1. 1 Knowing Your Customers Better Through Load Research Presented By: Lawrence M. Strawn Senior Retail Pricing Coordinator Orlando Utilities Commission September 21, 2004

2. 2 Agenda Background Define load research Getting started Meter sample selection Uses of data

3. 3 Orlando Utilities Commission Located in Orlando, Florida OUC provides electric, water and chilled water to Orlando, St Cloud and parts of unincorporated Orange and Osceola Counties. OUC net generating capability is 1,285 MW. The peak demand for FY2003 was 1,019 MW Winter and 969 MW Summer

4. 4 Orlando Utilities Commission The State of Florida is regulated OUC provides fully bundled service (generation, transmission and distribution) OUC electric customers: –Residential158,000 (86%) –Commercial 25,000 (14%) –Total183,000 OUC does not have a large industrial load

5. 5 Load Research The process of gathering, verifying, and aggregating interval meter data to determine the behavior and timing of consumer demand.

6. 6 Getting Started Determine your objective Metering equipment Hardware required Software required Required resources Determine meter sample selection Capital and ongoing costs

7. 7 OUC’s Load Research Objective Gain a better understanding of customer and customer class usage characteristics. Understand the costs associated with servicing varying usage characteristics. Develop rate strategies that send the appropriate rate signals to customers. Have necessary information available to develop innovative rates. Seasonal time of use rates. Curtailable rates. Photovoltaic rates.

8. 8 Sample Residential Shape

9. 9 Sample Commercial Shape

10. 10 Sample Load Shapes

11. 11 Metering Equipment Alpha solid state meter with load profile capability on circuit board. An internal modem is preferred. OUC’s meter alliance provider is Elster Electricity, LLC.

12. 12 Hardware Required Handheld Itron Laptop Computer Phone Line to Meter Server And / or

13. 13 Hardware Required

14. 14 Software Required OUC is using: –Premier Plus 4 –MV90 –Pervasive for MV90 Other products available –Stark –Datamatic Software is required to: –Download –Verify –Store –Organize –Display

15. 15 Required Resources Information Technology Billing Forecasting

16. 16 Required Resources Information Technology Billing Forecasting Meter Readers collect the interval load data using handheld Itrons. It takes approximately 3 - 5 minutes per meter to download the data

17. 17 Required Resources Information Technology Billing Forecasting Meter Operations collects the raw data, verifies it, and ensures it is stored correctly in MV90.

18. 18 Required Resources Information Technology Billing Forecasting Information Technology provides hardware and software support

19. 19 Required Resources Information Technology Billing Forecasting Commercial Account Reps use the data when working with customers to 1) ensure they are on the correct rate or 2) help them better understand their operations.

20. 20 Required Resources Information Technology Billing Forecasting Retail Pricing Coordinators use the data for the Cost of Service and Rate Design.

21. 21 Required Resources Information Technology Billing Forecasting Commercial Energy Auditors use the data to help customers understand how their usage affects their monthly bill

22. 22 Required Resources Information Technology Billing Forecasting Forecasting uses the data to prepare the sales forecast

23. 23 Meter Sample Selection

24. 24 Meter Sample Selection Review your load research objective. Assess what data you already have. Are there small customer classes or groups of meters you can gather 100% (census)? Which customer classes are too large to census and must be sampled?

25. 25 Number of Meters by Rate Class Rate Class kW SecondaryPrimary Standard Demand Time of Use Standard Demand Time of Use GSLD6,000 +1213 GSD11,000–6,000212719 GSD2 500–1,000894632 50-5004,66224870 GSND< 5015,915 Resn/a57,000 Homes75,000 Apartments

26. 26 Number of Meters by Rate Class SecondaryPrimary kW Standard Demand Time of Use Standard Demand Time of Use GSLD6,000 +1213 GSD11,000–6,000212719 GSD2 500–1,000894632 50-5004,66224870 GSND< 5015,915 Resn/a57,000 Homes75,000 Apartments CensusSample

27. 27 Determining Sample Size n = Z 2 x σ 2 E2E2 n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error

28. 28 Determining Sample Size n = 1.960 2 x σ 2 E2E2 n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error

29. 29 Population Standard Deviation Standard deviation (σ) of what? –Kilowatt hours. –Load factors. –Average market value per MWh. Market Price Vector –Florida Municipal Power Pool (FMPP) clearing house price vector Calculated the σ of the average market value per MWh for 100 GSD2 secondary meters ($3.77).

30. 30 Determining Sample Size n = 1.960 2 x 3.77 2 E2E2 n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error

31. 31 Determining Sample Size n = 1.960 2 x 3.77 2 $1.00 2 n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error

32. 32 Determining Sample Size 55 = 1.960 2 x 3.77 2 $1.00 2 By randomly sampling 55 meters within a customer class, OUC can be 95 percent certain that the sample’s market value per MWh will represent the population’s market value within plus or minus $1.00/MWh.

33. 33 Uses of Data Calculating Allocation Factors to Use in the Cost of Service and Rate Design

34. 34 Step 1: Calculate Customer Class Shapes HourMeter 1 Plus Meter 2 Plus Meter 3 Equals Class Shape 1514827 2617831 … 87599201039 87608211039 Total kWh61,320157,68078,840297,840

35. 35 Step 2: Scale to Forecast HourClass Shape Times (300,000 / 297,840) equals Class Shape (Scaled to Forecast) 12727.2 23131.2 … 87593939.3 87603939.3 Total kWh297,840300,000

36. 36 Step 3: Derive Residential/GSND Class Shape Res/GSND Line loss calculations must be included

37. 37 Step 4: Verify Shape of Res/GSND Compare average market value per MWh of calculated shape to that of sample shape. The two market values should be within $1.00 per MWh.

38. 38 Step 5: Compute Allocation Factors Monthly coincident peak (12CP) Non-coincident peak (NCP) Average and excess demands Average market value per MWh –By class –By time period within class

39. 39 Sample Load Shapes * Florida Municipal Power Pool (FMPP) Clearing House Price Vector

40. 40 Sample Load Shapes $39.85/MWh $39.00/MWh * Florida Municipal Power Pool (FMPP) Generation Clearing House Price Vector

41. 41 Time of Use Rates (Summer) * Florida Municipal Power Pool (FMPP) Clearing House Price Vector

42. 42 Time of Use Rates (Summer)

43. 43 Time of Use Rates (Summer)

44. 44 Other Uses of Load Research Data OUConsumption Online –Enables customers to understand their cost causation Totalize kW on contiguous site Look at cost for an individual customer Energy conservation programs