1. Using Economic Costs to Design Time-of-Use Prices: A Case Study American Public Power Association Business & Financial Conference Hyatt Regency Savannah, Georgia September 13-16, 2009 Presentation by: John M. Kelly Director of Economics and Research American Public Power Association Washington, D.C.

2. 1 Largely a “How to” Presentation … Rather than “Why?” But …

3. 2 Why Economic Costs? Should Rate Patterns be Based on: Tradition … inertia … and happenstance; OR A careful weighing of the relevant factors [costs] with a view of guiding consumers to make efficient use of the facilities that are available? — William Vickrey, 1955

4. 3 Why Economic Costs? 1.We are bombarded with information; 2.We have to sort the trivial from the important/relevant; 3.If we do not, we are lost in terminal overload; 4.The criteria for sorting must involve context and theory-- the larger perspective. [For example, “What does the term cost mean, what is relevant and what is not?”]. Stephen Jay Gould

5. 4 Why Economic Costs? Generally, Pragmatically … When prices reflect costs, good economic things happen when they don’t, bad things happen Intuitively what competitive business do They Work

6. 5 Why Economic Costs? Relevancy – Focuses on relevant cost of business decisions (how “bottom line” affected) Efficiency Efficient use of current facilities (lowers average Cost/price of Electricity Service Efficient consumption (residential & business) Sustainability -- Reflects prices in competitive market

7. 6 Why Economic Costs? Equity – Eliminates cross-class subsidies Transparency (v. traditional cost of service)

8. 7 Efficiency and the Structure of Electricity Prices R ate S tructure C apacity U tilization? Flat Rate ? ? ? ? ? ? ? Time-Varying Rate ? ? ? ? ?

9. 8 Efficiency and the Structure of Electricity Prices R ate S tructure C apacity U tilization ? Flat Rate 45% --55% Time-Varying Rate 65% -- 75%

10. 9 APPA DEED Project Sponsored by APPA Demonstration of Energy-Efficient Developments Practical and Useful TOU Pricing Methodology Grand Haven (MI) Board of Light & Power 13,000 customers $26 million revenues

11. 10 Task Essentially is to … 1. Forecast structure of wholesale power prices based on past prices and utility budget information … 2. Adjusting for market power and price anomalies so to reflect economic costs 3. Determine seasons and daily time periods

12. 11 APPA DEED Project (continued) Commons costs of power supply (generation plants/wholesale purchases) not allocated Wholesale Prices used to design structure of retail prices Quantitative methods used to determine seasons and time periods (versus visual inspection of graphs)

13. 12 Steps for Developing TOU Prices Based on Economic Costs (Short-Run Marginal Costs) Collect and Review 4-5 years of data on: -- Hourly Wholesale Market Prices -- Utility Sales and Revenues Determine Seasons Determine Daily Cost/Price Periods for Each Season Calculate Average Period Costs Estimate Revenue Impacts Adjust TOU Price Structure to Recover All Power Supply Costs

14. 13 Basic Data Four-Five Years of Data Wholesale Prices – MPPA (project) node prices in MISO wholesale market; Utility Sales and Revenue Budgets

15. 14 Organization & Preliminary Analysis of Data 1. Review 4-5 years of monthly hourly data, first graphically to identify and appropriately adjust for excessive prices and anomalies. 2. Compute average hourly prices for week day and weekend (plus holiday) hours. 3. Adjust increased hourly price by appropriate amounts based on future year budget estimates.

16. 15 Mean Hourly Costs (May- Sept)

17. 16 Precision/Accuracy Objection: Forecasts are not precise But … relative to what? Essentially flat prices (or some related standard); or Time-varying costs of electricity power supply?

18. 17 Costs: Traditional, Economic, Competitive, and Wholesale

19. 18 Precision/Accuracy “One puts more food on the table by shooting at flying ducks than at floating decoys.” William Vickrey

20. 19 Methods for Identifying Seasons and Daily Time Periods 1. Visual Inspection of Graphs 2. Visual Inspection of Graphs - aided by “data mining,” ANOVA, standard deviations, pivot tables, etc. 3. Economic Estimates of Welfare Losses

21. 20 Methods for Identifying Seasons and Daily Time Periods 4. *Cluster Analysis 5. *Regression Trees

22. 21 Mean Hourly Costs (May- Sept)

23. 22 Determining Seasons (Step 1): Correlation Matrix MayJuneJulyAugustSeptember May1 June0.95691 July0.93610.9961 August0.94540.9880.9891 September0.92180.920.9010.90531

24. 23 Determining Seasons (2): Sums of Absolute Differences of Hourly Costs (mill/kWh) Year 2007 May – June = 297 June – July = 267 Average of 2005 and 2006 July Costs May – June = 297 June – July = 159

25. 24 Mean Hourly Costs (January-May and September-December)

26. 25 Determining Hourly Cost Periods(Step 1): Cluster Analysis Dendrogram (Summer Weekdays)

27. 26 Determining Hourly Cost Periods (Step 2): Duda & Hart Stopping Rule ClustersJe(2)/Je(1)Pseudo T-squared 10.211382.12 20.292726.58 30.261825.38 40.45154.86 50.30311.5 60.177213.93 70.2396.37 80.32336.28 90.238315.98 100.09129.97 110.25262.96 120. 130. 140. 150.12297.13

28. 27 Determining Hourly Cost Periods (Step 3) Reviewing Resulting Hourly Clusters/Deciding on Number of Periods 1 st Cluster: 1 – 7 2 nd Cluster: 8 – 9 and 23 – 24 3 rd Cluster: 13 – 18 4 th Cluster: 10 – 12 and 19 – 22

29. 28 Hou rMonTueWedThu FriFriHourMonTueWedThuFri 1 1 2 2 3 3 4 27 4 5 5 6 6 7 7 8 46 8 9 9 10 62 11 73 11 12 13 14 15 97 15 97 16 17 18 19 20 71 20 21 61 22 23 41 23 24

30. 29 Revenue by Season and Time Period -- Hourly (LMP) Cost Season WinterMiddleSummerTotal Weekday1595,050447,902480,5941,523,546 21,699,1764,615,539959,7377,274,452 3409,970402,6961,828,1042,640,770 41,331,961.1,108,0972,440,058 Weekend1208,561326,723209,184744,468 2522,091702,699387,9311,612,721 3213,315.271,325484,640 Total4,980,1246,495,5605,244,79216,720,655

31. 30 Revenue by Season and Time Period (Mean Cost) Season WinterMiddleSummerTotal Weekday1546,847485,891493,8961,526,634 21,715,4924,319,627979,2307,014,349 3410,311426,1181,828,6642,665,092 41,411,395.1,149,3522,560,747 Weekend1198,711347,215173,016718,942 2507,990774,611352,5741,635,175 3224,944.251,704476,648 Total5,015,6896,353,4625,228,43616,597,587

32. 31 Revenue Adjusted for Price Elasticity of Demand SummerMiddleWinterTotal Weekday 1530,340507,701559,5141,597,555 2962,6224,836,8601,695,0017,494,483 31,826,963426,415412,7762,666,154 41,097,885.1,381,2782,479,163 Weekend 1178,786361,239199,170739,195 2354,700789,834520,2401,664,774 3245,793.220,664466,457 TOTAL5,197,0896,922,0504,988,64617,107,785

33. 32 Recovery of Power Supply Costs Determine Non-Power Supply Costs for Utility Determine Non-Power Supply Costs by Customer Class No Readily Accessible Estimates for GHBLP Estimated 70-75 percent 10-15 Percent Increase above wholesale-cost based price structure (Lincoln Electric System estimates about 10 percent) Mark up Price Structure Uniformly Mark up Price Structure Selectively

34. 33 DEED Methodology versus Traditional (FAC) Ratemaking 1. Standard/Core Methodology for Determining Costs and Prices (not secondary or option) 2. Power Supply Costs (generation and purchased power) not allocated 3. Rate Structure Based on Wholesale Power Market Prices

35. 34 DEED Methodology versus Typical TOU Pricing SRMC instead of LMRC More Pricing Periods Wider Price Range (Highs and Lows) More Reliance on Quantitative Methods for Determining Pricing Periods

36. 35 DEED Methodology versus Real-Time Pricing Real-Time Pricing -- Ideal or Goal but: Complexity Cost Customer: Understanding and Convenience

37. 36 Again … Accuracy/Precision versus Relevance “One puts more food on the table by shooting at flying ducks than at floating decoys.” William Vickrey