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AEIC Load Research Workshop Why Normal Matters

AEIC Load Research Workshop Why Normal Matters. By Tim Hennessy RLW Analytics, Inc. April 12, 2005. Normal Matters. Normalizing energy and demand is a routine matter. It allows plans to be developed under the “most likely scenario”

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AEIC Load Research Workshop Why Normal Matters

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  1. AEIC Load Research WorkshopWhy Normal Matters By Tim Hennessy RLW Analytics, Inc. April 12, 2005

  2. Normal Matters • Normalizing energy and demand is a routine matter. • It allows plans to be developed under the “most likely scenario” • It allows the comparison of loads without on a consistent basis • The choice of what is “normal” has an economic value.

  3. Normal’s economic impact • Simplistic Rate Making: • Forecast models are developed using history. • Normal weather is applied to the models to determine sales. • Costs are divided by the sales to get prices per unit. • Three possible points of contention: • Models, • Normal Weather • Costs. Were going to focus on the weather

  4. An example • A Utility develops a forecast, using degree days as a independent variable. • The utility chooses a 10 year basis for the Normal degree days. • The PUC says a 20 year basis is more appropriate • The utility determines that the different basis results in over $1M difference in revenue.

  5. The issue: Which basis is better? • Since 1972 there has been a general and consistent decline in DD. • Over the span, 59% of the periods the normal DD based on the previous 10 years are less that the normal DD based on the previous 20 years. • Since 1990, the 10 year normal DD is less than the 20 year normal DD in 87% of the periods.

  6. Approaches to determine the more accurate degree day basis • Analytical Approach: • Forecasting approach based on historical pattern. • Market based approach • What does the market place expect?

  7. Analytical approach • Compares how well a chosen normal DD basis predicts a future period. • The forecast horizon was defined as three years. • The three year average annual DD over starting in month m was compared to the normal DD use a 10 and 20 year basis, ending in month m-1.

  8. Comparison of Normal Degree Days Vs Forecast Period degree Days

  9. Comparison of Error between NDD and Forecast Period DDUsing a simple comparison Since 1920 Since 1990

  10. Conclusion • In comparing normal DD based on 10 years to those based on 20 years, the 10 year basis has the minimum amount of error. • However, the relationship between historical normal DD and forecast period DD can be used to determine a more accurate DD basis.

  11. Simple Forecast • The relationship can be quantified by using autoregression techniques that would use normal DD to predict the actual DD during a forecast period.

  12. Regression Results 10 Year 20 Year

  13. Comparison of Error Predicted Forecast Period DD and Forecast Period DD • The variability, the ranges, and the extremes are virtually identical • The variability of the prediction was substantially reduced by using the model.

  14. Comparison to Chicago Mercantile Exchange • The model generated forecasted DD based on 10 years is within 0.5% of the CME futures. • The modeled generated DD based on 20 years is with 1% of the CME futures. it does not materially matter if a 20 year or a 10 year basis is used if these bases are adjusted for historical patterns.

  15. Conclusions • DD have generally declined since the early 1970s. • The use normal DD to estimate average temperatures during a forecast period will, on average, overestimate the DD observed during that period. • If normal DD are to be used as an expected value during a forecast period, normal DD based a 10 year average will have an average smaller error, or less bias, than normal DD based a 20 year average. • If expected forecast period DD are estimated using autoregression techniques, the difference in estimated average annual forecast period degree days using normal DD based 10 year or 20 year averages are minimal.

  16. Market Value Of Degree Days • Weather Derivatives-A type of insurance • Most are private contracts, but are traded on the publicly Chicago Mercantile Exchange • Can use to determine the put a value on degree day basis.

  17. Example • If the PUC is correct, the Company would meet its revenue requirements when the NDD were 7,060. • However, the Company believes that NDD would only be 6,960. • If the Company’s NDD assumption is correct, it will lose $1M, or $10K/DD. • What value does the market put on DD between 6,960 and 7,060?

  18. Example • We can design a derivate that would pay $10K/DD, with a limit of $1M, for each DD over 6,960. • If the market values this derivative at $1M, that means it would expect that the NDD to be 7,060.

  19. Comparison Market values DD at $6.3K/DD

  20. Comparison of last 10 years Using each

  21. Conclusions • In our example, 6,960 may be a little to low, and 7,060 is too high. • If 7,060 is used, RR should be increased to make up for the projected shortfall. Alternatively, if 6990 is used, RR could be lowered a little. • The choice of Normal can have significant economic impacts. • The market can be used to put a value degree days.

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