplicity. It can also be used on any kind of wind turbine independent of the type or size of the rotor and generator. A more advanced model is required if transient behaviour and internal losses are to be modelled. The wind turbine is then better described by its efficiency characteristic or the Cp (X, 0 ) curve. This gives the ratio between mechanical power on the turbine shaft and the natural power in the wind field passing through the rotor area. The wind power is then calculated by the equation: (6.3)

The tip-speed ratio, X, is defined by: X = (Ot-r/V

Other symbols are defined as follows: Mechanical power on the turbine shaft Air density P Windspeed V Turbine angular velocity cot r Rotor radius 0 Blade pitch angle

(6.4)

(W) (kg/m3) (m/s) (rad/s) (m) (rad)

A more complex description could include the effects of averaging over the rotor disc. Also, account could be taken of fluctuations in wind speed within each time step of the model, but in many cases the above description is sufficient. The angular velocity of the turbine can be obtained by the swing equation. In a simple form this can be written as: dcot — = (/>w / cot - Z)tcot - T gen ) IJt where £>t

-

Tgen J\

-

represents the total mechanical losses in the rotor, gearbox and generator. is the mechanical torque from the generator. is the total moment of inertia of the rotor, gearbox and generator which must be referred to the rotor speed.

(6 5)

Modelling techniques and model validation

111

The generator torque, Tgen , must be found from a generator model, which in turn will be highly dependent upon the type of generator used and the chosen level of complexity. Modelling of electric machinery is covered by Stagg and El-Abiad (1968), and by Fitzgerald et al (1983). Other literature on this topic includes Tsitsovits and Freris (1983), Pierik and De Bonte (1985) and Hoeijmakers (1988). Diesel generator set The diesel generator set can, as was the case for the wind turbine, be modelled as a black box with diesel fuel consumption as input and electrical power as output. The fuel vs. power characteristic is often assumed to be linear or quadratic, see Fig. 6.2. Another common 'rule of thumb' is the assumption that the fuel consumption at idling is approximately 25 to 30 per cent of the consumption at nominal rated power.

Fig 6.2 Simple 'black box' model of a diesel generator set: fuel flow as input, electrical power as output

A more complex model could incorporate the first order dynamics of the engine and generator. Generator models are covered by Bleijs and Infield (1986) and by Eykhoff (1977). A simple first order linear model of a diesel engine with governor is shown in Fig. 6.3. Many wind-diesel plants have a clutch between the diesel engine and the generator. To be able to handle starts and stops of the diesel engine, the clutch must also be modelled.

Modelling techniques and model validation

172

Fig 63 First order model of a diesel generator set with governor The symbols are: cod coref 5 Jd mi Kc Po Ky Tp 7f ^dgen Z)d Jd

Engine speed, Governor reference speed. Governor gain constant. Time constant in governor. Diesel fuel consumption. Constant describing efficiency of the combustion. Motor chamber pressure when running idle. Stroke volume. Produced torque. Friction torque. Load torque from generator and clutch. Constant describing the frictional losses. Total moment of inertia of the engine, clutch and generator.

The differential equations describing diesel speed and fuel consumption in this model are: - = (Kv • (*c mf - p0) - Dd -cod - r

dgen

(6.6)

and dm?

(6.7)

Modelling techniques and model validation

173

Energy storage and dump load It is common to distinguish between short term and long term energy storage. Short term storage in wind-diesel systems as described in Chapter 4 can take the form of flywheels, hydraulic accumulator buffers or low capacity batteries. Long term storage can comprise larger battery banks or some kind of hydro pumped storage scheme. The purpose of an energy storage system is: To smooth the electrical frequency fluctuations caused by the turbulent nature of the wind. To better utilize the wind energy by charging the storage in periods with surplus wind power, and discharging when the consumer demand exceeds the average wind power. The dump load might come into action when the storage is fully charged, and the wind power still exceeds the load demand. The dumped energy can be used for example to supply a water heating system or alternatively the power output from the wind turbine can be controlled. Good models of the energy storage must reflect the specific qualities of the actual storage medium. This book does not describe the different types of storage and dump loads in any detail but relevant information may be found in Lipman (1988), in Slack and Musgrove (1986) and and in Bleijs and Infield (1986). A simple way of representing the storage in a long-term performance model is to consider the power flow of the total system, e.g: ' stor = * wgen + * dgen~Moad~~* dump~~* sysl

(°-°)

where Power available for storage. Negative power means discharging the storage. Electric power from the wind turbine generator. Electric power from the diesel generator. Pload

Load power consumption,

^dump

Power dissipated in dumpload.

^sysl

System losses excluding the storage losses.

Accumulated energy in the store, Wsior, can further be described by:

~ r stor

Moss

(6.9)

174

Modelling techniques and model validation

where Pioss is an estimated parameter representing the total losses in the storage system. Eq. (6.9) is necessary in long-term performance models only if the capacity time constant of the storage exceeds the time step of the model. Otherwise, Eq. (6.9) can be omitted or reduced to an algebraic equation, such as Eq. (6.8). Control system It is very important to construct a good model of the control system. The complexity of its description should be commensurate with the complexity level of the model as a whole. The implementation of control varies considerably from system to system so it is impossible to give a general description here. One of the most important applications of a performance model is to predict fuel consumption. Simulations done by Infield (1988) have shown that the prediction of fuel consumption is generally good if the diesel is kept running. However, if the diesel is allowed to stop, the fuel consumption will to a great extent depend on how long the engine runs. Making a model of the control system that can predict start/stop of the diesel engine with acceptable accuracy, is a difficult task. It is very important that this aspect of modelling is improved, especially if a model is to be created that can be used in economic analyses of wind-diesel systems at specific sites. Input data and output from the models The input to wind-diesel models are data describing the wind and the consumer load. The wind data most often consist of time series of wind speed measured at hub height. The wind direction need not be taken into consideration if it is assumed that the yaw control of the turbine works perfectly. If the yaw mechanism is modelled, the wind direction must of course also be taken into account. The appropriate time resolution of the wind speed measurements depends on the type of model to be used. In a dynamic model the time step should be as short as possible. It is difficult in practice, though, to obtain wind speed data with a resolution better than 0.5 to 1 second. In a long term performance model it would be ideal to use approximately 1 second data, however in reality this would be impractical since to simulate for example one full year of operation, would require excessive quantities of data which in any case would likely be impossible to find. Computer storage for such a quantity of data could also present a problem. The data commonly used in these models therefore consist of 10 minute mean wind speeds which is the international standard averaging interval for wind measurements. In addition to the mean wind speed, minimum and maximum values occurring during the interval are often available. If a resolution better than 10 minutes is needed, it is possible to generate time series with the desired resolution using these statistical parameters as input. Methods for generating windspeed time series with realistic spectral characteristics and probability distributions can for example be found inGoyder(1988).

Modelling techniques and model validation

175

As an alternative the system performance within the time intervals can be represented in a statistical way. Consumer load data in a performance model should be described by time series of load power or mean load power. Generally, the time resolution should be equal to the resolution of wind speed, but a slightly longer time step in the load data can usually be accepted. The output from simulation models typically consists of various time series describing the system performance. Depending on the type of model, different parameters are available. In a long term performance model the interesting parameters are power flow, i.e power produced or consumed from the wind turbine(s) diesel generator(s), storage and loads as functions of time. The number of starts and stops of the diesel engine, and the total consumption of diesel fuel are also important parameters describing the system performance. The benefits of modelling and simulation analysis are both technical and economic. The technical benefit is that different system configurations can be examined without having to do much experimental work. Provided that a calibrated model is available, this saves a lot of time and money during the design of a wind-diesel plant. The control system can also be designed, and to a great extent tested out before it is implemented in the real system. The economic benefit relating to performance models is that provided there are sufficient wind and load data available, it is possible to analyse the performance of any given system. From the results, the total energy cost can be calculated and the economics of different systems at different sites can then be compared. Numerical methods and model validation A time series model will generally consist of a set of differential and a set of algebraic equations. The equations will usually be non-linear and, in a simulation model, they must be solved numerically. The differential equations can be solved by simple numerical integration methods. The order of a performance model is normally not very high, and the integration is usually straightforward as long as the time step chosen is not too long. The last step in model development is validation. The final version will include many parameters describing the system, some of which will be known quite well beforehand, while others will be uncertain or unknown. If the model is to become a useful tool, and is to produce reliable results, all the parameters have to be verified. The verification should be carried out by comparing simulation results with measurements from real systems. Fig. 6.4 shows schematically how this can be done. Several methods for parameter estimation can be used on wind-diesel time series models. Some of these are discussed by Astr0m and Wittenmark (1984) and by Eyckhoff (1977).

Modelling techniques and model validation

176

Real output Process

^1+ Error

Input data

Simulated

f

output Model

p = Panam. vector p Parameter estimation

Fig 6.4 Comparison procedure for verifying parameter values

STATISTICAL MODELLING TECHNIQUES As a result of experience gathered from practical operation as well as from time domain simulation studies, it is often possible to derive some simple relationships to describe the more significant properties of dynamic systems as seen over a longer period of time. One important way to achieve the desired simplification is to develop statistical models. Such models represent a more empirical than analytical approach, in as much as they do not require a detailed physical knowledge about all system components and their interaction. On the other hand, statistical models can give excellent results, especially for long-term system behaviour and as a basis for economic assessments, provided the models are validated with the help of thorough measurements and long-term experience. One of the functions of wind-diesel modelling is to provide information on the long term performance of a given system operating at a given site. This information is important in the design of effective systems and indicates to a potential purchaser the expected fuel savings. Detailed time series modelling is not always an appropriate way to make this assessment for a number of reasons. For example, long time series of suitable wind and load data may not be available for the particular site and resources may well not exist to develop and run a detailed time series simulation model. In contrast, probabilistic calculations need only a highly simplified paramaterisation of the wind resource and the load and in a straightforward manner can provide evaluations of long term (eg annual) performance. Due to their nature such techniques are restricted to relatively simple wind-diesel systems such as a single wind turbine and a single diesel

Modelling techniques and model validation

111

generator set and they cannot deal with the fuel saving potential of large energy stores. However, since the major role of energy storage in a wind-diesel system appears to be to reduce the rate of diesel stop/starts to an acceptable level, thereby making the system more workable, these simplified approaches to system modelling do have an important function. Statistical calculations have been found to be in good agreement with measurements although admittedly in only a simple case of continuous diesel operation. (See results of the IEA validation reported later in this chapter and also by Infield (1988).) The more complex problem of intermittent diesel operation has been dealt with in the work of Infield through the introduction of some techniques from time series theory. So far these extensions have not been validated although they are in qualitative agreement with time series models. Modelling approach The statistical or probabilistic approach to modelling utilizes probability distributions for both the wind and the load. These density functions are manipulated to give a density function for the load on the diesel engine and hence the fuel consumption (assuming a given fuel characteristic for the engine). In what follows the most general wind turbine characteristic, described by Figure 6.5 is taken.

Wind turbine power

Wind speed

Fig 6.5 Notional wind turbine power characteristic

If we initially ignore the fuel consequences of energy storage and control strategies, the annual diesel fuel consumption can be calculated according to standard probability theory.

178

Modelling techniques and model validation

Let the wind turbine power output P as a function of the windspeed U be given by P(U) =

(6.10)

Then if the annual probability density function of wind speed is pu(U) the density function for wind turbine power, Pp(P), can be expressed as

dU dU_ dP

f°pu(U)dU vf

0

jVlpu(U)dU8(P-Pr)

(6.11)

where v c is the cut in windspeed, vf the furling speed, vr the rated speed, v' r the upper speed bound for rated power, and Pr the rated power. 8(x) is the Dirac delta function defined informally as

= oo for x = 0

J

8(x)dx=l

(6.12)

The change of variable involved in the first and second terms in the equation (6.11) is more easily recognised if we integrate both sides with respect to P. Illustrating this with just the first term,

(p

= b

= iiP=a P a

dU ^p

simply indicating that the contribution to the probability of power being between a and b on the rising part of the power curve is just the probability that the windspeed is b e t w e e n / " 1 ^ ) a n d / " (b), the corresponding wind speeds.

Modelling techniques and model validation

179

Assuming the annual probability density function pj(L), for the electrical load is known, the density function for the load seen by the diesel,/?d(Z)), can be expressed as

pd(D) = £

Pl(L) pp(L

- D)dL

(6-14)

where L is the system load. Note that it is assumed here that the wind speed and system load are independent random variables. If this is not the case the joint probability distribution function Pip(L, P) must be used. If the wind turbine rated power output is greater than the minimum load, then the diesel load, Z), can take negative values. In practice this surplus energy is absorbed by a dump load and

/7d(0) =pd(D < 0) = J

p6 (D)dD- b(D)

(6.15)

Let F(D) represent the diesel engine fuel consumption as a function of the load on the diesel, then the annual average fuel consumption is given by

<-£

F(D) = j

-t

_

F(D)pd(D)dD

F{D) J _

Pl(L)pp(L-D)dLdD

=p, =PT _ F(L-P)pp(P) since/)

Pl(L)dPdL

(6.16)

=L-P.

The operating regime represented is determined by the choice of the function F(D). For continuous diesel operation F(0) will take the value of the diesel fuel consumption running at zero load. F(0) is set equal to zero for intermittent diesel operation. Continuous diesel operation with a minimum diesel load, Dmin, is represented by setting F(D) = /7(Z)min ) for D ^Dm\n whilst intermittent diesel operation with a minimum diesel load is given by F(D) = F(Dmin) for 0
180

Modelling techniques and model validation

Overview of annual start/stop calculation This section provides a very brief introduction to the calculation techniques that have been developed to estimate, on a probabilistic basis, the annual rate of diesel start/stop cycling. Full details of the mathematics involved are included in Appendix 6B. A key simplification adopted to make the analysis tractable is the assumption of a constant system load (taken as the annual mean). So long as most start/stop cycling results from changes in wind turbine output rather than load, the approach should be reasonable. For the purposes of analysis the start/stop behaviour is formulated as a level crossing problem and standard results from time series theory are applied. The common diesel switching strategy of hysteresis can also be incorporated into this framework. The statistical and spectral characteristics of the wind time series are required to solve the level crossing equations. This is provided by taking a standard power spectrum, such as the Kaimal spectrum, to describe the turbulence induced fluctuations in the wind. Energy storage is included in this analysis by filtering the spectrum by a suitable first order filter. Annual results are calculated by integrating the functions derived from the level crossing analysis over one year with a specified probability distribution of wind speeds. The Weibull distribution is normally appropriate. Fuel penalties arising from the use of controls (including a minimum diesel run time) can be obtained from an extension of the annual fuel consumption calculation presented in the previous section. MODELLING PACKAGE SUMMARY To indicate the type and range of wind-diesel models which have been developed, a list is given in Table 6.1. This summarises the tools which have been produced by participants in the International Energy Agency's Research and Development programme on 'Decentralised Applications for Wind Energy' (IEA WECS R & D Annex VIII Project). The table shows the state-of-the-art of these models. The models are classified under four different categories, these being the various permutations of 'performance' or 'simulation' model with 'time series' or 'statistical' approach. As detailed in the foregoing sections, performance models only produce energy flows, fuel consumption and savings over a certain period and are mainly based on long term (10-60 min) average values of the input data (wind speed, load power, etc), whereas the simulation models are based on a (slow) dynamic model of the system with input data for relatively short intervals (1-60 s). These simulation models not only produce the performance of the system, but also calculate changes in powers, frequency etc, for each calculation interval.

Table 6.1 State-of-the-art of models considered by the members of the IEA programme Country

Switzerland

Canada

Canada

Denmark

United Kingdom United Kingdom Norway

Owner

EPFL-LEME

NRC

EMR

RISO

RAL

RAL

EFI

Type

S-T

S-T

S-T

P-T

P-T

P-S

S-T

Objective

Optimisation Power Qual. Control Strategy

Optimisation

Performance Control Strategy

Performance Control Strategy Operational Data

Performance

Performance

Control Strategy Power quality. Losses.

Application

Specific (bio-gas)

General

General

General

General

General

General

Input (1)

V, Pioad Characteristics

V, Pioad

V, Pioad

V, Pioad

V, Pioad Characteristics

V, Pioad, / Characteristics

V,Pload Characteristics

Energies U,f

Powers U,f

Energies,

Energies FC,FS

FS, Energies Diesel starts

FS, Energies Diesel Starts

UJ.FC

FS,FC

Output

(2)

Energies

Operating events Time step

1 s - 1 hr


1 s- 1 hr

Is - 1 hr

l-60s

N/A

0.5 - 2 s

Stage of Development

Partly validated

Not validated available Dec 89

Not validated available

IEA-trial validated

Validated

IEA-trial further validated

Not validated

Storage included

Storage included

Short/medium storage included

Remarks

P-S Performance Model based on Statistical approach

P-T Performance Model based on Time Series approach S-T Simulation Model based on Time Series approach

Country

Norway

Netherlands

Netherlands

USA

USA

USA

Sweden

Owner

EFI

ECN

Utrecht Univ.

UM/SERI

UM/SERI

UM/SERI

FOA

Type

P-T

S-T

P-T

S-T

P-T

S-T

S-T

Objective

Operational Costs

Optimsation Control Strat. Power Quality

Performance Optimisation

Control Strat. Performance Power Quality Optimsation Losses

System Design

Performance Optimsation Control Strat.

Application

General

General

General

General

General

Specific

General

Input")

V, Pioad V, Pioad Characteristics Characteristics

V, Pioad Characteristics

V, or Pwind Pioad (fixed)

V, Pioad Characteristics

V, Pioad (fixed)

V, Pioad

Output(2)

FS, energies Diesel starts

/, FS, FC, power FS, energies, Diesel starts Diesel starts

/, powers

FS Diesel starts

Time Step

10-60 min

0.5-2 s

10-60 min

10 min

1-60 min

1-2 s

ls-lh

Stage of Development

Commercially available

Validated

IEA trial validated

IEA-trial validated

Under validation

Limited validation

Ready to run

Remarks

Storage included

Storage included

No diesel stop. Low penetration

Storage included

Not in use any more

(1) V Pioad / Pwind

= = = =

wind speed load power turbulence intensity wind turbine power

(2) U

f FC FS

FC and power Energies, each 10 min Diesel starts

= = = =

grid voltage grid frequency fuel consumption fuel savings

Modelling techniques and model validation

183

Both types of the model can be further categorised according to their method of calculation. In time series models the input data consist of time series files, and calculations are executed for each time step, whereas in statistical models the input data consist of a statistical description of the input signals, and the model executes statistical calculations. For further details on any model, it is suggested that the reader contacts the appropriate national representatives who participated in the IEA programme. A list of addresses is supplied at the beginning of the book. TRIAL VALIDATION OF MODELS CONSIDERED BY THE AUTHORS As part of the IEA WECS R & D Annex VIII Project a systematic validation of a number of statistical and long term time series performance models was attempted. This section summarises the detailed report describing the trial validation exercise which was carried out between August 1987 and February 1988. The full report is 'IEA Trial Validation Exercise using ECN Wind-Diesel Data' by Infield (1988). The objectives were to ensure that the international exchange of data could be completed successfully, that the various national wind-diesel models could receive an initial assessment, and that problems with data quality, modelling performance and reporting format could be identified. For this initial exercise the modellers were supplied with the system performance data in addition to the system inputs. This was to assist in identifying any inadequacies present in the computer models, and also, as transpired, with the accuracy of the data supplied. It was not the intention to undertake a 'blind' validation exercise, although this option could be considered as a further future option. Reports for the trial validation were received from Canada, Denmark, Norway, United Kingdom, and USA, and these are included as appendices to the detailed report. It was apparent that the reporting format varied considerably from country to country and one purpose of this chapter is to make recommendations regarding the format which should be adopted in any future validation. The primary objective of the validation exercise was to identify techniques which could and had been used with confidence to model a variety of wind-diesel systems, in order to optimise systems for arbitrary locations and applications. As shall be seen, this objective was not realised. However, it is thought that it is still worthwhile to set the experience on record to help future researchers. The system Data were supplied by The Netherlands Energy Research Foundation (ECN) from the trial wind-diesel system which was operating at ECN. Details of the hardware which included a 50 kW diesel generating set and up to two 25 kW variable speed wind turbines are given in the report, T h e Dutch Autonomous Wind Diesel System' which formed Appendix 1 of the full report.

184

Modelling techniques and model validation

Seven data files were made available covering differing modes of the system. These were: File

Wind Speed

Load

Diesel Mode

1 2 3 4 5 6 7

Medium Low/medium Medium Low High High Medium/low

Variable Constant Constant Constant Variable Constant Zero

Continuous Continuous Continuous Continuous Fuel-off allowed Fuel-off allowed Fuel-off allowed

In the last three cases, where the diesel fuel was allowed to be cut off, the diesel and its generator still represented an additional 5 kW load on the system. All files comprised of 10 min averages (or equivalent totals). Data quality Early work on the trial validation showed that the question of data accuracy was important. In consequence, ECN indicated the expected performance of the different transducers used in the experiment to be as follows: Quality

Absolute Error

Power from turbine Consumer power demand Power to dump load Diesel electrical power Fuel consumption Diesel operating time Frequency (40-60 Hz) Wind speed

0.9 kW 0.9 kW 0.9 kW 1.6 kW 1 ml (resolution error) 1 sec (resolution error) 0.3 Hz 0.5 m/s

Particularly important for the modelling accuracy was the measurement of wind speed. The above quoted precision of 0.5 m/s refers to the performance of the cup anemometer. In practice the anemometer was not well placed, indeed at times it was in the wake of another wind turbine, and hence the measured wind speed was not always a reliable indication of the wind seen by the wind turbine rotor. In consequence some of the modellers (UK and USA) chose to model the system performance using both the measured wind speed and the measured wind turbine power output. Although the errors indicated for the power flows are small as a percentage of rated powers, at the relatively low powers recorded in the experiments these constitute a significant fractional error. These factors must be taken into account when assessing the accuracy of the simulation models.

Modelling techniques and model validation

185

Modelling results The models varied in their degree of sophistication. The versions of the American and Danish models and the long time-step Norwegian model applied here did not deal with diesel stops and starts. Without describing sub-ten minute behaviour, the Canadian model dealt with intermittent diesel operation in a simplified way. The Norwegian short time-step model examined the detailed dynamic' response of the system by synthesising 1 second wind data from the 10 minute averages and then using this as input to the model. The UK model dealt with this area of difficulty by applying a statistical approach making use of level crossing theory. However, none of the attempts by any of the countries to reproduce the measured stop-start operation of the diesel had succeeded. It is believed (as discussed in the UK supplementary report, Appendix 3 of the main report) that this was due to a failure of the system controller to comply with its specification. Because of these difficulties this summary of the validation exercise will concentrate on cases where the diesel either ran continuously, or did not run at all. Not all the models were applied to the complete set of data files. As pointed out in the US report, fuel consumption is somewhat insensitive to diesel load, resulting in better prediction of this parameter than of others describing the system. Consequently each model's ability to predict both diesel load and fuel consumption is reported here. Some model results gave total energy flows and fuel consumption. It is perhaps more useful to look at average rates, first because the numbers are then independent of the arbitrary length of the run, and second so that RMS errors can be seen in perspective. In addition they are then directly suitable for statistical testing. This has been done in Table 6.2 which summarises the performance of all of the models involved in the exercise. (It should be noted that the modellers have made different assumptions with regard to system losses and these account for much of the variation between results.) Techniques for model assessment Model validation is a relatively recent activity and in an attempt to rigorise the procedure some standard statistical approaches have been adopted. These however should be used with care. One common technique is to compare the measured data set with the equivalent calculated data set as if they were sampled at random from a normal probability distribution. In practice the assumption of normality may be questionable, and the fact that the time series will almost certainly have some well defined structure will distort the result, but it does provide a quantitative comparison. Usually the question being asked is whether the two data sets are likely (i.e. with a specified statistical confidence) to be drawn from the same population. For the examination of a single parameter (such as diesel load) the Student's V test can be applied. If a set of predictions is to be assessed simultaneously then the appropriate technique is Hottelling's 7' test. It seems from the literature that such an approach may be used to assess the quality of the model. This is

186

Modelling techniques and model validation

Table 6.2 Model assessment Calculated fuel, 1/h

RMS fuel Calculated RMS power error, 1/h * power, kW error, kW

Filel measured fuel: 3.671/h measured diesel load: -0.46kW

UK Canada

USA Denmark Norway

3.47 3.40 3.42 3.38 3.43

0.38 0.58 0.41

1.42 1.73 1.62

0.59

-1.20 -1.69 -1.50 -1.63 -1.28

1.53

3.80 3.76

0.64 0.68

0.59 0.33

4.02 4.32

-

-

-

-

3.91

0.73

1.44

3.38

3.88 3.85 3.89 3.84 4.00

0.32 0.35 0.31

1.04 0.80 1.06 0.78 1.94

2.24 2.47 2.24

3.24 3.26 3.32 3.20 4.15

1.66 1.65 1.58 0.90

-4.99 -5.00

0.55 0.54

-

-

-

File 2 measured fuel: 4.33 1/h measured diesel load: 4.08 kW

UK Canada

USA Denmark Norway File 3 measured fuel: 4.141/h measured diesel load: 3.01 kW

UK Canada

USA Denmark Norway

0.55

_ 1.61

File 4 measured fuel: 4.43 1/h measured diesel load: 4.84 kW

UK Canada

USA Denmark Norway

4.27 4.29 4.30 4.28 4.40

0.50

0 0 0 0

0 0 0 0

0.18 0.17 0.16

File 6 measured fuel: 0.01/h measured diesel load: -5.46 kW

UK Canada

USA Denmark Norway

-5.00 -6.00

1.15

Modelling techniques and model validation

187

misleading for it really only tells us if the two data sets are statistically distinguishable. Take for example the fuel consumption calculation provided by the UK model for the fourth data file. Overall the accuracy is within 4% and more impressively so is the RMS error of the individual predictions. Visual examination (Appendix 2 of the main validation report) indicates that the model accurately reflects the time history. However the results fail the *t' test comparison for any reasonable confidence level. This is because there is a small but quite consistent error (about 3.6%) in the predictions. In other words the two data sets can be statistically distinguished. Clearly this is not sufficient reason to regard the model as poor. Instead it should be interpreted to mean that the model performance could be improved - in this example some factor (or factors) which results in the consistent under-estimation of fuel consumption should be sought.

GUIDELINES FOR FUTURE VALIDATION EXERCISES Data requirements The nature of the validation depends on the extent and detail of the data available. For commercial systems only rudimentary data would be expected, consisting perhaps of monthly fuel consumption and energy delivery figures. Wind speeds would likely have to be taken from local meteorology station data. In this instance detailed validation is out of the question. Despite this, a model's ability to predict overall performance as reflected in fuel consumption is still important and should be tested under these conditions. More detailed validation requires high quality data sets covering a large number of parameters which are only really available from research, or well instrumented demonstration systems. This is the situation with which the IEA Annex VIII work has been primarily concerned. Very little needs to be said regarding guidelines. There are strong arguments for basing validation work on a time series of 10 minute averages, as was done for the trial validation exercise. This of course is consistent with the IEA Recommended Practices No 1 dealing with Wind Turbine Power Performance Measurement. The time series should be long enough to reflect the operation of the system in question, so for example systems with battery storage require longer data sets than systems with no, or only short-term, storage. A number of different time series representing different wind and load conditions are necessary to ensure that the models are validated over the full range of operational conditions. Great importance is attached to the quality of the data, and related to this the calibration of the transducers. This is discussed in more detail in Appendix 6C where particular attention is paid to dealing with errors and uncertainty. Data should be provided in a standard format with the first file summarising subsequent files. Within the IEA exercise, magnetic tape was found to be the most suitable

188

Modelling techniques and model validation

medium for exchanging data and obviously a common specification would have to be agreed between the participants in any future exercise. (It may be that PC disks could be a preferred means of data exchange.) Recommendations on reporting format The form of report will depend on whether the validation is undertaken 'blind' or not. If it is then only the simulated time series and summary statistics can be provided by the modellers. In this event an independent party would have to assess the accuracy of the models and for this they would probably require the calculated time series. The assessment procedure should be similar irrespective of whether the validation is performed 'blind' or whether the modellers themselves undertake an assessment of the accuracy of their work. Central to validation is the comparison of measured and predicted fuel consumption. Also important in assessing the quality of the models is a comparison of the major energy flows. The following parameters should be calculated for each of the data sets that are used in any further validation work. Units Error in mean overall fuel flow rate RMS error in fuel flow rate

1/h and % 1/h and %

Error in mean wind turbine output RMS error in wind turbine output

kW and % kW and %

Error in mean dump load power RMS error in dump load power

kW and % kW and %

Error in mean diesel load RMS error in diesel load

kW and % kW and %

For systems with energy storage Error in mean power into store RMS error of power into store

kW and % kW and %

Error in mean power out of store RMS error in power out of store

kW and % kW and %

For systems allowing diesel stop/starts Error in overall number of diesel starts RMS error of diesel starts

% %

Error in overall fractional run time RMS error in fractional run time

% %

Due to the difficulties outlined in the previous section no statistical comparison is

Modelling techniques and model validation

189

proposed. Should such tests be considered advisable then standard deviations for all the time series, calculated and measured, would be required. A further approach to assessment could be to undertake linear regression between the measured and calculated data, parameter by parameter. This would provide a best fit line through scatter diagrams, like those presented in the UK reports. The regression (or correlation) coefficient would indicate the consistency of the model, the intercept would indicate the presence of an offset and the gradient should be 1.0 for a good model. Statistical tests of the sort previously discussed would in this context be equivalent to calculations of whether the intercept is significantly non-zero and whether the gradient is statistically different from 1.0, given the degree of scatter present in the data. Most of the results produced in the IEA programme included time series plots for both the measured data and the model result. This is thought to be useful and it is recommended that such plots, for each parameter, should be included in any future validation report. Perhaps the comparison of theory with experiment on the same graph is the most satisfactory presentation. Scatter plots of calculated against measured values are also considered to be helpful.

ERROR PROPAGATION A consideration of error propagation is an essential part of model validation. For a model to be useful it must be validated. This involves comparing predictions from the model with results obtained from measurements. Model predictions never give results that are exactly the same as those obtained from experiments. Therefore, some assessment of the errors involved is necessary in order to evaluate whether the model is consistent with the test results. This helps to indicate whether further refinement, in either the measurements or the model, is called for. There are two main considerations in assessing the effect of errors on model validations: a) the accuracy of the test data, and b) the propagation of errors into the other parameters of interest. In the test data both mistakes and instrument precision may affect the results. Mistakes can be minimized by using a carefully thought-out test programme. This includes calibrating each sensor individually and then making end to end calibrations of the entire test system, using known inputs. Instrument precision is an indication of how close the actual value of a measured parameter is to the one shown. For many instruments precision is expressed in terms of a percentage of full-scale. This may cause unexpectedly large errors at the lower end of the range. For example, suppose a power sensor with a range of 0-100 kW and an accuracy of ±1 % of full scale was used to measure the power from a diesel engine of 10 kW then any measured value of the diesel's power would only be accurate to ±1 kW, or ±10 % of the diesel's rating. Frequently a sensor does not express directly the desired parameter, and the value of

190

Modelling techniques and model validation

that parameter must then be derived from the measurement and its accuracy assessed accordingly. An example of this might be in using diesel fuel flow measurements as an indication of energy savings. The measured flow would need to be combined with values for density and energy content of the fuel to obtain the required value. Accuracy of predictions It is often possible to estimate the error associated with test results by means of uncertainty analysis. If the model results are within the uncertainty range of the test result then the model may be judged to be consistent with the experiments. To ascertain whether an improved model is more appropriate it may be necessary to increase the accuracy of the measurements or to undertake different tests. Uncertainty analysis is a method for predicting the accuracy of a result of an experiment, given the precision of the various measurements. The method is described in detail in Appendix 6C. Parameter sensitivity studies In many cases a model is so complex that an analytical approach to uncertainty assessment is not possible. In addition, the predictions of a deterministic model cannot be expected to give results that are applicable to all possible situations which a winddiesel system might experience. One approach to both of these problems is to undertake performance sensitivity studies. Essentially, this involves exercising a model with a variety of inputs, corresponding to the range of instrument errors or operating situations that might be encountered. These studies are normally done by starting with a 'base case'. This corresponds to the condition that is believed to be most likely or most representative. The input parameters are then varied in some organised fashion and the effects on the model results are noted. One type of sensitivity analysis involves varying parameters one at a time and the fractional change in the output is then compared to the fractional change in the input. The results from a number of tests are often depicted graphically on a 'star' or 'spider' graph. The fractional changes in the input are plotted on the x-axis, the fractional changes in the output are plotted on the )>-axis. The base case is a zero. Lines are drawn through all the points that correspond to a particular parameter. Lines with shallow slopes indicate that the output is relatively insensitive to changes in input. A steep slope indicates greater sensitivity.

ACKNOWLEDGEMENTS 0yvin Skarstein (N), Kjetil Uhlen (N), David Infield (UK), and Jan de Bonte (N) are responsible for this chapter.

Modelling techniques and model validation

191

REFERENCES Astr0m, KJ. and Wittenmark, B., 'Computer Controlled Systems' Prentice-Hall, 1984. Bleijs, J.A.M. and Infield, D.G., 'Some Design Aspects of Wind-Diesel Systems Incorporating Flywheel Energy Storage', Proceedings from EWEC'86, Rome, Italy, Vol. 2, p203-208, 1986. Eykhoff, P., 'System Identification', Wiley, 1977. Fitzgerald, A. E., Kingsley, C, Umans, S. D., 'Electric Machinery', 7th edition, McGraw-Hill, 1983. Goyder, H.G., 'A Novel Method for the Numerical Simulation of Turbulence', Proceedings from BWEA10, London, UK, pl21-128, 1988. Hoeijmakers, M. J., 'A Simple Model of a Synchronous Machine with Converter' Proc. 8th Int. Conf. on Electrical Machines, Pisa, Italy, 12-14 Sept 1988. Infield, D., 'IEA Trial Validation Exercise using ECN Wind-Diesel Data', Rutherford Appelton Lab., UK, 1988. Lipman, N.H., 'A Review of Autonomous Wind-Diesel Strategies', Proceedings from BWEA10, London, UK, p325-334, 1988. Pierik, J. T. G., and De Bonte, J. A. N., 'Quasi Steady-State Simulation of Autonomous Wind/diesel Systems'. ECN Report 85-091, Petten, Netherlands, 1985. Slack, G.W. and Musgrove, P.J., 'A Wind-Diesel System with Hydraulic Accumulator Energy Buffer', Proceedings from EWEC'86, Rome, Italy, Vol. 2, pl85190, 1986. Stagg, G.W. and El-Abiad, A.H., 'Computer Methods in Power System Analysis, McGraw-Hill, 1968. Tsitsovits, AJ. and Freris, L.L., 'Dynamics of an Isolated Power System Supplied from Diesel and Wind', IEEE proceedings, Vol. 27, Pt.A, No.9, 1983.

APPENDIX 6A SIMPLE EXAMPLE OF USE OF STATISTICAL MODELLING The statistical model described in Chapter 6 can be implemented in a straightforward manner for preliminary evaluation of a potential wind-diesel system. One method is as follows: As shown in Equation 6.16, the average fuel savings (when the wind speed and load are assumed to be uncorrelated) is given by:

= JJ ~ ~J J ~' F(L' JL JLJ

U — U

i

P)pp(P)Pl(L)dPdL

—U U

— U

where: D F(D) = F(D) = L PrfP) = P\(L) = P

r

=

P

net load on the diesel = L-P diesel fuel consumption as a function of net diesel load average fuel consumption total system electrical load probability density function of wind turbine power probability density function of system load rated (maximum) power of wind turbine wind turbine power

The double integral may be approximated as:

F( D)

= Y, •_v J

in J

J

Y, F(L{ - P(Vji\ J J

-J

V

io

A, (V)dV J '

JVj-AV/2 J

m (L)dL

Lt-AL/2 (6a.2)

where: ^in out

= ~

L

lo

-

L

up

=

i

=

L

P(vp = AV AL

wind turbine cut-in wind speed wind turbine cut-out wind speed lower limit of load upper limit of load ith value of the load wind turbine power atyth wind speed probability density function of wind speed wind speed interval load interval

The first integral is the probability that the wind velocity lies between wind speeds Vj - AV/2 and Vj + AV/2. The second integral is the probability that the system load is between Q - AL/2 and L; + AL/2.

Appendix 6A

193

The most appropriate probability density function for wind velocity is usually given by the Weibull distribution:

where c and k are the Weibull scale and shape factors. These may be found analytically from the mean V and standard deviation o v of the wind speed. They are given approximately by: c = V (2.732 + 2.1846 / - 1.9361 exp(/) + 0.1827 exp(27)

(6a.4a) (6a.4b)

where (6a.4c) The integral of the Weibull distribution may be solved analytically as:

(6a.5) where V\o and VUp are, here, the limits of integration. Depending on the situation, either a normal or Weibull distribution could be more appropriate for the load distribution. In the case of a Weibull function the integral may be solved directly as above. For the normal it is more convenient to use an approximation:

L, where

ol

(6a.7)

and where x = mean and GX = standard deviation. It should be noted that by carrying out suitable calculations it is also possible to obtain an indication of the amount of dumped energy. Within limits, various control strategies may be simulated. These include

194

Appendix 6A

varying the number of diesels or the minimum acceptable diesel power level. It should be recalled, however, that the method assumes that the storage incorporated in the system is sufficient to prevent the short term variations adversely affecting the assumed control strategy. Varying the amount of storage is not possible with this simplified method. By way of illustration the statistical method was used to assess the site discussed by Skarstein and Uhlen (1989). The site characteristics are as follows: Wind Annual average wind speed = 6.9 m/s Standard deviation of 10 minute mean wind speeds = 4.1 m/s Load Average load = 47.5 kW Standard deviation of load = 21.6 kW Wind Turbine (nominally 99 kW): Rated power = 105 kW Cut-in wind speed, V'm = 4 m/s Wind speed at rated power, Vr, = 12 m/s Cut-out wind speed, VW, = 20 m/s For Vin < V< Vr, P = 20.32 - 11.202 V + 1.1753 V2 + 0.1338 V3 - 0.0092756 V4. Diesel Single 100 kW diesel with fuel use, in litres/hour, is given by: F = 0.08415 P r + 0246P (= 33.01/hr at full load).

The wind speed interval was taken to be 1 m/s and the load interval was half the load standard deviation. A Weibull load distribution was assumed. The method predicts that the average maximum wind power would be 31.4 kW (= 66 per cent of the average load). Predictions of fuel usage, dump power, and savings for various possible operating strategies are given below. As shown, the expected savings would be in the vicinity of 35-37 per cent. For comparison, the detailed model used by Skarstein and Uhlen (1989) indicated that 38 per cent of the diesel fuel could be saved for the minimal storage system. Given the limitations of the statistical approach, it may be seen that its predicted values are close to those of the more detailed model. In particular the results are much closer than those which might be surmised by simply comparing the average load and the maximum wind power.

Appendix 6A

195

Results Case

Fuel Use 1/hr

Dump kW

Savings %

Minimum diesel power = 30 kW 1 NoWTG 2 1WTG

20.6 13.6

0 14.5

0 35

Minimum diesel power = 0 kW 1 NoWTG 2 1WTG

20.0 12.6

0 11.2

0 37

ACKNOWLEDGEMENT James Manwell (USA) prepared this appendix.

REFERENCE Skarstein, 0., and Uhlen, K., 'Design Considerations with Respect to Long Term Diesel Savings in Wind-Diesel Plants'. Joint BWEA/EWEA/IEE Workshop on Wind-Diesel Systems, Rutherford Appleton Laboratory, UK, 8-9 May, 1989.

APPENDIX 6B DETAILED TREATMENT OF START/STOP CYCLING IN STATISTICAL MODELS Annual calculation techniques for diesel stop/start cycling When calculating annual numbers of diesel stop/start cycles we consider the load as being fixed at a constant value (the mean annual load). This is justified by the assumption that the cycling results primarily from changes in wind turbine output rather than from changes in system load. The calculation of diesel starts then becomes a level crossing problem and standard techniques can be applied as found in Blake and Lindsey (1973). In the simplest strategy shown in Fig. 6b. 1, the diesel is started whenever the wind turbine output falls below the load and stopped whenever the wind turbine output rises above the load. Thus if UL is the wind speed (assumed unique) at which the wind turbine output equals the load, we have a level crossing problem with wind speed as the time series. We are interested in the expected time E[T) between start/stops ie, lower/upper crossings.

Diesel starts occur at these crossings

Fig 6b. 1 Occurrence of diesel starts for a system with no dump load and no storage

A common diesel switching strategy involves hysteresis. Such a strategy is appropriate to a system which includes a dump load. In this case, shown in Fig. 6b.2, there must be some fixed power surplus of wind energy over load before the diesel is switched off. This too can be considered as a level crossing problem. We will denote this surplus as 'EXCESS' in wind speed terms. Referring to the figure, the diesel starts at the beginning of period T when the wind speed falls below UL . The diesel continues running throughout period 7'and T" until the wind speed rises to above UL + 'EXCESS'.

Appendix 6B

Wind speed i

197

T"

UL + excess

Fig 6b 2 Diesel switching strategy involving hysteresis: Starts occur at beginning ofT' and end ot T'". Stop occurs at end ofT " The diesel stays switched off and does not restart until the wind speed again falls below UL at the end of period T'". Here we want the expected time between successive start/stops cycles, ie,

E[T'+T" + T"% Calculating E[T'] is related to the level crossing calculation of the non-hysteresis case. E[T "] and E[T '"] are first crossing times which under suitable assumptions can be calculated. To be able to calculate these quantities of interest certain assumptions about the time series are required. These are that: i Over restricted time intervals (one hour in our case) the time series can be considered stationary. ii The time series can be represented by the Ornstein-Uhlenbeck process which has an auto-correlation function of the form

where (J is the inverse of the time constant of the process. An assumption of normal wind speed fluctuations over the restricted time interval is implicit to this process, a property supported experimentally by the work of Der Kinderen er a/. (1977). For discrete time intervals the auto-correlation is given by

198

Appendix 6B E[(Ut-U)(Ut

+

k-U)]

r

(6b.2)

where a is the variance of the sample. A two second unit of time interval is used here, for convenience and also, because time constants of this order arise naturally from the rotor inertia and rotor smoothing effects. From a general analysis, the expected number of stop/starts, E(Na), in unit time is given by:

E[Na]

= \ [ -r"(0) ] 1 / 2 exp - ^ J

(6b3)

where a is the number of standard deviations difference between £/L and U, the mean wind speed for the hour in question. This equation cannot be used directly since r"(0), the second derivative of r(t) with respect to t evaluated at t = 0, is unbounded for r(t) given by equation 6b. 1. It does however tell us that the form is

exp 1-=^ I

( 6 b .4)

and we can use an alternative method to calculate E[No\, the expected number of zero crossings (crossings of the mean). If we use the property that fluctuations from the mean are normally distributed (implicit in the Ornstein-Uhlenbeck requirement), then the joint probability distribution of successive fluctuations is

U

p(Uv U2) =

1

j yjTj exp ]

{Ul-U)2 + (U2-U)2-2r(Ul-U){

2-U)\

\

(6b.5) where r is the auto-correlation at unit time lag. The probability of a diesel stop is given by

uL,u2>uL]

=f '

L

ff 2 P ( u v u 2 ) d u x d u 2 U

(6b.6)

Appendix 6B

199

This approach has been used by Bossanyi and Anderson (1984). If UL is chosen as U, we have an expression for E[No]

E[N0]=j

I

P(UvU2)dUldU2

(6b.7)

This can be integrated to give E[NQ] = -rn COS" (r) starts per unit time.

.,, «.

It is then computationally more efficient to use (6b.6) for other values of a. Together (6b.6) and (6b.8) enable us, given r, to calculate the expected number of start/stops in a given hour for the simple strategy with no hysteresis. One way to estimate r, and from this p in equation (6b. 1), is to integrate up one of the more reliable wind power spectra. For the two second interval we want t°°

r(2) = J S(n) cos (Ann) dn

(659)

where S(n) is the power spectrum, n being frequency. For the results shown here the Kaimal spectrum (Kaimal, 1973) was used, ie nS{n)_ 2

0A64(f/f0) " l - f 0.164 (f/fo)5/3

c

(6b 10)

'

where/= nZ/U and/o = 0.041 Z/L Z is height above ground G/U is the turbulence intensity L is the integral length scale (120 m) If N(U) denotes the number ofstarts per hour calculated from (6b.6), (6b.8) and (6b.9), and bearing in mind that the unit of time was chosen as two seconds, for a specified value of UL , then the annual average number of starts per hour is given by

fN(U)W(U)dU 0

(65.H)

200

Appendix 6B

where W(U) is the annual probability distribution of hourly means, which is assumed here to be Weibull. Extension to include hysteresis As previously mentioned, we need to calculate E[T + V + 7y//] p(U
=

2N{U)

SeC

°nds

( 6b - 12 >

E[V] and E[T"] are obtained from the following expression for the first crossing time of an Ornstein-Uhlenbeck process

T(atob) = ] ~ - \ \

p(x)dx\dz

(6b.i3)

where /?(•) is again the normal distribution, a and b are in standard deviations from the mean, and here T is in units of the time constant for the process, ie. T' = T(UL to UL + EXCESS) T

(6b. 14)

T" = T(Uh + EXCESS to UL) T

(6b. 15)

and

where x is the time constant of the process, ie. x=l/p=l/(-lnr)

(6b. 16)

and the symbol ~ denotes that units of standard deviations from the mean have been used. Annual average hourly starts can be calculated in a similar manner to the previous case. The inclusion of storage It is not possible to formulate a mathematical model of storage which could be included in the framework adopted here. Instead storage can be represented by a low pass filter applied to the wind power spectrum. The relationship between the time constant of the filter and the storage capacity has to be established by using simulation runs. For a particular small system the time constant of the filter was found to be 2.4 times the storage time, Ts.

Appendix 6B

201

Thus the filtered spectrum, S (n)> is given by F

SF(n) =

S{n) s\r?(nn 2.4 r j =—52 (TO 2.4 7 S )

(6b.l7)

This filtered spectrum is then used in (6b.9) when calculating the auto-correlation and the annual starts can be calculated as before both with, and without hysteresis. Minimum diesel run time A minimum diesel run time provides an easy way of reducing the average rate of start/stop cycling. By this we mean simply that once the diesel has been started it runs for a minimum preset duration regardless of changes in load or wind turbine output. Let N(U) be the number of starts per hour with no minimum run time and N'(U) the number of starts with the minimum run time implemented. If MRT is the minimum run time in seconds then on average (and for MRT » t i m e between starts in the absence of the MRT strategy) each MRT can be considered to have absorbed a number of diesel starts given by: MRT/3600 N(U)

(6b. 18)

Thus each of the reduced starts accounts for 1 + I

-^r ._ JOUU

of the original starts, I

namely:

f

N(U) 3600

^ j = NiU)

(6b.l9)

and so the new reduced starts are given by N(U)

^

N'(U) is then used in the previous calculations of annual starts in place of N(U).

Fuel penalties due to control strategy The results of the level crossing analysis can be used to calculate fuel penalties arising from the use of switching control strategies to reduce diesel stop/start cycling.

202

Appendix 6B

Fuel penalties from hysteresis It will be recalled that in the analysis of hysteresis, V denoted the time taken for the wind power output to reach the switch off level of UL + EXCESS, from the power balance level UL . This is the additional amount of diesel engine running time associated with each start/stop cycle. The additional engine run time during each hour, denoted A(U), is therefore A(U) = r/(f7)7V((7)/36OO hours

(6b.21)

Thus the annual average extra run time is given by

r

A(U)W(U)dU

(6b.22)

where as before W(U) is the annual probability density of hourly mean wind speeds. The diesel will always be operating on its minimum load, Anin, during this extra running and so the total annual fuel penalty is

365 x 24 x F(Z) min )£ A(U)W(U) dU

(6b.23)

Fuel penalty from minimum run time A similar approach can be taken to calculating the penalty associated with a minimum diesel run time. Here the total run time per hour can be approximated by N'(U) • MRT/3600 hours

(6b.24)

Without a minimum run time the running time is given by the probability p(U < UL) The additional run time per hour, A(U), is thus A(U) = N'(U) • MRT/3600 - p(U
(6b.25)

The annual fuel penalty can then be calculated in a manner similar to the case of hysteresis. There is no straightforward way of calculating the impact of energy storage on annual fuel savings. Whether fuel is saved or not will depend heavily on the storage characteristics as well as on the wind and load regime. Since the small energy stores for which the level crossing analysis is appropriate have the primary function of providing acceptable operating conditions for the diesel rather than saving fuel, this area will not be explored in these notes.

Appendix 6B

203

ACKNOWLEDGEMENT David Infield (UK) prepared this appendix. REFERENCES Blake, I. F., and Lindsey, W. C, (1973). 'Level-crossing Problems for Random Processes', IEEE Trans. Vol IT-19, No. 3. Bossanyi, E. A., and Anderson, M. B., (1984). 'A Comparison of Techniques for Evaluating the Frequency of Wind Turbine Shut-downs'. Proc. Sixth BWEA Wind Energy Conference, UK, Cambridge University Press. Der Kinderen, W. J. G. J., van Mell, J. J. E. A., and Smulder, P. T., (1977). 'Effects of Wind Fluctuations on Windmill Behaviour', Wind Engineering, 1. Infield, D. G., Forbes, R., Goodyer, P., Lipman, N. H., Magraw, J. E., Reynolds, R., Bleijs, R. A. M., Freris, L. L., Jenkins, N., and Attwood, R., (1985). 'Further Progress with Wind-diesel Integration'. Proc. Seventh BWEA Wind Energy Conference, Oxford, UK, MEP. Infield, D. G., Slack, G. W., Lipman, N. H., and Musgrove, P. J., (1983). 'Review of Wind-diesel Strategies'. IEE Proc. Part A, 130, 613-619. Kaimal, J. C, (1973). 'Turbulence Spectra, Length Scales and Structure Parameters in the Stable Surface Layer'. Boundary Layer Meteorology, 4.

APPENDIX 6C UNCERTAINTY ANALYSIS Measurement error The following is a brief summary of uncertainty analysis. More details may be found in Kline and McClintock (1953), Kline (1985) and Abernethy et al. (1985). Any measurement has errors. The errors are the differences between the measurements and the true values. The total error, £, is usually expressed in two components: a fixed or bias error, B, and a random or precision error, e. These types of errors are shown in Figure 6c.1. Note that in the 7th' measurement, the total error, Ei, is given by

True Value

True Average Measurement

ith Random Error

i (ith measured value) Fig 6c. 1 Elements of measurement error

Precision index The precision error is estimated by taking N repeated measurements and calculating the precision index, S (which is an estimate of the standard deviation). S is given by:

205

Appendix 6C

Where Xi are the individual measurements and the mean of the measurements, x, is given by: N

(6c.2) /=!

Bias error Bias errors cannot be addressed statistically. They may arise due to instrument drift, changes in calibration, etc. They must be estimated. Combined errors Errors are divided arbitrarily into three categories: calibration, data acquisition, and data reduction. When K multiple errors affect a measurement, they are combined by the root sum square method. Specifically: 1/2

S=

Iv 1/2

X*,(6c.3)

where Si and Bi are the /th precision and bias errors respectively. Uncertainty of a parameter The uncertainty is a single number expressing a reasonable limit of error for a given parameter. It is obtained by combining the precision index and bias error. There is some discussion as to the appropriate method for doing this. For example, the accepted value of uncertainty, U, at the 95% confidence level is given by: 1/2

(6c.4)

where t is a function of the degrees of freedom used in calculating S. For large samples

Appendix 6C

206

(N > 30), t is taken to be 2. Otherwise the contribution to uncertainty is greater (t > 2) and must be calculated according to the Welch-Satterwaite formula as quoted by Abernethy ef 0/. (1985). Uncertainty of a result Errors may be propagated into derived calculations through the functional relationship between the result, /?, and the M parameters, Pi, of which R is a function. Precision indices and bias errors of the result are determined independently and then combined into the uncertainty. This is done as follows. Suppose: (6c.5)

Then, the precision index of the result, SR , is given by:

(6c.6) where Spi is the precision index of the ith parameter. Similarly 1/2

(6c.7) where Bp\ is the bias error of the ith parameter. Note that the precision index of the result may also be expressed in fractional form

as 1/2

SR/R =

(dlnR V

2

(6c.8)

In this case the terms ^. _ are known as influence coefficients and are constant. alnPi The fractional bias error may be found in a similar manner. The uncertainty in the result, £/R, at 95% confidence is expressed by:

Appendix 6C

207 2

J

(6C.9)

Uncertainty analysis example calculation The following is a simplified example of determination of uncertainty applied to the calculated wind velocity, V, in a wind tunnel, using a pitot tube, according to the relation:

L

F

a J

(6c.lO)

where h 7a Pa R

= = = =

the velocity head as indicated by a manometer attached to the pitot tube. air temperature. air pressure. ideal gas constant.

Previous calibration has shown the fractional precisions to be: Sh/h STJTa Sp //>.

= + 0.02 = + 3.4xlO- 4 =+1.67xlO" 3

The bias errors were found to be zero. The influence coefficents are: dlnV dlnV " dlnh dlnV

= -1/2

The fractional precision in velocity is:

Assuming t = 2, the fractional uncertainty in velocity is:

t/v/V = h ? + n r

= 0-02007 (2%)

(6c.l2)

208

Appendix 6C

Note that brief examination of this simple exercise also points out clearly where the source of major error lies, namely in the velocity head. To improve the accuracy of the calculated velocity, the measurement of velocity head would have to be improved. On the other hand, improvements in the accuracy in measuring ambient temperature or pressure would be of no value.

ACKNOWLEDGEMENT James Manwell (USA) prepared this appendix. REFERENCES Abernethy, R.B., Benedict, R.P., and Dowdell, R.B., 'Asme Measurement Uncertainty,' ASME J. Fluids Engineering, 1985. Kline, SJ. and McClintock, F.A., 'Describing Uncertainties in Single-Sample Experiments,' Mechanical Engineering, 75, 1953. Kline, S.J., 'The Purpose of Uncertainty Analysis,' ASME J. Fluids Engineering, 1985.

7 Installation and monitoring of wind-diesel systems

Wind-diesel systems by virtue of their capital, operational, and maintenance costs are most often considered for use in remote, isolated and poorly accessible regions of the world where power from conventional large or national electricity grids is not available. In most instances where wind-diesel electrical generation systems are contemplated there are few human, technical, and physical resources available to design, build, install, operate, and maintain the system. When designing the system and planning its subsequent installation, operation, and maintenance, it is therefore vital to take due regard of the characteristics of the available resources. Some of the principal matters or issues that must be considered and resolved by potential installers and operators are outlined in the sections that follow. The subjects have been organized more or less in the order in which events will proceed during realisation of a project. Because of the very wide potential range of project sizes, configurations and site conditions, variable weighting, based on experience and solicited advice of experts, should be given by the developer to each of the items.

THE IMPORTANCE OF VERIFICATION TESTS Developers and purchasers of wind-diesel systems for installation in remote and isolated locations must have a high level of confidence that the system will have high reliability and perform as claimed or specified. The degree to which this confidence can be confirmed will be influenced greatly by the verification of performance and reliability achieved through a rigorous and well documented testing and evaluation programme carried out during system development, commissioning and subsequent operation of the system. As well as contributing to initial confidence such testing and ongoing monitoring will also contribute to longer and more reliable and efficient performance of the system throughout its lifetime. Pre-delivery tests If the system is to be installed in a remote location an essential stage of the plan is to confirm system operation and completeness prior to shipment. This should involve

210

Installation and monitoring of wind-diesel systems

prototype testing and/or a commissioning test of the completely assembled system using the actual or an identical or simulated wind turbine and load characteristics of the ultimate site. Development testing Development testing is that series of testing activities carried out in the manufacturer's or supplier's workshops and laboratories to fully evaluate the performance characteristics of each of the system components and assemblies under simulated end use conditions. These must reliably and realistically replicate the conditions that the system will experience in the planned application and can include but need not be limited to: a Load and demand characteristics. b Wind variability and climatology. c Ambient temperatures. d Other natural events and environmental conditions (icing, bugs, snow, rain, salt air, etc.). e Operator and maintenance schedules. / Component failures. g Normal and emergency operational routines. Development testing should be thorough and should not assume that the performance of complex combinations of components and systems can be accurately indicated by individual performance testing of the components. The final development test should be performed on a system that is complete or can be verified to be identical to that to be installed. Development testing should lead logically to later commissioning testing in situ. A testing report should be provided with the equipment to describe the tests performed and results obtained. The report should describe fully the methodology of the testing and provide sufficient information to allow later confirmation or comparison of performance in the field during commissioning trials. Calibration accuracy of transducers and other apparatus to be incorporated into the equipment for commissioning and operational monitoring should be confirmed during development testing.

THE PRE-INSTALLATION PHASE Planning Once a wind-diesel system has been selected, designed and approved for installation at a site the process of installation can commence. When developing a plan or schedule for any construction project consideration should be given to possible delays resulting from weather, transportation, availability of tools, and availability of equipment and

Installation and monitoring of wind-diesel systems

211

labour. A documented work plan clearly indicating the most critical path and logical sequence of events should be adopted, followed and adapted as necessary to complete the work in the most proficient manner. Transportation Transportation of wind-diesel equipment and components will often involve the use of non-conventional means of packaging, handling and transportation if the destination is remote and not served by normal rail, truck, ship and air carriers. Means such as barges, heavy chartered air transport, helicopter, and various over-land hauling methods and combinations may be necessary to deliver the equipment to the installation site. Scheduling of transport relative to other stages and phases of a project could be critical because of long intervals between available shipping dates. These can be caused by circumstances of weather and seasonal climate, carrier space, carrier demand and requirements for special or unusual transport and handling equipment. For these reasons it is often necessary to reserve and guarantee space well in advance of requiring the actual transport. Local access to the site with all equipment and materials must also be confirmed. Insurance It is the shipper's responsibility to obtain insurance against loss or damage of equipment and to confirm that the carrier is able to obtain insurance to protect against loss or damage to equipment while within his care and control. Packaging Packaging of equipment for shipment may often require special consideration of weather, environmental and mechanical conditions during transport. If equipment is to be stored or left in unsecured marshalling areas, packaging which prevents tampering and pilferage of parts may be required. An inspection of the equipment should be made at the destination and responsibility for further handling, storage and installation transferred accordingly. Handling In many sites or communities where wind-diesel systems are to be installed there may be few resources for local handling and surface transport, or for moving the equipment to the actual installation site(s). The carrier often is only capable of off-loading immediately adjacent to the transport equipment and therefore careful consideration must be given to the next steps. The developer must then consider carefully the size and weight of components in relation to the handling and transport at the ultimate destination.

212

Installation and monitoring of wind-diesel systems

Development permits Permits to build structures and install equipment such as wind turbines will often be required by national, regional, municipal or community councils. The developer of the project is usually responsible for determining which permits are necessary and for obtaining them. It is advisable to complete this activity well in advance and prior to commitment to a project. In many instances it will also be necessary to show evidence that no adverse environmental, safety or nuisance effects will be caused by the planned system on the site selected. Construction Generally wind-diesel systems will differ widely in terms of the construction activity required to complete the installation. The principal construction and installation related activities for a project or task can, however, usually be described by a combination of the following. a b c d e

f 8 h i j

k I m n 0

P Q

r s t

u V

w X

Mass foundations. Pilings and piers. Soil anchors. Slab foundations. On site buildings. Prefabricated structures. Towers. Wind turbines. Underground cabling. Distribution power lines. Access roads. Surface drainage systems. Fences. Battery storage. Electrical distribution systems. Earthing or grounding system. Electrical controls. Meteorological towers. Diesel electric generators. Liquid fuel storage and distribution systems. Fire protection systems. Telecommunication systems. Monitoring systems. Lightning protection systems.

Assuming that the equipment is appropriately designed and documented many of these tasks can be carried out with the same ease and using similar techniques to those

Installation and monitoring of wind-diesel systems

213

used for construction and installation of similar equipment in conventional locations. Some others however may often require special consideration of site conditions and resources available. Often the design and subsequent installation of a system must take into account the availability and cost of such resources as:

a b c d e

f g h i j

k I m n 0

P

Skilled tradesmen. Construction labour. Transport. Electrical tools. Mechanical tools. Hoisting and lifting equipment. Excavation equipment. Drilling equipment. Hauling equipment. Lodging and food. Building materials. Water. Cement powder. Concrete aggregate. Fuel. Temporary heat and electricity.

This list can be considered as a partial checklist for assessing the necessary resources when planning an installation. Foundations Foundations for wind turbines and other structures must be given particular attention in the design and implementation of a project. They are the essential physical interfaces between the equipment and the actual site. Although foundation materials may be shipped or transported to the site with the equipment, they invariably must be constructed on location. It is evident then that at remote sites concrete structures will be exceedingly expensive if aggregate and water is not locally available and alternative foundation systems should be investigated. Soil condition and quality will also influence choice of foundation type and can pose challenging design problems when combined with resource availability, the weather and the climatic conditions. This is particularly true for foundations for wind turbines which must withstand overturning and dynamic loads and where even minor movement could pose a serious problem. Soil conditions which pose the greatest problems are characterized by: a b c

Rain storms. Surface water. Swelling clay.

214

Installation and monitoring of wind-diesel systems d e / g h /

Seasonal frost action on soils. Permafrost - particularly if in boulder, sand, clay matrix and in areas with an active seasonal thawing layer. Peat bogs and boulder plains. Sand. Poorly drained clays with high frost penetration. Land slides.

Under these conditions and where uncertainty of soil engineering data exists it is very prudent to seek expert advice and to be able to provide consultants with accurate specifications of loads and other requirements as necessary to design safe foundations at a minimum cost.

THE ASSEMBLY AND ERECTION PHASE Procedures and techniques Assembly and erection of equipment for wind-diesel systems should be specified during system design and such specifications must also consider the availability of equipment required on site to perform certain tasks. Depending upon the nature of the equipment to be assembled and erected the installer should consider the availability, capacity, cost and safe use of equipment such as: a b c d e

f g h i j k I

Large and small cranes. Transport trucks. Traction and earth moving machines. Excavation machines. Drilling and boring machines. Concrete mixers. Winches. Welding and oxygen cutting apparatus. Portable heaters. Portable shelters. Portable electric generators. Mechanical tools.

Assembly and erection personnel Erection of wind turbines is a specialised activity and should be performed by trained and experienced persons using approved and documented techniques and procedures with proper and approved tools and other equipment. Where it is necessary to adapt procedures or tools and equipment because of site conditions or availability, the installer should obtain engineering advice from a reliable source and the approval of the project manager or other responsible authority. The availability, capability, and skills of local

Installation and monitoring of wind-dieseI systems

215

workers and tradesmen should also be determined in advance. Inspection and standards Upon completion of installation the developer should inform electrical, utility and industrial equipment safety inspectors with jurisdiction in the area for inspection and approval of the system. Weather Many of the most interesting locations for wind-diesel systems are in areas where weather, climate and related conditions may make work very difficult during certain seasons and as a result the interval or weather window during which construction work is reasonably possible may be very short. Examples of such situations are in Canada's high Arctic communities where only a few weeks in mid-summer are free of snow and freezing weather during which the ground has thawed so that cable burial, foundations and other surface work may be performed easily. Light conditions are also optimal during long hours of daylight so that work may proceed to the limit of worker endurance. At this time, however, insects such as black flies, 'no-see-ums' and mosquitoes can make life miserable for those working outdoors. For such areas a good insect repellent is essential. In contrast in equatorial areas high temperatures and humidity may contribute to low productivity of workers unaccustomed to such conditions. Rainy seasons may make transportation and construction work slow and difficult.

COMMISSIONING Once a system has been installed and all preliminary component testing and calibration has been completed, the complete system should be commissioned according to a commissioning trials programme specified during system design. Although the commissioning trials may have to meet specific requirements of the customers or clients or their agents, the system should also be sufficiently tested and results compared with those obtained during development tests. The developer should confirm performance and reliability prior to handing the system over to the operators. If possible, results should be compared with those from development or factory tests. The minimum acceptable general requirements for commissioning should include: a Confirmation of operation of all wind turbine safety devices and system protection over a full range of load and wind speeds. b Confirmation of wind turbine performance and availability during 400 hours of operation during which power output will exceed 50% of rated output for at least 100 hours. It should be verified that availability exclusive of weather or atmospheric induced effects such as icing, exceeds 95%. During this time no manual intervention should be

216

Installation and monitoring of wind-diesel systems

required to reset or restart the system. c

Typical performance testing could include measurement and recording of: (i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (iv) (x) (xi)

wind speed ambient temperatures wind turbine(s) power diesel power and reactive power system voltage system frequency load power and reactive power diesel fuel consumption kWhr produced and supplied operating interval operation of control and protection alarms.

d Confirmation of proper operation of all controls, communication, monitoring safety and alarm systems. e

Confirmation of diesel generation heat rates and fuel savings.

/ Confirmation of proper operation of all load management, storage, dump load and frequency control equipment during all intended modes of operation. g The complete system should be tested to determine its response to irregularities in load and wind turbine output. These tests should determine system resonances and transient behaviour. Typical tests should involve measurement of the system response to step function load disturbances from 10% to full load and to sinusoidally varying loads of varying frequency and amplitude. h The system should also be tested to ensure safe and proper response or shut down resulting from failure, fault or forced outage of one or more of the generation sources. In installations with storage or load management features, proper response should be confirmed and adjustments made to optimise performance. / Power quality parameters such as system power factor, frequency and voltage stability and duration, harmonic content, flicker and telephone interference factors should be measured under a range of operational conditions and confirmed to be within acceptable limits.

MONITORING The data, information and observations obtained during operational monitoring of wind-diesel power supply systems are necessary to ensure ongoing performance and reliability. Ongoing monitoring and recording of operation, maintenance and performance of equipment is normal practice in conventional utility operation and is essential to good management and reliable operation of the plant. In remote locations, diesel generators in combination with wind turbines, storage systems and sometimes sophis-

Installation and monitoring of wind-diesel systems

217

ticated controls should be similarly or more intensively monitored. Also because of the often disperse location of the system components it will often be desirable to use fully automated instrumentation and data logging equipment. Frequently it will also be desirable to equip monitoring systems with modems, telecommunication and/or satellite communication systems to enable remote collection of data and to exercise control over the system. Monitoring strategy Wind-diesel systems may differ widely in configuration and size depending upon end use requirements and choices made by the designer to meet those requirements. The means and specific characteristics of monitoring systems may also vary significantly. However there should be some general strategies employed to enable comparison of performance with other systems or alternatives. When developing the specific strategy for monitoring a wind-diesel system the following matters should be considered to identify the parameters and factors to be measured and recorded. Power Performance

- wind turbine - diesel generator - storage

Meteorological Factors

- wind speeds and distribution - direction - ambient temperature - icing, snow fall, hail, rain

Load

- direct load pattern - dump load - storage

Fuel Consumption

- total - per unit

Operation

- diesel generation hours - no. starts - wind turbine hours - breaker actuation

Sampling Time Averaging Interval Operational Log Maintenance Log

- diesel engine temperature - diesel engine oil pressures

218

Installation and monitoring of wind-diesel systems

Monitoring equipment System monitoring should be performed with an automated data acquisition system that measures and records system performance data on a continuous basis. Such systems are developing rapidly and are a very cost effective and efficient means of recording both technical and economic performance of power generation plants of all types and sizes. An online monitoring system can in fact be only a component of the overall system control and may also be programmed to provide fault diagnosis. Smaller systems (<10 kW) may not be able to justify the cost of continuous monitoring. Monitoring systems should be configured to provide for high data sampling rates (0.1-1.0 Hz) and longer averaging intervals (10 min -1 hr) to produce performance summaries. The system should monitor many aspects of system performance such as: a b c d e / g h /

Fuel consumption and rate. Electrical energy generation. Loads. System voltage. System frequency. Engine starting events. Wind and climatic data. Outages and system faults. Operation of control and protection devices.

The system should also be set up to record planned and unplanned maintenance activity and to flag or set alarms for maintenance required. The maintenance schedule is thus tied directly to system operation and performance. Modern data loggers enable accurate measurement or sampling of many parameters at frequent intervals. Small modular systems can measure and record 16 parameters at rates of up to once each second. Larger PC based systems can sample 64 or more points at rates exceeding 100 Hz. Most are capable of measuring 12 to 16 bit data and have on-board processing to compile averages and standard deviation and to perform other processing and data classification routines such as: i ii iii iv

Binning for histogram and power performance, Recording maximum and minimum events, Setting alarms, Providing outputs for control purposes.

Measurements for monitoring purposes are most useful if compiled and stored in a time series for later processing to produce performance summary reports including charts and diagrams. Communication with most data loggers can be by modem or telemetry and it may be possible in many cases to interrogate and program them remotely making comprehensive monitoring programmes inexpensive and practical. Systems which have a high

Installation and monitoring of wind-diesel systems

219

speed or burst mode of operation can monitor a few selected parameters on demand and are very useful for carrying out fault analysis of remote systems. Systems with built in memory capacity of about 16 K with expansion capability to 64 K and upwards are desirable. Such systems usually have facilities to enable retrieval of data by modem to a PC computer and/or locally by magnetic tape or removable EPROMS. This type of system should always be considered since it offers such efficient means of investigating performance and diagnosing operation problems. One modest PC based computer can be used to communicate with and process data from several such systems. Data logging equipment and associated hardware should have a combined accuracy target of 1-5% or better and have at least 8 bit resolution. Most currently available systems are now 12 or 16 bit making good resolution easy. Signal levels should of course be conditioned to match the logger's input range, otherwise poor resolution can occur. Maintaining calibration accuracy and precision of transducers, sensors and instruments in the field is one of the greatest challenges to efficient monitoring of systems and one that must not be overlooked. Systems and devices that are particularly prone to deterioration, wear and damage due to normal operational events and weather conditions should be paired to allow for comparison and faults identification.

OPERATION AND MAINTENANCE Wind-diesel power systems require high levels of competent maintenance to achieve reliable operation and anticipated life expectancy. Failure to provide proper maintenance will result in frequent system or component failures or forced outages and may lead to catastrophic failure of engines, generators, wind turbines, and associated equipment. Proper operation and maintenance can only be carried out by qualified and trained personnel who are suitably equipped with tools, spare parts and other necessary resources. When establishing operational and maintenance infrastructure needs, developers should consider the following. Operating personnel Because wind-diesel systems may vary in capacity from a few kilowatts to several megawatts staffing requirements may vary accordingly, from a part time technical operator/maintainer to a team of several such persons. In general, however, because of the relative similarity of the equipment it is desirable that all of the technical personnel be capable of performing the majority of operational, maintenance and repair activity. This policy although perhaps leading to some redundancy in capability will provide continuity in operation when staff turnovers occur. Such turnovers occur with higher frequency in remote and isolated areas and are a continual problem in supplying services in such locations.

220

Installation and monitoring ofwind-diesel systems

Although special training in operation and maintenance of specific wind-diesel systems should be provided to personnel, they should be recruited as skilled tradesmen, technicians, or technologists with experience in the general area of electro-mechanical technology and with demonstrable ability to operate, maintain and diagnose or troubleshoot such systems as: a b c d e / g h /

Diesel engines. Engine governors. Mechanical drive components. Electrical generators and controls. Electrical motors and controls. Programmable logic controllers. Electrical switch gear and protection devices. Hard wired electronic controls. Electrical, electronic, and mechanical instrumentation devices.

They should also be able to learn quickly to operate proficiently, maintain and diagnose or trouble-shoot: j k /

System operational faults. Wind turbine generator systems. Computerized data acquisition systems and instrumentation.

Documentation A complete 'as built' current system engineering description and operational, maintenance, fault diagnosis and repair information should be provided in drawings and manuals. Spare parts Sufficient spare parts and components should be stocked on site to enable full repair of all systems at short notice. Exceptions can be made for diesel engine main block and head assemblies and diesel generators. In multiple diesel generator installations reserve diesel generation capacity will usually protect against engine/generator forced outages. In single engine installations alternative emergency generation, storage and load shedding may be provided. An inventory of spare parts should be maintained on the monitoring and data system.

ACKNOWLEDGEMENTS The author of this chapter was Malcolm Lodge (CAN).

8 Assessing the economics

Throughout the world there are hundreds of thousands of villages, remote communities, islands and commercial sites which do not have power or are supplied on an individual basis by small gas or diesel generator sets, small wind turbines and photovoltaic systems. Many of these sites are located in countries where centralized utility systems only exist in urban or industrial areas. The average cost to extend utility power lines, not including new power plant capacity, is approximately $15,000 to $30,000 (US) per kilometre at 1990 prices. Diesel engine driven generating sets have the largest market share by far of all sources of remote power. Over 10 million diesel generator sets are utilized world-wide to provide power in locations remote from electrical grids. Unlike most energy sources which have a high capital cost with high operational costs, diesel generating sets have a low capital cost and high operating cost. Diesel engine manufacturers are conducting research to increase reliability, reduce emissions, and reduce manufacturing and operating costs. The high costs are due primarily to the cost of purchasing the diesel fuel and delivering it to where it is needed. Operation and maintenance of the diesel generators may also contribute to high cost at remote sites. These costs generate a powerful economic incentive to find more cost effective alternatives. For many remote locations, the most attractive option is to be able to generate a significant amount of the power at the site. When the wind resource is good a wind-diesel system is an obvious candidate. In small high reliability situations, such as for telecommunications, the diesel or other fossil fuel generators are likely to be designated as back-up sources in order to ensure continuity of supply. The costs of generating electrical energy from diesel engine sets generally vary from $0.15 to $0.50 per kWh compared to an average cost of $0.05 -$0.15 per kWh for wind power. Wind power costs are higher for small d.c. systems with battery storage. However, the addition of wind energy into a diesel powered supply system may necessitate the use of additional equipment and controls, as discussed elsewhere in this book, thus raising the cost of the wind power component. Therefore, the actual discussion on whether to use wind-diesel systems instead of pure diesel generation will, in the end

222

Assessing the economics

be based on an assessment, according to some realistic method, of the ability of the wind turbine(s) to pay for themselves and the additional equipment by means of fuel and other savings. Other benefits such as reduced emissions and social value of the electricity, may be more difficult to evaluate in an economic framework, but should be included in the assessment. The intention of this chapter is to introduce the various factors affecting the economics of a wind-diesel system and to give an overview of the methodologies commonly used in economic assessments. The actual values to be used must be defined for each specific case, as they are generally very site specific. This chapter is broader in scope than the recommended practice of the type edited by Nittenberg (1983) which deals with estimation of costs of energy from (grid-connected) wind turbines.

THE ECONOMIC INCENTIVES The primary economic incentive to use wind power as opposed to diesel is the high delivered cost of diesel fuel. Thus the main objective for most wind-diesel systems is to reduce the fuel use enough so that the added costs associated with the system are more than made up by the fuel savings. In some situations the existing diesel grid may already be operating at its limit. In such a case the addition of wind power could augment the existing capacity. For other applications, such as mountain top telecommunications facilities, the addition of wind power can help to extend the fuel supply, thereby reducing the frequency of re-supply. The reduction in the number of journeys can result in substantial cost savings. The use of wind power to reduce the load on the diesels may also result in reduced operation and maintenance costs of the diesel generators themselves. For example it may be possible to extend the time between overhauls and replacements. The extent to which this can occur depends on the method of operation of the equipment and cannot be assumed to apply in all cases. In particular allowing diesels to run for extended periods at very low load may actually be detrimental to the diesel's longevity as shown by Reynolds (1985) and Collier (1986), although methods are being devised to ameliorate this problem as Hughes et al. (1990) and Lundsager and Sherwin (1990) have indicated. Some final environmental benefits, which are difficult to quantify, and are brought about by reduced diesel operation, are reduced pollution and noise.

COST PARAMETERS There are a number of costs that affect the economics of a potential wind-diesel system. Except for the wind and load conditions, which are treated in Chapters 3 and 2, respectively, the key ones are described below.

Assessing the economics

223

Fuel The most important consideration is the delivered cost of diesel fuel itself. In remote sites the cost may be very high and in some cases fuel may have limited availability. An additional fuel related cost is that of storage. This cost may be increased in cold climates where the fuel may have to be heated or treated with Kerosene. System dependent capital costs Wind-diesel systems by their very nature are more complex and capital intensive than straightforward diesel systems. Diesel generators In many diesel electric systems, the diesels have already been installed and will not be changed when wind turbines are added. In such cases the cost of the diesels themselves may not enter into the economic analysis of the wind-diesel hybrid. In other cases, however, new diesels may be needed or the existing diesels may be modified. For example, a clutch may be added between the diesel engine and its synchronous generator as discussed in previous chapters or additional equipment for temperature control may be required for low load operation as discussed by Hughes et al (1990) and Lundsager and Sherwin (1990). These costs would then have to be included. Costs for new diesel generators typically range between $250 and $500 for every kW of rated capacity, depending on the size. Wind turbines Adding wind generation to a diesel electric system will entail a significant expenditure. The most expensive components will normally be the wind turbines themselves. Typical costs for wind turbines (1991) are in the range of $ 1000 to $2000 per rated kW. Generally the smaller machines have higher costs per kW. Control systems When combining components together into a wind-diesel system, a supervisory control system is usually required. The cost of this control system will of course depend on the complexity of the wind-diesel system and its operating strategy. Ancillary systems Connection of any wind turbine to a low voltage grid requires the use of electrical switch gear, etc. Usually this will be included in the standard controller system supplied with the wind turbine and will therefore be covered as part of the wind turbine costs. In most basic wind-diesel systems, additional components such as the dump load, the dump load controller etc, will have to be added to allow wind turbines to be connected

224

Assessing the economics

to the local grid of the system. The costs of these are modest compared to those of the wind turbine(s). Many wind-diesel systems may also incorporate other relatively expensive components. This is particularly true when some storage is included. For example, when battery storage is used, the cost of the batteries themselves as well as the rectifier and inverter can be significant. Typical costs for lead acid batteries per kilowatt hour of storage capacity range from $80 to $200. Nickel-cadmium batteries may cost $700 to $800 per kWh. Rectifiers cost from $200 to $400 per kW of rating. Inverters cost from $500 to $1000 per kW installed. These last two components rely heavily on power electronics, whose costs have been dropping rapidly in recent years. Thus typical costs should also decline significantly in the near future. Some wind-diesel systems use flywheels for short term storage. The cost of these, together with associated power convenors and controls must also be included where appropriate. System dependent operating and maintenance costs Because of added complexity, it is also important to review the O & M costs associated with a combined system. They will be different from those which may previously have been experienced with an old diesel only system. Diesel generator set Diesel generators may require a significant amount of time and materials to keep them operating satisfactorily. This cost can change with the addition of wind turbines. When it does, the difference should be accounted for in the economic assessment. Some of the factors to consider are the following: *

Number of operating hours per year

*

Number of starts and stops

*

Number of cold starts

*

Service labour rate

*

Spare parts costs

*

Labour parts inflation rate per year

*

Travel costs including direct and indirect costs

*

Lubrication oil cost at site

*

Fuel/oil inflation per year

*

Sump size

*

Oil change interval

Assessing the economics

225

*

Number of hours between scheduled minor overhauls

*

Number of hours between major overhauls

*

Fuel consumption per hour

*

Lubrication oil consumption per hour

Table 8.1 provides a breakdown of typical operating costs. The percentages may vary greatly depending on the diesel load factor, quality of fuel, maintenance practices, specific cost of parts, fuel, lubrication oil and labour and environmental factors such as ambient temperature and altitude. Of all the operating costs, fuel is by far the largest and is usually considered separately.

Table 8.1 Typical breakdown of operating costs for a diesel gen-set Total % Fuel Lubrication oil Scheduled maintenance Parts Overhauls

80-85 4-6 5-7 2-4 3-5

Wind turbine Wind turbine operation and maintenance may also be a significant cost. In large wind farms such costs are typically of the order of $0.01 to $0.02 per kWh of electricity generated. In the wind-diesel application, these costs may well be higher depending on location and training of the staff. Wind turbine O & M costs are frequently estimated in terms of percentage of original capital cost per year. Numbers used are typically of the order of a few percent. It must be emphasized, however, that in remote applications a service call can be very expensive or not possible when needed. It is ill advised to assume too low a value for the O & M costs. Site dependent costs There will always be site specific costs which may differ widely from one location to another. These include the following:

Access Access must be provided to the wind turbine both for installation as well as routine operation and maintenance. This will typically require an access road which is suitable for use by heavy equipment (e.g., cranes, trucks etc) at least part of the year. Small systems in remote locations may also require aircraft, helicopter or other special access

226

Assessing the economics

methods. The access cost will be heavily dependent on the terrain. It will also be relatively insensitive to the number of machines. Thus for a large installation it will be a smaller percentage of the total wind system cost. Access may also be limited by (Arctic) weather or by limited water transport facilities and suitability of vessels. Installation Installation of the wind turbines, additional components or the complete wind-diesel system will also entail a significant expenditure. In addition to access, key considerations are the cost of the foundations and additional power lines. A crane, if needed, may be quite expensive and should be avoided in remote locations. Installation costs taken in a wider sense will also include commissioning tests and training of operation, maintenance and repair (OMR) staff. In installations involving a new type of system or innovative technology, extensive commissioning tests may be desirable. As the success of the installation depends heavily on qualified OMR of 'new' components such as wind turbines, storage components etc, a training programme will be needed even for experienced diesel OMR staff. The cost of these may have to be included in the installation costs. Special environmental factors Some sites may require certain expenditure to safeguard the local environment or to protect the populace. Other considerations Certain components may have to be replaced during the operational lifetime of the system. In severe climates such as found in the Arctic or humid tropics, more frequent maintenance, more frequent replacement of components, or special components may be required. Some wind turbine manufacturers stipulate that the blades should be replaced at half of the wind turbine design life, and storage schemes using batteries may have to incorporate replacement of batteries at regular intervals. This should be reflected in the financial and operating analysis. Economic considerations of new vs retrofit systems The question of whether a wind-diesel system should be a retrofit to an existing diesel installation or should be completely new may also be resolved with an economic analysis. One consideration is that a newer system may be more fuel efficient due to improvements in the engine caused by reduced friction, better design in the mechanical components, and better combustion techniques. Substitution of existing diesels with new ones would appear to be more relevant with high wind power penetration systems. In larger diesel power plants where moderate wind power penetration is envisaged, a retrofit solution would probably be the most

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227

economically attractive. Substitution of multiple smaller diesels for fewer larger ones may result in substantial fuel saving by allowing better load matching and shutting down of one or more diesels. The use of two diesel sets instead of one with combined rated power being equivalent, will result in some fuel savings, because the fuel consumption at zero load is nearly proportional to the rated power. As reported by Skarstein and Uhlen (1989), the use of several small diesels with an appropriate loading strategy allows system operation to be more efficient at any load, than would be possible if one large diesel was used . Operating strategies The modelling techniques outlined in Chapter 6 will enable the system performance to be evaluated. The system performance will of course depend on the operating strategy. Some key aspects include: *

Generator selection

*

Predictability of load and wind resource

*

Load management

*

Stability and power quality considerations

*

Dumped power usage and valuation

*

Waste heat usage and valuation

INDIRECT FINANCIAL CONSIDERATIONS OF TOTAL ENVIRONMENTAL AND SOCIAL COST In some situations incentives are made available to encourage the use of renewable or local energy sources. One of the reasons for this is to help ensure that the true cost to society of energy production is distributed equitably. On the other hand, sometimes fuel use is subsidised to encourage economic development or for other reasons. A study dealing with this and reported fairly recently by Hohmeyer (1988) was made under contract with the Directorate-General for Science, Research and Development of the Commission of the European Communities. This study surveyed these costs in an attempt to give a first systematic evaluation of the external effects and costs of energy systems. Although the study was conducted within the economic and administrative framework of the Federal Republic of Germany its results may eventually lead to a generally accepted rate for costing the environmental benefit, or avoided environmental harm, of using renewable energy systems. The report concludes that when the positive and negative effects of electricity generated on the basis of wind energy are considered (with the external costs of present electricity generation included as avoided costs) the total external benefits including

228

Assessing the economics

environmental ones exceed 0.06 DM per kWh. This corresponds to an amount of approximately ECU 0.03 or US$ 0.035 per kWh which may safely be ascribed to the use of wind energy, and it may therefore also be used to assess the economics of a wind-diesel system.

DIRECT FINANCIAL CONSIDERATIONS In addition to the system costs described above there are other parameters that must be considered in an economic analysis. For example, when the project is financed the interest rate and term of the loan must be taken into account. The fuel inflation and general rates are also important. These numbers are often assumed to be different, with the fuel inflation rate somewhat higher. Due to the great uncertainty associated with any prediction about the future it is suggested that a sensitivity analysis be undertaken. The opportunity cost or discount rate may also be relevant. This reflects the other options one has for investing money. It is typically used for putting cash flows on an equal basis.

ECONOMIC APPRAISAL METHODOLOGY This section provides an overview of a methodology for assessing the economics of a system, based on what has been discussed above, in this chapter and elsewhere in the handbook. The assessment is an interactive process, as each step typically will be influenced by the other steps. Furthermore, the total range of possibilities is very large, and therefore one will typically divide the assessment into two phases: 1 A concept study, in which a number of possible relevant concepts are roughly estimated, by fast approximate techniques. Based on this, the concepts are rated against each other in order of priority. 2 An assessment of a few of the more promising systems, using more detailed techniques and parameters, that are as accurate as possible. It will often be very useful to establish how sensitive the overall economics are to certain parameters by variation of, say, component costs or fuel prices. The individual steps of the iterative assessment in Phases 1 and 2 above may be illustrated as shown in Figure 8.1. The amount of fuel that will be saved through the use of the possible system concepts is estimated by means of simulation techniques, such as those described by Skarstein and Uhlen (1989) and Lundsager and Johnson (1990). Fuel savings can only be identified if the fuel requirement of the diesel-only (no wind turbine) case is known. The calculation of this quantity produces a base line for comparison. When possible, the base line prediction is compared to actual fuel consumption data to ensure that the model used gives plausible results, for the situation at hand. The assessment of a concept could be done according to the steps outlined below,

229

Assessing the economics

Concept Study

Choice of System

Rough Assessment

Ranking

Detailed Assessment

Fig 8.1 Iterative assessment of concepts where reference is given to the chapter in which the techniques of the step are described: a Use consumer load assessment (Chapter 2) and wind resource assessment techniques (Chapter 3) to establish wind and load data, generally applicable as time histories. b Include other factors in the choice of one or more prospective system concepts to be assessed for the present application (Chapter 2). c Choose system components believed to be suitable for the concepts (Chapter 4), and establish one or more system simulation model(s) capable of representing the concepts in the desired way (Chapter 6). d Simulate the system behaviour under the conditions of step *a\ assessing the system performance in terms of fuel savings and/or power quality as desired (Chapter 4). Sensitivities may be assessed using parameter variation in successive runs. e Estimate economic parameters such as capital costs, OMR costs, etc, for the concepts (Chapter 8).

230

Assessing the economics

f Evaluate and assess the economic performance of the system using the techniques outlined in this chapter under Economic Assessment Methods (p.231). This is an iterative process which may be outlined in block diagram fashion as shown in Figure 8.2. An example on this process is given by Lundsager and Johnson (1990).

Load and Wind Data

± System Sim. Model

Parameter Variation

System Costs Simulation Fuel Saving

Evaluation of Economy

I Choice of Solution

Fig 8.2 Block diagram of iterative approach required to optimise economy of a wind-diesel system

This iterative process ends up in a choice of solution(s). The actual economic criteria will depend on who is making the decision. For example taxes, fees, subsidies, etc, would be control variables in a macro-scale environment, ie on a national basis, but would be given constraints for private commercial enterprises. It should still be kept in mind, that some of the important factors are difficult to express in purely economic terms. Therefore the final decision may be influenced by what could be termed social and political factors.

ECONOMIC ASSESSMENT METHODS For a wind-diesel system to be installed as a commercial venture, it must be shown that it is economic according to some realistic method of assessment. There are three

Assessing the economics

231

techniques that are commonly used for such assessments, two of which are based on life cycle cost analysis: a The simplest is the determination of the system's payback period. It has the virtue of simplicity and it provides a rough measure of the system's economic viability. It may also be supplemented by other simple measures. b A method often preferred by operators is based on levelized cost of energy. It takes into account such factors as loan interest rates, inflation, operation and maintenance costs, system life, the time value of money, as well as the overall first cost of the system and annual value of the power. It should be pointed out that the value of the power is not equal to the cost of the power. The value of the power will be determined by a sound economic analysis. c The Internal Rate of Return (IRR) is often preferred by investors, donors and other financiers. It is based on an assessment of the value in terms of capital of the entire undertaking, referred to a common point in time by annuity methods. The method includes a cash flow analysis year by year, in which events such as component replacements, etc., are incorporated. More details on economic assessments have been reported by Nittenberg (1983), Freeman (1981), Smith (1973), and Ruegg (1976). It is important to realise that the methods described here are useful for initial appraisals. Detailed reviews of cash flows throughout the life of the system must always also be examined to ensure for example that annual receipts will always be sufficient to service bank loans and pay operating costs etc. Payback period analysis This simple payback period approach normally involves predicting the annual value of the power produced and the total cost of the system. The payback period in years is the ratio of the total cost to the annual value of the power. That is: Payback Period = System Cost/Annual Value of Power

(8.1)

The payback method has the virtue of great simplicity and it does provide a rough measure of a system's economic viability. However, it omits many factors which may have a significant impact on a system's cost effectiveness. The simple Payback Period analysis may be supplemented by two other simple measures, both based on a simple annuity consideration: One is Estimated Cost of Energy as demonstrated by Skarstein and Uhlen (1989). Based on estimates of running costs per year for the system (including capital recovery costs) and energy produced per year, the estimated cost per kWh is:

232

Assessing the economics Estimated Cost per kWh = Running Costs/Energy Produced

(8.2)

The other is Estimated Cost of Fuel Savings as demonstrated by Lundsager and Sherwin (1990). This measure is based on estimates of running costs (including capital recovery costs) per year for the additional equipment necessary to include wind energy, thus excluding the costs for standard diesel operation, and estimated fuel savings per year compared to diesel only operation. The estimated cost per litre of fuel saved is given by: Estimated Cost per litre = Additional Running Costs/Total Fuel Savings

(8.3)

This measure has the benefits and drawbacks of being a relative rather than an absolute measure. Present value concept A key concept in the two other methods, based on life cycle costing is that of 'present value' (also referred to as 'present worth'). This can apply either to a single item or a series of cash flows associated with an investment. In all cases the present value is obtained from a value at an arbitrary point in time by discounting it back to a specified reference time. This procedure is designed to reflect the lost opportunity cost of capital. Let C; denote a cost of C to be incurred / years ahead of the reference time. Then the Present value PV of the cost, ie, the cost evaluated at the reference time, is:

(8 4)

O^df

'

where d is the discount rate (expressed as a fraction). Closely related is 'Net Present Value' (NPV), which is the sum of all relevant present values. For example the NPV of a single cost C for n years is:

(8.5)

If the single cost C is inflated at an annual rate e the annual cost Ci becomes

and the NPV becomes n

~

i

""

C

(8.7)

Assessing the economics

233

These formulae also hold if the cost C is a different amount in different individual years. The NP^can be used on its own as a measure of economic value when comparing investment options. When comparing two systems the configuration with the lowest NPV is the one that would be selected. This value may be found by calculating the total costs of the system for each year of its anticipated life, including debt service, operation and maintenance, inflation, etc. The annual values are then discounted back to the first year to put all costs in an equivalent form, and then summed up. When both general and energy inflation rates and operation and maintenance costs (as a fraction of system capital cost) are assumed to remain constant over the system life and the loan for the system is repaid in equal instalments, NPV may be found by the following equation:

(8.8) where: Ad Ap b d N FL FC e / L C OM

= initial payment on the system = annual payment = (C - Ad) X [a, n] = loan interest rate - market discount rate = period of loan = amount of fuel consumed per year - unit cost of fuel = inflation rate of fuel = general inflation rate = life of system = capital cost of system = annual operation and maintenance cost fraction (of capital cost)

The Present Worth Factor Y [a, n] represents the Present Value of a series of payments, ie what $1 to be paid at the end of each period is worth today. The value is given by

\ —a = n if a = 1

(8.9)

The inverse of the Present Worth Factor Y [a, n] is the Capital Recovery Factor X[a,n]:

234

Assessing the economics X [a, n] = \IY [a, n] \-a ~a-an+1

The Capital Recovery Factor X [a> n] represents the mortgage payment to amortize a loan of $ 1, ie the annuity payable at the end of each period to represent a Present Value of$l. Sometimes slightly different versions of the Present Worth Factor or the Capital Recovery Factor are used, depending on the point in the first year to which the values are referred. The simplest method mathematically, which is the one used here, assumes that the cost basis starts at the beginning of the first year, all values are discounted to the beginning of the first year, and payments are made at the end of the year. All of the methods are essentially similar and any of them may be used in comparisons as long as consistency is maintained. The above discussion assumes that all relevant indices (e.g., inflation, interest discount, fuel use, load supplies, etc.) remain constant over the analysis period. When this is not the case the summation formulae given above cannot be used and cash flows for each year must be calculated explicitly. It is frequently more informative to use one of two concepts derived directly from net present value. Levelized cost of energy The first concept is that of Levelized Cost of Energy, CL. That is the value of energy in $/kWh which, if held constant over the lifetime of the system, would result in the same NPV as calculated above. It is obtained as follows: CL = NPV - X [1/(1 + d), L]/APP

t8 -1 !)

where L

= Lifetime of the system

APP

= Annual Power Production

Another way to express this is that the levelized cost per kWh times the annual power production makes the annual mortgage needed to amortize the net present value of the system. Internal rate of return (IRR) The second concept is that of Internal Rate of Return, IRR, which is often used by utilities in assessing investments. The IRR is that discount rate which results in a NPV of zero

Assessing the economics

235

for the savings resulting from using one system rather than another. In this case the comparison is between the costs C d z- and C wdi - fordiesel only and wind-diesel systems, respectively, incurred i years ahead. In terms of a formula, the IRR is the discount rate such that:

(812)

In an economic assessment two measures of IRR are used. The Financial Internal Rate of Return (FIRR) is calculated from the above equation using the actual fuel costs at the location. It relates to the cost of supplying the energy. The Economic Internal Rate of Return (EIRR) is calculated from the above equation using the value of fuel savings instead of actual fuel costs. Thus the EIRR relates to the value to the society of using this kind of energy supply. The EIRR is used to evaluate one type of energy supply against other types of investments. Thus the EIRR is the measure to use to include social and environmental costs in a 'hard' economic analysis, for instance by adding the cost mentioned in the section dealing with the Indirect Financial Consideration to the value of fuel savings used in determining the EIRR. Life cycle cost analysis The life cycle cost method brings all capital including the recurring costs (such as fuel and maintenance) and non recurring costs (such as major overhauls) to present day values using established discount and escalation factors. Discount rates generally vary between 5 and 20 per cent. Values of 5, 7, 12 and 20 per cent have typically been used. The 12 per cent discount rate is an average value commonly used by the World Bank and many other similar institutions and countries. The lower rates like 5 and 7 per cent are generally used where special funds are used to provide good terms to developing countries. 18-22 per cent rates are generally used by most commercial institutions. Projects with high risk might go even higher. Escalation factors include provisions for rises in on-going costs such as those for fuel, labour and parts. For modelling purposes these are tied to known trends for those specific items, or to general escalation factors such as consumer price indices and known or projected inflation factors. These factors may be presented in two ways; the first being the total life cycle cost for the wind-diesel system being analysed, and the second being the life cycle energy cost per kWh. The first is the total of the capital cost and discounted operating cost over a fixed term or expected life of the system. The second method is the discounted cost per kilowatt hour of usable energy delivered by the system.

236

Assessing the economics

The constants to be considered are: Term of the life cycle This is generally based upon the major element with the longest life, in this case probably the wind turbine, 20 years. Discount rate This is the rate as discussed above. Fuel escalation This is the established or projected rate for fuel price escalation or inflation. Non-fuel escalation This is the established or projected rate of price escalation or inflation on these items: *

Parts

*

Lubrication oils

*

Labour

*

Travel

*

Miscellaneous

Fuel prices Cost of the fuel per litre delivered to the site. The costs to be considered are: Capital cost These are the costs expected before the beginning of operations. They include cost of purchase and installations of the complete system including diesel, wind turbines, storage and switching devices. When financing of capital cost occurs the cost of money and opportunity cost should be considered. Recurring fuel costs These are the total costs of the fuel utilised at the site and are generally calculated by multiplying the fuel price times the amount of fuel utilised. Recurring costs (other than fuel) These are the costs of operation and maintenance and include parts and labour. Non-recurring costs These are costs associated with replacing items or carrying out major overhauls during

Assessing the economics

237

the lifetime of the system. These may include storage devices such as batteries, switch-gear, and major wind turbine or diesel overhauls. These costs must be based on the expected point of overhaul or replacement during the wind-diesel system's life. Some may have a more frequent occurrence than others. The cost is then determined by dividing total costs by the number of years of life or use to get the average annual cost. Therefore the total true life-cycle cost of the wind-diesel system may be defined as the total of the capital costs plus the recurring fuel costs, recurring costs (non fuel) and the total of non recurring costs. Total Lifetime Cost = Capital Costs + Recurring Fuel Costs + Recurring Costs (Non Fuel) + Non Recurring Costs.

,___ Total Lifetime Costs ~ Cost ofc energy per kWh = ——_ . . . . Useful life x Annual energy output

(8.13a)

(8.13b)

Difficulties in analysis may occur when the energy provided has different values or uses such as can be the case between prime power and dump power. Return on investment analysis Different financiers or purchasers of schemes will use different assessment methods, which will usually reflect their different financial objectives. Banks supplying investment capital, for example, may well use payback methods to assess risk and rate of return methods in order to assess long term profitability. Generally, public bodies will use lower discount rates than private investors, and similarly will have lower expectations of required rate of return. Loss of load expectation (capacity value) Another aspect of the economic evaluation of wind/diesel systems is that of the value of the additional effective capacity contributed by the addition of the wind turbine(s). This value is in addition to that relating to fuel savings and is not included in the above analysis. The approach to this question is through the concept of 'Loss of Load Probability' (LOLP), also known as 'Loss of Load Expectation' (LOLE). This approach is used by utilities in evaluating the need for generation capacity. The LOLP approach is most applicable where a new power system is being planned or an existing system is going to be expanded. LOLP uses statistical techniques to

238

Assessing the economics

describe the reliability of a system in terms of the probability that the load will not be met for some small fraction of time. The calculation of the capacity value of the wind component of a wind-diesel system is done as follows. First of all the wind-diesel system is configured so that it will meet the load with a specified degree of reliability. The inclusion of wind will decrease the probability that the load cannot be supplied when compared to the same system with no wind component. This is true even though wind is not dispatchable in the conventional sense, because no component is 100% available. There is a finite probability that the wind turbines may be generating when a diesel is out of operation, thus enhancing the overall system's ability to meet the load. The wind component is then removed and the diesel capacity is increased until the load can be met with the same degree of reliability. The wind capacity credit that the wind generation may be given is the difference in total diesel capacity in the two cases. The value of that capacity, which can be considered by conventional economic analysis techniques, is the capital saving resulting from the need to purchase fewer or smaller diesel generators. The application of the LOLE concept in the assessment of wind-diesel systems has been demonstrated by Tande (1991). Sensitivity analysis Many of the parameters entering the economic assessment of a wind-diesel system are defined with considerable uncertainty. The introduction of the system in a remote community will induce changes that may affect the assumptions considerably and sometimes unexpectedly. Therefore the economic assessment should include parameter variations in order to assess the sensitivity of the results to variations in key parameters, as indicated in Figure 8.3. Such parameters should include: *

Wind conditions (e.g., average speed)

*

Consumer demand

*

Interest rates

*

Fuel costs (for financial evaluation)

*

Fuel savings value (for economic evaluation)

*

Operation, maintenance and repair rates

*

Component lifetimes

The sensitivity is often represented in 'spider graphs'. The example in the figure, taken from Danida (1990) shows the sensitivity of economic outcome of a proposed

Assessing the economics

239

project with regard to various technical and economic parameters. The percentage change in the expected internal rate of return EIRR is shown on the vertical axis versus the deviation of each parameter (with all others being kept unchanged) shown on the horizontal axis as the percentage deviation from its assumed value.

Currency exchange rate

Fuel price

i LU

Equipment

s

1 Local costs

o Wind speed

-4 -40

I -20

I

I 0

X Fuel save

I

20

40

Deviation (%)

Fig 8.3 Sensitivity of expected internal rate of return to parameter variation for an actual wind-diesel system Such sensitivity analysis is essential when assessing the economic risk in an installation. The example of Figure 8.3 shows that the economic viability of the project at hand is in essence determined by the wind conditions while also being strongly dependent on the value of fuel savings and the cost of equipment. Local financial considerations The effectiveness of a wind-diesel system and its economic viability are affected by the tariff structure which is used to (partly) finance its installation. It is important that consumers should be dissuaded from using excessive amounts of low grade energy when insufficient wind or stored energy is available, therefore a graded tariff structure is often to be preferred. It is important, when carrying out the economic assessment, to include all relevant local taxation and subsidy factors. To encourage development of non-polluting sources

Assessing the economics

240

of energy, local, national and international grant schemes may be available. These may take the form of capital grants, soft loans, or subsidies based on energy production. Depending upon the local tax structure, it may be possible to obtain advantage by setting against tax the capital cost of the installation.

ECONOMIC ASSESSMENT EXAMPLE To help consolidate the theory of the various economic appraisal methods described in this chapter, an example is now presented showing their use. The example assesses the relative merits of installing a wind-diesel rather than a diesel-only system at a hypothetical location. It is assumed that the existing diesel is due for replacement. For the sake of clarity, it is further assumed that annual system demand will remain fixed throughout the lifetime of the plant. System parameters Average system load, SL Diesel rated power Mean diesel usage - no wind turbine Wind turbine rated power Average annual wind speed Annual wind speed variability Average wind turbine power Useful wind turbine power (based on 25% lower limit on diesel loading) Mean diesel usage with turbine

96 kW = 841 000 kWh/year 200kW 42.4 litre/hr = 371 000 litre/year lOOkW 7.54 m/s 0.47 30.9 kW 21.1 kW 34.8 litre/hr = 305 000 litre/year

Economic parameters Wind turbine cost Diesel cost System life Initial payment Term of loan Interest General inflation Fuel inflation Discount rate Diesel fuel cost Wind turbine 0 & M cost Diesel 0 & M cost

_ L Ad N b i e d FL OMW OMD

$1500/kW installed $350/kW installed 20 years $30 000 10 years 10%

5% 6% 9% $0.50/litre 2% of capital cost/year 5% of capital cost/year

241

Assessing the economics

Levelized cost of energy To derive the levelized cost of energy, the net present values (or in this case, net present costs) of installing the power supply system must be assessed. It is helpful first of all to calculate the relevant present worth and capital recovery factors from equations 8.9 and 8.10. Capital recovery factor for interest payments, CRFP

0.162745

Present worth factor of interest payments, PWFP

6.4177

Present worth factor of fuel costs, PWFF

15.114

Present worth factor of O & M costs, PWFO

13.822

Capital recovery factor for system income, CRFI

0.109546

This enables the net present values to be calculated in accordance with equation 8.8. Comparing the two system options:

Capital cost

C

Diesel Only $70 000

Initial payment on system

Ad

$30 000

$30 000

Loan

C-Ad

$40 000

$190 000

Annual payment, Ap

(C - Ad) x CRFP

$6 510

$30 922

NPV of annual payments

Ap x PWFP

$41 779

$198 448

Fuel costs per annum(year 1)

FLxFC

$185 500

$152 500

NPV of fuel consumed over system lifetime

FL x FC x PWFF

$2 803 647

$2 593 176

0 & M costs (year 1), OMC

ECxOM

$3 500

$6 500

NPV of 0 & M costs over system lifetime

OMC x PWFO

$48 377

$89 843

$2 923 803

$2 623 176

$0.38/kWhr

$0.34/kWhr

Total NPV of system costs, TNPV Levelized cost of energy

(TNPV x CRFT)/SL

Wind Turbine and Diesel $220 000

242

Assessing the economics

This shows that it is worthwhile to make the bigger investment required to include a wind turbine in the system. This type of analysis is very useful, but should always be treated with caution. In particular, it can disguise cash flow difficulties. For instance, if it is assumed that the levelized cost of energy is the tariff actually applied and that it does not increase with inflation, then in the latter years of the debt repayment period, the income will be insufficient to meet all the annual system costs. A useful exercise for the reader would be to confirm this. Simple payback In the example, if it had been assumed that the diesel did not require to be replaced, then a very simple payback calculation based upon savings in fuel would be possible: Total cost Annual value of energy Payback

=100 kW x $1500/kW = $150 000 = (371 000 - 305 000) litre/year x $.50/litre = $33 000/year =$150 OOO/($33 000/year) = 4.5 years

Although the above ignores important factors such as the increased maintenance costs, it is commendable for its simplicity. Returning to the original example, where total system replacement was being assessed, the simple payback technique does not work so easily. The difficulty is in assigning a 'value' to the electrical energy generated. This is overcome if a tarrif (income) structure is specified. Therefore, for the sake of argument, let us assume that the system operators will receive $0.30 for every unit of electricity sold.

For the system without a wind turbine: Total capital cost

= $70 000

Annual costs

= $185 500(fuel) + $6 510 (bank repayments) + $3 500 (O & M) = $195 510

Total annual income =$252 300 Payback occurs (ie total income exceeds total expenditure) in this case after 1.23 years. For the system with a wind turbine: Total capital cost

= $220 000

Assessing the economics Annual costs

243

= $152 500 (fuel) + $30 922 (bank repayments) + $6 500 (O & M) = $189 922

Total annual income = $252 300 Payback occurs after 3.53 years Based upon this evaluation, the best decision would be not to invest in the wind turbine which is contrary to the conclusion of the NPV analysis. The payback method fails to identify the long term potential of the combined wind-diesel system, which the NPV and levelized cost of energy analyses bring out.

ACKNOWLEDGEMENTS The principal authors of this chapter were: R. W. Sherwin (USA), P. Lundsager (DK), J. Manwell (USA), and D. Infield (UK). The early input to this chapter by R. Horbaty and M. Real of Switzerland is appreciated by the authors. The authors additionally would like to thank 0 . Skarstein and K. Uhlen of the Norwegian Electric Power Research Institute and J.O.G. Tande and P. Norgaard of RIS0 National Laboratory, Denmark, for comments, suggestions and data. REFERENCES Collier, M.R. (1986) Further Wear Analysis of a Diesel Engine Operating under Wind-diesel System Conditions. Science and Engineering Research Council Energy Research Group Report, Rutherford Appleton Laboratory Chilton, Didcot, Oxon, OX110QX. Danida (1990) India: Wind-Diesel Systems for Lakshadweep Islands - Feasibility Study and Project Proposal. Danida Ref No: 104.0.27:Ind.l/l. Feasibility Report prepared by Danida Feasibility Mission with participants from RIS0 National Laboratory (Denmark), Denconsult (Denmark) and Energy Consultants (India). Freeman, T. (1981) IEA Report Task 1, Subtask D, Optimization, IEA. Hohmeyer, O. (1988) Social Costs of Energy Consumption - External Effects of Electricity Generation in the Federal Republic of Germany. Springer-Verlag Berlin Heidelberg, 1988. Hughes, P.S., Johnson, B., Sherwin, R.W., and Stein, W. (1990) System Stability and Penetration Study for Wind-diesel Hybrid Systems. Prepared under DOE/SERI co-operative agreement No DE-FLOZ- 87CH 10344 AY/Atlantic Orient Corp. Lundsager, P., Johnson, J.P. (1990) Logistic Computer Simulations and Economic Assessment of High Penetration Wind Energy Systems for the Peoples Republic of China. Proceedings of the 12th BWEA Conference, Norwich, UK, 1990. Lundsager, P., Sherwin, R.W. (1990) Using Simple Wind-diesel Systems without Energy Storage to Obtain High Penetration and Market Acceptance in the Near

244

Assessing the economics Future, Proceedings - American Wind Energy Association - Windpower '90, September 25 - 28. Washington DC, USA. Nittenberg, Jan, ed. (1983) Estimation of Cost of Energy from Wind Energy Conversion Systems, DEA Expert Group Study on Recommended Practices for Wind Turbine Testing and Evaluation. First edition. Reynolds, R.F. (1985) Diesel Engine Wear Evaluation in a Wind-diesel System. Science and Engineering Research Council Energy Research Group Report, Rutherford Appleton Laboratory Chilton, Didcot, Oxon, OX11 OQX. Ruegg, R. T. (1976) Evaluating incentives for Solar Heating, National Bureau of Standards, NBSIR 76-1127. Skarstein, 0., and Uhlen, K. (1989) Design Consideration with Respect to Longterm Diesel Saving in Wind/diesel Plants. Wind Engineering Vol 13, No. 2, 1989. Smith, G. W. (1973) Engineering Economy, 2nd Ed. Iowa State Univ. Press, Ames, Iowa. Tande, J.O.G. (1991) The Economics of Wind Power in Local Power Systems - A Methodology Description. Department of Meteorology and Wind Energy, RIS0 National Laboratory, RIS0-M-2928, RIS0 National Laboratory 1991.

Index

Aerodynamic performance, 41 Agriculture, 50 Anemometer(s): Data, 57 Annoyance, 46 Electromagnetic interference, 47 Noise, 47 Shadow/sunlight flicker, 47 Visual impact, 46 Auxiliary heat loads, 115 Averaging interval, 85 Averaging period, 57, 84

Batteries: Cyclic charging, 18 Lead-acid, 11,119 Nickel-cadmium, 120 Sodium-sulphur, 120

Relative humidity, 41 Snow, 42 Solar radiation, 41 Windborne contaminants, 43 Combined Heat and Power (CHP), 38 Commissioning, 215 Consumer load assessment: Category classification, 31 Extrapolation of load data, 33 Required data, 27 Control: Diesel engine, 105 Economics, 223 Power, 11 Systems, 96, 124-126 Time series modelling, 174 Wind turbine, 114 Cp-lambda curve, 170 Cyclic charging, 18

Case studies, 140 Foula Electricity scheme, 145-157 Froeya Demonstration, 140-145 The RAL/ICST wind diesel research facility, 157-164 Climatic conditions (general), 40 Extreme temperature, 41 Freezing, 42 Lightning, 42 Rain, 41, 42

Data recording, 85 Diesel engines, 104-110 Aspiration, 105 Control, 105 Cooling, 105 Fuel consumption, 10, 95, 172 Fuel type, 104 Generator, Choice of, 104 Equipment considerations, 110

246

Diesel engines Generator (cont) Modelling, 171 Modes of operation, 110 Rating of (calculations), 133-139 Types, 109 Heat recovery, 106 Loading, 9, 95 Operating cycle, 105 Operational considerations, 106 Size, 108 Speed, 105 Water temperature, 107,108 Direct financial considerations, 228 Dump loads, 11,12,38, 115 Types, 116 Modelling, 173 Economics, 221 Ancillary systems, 223 Appraisal methodology, 228 Assessment: Example, 240 Methods, 230 Control systems, 223 Cost parameters, 222 Diesel generators, 223, 224 Fuel, 223 Incentives, 222 Installation, 226 Internal rate of return, 234 Levelized cost of energy, 234 Example, 241 Life cycle cost analysis, 235 Local financial considerations, 239 Maintenance, 224 New-vs-Retrofit, 226 Operating, 224, 227 Other considerations, 226

Index

Payback period analysis, 231 Example, 242 Present value concept, 232 Site access, 225 Wind turbines, 223, 225 Electrical: Decentralised sites, 7 Generator options, 14, 100 A.C. induction, 102 A.C. synchronous, 101 D.C, 100 Loads, 6, 7 Modelling load demands, 36 Electromagnetic interference, 47 Employment, 50 Energy storage (general), 11 Battery, 11,18,19, 119 Flywheels, 12,19, 120-122 Hydraulic/pneumatic accumulators, 12, 20, 122 Integrated system, 21 Modelling, 173, 200 Pumped hydro, 13, 123 Rating of, 123, Selection, 117 Use of, 123 Error propagation, 189 Extreme winds, 40 Finance, 46 Grants, 46 Financial considerations Direct, 228 Indirect, 227 Flora and fauna, 49 FLOWSTAR wind model, 79 Flywheels, 12, 19, 120-122 Foula Electricity scheme, 145-157 Foundations, 39 Frequency variations, 99

247

Index Froeya Demonstration, 140-145 Glossary (Wind terminology), 90-94 Guidelines terrain model, 64

Horizontal Axis Wind Turbine (HAWT), 112 Hydraulic/pneumatic accumulators, 12,20,122

IEA, 99, 111,165 Indirect financial considerations, 227 Installation, 209 Construction, 212 Costs, 226 Development, 210 Foundations, 213 Inspection, 215 Permits, 212 Planning, 210 Pre-delivery, 209 Pre-installation, 210 Standards, 215 Instrumentation, 57 Insurance, 44 Intermittent diesel strategies, 96

Land use and purchase, 40 Lightning protection, 40 Load management, 14, 22,127

Maps, 57, 62, 64 Market potential, 22 Measure-correlate-predict model, 56, 76,81 Meteorological information, 56

Monitoring, 209, 216 Equipment, 218 Strategy, 217 MS3DJH/3R wind model, 67, 68

Networks: Grid, 5 Diesel, 5 Noise, 47

Operation: Basic system with no storage, 19 Classification of existing systems 15,17 Combinations, 8 Documentation, 220 Engine start-stop strategy, 11 Existing systems, 16 Handling, 211 Insurance, 211 Multiple diesel/single wind turbine, 21 Multiple wind turbine/multiple diesel, 21 Multiple wind turbine/single diesel, 21 Packaging, 21 Permits, 212 Personnel, 219 Spare parts, 220 System potential, 8 Transportation, 211

Permits, 212 Planning, 45, 46, 50 Power: Active, 101 Control, 11

248

Power: (cont) Converters/invertors, 100,103 Curve, 111 Diesel, 6 Electronics, 103 Quality of, 44, 97, 99 Reactive, 101, 102 Wind, 7 Probability density function, 178,193 Pumped hydro, 13, 123 Quality of life, 51

RAL/ICST wind diesel research facility, 157-164 References, 24-26, 51-53, 88-90, 131-132,191,195,203,208, 243-244 Reliability, 44, 97

Safety, 40, 43, 126-127 Security, 45 Shadow/sunlight flicker, 47 Site inspection, 62 Site selection, 55, 61 Decision process, 86 Siting handbooks, 77, 82 Standards, 43, 47 Statistical modelling, 176-180 Hysterisis, 200 Start/stop cycling, 196 Storage, 200 Summary, 180-182 Validation, 183-189 Surface roughness, 61, 63, 64, 68, 69 System control, 96, 124-126 Long-term, 130 Short-term, 128 System design, 95

Index

System operation, 95 Continuous diesel, 95 Intermittent diesel, 96 System selection, 22 Time series modelling, 166 Control system, 174 Diesel generator, 171 Dump load, 173 Dynamic model, 167 Energy storage, 173 I/Pdata, 174, 194 Long-term performance, 168 O/Pdata, 195 Short-term performance, 168 Summary, 180-182 Use of, 192 Validation, 175, 183-189 Wind turbine, 169 Tourism, 51 Training, 45

Uncertainty analysis: Bias error, 205 Calculation of, 207 Combined errors, 205 Measurement error, 204 Precision error, 204 Uncertainty of parameter, 205 Uncertainty of result, 206 UNIPEDE, 31, 34, 35, 37, 39, 99

Variable speed drives, 103 Variable speed system, 103 Vertical Axis Wind Turbine (VAWT), 112 Visual impact, 46 Voltage variations, 98 Harmonic distortion, 99

249

Index Voltage variations (cont) Three phase imbalance, 99

WASP wind model, 80 Weibull distribution, 59, 73,193 Wind atlases, 77, 82 Wind data, 59 Wind measurement, 55, 56, 63 Balloon measurements, 84 Calculation of long-term mean wind speed, 59, Choice of equipment, 83 Kite measurements, 84 Local knowledge, 60 Long-term study, 85 Short-term study, 83 Turbulence, 60, 64, 73 Wind models, 55, 56, 74, 75, 77 FLOWSTAR wind model, 79 Guidelines terrain model, 64 Application, 66 Input data, 64 Internal boundary layer, 68,69, 71 Calculation of surface roughness, 68 Calculation of terrain elevation effect, 66 Use of results in screening sites, 73 Limitations of models, 80 MS3DJH/3R model, 67, 68 Nonlinear effects, 82 Validation of models, 80

WASP wind model, 80 Wind power, 59 Availability, 9 General, 6 Machine control, 11 Wind profiles, 58 Interannual variations, 59 Seasonal variations, 58 Wind resource (general), 54 Wind tunnel studies, 82 Wind turbine : Blades, 59, 113 Fixed pitch, 113 Tip-speed ratio, 170 Variable pitch, 113 Control, 114 Modelling, 169 Options, 112 Generators, 114 HAWT, 112 VAWT112 Permits, 212 Power curve, 111 Selection, 111 Sizing, 115 Yaw, 113 Wind turbine siting: Access, 39 Foundations, 39 Land use and purchase, 40

Yaw, 113

Editors' note

The following persons assisted greatly in the creation of this book, either by participating in the parent co-operative technical programme, or by providing text for the various chapters. Without their help and that of their organisations, this book would not have been possible. Dr J. Walmsley, Atmospheric Environment Service, 4905 Dufferin Street, Downsview, Ontario, M3H 5T4, CANADA Mr R. Rangi, Energy, Mines and Resources, 580 Booth Street, Ottawa, Kl A OEY, CANADA Mr M. A. Lodge, Island Technologies Inc, Charlottetown, Prince Edward Island, CIA 719, CANADA Mr P. Lundsager, Test Station for Windturbines, RIS0 National Laboratory, PO Box 49, DK 4000, Roskilde, DENMARK Mr F. Van Hulle, Mr J. Pierik, ECN, PO Box 1, 1755 ZG Petten, NETHERLANDS Dr S. J. Reid, Ministry of Transport, New Zealand Meteorological Service, PO Box 722, Wellington 1, NEW ZEALAND Dr 0. Skarstein, Mr K. Uhlen, Mr T. Toftevaag, Norwegian Electric Power Research Institute (EFI), Sem Saelandsvei No 11, N-7034, Trondheim, NORWAY Mr F. Martin, Instituto de Energias Renovables/CIEMAT, Avda Complutense 22, DP 28040, Madrid, SPAIN Mr G. Svensson, Swedish State Power Board, Dept UP, S-162 87, Vallingby, SWEDEN Dr O. Carlson, Department of Electrical Machines and Power Electronics, Chalmers University of Technology, S-41296, Gb'teborg, SWEDEN Mr R. Horbaty, Oekozentrum Langenbruck, Schwengistrasse 12, CH-4438, Langenbruck, SWITZERLAND Dr S. Kessler, Mr Markus Real, Alpha Real AG, Wind Energy Department, Feldeggstrasse 89, CH-8008, Zurich, SWITZERLAND Dr L. Dubai, Office Federal de l'Energie, Bern 3003, SWITZERLAND Dr D. Infield, Dr J. Halliday, Rutherford Appleton Laboratory , Chilton, Didcot, Oxon,OX110QX,UK

Mr G. Elliot, Mr R. S. Hunter, National Wind Turbine Centre, National Engineering Laboratory Executive Agency, East Kilbride, Glasgow, G75 OQU, UK Mr R. Sherwin, Atlantic Orient Corporation, Norwich, PO Box 1097, Vermont 05055, USA Professor J. Manwell, Department of Mechanical Engineering, University of Massachusetts, Amherst, USA

Some of the authors and technical participants

XI

One author's interpretation of an integrated wind-diesel system! (courtesy Malcolm Lodge, Canada)