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When Normal Weather Is Not Normal

When Normal Weather Is Not Normal. AEIC Load Research Workshop April 2006. Southern Company. One of the largest producers of electricity in the United States Service area 120,000 square miles in 4 states Nearly 4.1 million customers Population of 12 million

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When Normal Weather Is Not Normal

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  1. When Normal Weather Is Not Normal AEIC Load Research Workshop April 2006

  2. Southern Company • One of the largest producers of electricity in the United States • Service area • 120,000 square miles in 4 states • Nearly 4.1 million customers • Population of 12 million • More than 27,000 miles of transmission lines • 79 generating stations • Nearly 39,000 megawatts of generating capacity • Sources of generation: 69% coal; 16% nuclear; 3% hydro; and 12% oil and gas.

  3. Southern Company Subsidiaries • Alabama Power • Georgia Power • Gulf Power • Mississippi Power • Savannah Electric • Southern Nuclear • Southern Power • Southern Company Services • Southern LINK

  4. Southern Company Service Area Georgia Power Georgia Power Alabama Power Alabama Power Savannah Electric Gulf Power Gulf Power Mississippi Power

  5. Applications for Normal Weather • Energy & Demand Forecasts. • Energy & Demand Growth Rates. • Flat Rate Pricing. • Demand Side Management Program Evaluation/Assessment.

  6. Defining Normal Weather • NOAA 30-year Average • Self-Defined Historical Period Average • NOAA Typical Meteorological Year (TMY) • Rank & Average Method Pros & Cons: • Averaging methods good for energy forecasting, but lack temperature extremes necessary for demand forecasting. • TMY provides temperature variations day to day, but still may not contain the extremes.

  7. Rank and Average Method

  8. Analysis of Ranking Methods • Choices of Ranking Methods • Monthly • Seasonally • Annually • Based on the method selected, you can effect which (when) the peak will occur.

  9. Comparison of Ranking Methods

  10. Selecting a Reference Year

  11. Selecting a Reference Year • Randomly select any year • Select a year based on minimum variance

  12. Calculation of Variance Before and After the Ranking

  13. Reference Year based on Minimum Variance

  14. Selecting a Reference Year • Randomly select any year • Select a year based on minimum variance • Use the period average as the reference year

  15. Reference Year based on Period Average

  16. Reference Year based on Period Average

  17. Conclusion • Rank and average method produces a normal profile appropriate for capturing extreme weather conditions. • Seasonal or Annual ranking is preferable than monthly ranking to determine typical extreme temperatures. • Using the period average as reference year is preferable over other selection methods. • it captures the peak temperature on the same day as period average. • It reduces the volatility from day to day across the year.

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