MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATES

MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATESDR MIKE ANDERSONGROUP TECHNICAL DIRECTOR

THURSDAY 15TH JULY 2010

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OUTLINE OF PRESENTATION

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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

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KEY MILESTONES

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QUANTIFYING UNCERTAINTY

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PRODUCTION ESTIMATES - METRICS

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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 P50 - 50% 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

PRODUCTION ESTIMATES – EXAMPLE

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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) = 76.38 GWhr/year (100%)

P90 (10 year) = 68.43 GWhr/year (89.6%)

P99 (1 year) = 52.42 GWhr/year (68.6%)

Clearly uncertainty is having a large impact upon the P90 and P99 AEP estimate.

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TYPICAL ONSHORE UK WIND FARM LAYOUT

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ASSESSMENT OF ENERGY YIELD – MAJOR COMPONENTS

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Net Yield

Long Term Wind Resource

Wind Flow Model

Turbine Model

Wakes Loss

Electrical

Loss Adjustment Factors

Reference

Net

85%

ASSESSMENT OF ENERGY YIELD – UNCERTAINTY ELEMENTS

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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

WIND SPEED MEASUREMENT – CHOICE OF INSTRUMENTATION

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Good Practise(+/-3.0% in energy

Poor Practise

(+/-7.0% in energy)

WIND SPEED MEASUREMENT – EFFECT OF HEIGHT

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Uncertainty

Increase in uncertainty for every

10m difference in height is 1%

in wind speed and ~2% in

energy

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)

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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

LONG TERM WIND SPEED ESTIMATE

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LONG TERM WIND SPEED ESTIMATE - UNCERTAINTY

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Good Poor

Instrumentation 1.5% 3.5%

Data collected 3 years 6 months

Reference period 10 years 3 years

Extrapolation to hub height

0% 3%

Error in Long Term Estimate

3.3% 6.9%Error in Energy Production 5.7% 11.9%

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ENERGY YIELD ASSESSMENT – TREE UNCERTAINTY

CAN BE MINIMISED BY

MEASURING AT HUB HEIGHT

AND AT MULTIPLE

LOCATIONS

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ENERGY YIELD ASSESSMENT – FLOW MODEL UNCERTAINTY

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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

TYPICAL OFFSHORE UK WIND FARM LAYOUT

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Multiple Masts

Single Mast

1 mast~15%

2 mast~10%

3 mast~7%

THE IMPACT OF UNCERTAINTY UPON DEBT (Change to 10 year P90)

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500 MW Offshore wind farm (40% capacity factor)

Scenario P90 +2% P90 +1% Base P90 -1% P90 -2% P90 -5%

P50 Energy Yield (MWh/year) 1,752,000 1,752,000 1,752,000 1,752,000 1,752,000 1,752,000

P90 Energy Yield (MWh/year) 1,576,800 1,559,280 1,541,760 1,524,240 1,506,720 1,454,160

Debt (£k) 1,342,539 1,324,756 1,309,801 1,286,469 1,274,354 1,222,300

Total Equity Requirement (£k) 429,781 446,299 460,190 481,862 493,116 541,467

Debt Proportion (%) 75.80% 74.80% 74.00% 72.80% 72.10% 69.30%

Change in Debt (£k) 32,738 14,955 0 -23,332 -35,447 -87,501

25 MW Onshore wind farm (35% capacity factor)


P50 Energy Yield (MWh/year) 77,400 77,400 77,400 77,400 77,400 77,400


Debt (£k) 38,936 38,370 37,804 37,240 36,646 34,915

Total Equity Requirement (£k) 17,818 18,344 18,869 19,394 19,945 21,553

Debt Proportion (%) 68.60% 67.70% 66.70% 65.80% 64.80% 61.80%

Change in Debt (£k) 1,132 566 0 -564 -1,158 -2,889

THE IMPACT OF UNCERTAINTY UPON DEBT (Change to 1 year P99)

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25 MW Onshore wind farm (35% capacity factor)




Debt 35,373 34,487 33,545 32,604 31,608 28,574

Total Equity Requirement 21,128 21,950 22,826 23,700 24,626 27,443

Debt Proportion (%) 62.60% 61.10% 59.50% 57.90% 56.20% 51.00%

Change in Debt (£k) 1,828 942 0 -941 -1,937 -4,971

IMPACT OF CLIMATE CHANGE

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WAS THE WINTER OF 2009/2010 NORMAL?

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SKIING IN HERTFORDSHIRE

STORMY WEATHER IN CORNWALL

ABNORMAL? NORMAL?

UKCP09 SCENARIOS – PREDICTED CHANGES IN MEAN WIND SPEED

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Predicted changes in surface wind speed (%), for 2070-2099 minus 1962-1990.

Derived from 11 ensemble members of the HadCM3 global climate model (PPE_RCM).

Medium emissions scenario.

Brown et. al. 2009

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Climate Projections

UKCP09 SCENARIOS – PREDICTED CHANGES IN MEAN WIND SPEED

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Percentage changes in surface wind speed for winter months for 2070-99 relative to 1961-90 for 3 climate models.

Brown et. al. 2009

CHARACTERISTICS OF THE NORTH ATLANTIC OSCILLATION INDEX (NAO)

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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)

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Prolonged +ve phase

CHARACTERISTICS OF THE NORTH ATLANTIC OSCILLATION (NAO)

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PREDICT PRODUCTION

Use geostrophic wind data to enable a long

term record from 1961 to be generated

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NORMALISED SEASONAL PRODUCTION TREND

MONTHLY PRODUCTION AND NAO INDEX

December 2009 – February 2010

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NORTH ATLANTIC OSCILLATION – FUTURE CHANGE

Gillett et. al. 2003

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. 200331


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


Goodkin et. al. 2008

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 1990’s 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.

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SUMMARY OF PRESENTATION

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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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MINIMISING UNCERTAINTY IN PRODUCTION ESTIMATES