ETSU-R-99 A Review of the UK Onshore Wind Energy Resource

Transcription

1 ETSU-R-99 A Review of the UK Onshore Wind Energy Resource F Brocklehurst

2 February 1997 ETSU-R-99

3 A REVIEW OF THE UK ONSHORE WIND ENERGY RESOURCE Fiona Brocklehurst ETSU First published February 1997 Crown Copyright 1997

4 This report was commissioned from ETSU by the UK Department of Trade and Industry. It presents the views of ETSU. It does not necessarily represent Government policy

5 EXECUTIVE SUMMARY This document describes improved estimates of the UK wind energy resource and the cost of exploiting it, derived in 1996 as part of the DTI s Wind Energy Programme Area. This has comprised a major review of the UK s onshore wind resource-cost which has examined most aspects of ETSU s approach to its estimation. The review has highlighted significant revisions in the resource-cost estimates due to two main factors: new capabilities and data produced using a Geographic Information System (GIS) and changes or new information on recent advances in the wind energy industry and the UK as a whole. The key points arising from this review are: Assuming larger turbines with hub heights higher above ground level increases the resource considerably. The current resource estimation methodology takes into account more factors and should give a more realistic overall indication of the possible resource. Previous estimates of cost reductions by 2005 and 2025 seem to have been conservative based on current trends. (Capital costs in 1996 are about 800/kW compared to 1000/kW in 1992.) The accessible resource estimate using the new methodology is a considerable increase on the previous estimate. A new methodology has been developed to incorporate clustering and proximity analyses into the main analysis. This provides an arbitrary estimate of the effect that the planning system might have on the size of the resource. It is difficult to make an assessment of how the land planning system will affect the resource. A build rate of about 400MW a year of installed capacity seems sustainable at present within Europe. If the worldwide wind energy market continues to grow this should have doubled by i

6 As it now stands the current electricity network is a major restriction on developing the wind resource. This is particularly the case in Northern Ireland and Scotland where under current conditions (without major reinforcement or changes in operating conditions) approximately 1.6% and 2.6% respectively of the resource after clustering and proximity analysis can be developed. Generating 10% of the UK s electricity requirements from wind energy would require major investment and changes in methods of operation of the electricity infrastructure. The maximum practicable resource in 2005 is estimated to be between 2,460GWh/y and 16,930GWh/y, or 870-6,160MW installed capacity. The maximum practicable resource in 2025 is estimated to be between 2,460GWh/y and 30,000GWh/y, or ,350 MW installed capacity. ii

7 CONTENTS 1. INTRODUCTION 1 2. OVERVIEW OF NEW METHODOLOGY AND DATA 3 3. REVIEW OF COST PREDICTIONS Current costs Future cost reductions Increase in capital costs for higher wind speed sites Choice of cost-effective annual mean wind speed (amws) 8 4. FEASIBLE RESOURCE Feasible resource description Feasible resource estimates Sensitivity analysis of feasible results Introduction Discussion of effect of variation of physical constraints ACCESSIBLE RESOURCE Accessible resource estimates Effect of different designations on the resource Sensitivity analysis of physical factors on the accessible resource Comparison with estimates from previous methodology Overall size of the resource Split of the resource by country REVIEW OF FACTORS AFFECTING THE PRACTICABLE RESOURCE RESOURCE FOLLOWING CLUSTERING AND PROXIMITY ANALYSIS Description of methodology Estimates from analysis Comparison with previous results Sensitivity of estimates to clustering and proximity parameters 31 iii

8 8. BUILD RATE REVIEW OF RESTRICTIONS DUE TO THE ELECTRICITY NETWORK National operational limits Overview Scotland Northern Ireland Summary Transmission limits ESTIMATED PRACTICABLE RESOURCE The effect of the planning system on resource The effect of the electricity network on the resource Operational limits for non-firm power Restrictions due to limits on the transmission and distribution networks Current restrictions on wind resource due to electricity networks Specific questions to be answered from the practicable resource estimates Is the figure previously quoted for practicable wind energy resource of 10% UK electricity consumption achievable? How much could wind energy contribute to the UK electricity supply by 2010? Estimates of the maximum practicable resource in 2005 and SUMMARY ACKNOWLEDGEMENTS REFERENCES APPENDIX 60 iv

9 1. INTRODUCTION It is a requirement of the New and Renewable Energy Programme that resource-cost data be produced for each Programme Area in a standard format and that this data be updated every year. This report describes the first major review of onshore wind resource-cost estimates since the data used in An Assessment of Renewable Energy for the UK 1 was produced. This revision has examined most aspects of our approach and should form the basis of subsequent resource-cost estimates for the next few years. The report deals firstly with the changes in systems and data used to produce the estimates and presents the results of the high-level resource that result from it. Then it reviews the factors that influence the practicable resource and discusses the range of estimates that result. Finally, there is a summary section. The review has generated an enormous amount of data. Selected data are presented in the main part of the report; the remainder is summarised in tables in the appendices. 1

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11 2. OVERVIEW OF NEW METHODOLOGY AND DATA The changes in producing these new resource-cost estimates can broadly be split in two: those due to new capabilities and data available to a Geographic Information System (GIS); and those due to changes or new information gathered on the situation in the wind energy industry and the UK as a whole. The former are summarised in tables in Appendix A1. This section gives a brief overview. As wind is a geographically dispersed resource, producing resource estimates requires geographically-based analysis. The first national estimates using the UK wind speed database were produced using what was effectively a custom-written GIS. This was very effective but inflexible and as a result no revised estimates for the whole of the UK have been produced since Since then appropriate software and hardware have made rapid progress and in 1995 ETSU purchased a fully commercial Geographic Information System, SPANS GIS. This made it possible to incorporate new digital data sets and develop new analyses, replacing the existing methodology which, various regional studies have shown, contained inaccurate and out of date material. The new data sets and analysis in SPANS improve the ability to say where and how many wind turbines, given certain assumptions, it should be possible to place in a given area. Almost all the data on physical and institutional constraints is new and turbine density is calculated using new data and with a fresh approach. Analysis has been added to refine the resource a stage further than the accessible level in a mechanical way. As part of the regional studies two separate algorithms were written to cluster turbines into wind farms and then to apply a proximity constraint to these farms. Procedures which exist within SPANS have been used together with additional programming to derive an approach which has a similar effect to these two algorithms. This methodology is described in more detail in Section 7. The changes without those due to the change in hardware and software systems largely affect the estimates of the practicable resource and are discussed in Sections 8 and 9. One exception is the size of the wind turbine used and, related to that, the choice of data set from the UK wind speed database. The national resource was calculated using a typical 300kW turbine and wind speed data at 25m above ground level (agl). More recent regional studies resource estimates have been based on a 400kW turbine and the 25m agl wind speed height. Currently, the average size of turbine installed in wind farms is 600kW (with a rotor diameter of about 42m and a hub height 40-50m agl). Therefore this analysis uses a power curve for a typical 600kW turbine and the 45m agl wind speed data to match its hub height. In fact, data on a family of three 600kW turbines has been used. These have the advantage that they are designed to generate in sites with a very large range of annual mean wind speeds (amws) from 4m/s (with the large rotor diameter of 44m and a wind speed cut-out of 20m/s) to 14m/s (with a rotor diameter of 39m and a wind speed cut-out of 30m/s). The remaining change is a revision of the cost of energy. This is covered in Section 3. 3

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13 3. REVIEW OF COST PREDICTIONS 3.1 Current costs Previous published values of wind energy costs assumed installed capital costs of around 1000/kW and annual operational costs for a 400kW turbine of around 12,500. Indications are that current capital costs are considerably lower, at around 800/kW, but annual costs are similar. The average costs for a 600kW turbine, as used in the resource calculations, are shown Table 3-1. These assume that the turbine is in a medium sized typical wind farm. Table costs for 600kW turbine Capital Costs Annual Costs Ex-factory cost 285,000 Operation & maintenance 9,000 Commissioning & installation 45,000 Local rates 3,843 Civil engineering 45,000 Land rental 2,000 Electrical engineering (including grid connection) 75,000 Insurance 2,700 Miscellaneous* 30,000 Reactive power charges 1,400 Total installed cost 480,000 Total annual cost 18,943 *includes development costs, cost of obtaining planning permission, arranging finance etc. 3.2 Future cost reductions In the last major review of resource-cost data (R82 1 ) ETSU predicted a reduction in cost of energy of 25% by 2005 and 30% by 2025 from 1992 values. As stated in the Sections above, capital costs have already reduced by about 20%. Energy efficiency has increased due to improvements in designs of turbines and increases in size of turbines (from 300/400kW to 600kW rated capacity) increasing hub height and thus increasing the wind speed. The cost of energy for an equivalent site has reduction by approximately 20-30% between 1992 and 1996 (without correcting for inflation). Clearly then, the cost reductions prediction in R82 look conservative. A review of recent papers on this subject, notably from the Special Topic Conference on the Economics of Wind Energy in Finland in September ,3 and a mini-review of wind energy by IEA members 4, shows that predictions of future cost reductions vary widely with the most optimistic being 40% by 2000 and 55-60% in the long term. Common assumptions of all the estimates are that they will be due to a combination of increases in efficiency and reductions in costs. The latter factor is very dependent on the size of the market; the more optimistic estimates assume a buoyant worldwide market for wind energy. 5

14 One difficulty in interpreting these estimates is that the current cost of energy varies considerably in different countries so the basis of the predictions varies. Current costs of energy, under NFFO-3 and SRO-1 contracts, are amongst the lowest in Europe, but this is largely due to the higher average wind speeds, and capital costs are probably no cheaper and possibly still more expensive than those in other parts of Europe, particularly Germany (where almost all turbines installed are domestically produced, in contrast to the UK). A reasonable approach seems to be to use the same reductions as stated in R82, 25% by 2005, 30% by 2025, but use the 1996 costs as given in Table 3-1 as the base case (ie the same percentage cost reductions over a shorter period). In the long term wind energy in the UK will need to compete in the electricity market with conventional forms of generation. The primary issue for most customers will be cost of energy. The resource distribution in the UK is such that the resource available increases with the cost of energy. Turning this around this means that the wind resource at a fixed market price increases if the cost reduction increases. Figure 3-1 shows this effect for a practicable resource estimate case defined in Section 10.4, for a market price of 3p/kWh and assuming a discount rate of 8%. 6

15 resource in GWh/y 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 UK Northern Ireland Scotland England & Wales 0 0% 5% 10% 15% 20% 25% 30% 35% cost reduction from 1996 values in % Figure 3-1 Effect of cost reduction on resource available at a given market price - Resource available against cost reduction with a price threshold of 3p/kWh (UK optimistic practical resource 2005, 8% discount rate) 3.3 Increase in capital costs for higher wind speed sites Wind farm costs are inevitably very site specific and it is not possible to fully reflect this in a national estimate. However, it was thought that, on average, sites with high annual mean wind speeds would be less accessible than lower wind speed sites and therefore infrastructure costs (installation, civil and electrical engineering), would be higher. As reported in R82, installed costs were multiplied by a factor which was 1.0 for sites with an amws of 7.5m/s or less and increased linearly to 1.25 for sites with an amws of 10m/s or greater. This approach and the figures used were independently confirmed by David Milborrow in his review of NFFO-1 and 2 wind farms 5. There was scatter in the data but he found a 16% increase in installed costs for an increase in amws of 1.5m/s, showing very good agreement with our estimates. This factor has to be adjusted for the step change in the height agl of wind speed estimates from 25m to 45m. Correcting for the change in heights, 7.5m/s and 10m/s at 25m agl correspond approximately to 8m/s and 11m/s at 45m agl. Thus the capital cost correction has been applied linearly between 1.0 for 8m/s to 1.25 at 11m/s. 7

16 3.4 Choice of cost-effective annual mean wind speed (amws) As a development of the regional resource studies it has been necessary to make a judgement on what proportion of the resource is likely to be cost-effective in the medium to long term. Making a decision about the cost-effective resource has been used to select which part of the resource is considered for the clustering and proximity stages of the resource estimation methodology. As in our model the cost of energy is directly related to wind speed, applying a cost limit is therefore equivalent to applying a wind speed threshold. The value which has been used previously is an amws greater than or equal to 6.5m/s at 25m agl, which is approximately equivalent to 7m/s at 45m agl. Using the current costs given in Section 3.1 this is equivalent to 4.9p/kWh and 6.9p/kWh (8% and 15% discount rate respectively). Applying the cost reductions in Section 3.2, in 2005 costs this is equivalent to around 3.6p/kWh and 5.1p/kWh (8% and 15% discount rate) and in 2025 costs to around 3.4p/hWh and 4.8p/kWh (8% and 15% discount rate). These costs indicate that the 7m/s value is a reasonable cut-off point and this threshold has been used in this assessment. 8

17 4.1 Feasible resource description 4. FEASIBLE RESOURCE The feasible resource is an estimate of the resource if areas which are unsuitable for siting wind turbines for physical reasons are removed from consideration. The data used to establish where these areas are consisted of two data sets: one has information equivalent to that on a standard map - settlements, roads, rivers and so on the other gives the land cover of an area derived from remote sensing data (ie gathered by satellite). The land cover data distinguishes between 21 different types of cover. The land cover types were assessed as being suitable for turbine siting or unsuitable (for example, arable land is suitable, beach is unsuitable). In the form used for the resource estimate the land cover data is in the form of percentage occupancy of each 1 sq km for each land cover type. The area available for siting turbines, prior to adding the constraints of the map data with the buffers listed below, depends on the proportion of each sq km which is occupied by suitable land cover. The base case conditions used for the feasible resource estimates are: 600kW turbine. maximum turbine density of 9MW/sq km or 15 turbines/sq km. energy resource reduced by 2% to allow for availability, 5% to allow for wake losses and 5% to allow for electrical losses. 45m agl wind speed data. maximum turbine density of 3.6MW or 9 turbines/ sq km when comparing with previous resource estimates with a 400kW turbine. wind speed cut-off 7m/s at 45m agl and 6.5m/s at 25 magl. turbine density based on land cover class (as described above), then areas removed for various physical constraints, with 100m buffer around linear features such as roads, rivers etc. 100m buffer around woodland. 400m buffer around settlements (calibrated using Ordnance Survey AddressPoint data). 9

18 areas with slope over 10º excluded. 6km buffer around airports. Data on settlements are considerably less detailed for Northern Ireland and a slightly different approach was taken to try to compensate for this. In additions to the areas listed as settlements in the land cover data and the AA data set, it was assumed that there was ribbon development of dwellings near all but trunk roads and motorways. This approach was based on visual inspection of high-scale maps which indicated that this methodology was a reasonable approximation. However, this obviously distorts the results of the sensitivity analysis as presented in Appendices A2 and A4 and discussed in Sections 4.3 and 5.3. It is hoped that in future we will be able to identify better data sets and be able to use the same methodology in Northern Ireland as in Great Britain. 4.2 Feasible resource estimates Estimates are as shown in Table 4-1, Table 4-2 and Table

19 Table 4-1 All of resource Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 179, , Wales 27,103 59, Scotland 183, , Northern Ireland 41, , Total 431,298 1,045,626 Table 4-2 Resource with amws at 45m agl 7m/s Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 43, , Wales 12,153 32, Scotland 139, , Northern Ireland 27,789 78, Total 223, ,787 Table 4-3 Area in square kilometres Area % of total area (sq km) England 130, Wales 20, Scotland 78, Northern Ireland 13, Total 244,000 11

20 Less of the high wind speed resource is in England and more is in Northern Ireland, and more particularly Scotland, compared with their land area. This is not surprising given the estimated wind speed distribution. 4.3 Sensitivity analysis of feasible results Introduction While they reflect as far as possible current practice the choices of buffers and threshold values used in deriving the feasible resource, are to some extent arbitrary. Therefore it is important to establish how changes in these values affect the resource. Furthermore, it is important for some other aspects of the Wind Programme Area to establish the effect of these choices on the size of the resource available. For example, if the restrictions on acceptable noise levels from wind farms at dwellings are increased, wind farms would have to be sited, on average, further away from dwellings. Each parameter was varied separately, as shown in Table 4-4. Table 4-4 Variation of physical parameters for sensitivity analysis Physical constraint Base case 1st variant 2nd variant areas with slope above a certain threshold excluded buffer applied to linear features, roads, railways etc 10º slope 8º slope 15º slope 100 m 50 m - buffer applied around dwellings 400 m 800 m 1500 m buffer around woodland 100 m no buffer 200 m Finally, it is helpful when comparing the estimates of the resource using the previous methodology to be able to identify the effect that different changes are having. In order to do this, two further variations were applied: the resource was estimated using the wind speed data at 25m agl with the 600kW turbine and the 400kW turbine that had been used for previous regional studies. The results are presented for each country separately and for Great Britain in the tables in Appendix A2 and discussed in the next Section. 12

21 4.3.2 Discussion of effect of variation of physical constraints In all cases increasing the buffer around settlements has the strongest effect on resource. As would be expected, the effect of the change in constraints is highest in England, the most densely populated country, and lowest in Scotland, the least populated (in Great Britain). The effect is less on the high wind speed resource, again as expected as these areas are generally less densely populated. (For Northern Ireland the high wind speed resource with larger settlement buffers applied is a lower proportion of the overall resource than for England. This is almost certainly due to the different approach to settlement, described in Section 4.1.) The next highest sensitivity varies from country to country. For Scotland, Wales, Northern Ireland and the high wind speed resource in England, the next strongest effect is due to variation in threshold slope. The effect is stronger on the high wind speed resource, as expected given that, generally, high wind speeds occur in more complex terrain. The buffer around linear features such as roads, rivers, canals etc is the second highest effect for England considering all the resource, the third highest for Scotland and the least sensitive for Wales and Northern Ireland. To a large extent this reflects the extent and distribution of the road network in each country (there is a lower density of roads in high wind speed areas in England). The Northern Ireland results for this effect are inevitably anomalous, see Section 4.1. Finally, the degree of buffering around woodland areas seems to have a stronger effect on the resource in Wales than in Scotland, England and Northern Ireland. In all cases the effect is less at higher wind speeds, as would be expected as tree growth is slower and woodland less common in high wind speed areas. Overall for Great Britain, the order of sensitivity is: settlement, slope, linear features, and woodland, with the effect on the higher wind speed resource increased from the total resource only in the case of slope, as would be expected. The resource for old conditions (lower wind speed height, lower turbine density, smaller turbine) in terms of energy output is about 30% of that using new conditions due to three factors, roughly: 1. lower turbine density (reduces resource by ratio of capacity density, 3.6/9MW/sq km -= 60%) 2. lower energy capture due to lower wind speeds (reduces resource by 15%) 3. lower energy capture due to smaller turbine (reduces resource by 10%). The resource left = 40 x (1-0.15) x (1-0.1) = 30%. The increased energy capture due to the larger turbine is less noticeable at higher wind speed. 13

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23 5.1 Accessible resource estimates 5. ACCESSIBLE RESOURCE The accessible resource is a refinement of the feasible resource whereby areas which are more sensitive to the environmental impact of wind energy development are further excluded from consideration. As for so many aspects of wind farm siting, this is fundamentally a sitespecific issue and therefore it is difficult to take account of this accurately on a national scale. The approximation used excluded nationally designated areas from consideration. This is in no way an endorsement of the view that any wind energy development in these areas is unacceptable. There may well be locations where there is no conflict between the reason for the designation and wind energy development. Conversely, there may be areas which are not nationally designated where the environmental impact of wind farms is greater than that within designated areas. The assumption underlying this approach is that over a large scale these factors will balance out and that this will give a reasonable estimate of the size of the resource. Unfortunately this means that the resulting distribution of the resource on a local scale will not reflect that which could be developed considering local effects. One complication to this approach is that landscape designations are different in Scotland; specifically National Parks and Areas of Outstanding Natural Beauty (AONBs) do not exist. As part of the 1993 assessment of the renewable resource in Scotland considerable data were collated on regional designations in Scotland in addition to national designations. As this data was all valid for the same timescale and already held in suitable digital form it was decided to apply these designations in addition to the national designations as a valid further refinement of the resource. The combination of designations is as shown in Table 5-1, together with details of which apply in which countries: 15

24 Table 5-1 Designations as applied to derive the accessible resource Designation title National and regional designations applied (abbreviation) in Scotland in rest of UK National Parks (NPs) Areas of Outstanding Natural Beauty (AONBs) National Nature Reserves (NNRs) Sites of Special Scientific Interest (SSSIs) Greenbelt Regional Parks National Scenic Areas (NSAs) High Landscape Sensitivity Areas (HLSAs, Highlands) Areas of Great Landscape Value (AGLVs) Medium Landscape Sensitivity Areas (MLSAs, Highlands) Areas of Regional Landscape Importance (RSAs) NNRs SSSIs Areas of Archaeological Importance Greenbelt Countryside Around Towns (CAT) Do not exist in Northern Ireland AONBs NNRs Two equivalent designations in Northern Ireland, Areas of Scientific Interest (ASIs) and Areas of Special Scientific Interest (ASSIs) No Greenbelt in Wales or Northern Ireland Estimates using the current methodology are shown in Table 5-2 and Table 5-3 below, with estimates using the previous methodology shown for comparison in Table 5-4 and Table

25 Table 5-2 Accessible resource, all data Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 114, , Wales 16,772 34, Scotland 89, , Northern Ireland 31,685 79, Total 251, ,416 Table 5-3 Accessible resource with amws at 45m agl 7m/s Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 14,135 36, Wales 6,156 16, Scotland 68, , Northern Ireland 20,565 56, Total 109, ,854 Table 5-4 Accessible figures from old methodology (R82), all data Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England and Wales 85, , Scotland 83, , Northern Ireland 16,049 33, Total 185, ,730 Table 5-5 Accessible figures from old methodology (R82), with amws at 25m agl 6.5m/s 17

26 Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England and Wales 10,662 23, Scotland 55, , Northern Ireland 10,532 25, Total 76, ,430 Resource-cost curves are shown in Figure 5-1 and Figure 5-2. Resource-cost tables for the accessible resource are in Appendix A3. energy GWh/year 600, , , , ,000 UK Scotland Northern Ireland England Wales 100, cost at 8% discount rate p/kwh Figure 5-1 Accessible resource at 1996 costs, 8% discount rate, by country and for UK 18

27 600, ,000 energy GWh/year 400, , , , % % % % cost limit p/kwh Figure 5-2 UK Accessible resource, different time-scales and costs 5.2 Effect of different designations on the resource In order to determine which designations have most effect on the resource each designation was applied separately. The results for each country separately and for Great Britain are in the tables in Appendix A4. Landscape-based designations cover relatively large areas and are generally in hilly or mountainous areas. It is therefore not surprising that they have a strong effect on the resource in general and a stronger effect on the high wind speed resource. The relative effect of the different designations is largely illustrative of the area they occupy in the different countries: in England AONBs have more effect than National Parks (NPs), whereas in Wales the opposite is true. There are no National Parks in Northern Ireland. Of the two designations designed to protect flora, fauna and ecosystems, Sites Of Special Scientific Interest (SSSIs) and National Nature Reserves (NNRs), the latter has the least effect of any of the designations. This reflects their relatively small number and size. SSSIs are much more numerous and can be quite extensive. In Wales this has the next strongest effect on resource after NPs and ahead of AONBs. In England and Wales (and Great Britain overall) SSSIs have more of an effect on the high wind speed resource which suggests that these designations are concentrated in the higher wind speed areas. Areas of Scientific Interest (ASIs) and Areas of Special Scientific Interest (ASSIs) in Northern Ireland are both roughly equivalent to SSSIs. In countries where it applies, Scotland and England, the Greenbelt designation has quite a noticeable effect, which is smaller for the high wind speed resource, as would be expected (cities are generally in low wind speed areas). 19

28 5.3 Sensitivity analysis of physical factors on the accessible resource Sensitivity analysis of the effect of buffer distances and thresholds as described for the feasible resource in Section 4.3 and listed in Table 4-4 were repeated for the accessible resource. The results of this are presented for each country separately and for Great Britain in the tables in Appendix A4. Note: the Northern Ireland results are somewhat anomalous for reasons described in Section 4.1. Applying a wind speed threshold has a greater effect on the accessible resource than the feasible resource in England and Wales and Great Britain overall. This indicates that more of the high wind speed resource has been removed by the additional constraints. As for the feasible resource, in all cases increasing the buffer around settlements has the strongest effect on the accessible resource. In fact, the sensitivity to settlement buffers is increased from the feasible resource case. This indicates that, as would be expected, designated areas are less densely populated. As before, the effect of the change in constraints is highest in England, the most densely populated country, and lowest in Scotland, the least populated in Great Britain. The effect is less on the high wind speed resource, again as expected as these areas are generally less densely populated. The next highest sensitivity varies from country to country. For Scotland, Northern Ireland and Great Britain overall the next strongest effect is due to variation in threshold slope. The effect is stronger on the high wind speed resource, as expected given that, generally, high wind speeds occur in more complex terrain. This is the third sensitivity for Wales and the least sensitive for England, indicating that the resource in these areas no longer contains much complex (high slope) terrain. The buffer around linear features such as roads, rivers, canals etc is the second highest effect for England, the third highest for Scotland and the least sensitive for Wales and Northern Ireland. As for the feasible resource, the degree of buffering around woodland areas seems to have a stronger effect on the resource in Wales than it does in Scotland, England or Northern Ireland (with England having the lowest sensitivity). This is the second highest effect on the resource for Wales. The effect on the resource size of changing the wind speed data height and the turbine are very similar to those for the feasible resource. 20

29 5.4 Comparison with estimates from previous methodology Overall size of the resource The accessible resource derived using the new methodology is significantly greater than with the old. This is even more the case for the high wind speed resource than for the resource taken as a whole. In both cases the increase is greater in terms of energy than for the capacity. The most obvious reason for the increase in capacity is the change in the maximum capacity density used, from 3.6MW/sq km to 9MW/sq km. The resource in terms of energy is effected by the same factor but is also increased further by the change in wind speed estimates used, from 25m to 45m above ground level, and the larger, more energy efficient turbines used (400kW to 600kW) If simplistic factors are applied to the new resource figures to take account of the increased turbine density and energy output, the new resource would be less than the old figures. This is due to a combination of other changes in the methodology which have to some extent counter-balanced the increase in turbine density: applying a new physical constraint (no resource in areas of severe slope) using different data sets for land use/cover and a different approach to turbine density calculation, particularly in how to take account of settlements and isolated dwellings using up to date data for designations and in some cases, applying new constraints, though others are no longer applied (Environmentally Sensitive Areas and Local Nature Reserves) These changes have reduced the area considered effectively accessible. The new accessible resource is thus larger but concentrated in fewer, higher wind speed areas than the old resource which was very diffuse. The clustering and proximity analysis which will follow will accentuate this effect. Once this is complete the overall result should be a more accurate indication of the possible scale of wind energy development in the UK. NB The values for accessible resource presented above are those with a maximum density of kW turbines per sq km or an installed capacity density of 9MW/sq km. This is the central case to be considered. A high density case (29 turbines/sq km, 17.4MW/sq km) and a low density case (7 turbines/sq km, 4.2MW/sq km) will also be considered when undertaking clustering and proximity analysis. However, at the accessible stage, resource simply scales with maximum turbine density so the results for all three cases (which would be in the ratio 0.47:1:1.93) have not been presented Split of the resource by country As has been noted earlier (Section 4) it was not possible to treat Northern Ireland in the same way as Great Britain in assessing the effect of settlements on the resource because the available data sets were less detailed. Northern Ireland appears to contribute more to the resource than would be expected given either its area (Table 4-3) or results using the previous methodology (Table 5-4 and Table 5-5). This is probably due to the fact that the accessible resource estimate for Northern Ireland is probably still not taking full account of the effect of settlements and is therefore an overestimate. 21

30 Table 5-6 shows the split of the resource within Great Britain for the current and previous methodologies. Table 5-6 Split of resource within Great Britain % of total GB capacity % of total GB energy % of total GB % of total GB capacity wind energy wind speed threshold speed threshold old England and Wales old Scotland new England and Wales new Scotland Table 5-6 shows that less of the accessible resource is in Scotland relative to England and Wales in the current methodology than was the case using the old methodology. This effect is slightly less for the high wind speed resource. This is probably due to two changes in the methodology: 1. areas with a severe slope have been removed from the resource and this has a greater effect on the resource in Scotland than it does in England and Wales (see Section 5.3) 2. more designated areas have been removed from inclusion in the accessible resource in Scotland (see Section 5.2). 22

31 6. REVIEW OF FACTORS AFFECTING THE PRACTICABLE RESOURCE In reality the accessible resource is limited by three further restrictions: the need to group wind turbines together so that they are financially and practically viable and to minimise their environmental impact the rate at which the wind farms can be built the restriction placed on the resource by the limits of the electrical network When these restrictions are applied to the accessible resource the remainder is called the practicable resource. The following three Sections describe these restrictions. 23

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33 7. RESOURCE FOLLOWING CLUSTERING AND PROXIMITY ANALYSIS 7.1 Description of methodology As part of the regional studies (see particularly references 6 and 7) two algorithms were developed to cluster turbines into wind farms with a specified size and shape range and then to apply a proximity constraint, removing those which were too close together. These were effectively stand-alone programs, although the results could be imported into the GIS. The approach that these programs took is not one that can be translated into a SPANS GIS environment. Furthermore, the approach is somewhat protracted and would probably be impracticable for very large areas, such as the whole of Great Britain. There are no equivalents to these procedures within SPANS. More surprisingly perhaps, considering the large numbers of SPANS users, no similar procedures appear to have been developed by users which could have been used as a starting point. As a result ETSU has had to develop appropriate codes using a combination of SPANS applications and custom-written code from scratch. The advantage of this is that, while the procedures are still non-optimised, ie they are to some degree arbitrary and therefore may not estimate the maximum resource, they should be more robust and more consistent than the stand-alone algorithms used previously. This change from stand-alone algorithms to analysis within SPANS has necessitated a number of changes in approach, the main one being that there are no longer two distinct stages in the process whereby wind farms are formed and then some are eliminated. Instead, the requirement that the centres (more exactly centroids) of the wind farms are a certain distance apart influences how the wind farms are formed in the first place. This should make more efficient use of the resource. The data that are the basis of the analysis are largely the same. A wind speed threshold is applied to the accessible resource to take account of commercial viability. One difference is that the number of turbines in each square is rounded to give integral turbines. The basic parameters which are used to try and produce a realistic indication of how the resource might be developed are still the same. Estimates were made of reasonable values with variations to identify how sensitive the resource is to these values. The base case values and the values used in sensitivity analysis are shown in Table

34 Table 7-1 Input parameters to clustering and proximity and variants used for sensitivity analysis Input parameter Base case 1st variant 2nd variant Maximum turbine density 15 per sq km 7 per sq km 29 per sq km Minimum farm size 20 turbines 10 turbines - Maximum farm size 50 turbines 100 turbines - Maximum sparseness (15 turbine case) Minimum centroid separation 7km 11km 15km These values are largely self explanatory. To some extent larger wind farms are more cost effective, hence the requirement for a minimum farm size. The results show that altering the minimum farm size has a relatively small effect on the resource on a national scale, though it may well have considerable effect in areas where wind speeds are lower and therefore the resource above the wind speed threshold is limited. Similarly, there are a limited number of locations where very large wind farms are environmentally acceptable. There is only one wind farm in the UK which has more than 50 turbines (Llandinam) and if this were expressed in terms of installed capacity it would fall beneath the base case maximum wind farm limit (Llandinam capacity 28.8MW, maximum size limit 30MW). The minimum sparseness value does require some further explanation. The costs of a wind farm depend to some extent on its shape and extent. A farm which has turbines spread diffusely over several kilometres will generally be more expensive to install (longer electricity connections between turbines and access roads) and operate (possibly higher land rental) than a more compact farm. The algorithms used for these stages always try to create the most compact farm possible within certain limits. The sparseness is a way of expressing this compactness in numerical terms, such that the higher the sparseness the more diffuse the site. Thus each farm is required to have a sparseness below the test value - if a farm is too sparse the program removes the furthest km squares until the sparseness falls below the limit or the number of turbines in the farm falls below the minimum and the farm is rejected. The sparseness values obviously depend on the maximum turbine density. For previous analyses using the old methodology and algorithms, starting with a maximum turbine density of 9 turbines per square, maximum sparseness values of 0.33 to 0.5 were used. Equivalent sparseness values of the 0.33 figure for 7, 15 and 29 turbines per sq km are 0.43, 0.20 and A value below and above this base case were used for runs for the 7 and 15 maximum turbine density cases. 26

35 It has already been stated (in Section 5.1) that the accessible resource estimate assumes areas which are nationally designated do not contribute to the resource, which means that the estimated distribution of the resource will be inaccurate on a local scale. It must be stressed that the proximity analysis is a further, very crude, attempt to take account of the environmental effect of wind farms, in particular their cumulative effect, and how different degrees of sensitivity to this might affect the size and distribution of the resource. This analysis does not imply that it is unacceptable to build wind farms close together. It is merely trying to establish the effect that applying this kind of constraint would have. The proximity analysis simply ensures that the centroids of the nominal wind farms are at least the minimum specified distance apart; edges of the farms may be considerably closer than this. For example, if two nearby farms each consist of four 1km squares (each having 15 turbines) and their centroids are 7km apart, then the shortest distance between any part of the farms will be about 5km. The resulting estimates will be only broadly indicative of the size of the resource and the distribution of the nominal wind farm positions will be an inaccurate representation of the pattern of wind energy development on a local scale. 7.2 Estimates from analysis The following estimates show the results of three cases of clustering and proximity analysis: the base case, the case that gave the smallest resource and the case that gave the largest resource. The sensitivity of the estimates to different parameters is discussed in Section 7.4, with detailed results given in Appendix A5. Table 7-2 Base case results Capacity (MW) Energy (GWh/y) Number of nominal wind farms % of total capacity % of total energy England 3,240 8, Wales 1,591 4, Scotland 11,594 36, Northern Ireland 3,044 8, Total 19,469 57,

36 Table 7-3 Minimum case results (Base case + turbine density of 7 turbines/sq km and minimum centroid separation of 15km) Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 907 2, Wales 398 1, Scotland 3,401 10, Northern Ireland 927 2, Total 5,633 17,154 Table 7-4 Maximum case results (Base case + maximum farm size of 100 turbines) Capacity (MW) Energy (GWh/y) % of total capacity % of total energy England 4,198 11, Wales 1,907 5, Scotland 16,799 52, Northern Ireland 5,024 14, Total 27,928 82,752 Table 7-5 Base case results as a fraction of accessible results % of total accessible capacity % of total accessible energy % of accessible capacity wind speed threshold % of accessible energy wind speed threshold England Wales Scotland Northern Ireland UK

37 Table 7-6 Maximum practicable figures from old methodology, all data 5.5m/s as published as maximum practicable in R82 Capacity (MW) Energy (GWh/y) % of total capacity % of total energy % of accessible energy England and Wales 7,058 10, Scotland 14,544 36, Northern Ireland 2,273 4, Total 23,876 51, Table 7-7 Maximum practicable figures from old methodology (R82), with amws at 25m agl 6.5m/s Capacity (MW) Energy (GWh/y) % of total capacity % of total energy % of accessible energy England and Wales 1,066 2, Scotland 11,137 31, Northern Ireland 1,580 3, Total 13,783 37,

38 energy GWh/year 60,000 50,000 40,000 30,000 20,000 UK Scotland Northern Ireland England Wales 10, cost at 8% discount rate p/kwh Figure 7-1 Base case practicable resource 7.3 Comparison with previous results The practicable base case resource derived using the new methodology is of the same order in terms of energy, comparing the whole resource from the previous methodology (Table 7-6). Comparing feasibly economic practicable resource, ie above the wind speed threshold in both cases, Table 7-2 and Table 7-7, the new resource is significantly greater. In the previous methodology, the practicable resource was derived from the accessible by applying success factors to the accessible resource which varied on the population density of the country, but in energy terms averaged at around 16.1% of the resource 5.5 m/s and 18.2% of the high wind speed resource. The comparison between practicable and accessible resource estimates derived using the new methodology, shown in Table 7-5, shows that coincidentally an almost identical average factor applies for the high wind speed resource using the old and new approaches. Table 7-2 shows that the majority of the practicable resource, about 60%, is still in Scotland. However, compared to the figures for high wind speed resource using the old methodology in Table 7-7 less of the resource is in Scotland compared to the rest of the UK using the new estimate. Figure 7-1 shows the practicable resource-cost curve without applying any further restrictions (eg electricity network limitations). Figure 7-2 shows the effect of lifetime and discount rate on the cost of the resource. 30

39 energy GWh/year 60,000 50,000 40,000 30,000 20, yr 8% 20 yr 15% 15 yr 8% 15 yr 15% 10, cost limit p/kwh Figure 7-2 Effect of lifetime and discount rate on cost of resource 7.4 Sensitivity of estimates to clustering and proximity parameters The results of varying the clustering and proximity parameters illustrate the different patterns of distribution of higher wind speed areas and estimated potential turbine sites in different parts of the UK. The results are given in Appendix A6. In Northern Ireland there are large areas of high wind speed saturated with turbines. (To a certain extent this is not a true representation of the resource as the effect of settlements on the resource is not fully taken into account as discussed in previous Sections.) This means that reducing the maximum turbine density by more than half only reduces the resource by 20% from the base case. Under most conditions most wind farms have close to the standard maximum number of 50. The strongest effects on the resource are increasing the maximum farm size (this has a strong effect even at the lower maximum turbine density) and increasing the minimum separation distance. Rather oddly, increasing the turbine density to 29 turbines per sq km appears to slightly decrease the size of the resource. The distribution of resource in Scotland appears to be similar to that in Northern Ireland though less extreme. Again, there are large areas with high wind speeds and low population density. Reducing the maximum turbine density by half reduces the resource by about 40% from the base case. Increasing the maximum turbine density by a factor of two from the base case only increases the resource by about 10%, probably due to the fact that most farms are already at close to their maximum size. Increasing the maximum farm size has a significant effect. Increasing the minimum separation distance has the strongest effect. However, reducing the maximum sparseness criteria, eliminating farms that are too diffuse, also has a very considerable effect on the resource, in contrast to the Northern Ireland cases. 31

40 England and Wales exhibit a pattern very similar to one another and different to Northern Ireland. This is not unexpected - there are fewer high wind speed areas, which in general are more spread out, and the population density is higher so the turbine density is in general lower. For England and Wales, reducing the maximum turbine density by half reduces the resource by about 50% from the base case. Increasing the maximum turbine density by a factor of two from the base case only increases the resource by about 30%; again as for Scotland, many farms are already at close to their maximum size. Decreasing the minimum farm size has a big effect on the resource increasing it significantly, particularly for the low turbine density case. Increasing the maximum farm size has a significant effect on the base turbine density case (this gives the maximum resource) but not on the low turbine density case. Increasing the minimum separation distance has the strongest effect on the base turbine density case. However, for the lower turbine density changing the maximum sparseness has the greatest effect, in contrast to both Scotland and Northern Ireland. Overall, this case, low turbine density and low maximum sparseness, gives the minimum resource for England and Wales. Comparing the typical size of farms for different conditions for Northern Ireland and Great Britain as a whole confirms the differences in the distribution of the resource between the two regions. In Northern Ireland the median and the mean farm size are both close to 50 turbines, even for the lower turbine density cases. For the base turbine density case, increasing the maximum farm size to 100 turbines increases both the median and the mean considerably. In Great Britain, for the base case density cases, the median and mean are around 50, but for the lower turbine density cases they fall to around 30. In Northern Ireland the resource is very abundant and therefore the size of the resource is relatively insensitive to the parameters which select what type of wind farm is acceptable. The greatest effect on the resource is the value selected for the minimum separation of centres of farms. In Great Britain the resource is more diffuse and the type of farm considered acceptable and the minimum separation of farms are equally important in determining the estimated size of the resource. 32