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MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATES DR MIKE ANDERSON GROUP TECHNICAL DIRECTOR THURSDAY 15 TH JULY 2010 1.

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Presentation on theme: "MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATES DR MIKE ANDERSON GROUP TECHNICAL DIRECTOR THURSDAY 15 TH JULY 2010 1."— Presentation transcript:

1 MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATES DR MIKE ANDERSON GROUP TECHNICAL DIRECTOR THURSDAY 15 TH JULY

2 OUTLINE OF PRESENTATION 2 1.Introduction to RES 2.Assessment of Energy Yield 3.Assessment of Uncertainty 4.Techniques for Minimising Uncertainty 5.Impact of Climate Change 6.Summary

3 3 KEY MILESTONES First wind farm, Carland Cross 15 x 400kW, 6MW 1992 King Mountain, Texas, worlds largest wind farm at 218MW 2001 Beaufort Court and first development of other renewable technologies and sustainable building design GW milestone; launch of on-site renewables businesses 2006 launch of Inbuilt 2007 passed 2GW and 3GW milestones 2008 passed 4 GW milestone

4 4 QUANTIFYING UNCERTAINTY 4

5 PRODUCTION ESTIMATES - METRICS 5 To quantify the future annual energy production (AEP) we need a metric. Depending upon the lender different metrics are used to size the debt. 10 year P % chance of exceeding your estimate. 10 year P90 – 90% chance of exceeding your estimate. 1 year P99 – 99% chance of exceeding your estimate. In sizing the debt each metric will be used with a different DSCR. Since the credit crunch lenders are now more interested in reducing their exposure so the P90 and P99 are becoming widely used. Compounded by Silly money financing stupid projects. 5

6 PRODUCTION ESTIMATES – EXAMPLE 6 For a typical UK onshore wind farm rated at 26 MW with a net capacity factor of 33.5% typical values are: Uncertainty (1 standard deviation) in 10 year estimate = 8.1% P50 (10 year) = GWhr/year (100%) P90 (10 year) = GWhr/year (89.6%) P99 (1 year) = GWhr/year (68.6%) Clearly uncertainty is having a large impact upon the P90 and P99 AEP estimate. 6

7 TYPICAL ONSHORE UK WIND FARM LAYOUT 7

8 ASSESSMENT OF ENERGY YIELD – MAJOR COMPONENTS 8 Net Yield Long Term Wind Resource Wind Flow Model Turbine Model Wakes Loss Electrical Loss Adjustment Factors Reference Net 85%

9 ASSESSMENT OF ENERGY YIELD – UNCERTAINTY ELEMENTS 9 Net Yield Long Term Wind Resource Wind Flow Model Turbine Model Wakes Loss Electrical Loss Adjustment Factors 5.7% 2.4% 1.5% 0.8% 0.4% 2.0% 8.1% Reference Net 10 year time horizon

10 WIND SPEED MEASUREMENT – CHOICE OF INSTRUMENTATION 10 Good Practise (+/-3.0% in energy Poor Practise (+/-7.0% in energy)

11 WIND SPEED MEASUREMENT – EFFECT OF HEIGHT 11 Uncertainty Increase in uncertainty for every 10m difference in height is 1% in wind speed and ~2% in energy

12 LONG TERM WIND SPEED ESTIMATE REQUIRES EITHER A LONG RECORD OF ON-SITE MEASUREMENTS (>4 YEARS) OR CORRELATION WITH AN EXISTING REFERENCE STATION USING A NUMERICAL TECHNIQUE CALLED MEASURE CORRELATE PREDICT (MCP) 12

13 13 LONG TERM WIND SPEED ESTIMATE - MCP Measure wind speed and direction on site for minimum 12 months. Correlate to concurrent data from a reference site with a long-term record of wind data (10-20 years), e.g. met station or airport. Predict the long-term wind speed on site by applying the derived correlation to the historic data from the reference site and combine statistically with the site measured data. Reference site Wind Farm site Concurrent dataHistoric data

14 LONG TERM WIND SPEED ESTIMATE 14

15 LONG TERM WIND SPEED ESTIMATE - UNCERTAINTY 15 GoodPoor Instrumentation1.5%3.5% Data collected3 years6 months Reference period10 years3 years Extrapolation to hub height0%3% Error in Long Term Estimate3.3%6.9% Error in Energy Production 5.7%11.9%

16 16 ENERGY YIELD ASSESSMENT – TREE UNCERTAINTY CAN BE MINIMISED BY MEASURING AT HUB HEIGHT AND AT MULTIPLE LOCATIONS 16

17 ENERGY YIELD ASSESSMENT – FLOW MODEL UNCERTAINTY 17

18 18 Flow models are normally used to calculate the flow around the site. These models are initiated from one or more fixed mast locations. The errors in the model increase with terrain complexity and distance from the mast location. ENERGY YIELD ASSESSMENT – FLOW MODEL UNCERTAINTY These errors are difficult to quantify but are in the range 2% to 5%. Single Mast

19 TYPICAL OFFSHORE UK WIND FARM LAYOUT 19 Multiple Masts Single Mast 1 mast ~15% 2 mast ~10% 3 mast ~7%

20 THE IMPACT OF UNCERTAINTY UPON DEBT (Change to 10 year P90) MW Offshore wind farm (40% capacity factor) ScenarioP90 +2%P90 +1%BaseP90 -1%P90 -2%P90 -5% P50 Energy Yield (MWh/year)1,752,000 P90 Energy Yield (MWh/year)1,576,8001,559,2801,541,7601,524,2401,506,7201,454,160 Debt (£k)1,342,5391,324,7561,309,8011,286,4691,274,3541,222,300 Total Equity Requirement (£k)429,781446,299460,190481,862493,116541,467 Debt Proportion (%)75.80%74.80%74.00%72.80%72.10%69.30% Change in Debt (£k)32,73814, ,332-35,447-87, MW Onshore wind farm (35% capacity factor) ScenarioP90 +2%P90 +1%BaseP90 -1%P90 -2%P90 -5% P50 Energy Yield (MWh/year)77,400 P90 Energy Yield (MWh/year)69,44868,67467,90067,12666,35264,030 Debt (£k)38,93638,37037,80437,24036,64634,915 Total Equity Requirement (£k)17,81818,34418,86919,39419,94521,553 Debt Proportion (%)68.60%67.70%66.70%65.80%64.80%61.80% Change in Debt (£k)1, ,158-2,889

21 THE IMPACT OF UNCERTAINTY UPON DEBT (Change to 1 year P99) MW Onshore wind farm (35% capacity factor) ScenarioP99 +2%P99 +1%BaseP99 -1%P99 -2%P99 -5% P50 Energy Yield (MWh/year)77,400 P99 Energy Yield (MWh/year)56,31255,32654,29753,23052,13048,670 Debt35,37334,48733,54532,60431,60828,574 Total Equity Requirement21,12821,95022,82623,70024,62627,443 Debt Proportion (%)62.60%61.10%59.50%57.90%56.20%51.00% Change in Debt (£k)1, ,937-4,971

22 IMPACT OF CLIMATE CHANGE 22

23 WAS THE WINTER OF 2009/2010 NORMAL? 23 SKIING IN HERTFORDSHIRE STORMY WEATHER IN CORNWALL ABNORMAL? NORMAL?

24 UKCP09 SCENARIOS – PREDICTED CHANGES IN MEAN WIND SPEED 24 Predicted changes in surface wind speed (%), for minus Derived from 11 ensemble members of the HadCM3 global climate model (PPE_RCM). Medium emissions scenario. Brown et. al Climate Projections

25 UKCP09 SCENARIOS – PREDICTED CHANGES IN MEAN WIND SPEED 25 Percentage changes in surface wind speed for winter months for relative to for 3 climate models. Brown et. al. 2009

26 CHARACTERISTICS OF THE NORTH ATLANTIC OSCILLATION INDEX (NAO) 26 High NAO Low NAO Increased/stronger westerlies. Warmer temperatures and increased precipitation. Reduced/weaker westerlies. Colder temperatures and reduced precipitation. Measure of the pressure difference between the permanent low-pressure system over Iceland and the permanent high-pressure system over the Azores (the Azores high) 26

27 27 Prolonged +ve phase CHARACTERISTICS OF THE NORTH ATLANTIC OSCILLATION (NAO) 27

28 28 PREDICT PRODUCTION Use geostrophic wind data to enable a long term record from 1961 to be generated

29 29 NORMALISED SEASONAL PRODUCTION TREND

30 MONTHLY PRODUCTION AND NAO INDEX December 2009 – February

31 NORTH ATLANTIC OSCILLATION – FUTURE CHANGE Gillett et. al Most climate models simulate an increasing trend, with pressure decreases over the far North Atlantic and pressure increases in middle latitudes. Details vary considerably from model-to-model, and the simulated trends are smaller than observed Gillett et. al

32 NORTH ATLANTIC OSCILLATION – FUTURE CHANGE The IPCC 4th assessment report states: Sea level pressure is projected to increase over the subtropics and mid- latitudes, and decrease over high latitudes (order several millibars by the end of the 21st century) associated with a poleward expansion and weakening of the Hadley Circulation and a poleward shift of the storm tracks of several degrees latitude with a consequent increase in cyclonic circulation patterns over the high-latitude arctic and antarctic regions. Thus, there is a projected positive trend of the Northern Annular Mode (NAM) and the closely related North Atlantic Oscillation (NAO) as well as the Southern Annular Mode (SAM). There is considerable spread among the models for the NAO, but the magnitude of the increase for the SAM is generally more consistent across models. 32

33 NORTH ATLANTIC OSCILLATION – FUTURE CHANGE Goodkin et. al From using coral (Bermuda) as a proxy for sea surface temperature it has been possible to construct a record of the NAO from 1781 to 1999 and this has led to: Prolonged period of positive phase in 1990s led to the suggestion that anthropogenic warming was affecting the NAO. Insufficient evidence to support this conclusion. Coral marine records shows that multidecadal frequencies are correlated to shifts in hemispheric mean temperatures. Climate change seems to be acting to increase NAO variability suggesting that periods of prolonged intervals of extreme positive and negative NAO Index will probably increase. 33

34 SUMMARY OF PRESENTATION 34 1.Poor choice of anemometry can lead to large uncertainties. 2.Measure at hub height. 3.Install more than one mast for large or complex sites. 4.North Atlantic Oscillation has a major impact upon production in the UK 5.Impact of Climate Change is uncertain. 6.Invest in projects which have been engineered and developed to a high standard.

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