ARKANSAS PUBLIC SERVICE COMMISSYF cc7 DOCKET NO U IN THE MATTER OF ON THE DEVELOPMENT OF COMPETITION IF ANY, ON RETAIL CUSTOMERS

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1 ARKANSAS PUBLIC SERVICE COMMISSYF cc7 L I :b; -Ir '3, :I: 36 DOCKET NO U 1.. T " ~..-.ij IN THE MATTER OF A PROGRESS REPORT TO THE GENERAL ASSEMBLY ON THE DEVELOPMENT OF COMPETITION IN ELECTRIC MARKETS AND THE IMPACT, IF ANY, ON RETAIL CUSTOMERS OF ON BEHALF OF AMERICAN ELECTRIC POWER COMPANY, INC. AND SOUTHWESTERN ELECTRIC POWER COMPANY SEPTEMBER 4,200 1 APSC DOCKET NO U 1 I

2 TESTIMONY INDEX SUBJECT PAGE I. INTRODUCTION APSC ORDER NO. 18 REQUIREMENTS FORECAST METHODOLOGY... 5 IV. CONCLUSION EXHIBITS EXHIBIT TEH-1 EXHIBIT TEH -2 EXHIBIT TEH -3 EXHIBIT TEH -4 EXHIBIT TEH -5 EXHIBIT TEH -6 Consumer Price Index Forecast Mwh Sales Forecast Peak Load Forecast Short-Tern Forecasting Model Long-Term Forecasting Model Forecast Interpolation Process APSC DOCKET NO U 2

3 1 2 Q. 3 A. 4 I. INTRODUCTION PLEASE STATE YOUR NAME, POSITION, AND BUSINESS ADDRESS. My name is Tom E. Hough and I am employed by American Electric Power Service Corporation, a subsidiary of American Electric Power Company, Inc. (AEP), a global 5 energy company based in Columbus, Ohio. AEP is the parent company of Q. 10 A Q. 22 Southwestern Electric Power Company (SWEPCO). My position at AEP is Senior Forecast Consultant in the Economic Forecasting Department. My business address is 2 West Second Street, Tulsa, Oklahoma PLEASE DESCRIBE YOUR QUALIFICATIONS. I received a Bachelor of Business Administration degree in Finance from New Mexico State University, Las Cruces, New Mexico, in I received a Master of Science degree in Agricultural Economics from the same institution in In 1988 I was hired by Public Service Company of Oklahoma (PSO), a subsidiary of Central and South West Corporation (CSW) as an economist in the Corporate Planning Department. In 1994 I became the Forecast Consultant at West Texas Utilities Company (WTU), another subsidiary of CSW. In 1996 I was named to my current position, and retained that position and title with the closing of the AEP/CSW merger in June I am responsible for developing and communicating forecast issues for all AEP operating companies, including economic forecasts, sales forecasts, and demand forecasts. HAVE YOU PREVIOUSLY FILED TESTIMONY BEFORE REGULATORY COMMISSIONS? APSC DOCKET NO U 3

4 1 A Q. 5 A. 6 7 Yes. I have filed testimony before the Public Utility Comniission of Texas on behalf of WTU in Docket No and Docket No , and on behalf of Central Power and Light Company (CPL) in Docket No WHAT IS THE PURPOSE OF YOUR TESTIMONY? The purpose of my testimony is to present: 1) the general inflation rate used for the analysis as stated in Section I.A(l) of APSC Order No. 18, Docket No. 190-U (Order No. 18); ) 3) the mwh sales and load growth forecasts for AEP s subsidiary Southwestern Electric Power Company (SWEPCO or Company) as stated in Sections I.B(4) and I.C(4) of Order No. 18 and the forecast methodology used to develop the sales and load growth forecasts; the DSM programs affecting projected peak loads as stated in Section III.D(S) of Order No APSC ORDER NO. 18 REQUIREMENTS 16 Q A WHAT DOES APSC ORDER NO. 18 REQUIRE REGARDING AN INFLATION RATE? Order No. 18 requires that each party specify the general inflation rate assumed in its analysis. AEP uses the Consumer Price Index for Urban consumers (CPI), a broadbased measure of inflation. The CPI forecast AEP uses is provided by Economy.com, Incorporated, and is shown in EXHIBIT TEH-1. APSC DOCKET NO U 4

5 WHAT DOES APSC ORDER NO. 18 REQUIRE REGARDING MWH SALES 2 3 A Q- 8 9 A Q A. GROWTH? Order No. 18 requires that MWh sales growth be included in the rate projections under continued regulation and under retail open access. EXHIBIT TEH-2 shows AEP s Mwh sales forecast for S WEPCO s Arkansas jurisdiction. The methodology used to develop the MWh forecast is discussed below. WHAT DOES APSC ORDER NO. 18 REQUIRE REGARDING EXPECTED LOAD GROWTH? Order No. 18 requires that expected load growth be reasonably reflected in projecting the market-clearing prices that retail customers must pay. EXHIBIT TEH-3 shows AEP s peak load forecast for SWEPCO s Arkansas jurisdiction. The methodology used to develop the peak load forecast is discussed below. WHAT DOES APSC ORDER NO. 18 REQUIRE REGARDING DSM PROGRAM IDENTIFICATION? Order No. 18 requires that SWEPCO provide its current DSM programs and their 16 projected MW impacts for years 2002 through SWEPCO has no DSM 17 programs currently in place or projected in its Arkansas jurisdiction Q FORECAST METHODOLOGY HOW DID AEP PREPARE THE KWH ENERGY AND KW DEMAND FORECASTS THAT WERE USED IN THIS CASE? APSC DOCKET NO U 5

6 A. Two distinct methods were used for forecasting kwh. First, regression models with time series error terms were used to forecast kwh sales up to 18 months ahead (short-term). These models use the most recent customer count, kwh sales data, weather data (in the form of degree days), and indicator (dummy) variables where 5 needed. Models are estimated and evaluated in an iterative process. EXHIBIT TEH-4 shows the general form of the equations used in the short-term forecasting process. The long-term process starts with an economic forecast provided by Economy.com for the United States as a whole, each state, and regions within each state. These forecasts include forecasts of employment, population, and income. The long-term kwh forecast uses econometric models incorporating the economic forecast to produce a forecast of annual kwh sales. Inputs such as regional and national economic and demographic conditions, energy prices, weather data, customer-specific information and informed judgment are all utilized in producing the forecasts. EXHIBIT TEH-5 shows the general form of the equations used in the long-term forecasting process. The results of the kwh sales models. in turn, are inputs to the load (or kw) model. This filing uses projections based on both the short-term and long-term processes. Q. WHY DOES AEP USE DIFFERENT PROCESSES FOR SHORT-TERM AND LONG-TERM KWH FORECASTING? A. AEP uses processes that take advantage of the relative strengths of each method. The regression models with time series error terms use the latest available sales and APSC DOCKET NO U 6 TOM E. I-IOUGH

7 Q A weather information to represent the variation in kwh sales on a monthly basis for short-term applications like capital budgeting and resource allocation. While these models produce extremely accurate forecasts in the short run, without logical ties to economic factors, they are less capable of capturing the structural trends in electricity consumption that are important for longer term planning. The long-term process, with its explicit ties to economic and demographic factors, is more appropriate for longer term decisions such as capacity planning and distribution planning issues. HOW WERE THE SHORT-TERM AND LONG-TERM RESULTS USED TO PRODUCE THE KWH FORECAST PRESENTED IN THIS CASE? Forecast values for the year 2001 are taken from the short-term process. Forecast values for years after 2001 are the values obtained by interpolating between the 2001 short-term values and the long-term values. The interpolation process weights the short-term results more heavily early in the interpolation period ( ), and weights the long-term results more heavily later in the interpolation period, taking 15 advantage of the relative strengths of each process described above. This Q A. 22 interpolation ensures that any discontinuity between the short-term and long-term results is minimized. EXHIBIT TEH-6 is a detailed example of the interpolation process for the residential class. HOW WERE CLASS KWH ENERGY SALES FORECASTS TRANSLATED INTO A PEAK LOAD FORECAST? To forecast peak kw, AEP used the same algorithms as those in the Hourly Electric Load Model (HELM), originally developed by the Electric Power Research Institute. APSC DOCKET NO U 7

8 AEP used the methodology from HELM to forecast hourly load. Additional inputs include weather data, load shapes, transmission and distribution losses, and calendar information. The output from the model includes hourly loads for each operating company for the entire forecast period. AEP used a model that calculates the hourly distribution of loads based on kwh sales forecasts, load shapes, and weather response functions (WRF) for specific end-use categories. The calculated hourly loads for all end-uses are added together to form total hourly load. Specifically, the model calculates an hourly load shape for each end-use. There are two distinct methods used to calculate hourly loads, depending on whether the end-use is weather-sensitive or not'. For end-uses that are not weather-sensitive, the annual kwh energy sales forecast is first allocated across the twelve months of the year. The monthly kwh are then allocated to the individual days of each month. Daily kwh are allocated based on specific day types, such as weekday, weekend, or holiday. The daily kwh are then allocated to the 24 hours of the day, based on season and day type load shapes. For weather-sensitive end-uses, the model calculates daily kwh based on a WRF, defined for all combinations of specified seasons, day types, and daily weather variables. The weather variable used by the model is average daily temperatures. ' Weather-sensitive end uses are air conditioning and heating. Non weather-sensitive end uses include lighting, refrigeration, cooking, etc. APSC DOCKET NO U 8

9 The average daily temperature is determined by averaging the daily high and daily low temperatures. The forecast of daily normal average temperatures was developed by selecting twelve representative historical months and combining them to form a year. Different WRFs are defined according to the average temperature values recorded on any given day. WRFs are then applied to weather parameters to yield daily kwh for all customers. Daily kwh are then compared against total annual kwh to determine the distribution of kwh over the calendar year, resulting in daily energy percentages. These daily percentages are then applied to the annual kwh forecast to determine the daily distribution of forecast kwh. The final step is to allocate the daily kwh to hours based on season and day 12 type specific load shapes. The calculated hourly megawatt (MW) loads for all 13 specified end-uses are then added together to form the total hourly MW load profile 14 for the Projected Period. Planned demand-side management impacts (modeled Q. 18 A independently), an hourly MW load profile for AEP facilities, and system loss factors are then added to determine total MW load. WHAT ARE THE DATA SOURCES USED IN THE FORECAST? All kwh sales and peak load data are taken from Company billing and operational records. The weather data is provided by the National Oceanic and Atmospheric Administration, from weather stations in SWEPCO s service territory. The economic forecasts are based on data gathered by federal, state, and local authorities, as well as propriety sources of Economy.com. APSC DOCKET NO U 9

10 1 2 Q. 3 IV. CONCLUSION IS THE METHODOLOGY USED TO PRODUCE REASONABLE? THESE FORECASTS 4 A. 5 6 Q A. 10 Q. 11 A. Yes. The necessary data comes from reliable sources. The kwh and kw forecasts use techniques that are widely accepted in the electric utility industry. ARE THE INFLATION RATE ASSUMPTION, KWH SALES FORECASTS AND THE PEAK KW DEMAND FORECASTS OVER THE FORECAST PERIOD REASONABLE FOR USE IN THIS PROCEEDING? Yes. DOES THIS CONCLUDE YOUR? Yes. it does. APSC DOCKET NO U 10

11 Exhibit TEH-1 Consumer Price Index Forecast Exhibit TEH CPI 6/ CPI 7/ CPI dated was used by SWEPCO in its latest forecast CPI dated 7/2001 is the latest available forecast from Economy.com.

12 Exhibit TEH-2 MWH Sales Forecast MWh Sales by Class - SWEPCO Arkansas Residential , ,012, ,027, ,042, ,073, ,088, ,103, ,118, ,133, % 1.5% 15% 1.5% 1.4% 1.4% 1.4% 1.4% 1.4% Commercial , , ,015 1,010,432 1,035,848 1,061,265 1,086,681 1,112,098 1,137, % 2.7% 2.6% 2.6% 2.5% 2.5% 2.4% 2 3% 2.3% Industrial 1,755,052 1,803, % % 1,899, % 1,948, % 1,996, % 2,044, % 2.092; % 2,141, % 2.189, % Other Retail , % 108, % 109, % 109, % 110, % 111, % 112, % 112, % % Total Retail 3.768,057 3, % 3,947, % 4,036, % 4,126, % 4, % 4,305, % 4,395, % % % Wholesale (On-System) , , , , , , , % 2.0% 1.9% 1.9% 1.9% 2.0% 1.9% 1.8% 1.8% Total On-System 4,476,199 4,579, , ,996,076 5,100,971 5, ,309,903 5,414, % 2 3% 2 2% 2.2% 2.1% 2 1% 2.0% 2.0% 2.0% 14% 2.5% 2.5% 0.7% 2.2% 19% 2.1% Exhibit TEH-2

13 Exhibit TEH-3 Peak Load Forecast Exhibit TEH SWEPCO-AR ,009 1,027 1,050 1,070 PCt 2.2% 2.2% 1.9% 2.5% 2.0% 2.0% 1.8% 2.3% 1.9% Average Growth 2.1 %

14 EXHIBIT TEH-4 Short-Term Forecasting Model (Generalized Equations ) KWH Sales = f ( Lagged Sales, Current Weather, and Lagged Error Terms ) KWH/Customer Sales = f ( Lagged Sales, Current Weather, and Lagged Error Terms ) Customers = f ( Lagged Customers and Lagged Error Terms ) Price = f ( Lagged Price, Usage, and Lagged Error Terms ) Prepared by: Reid Newman Sponsored by: Mark Gilbert

15 EXHIBIT TEH-5 Long-Term Forecasting Model ( Generalized Equations ) Residential KWH Sales = f ( Employment, Weather, Energy Prices ) Commercial KUH Sales = f ( Employment, Weather, Energy Prices ) Industrial KLYH Sales = f ( Industrial Production, Energy Prices ) OitherRetail KWH Sales = f ( Employment ) Customers = f ( Employment ) Prepared by: Reid Newman Sponsored by: Mark Gilbert

16 ~~ ~ Exhibit TEH-6 Exhibit TEH-6 Forecast Interpolation Process The interpolation between the short-term and long-term forecast results is: 1) a simple linear connection of two points; and 2) the application of the slope of the two points to each month of the forecast period from 2002 to This procedure is performed on each revenue class. The tables below outline the interpolation process as applied to the residential sales on an annual basis. Table A contains the results of the short-term forecast procedure, Table B contains the long-term annual forecast results, and Table C contains the results of the interpolation. Table D shows the results of the linear regression results which produced the data in Table C. Table A Short-Term Monthly Sales Forecast I Residential Year Month 1 2 MWh 86, Table B Initial Long-Term Annual Sales Forecast Year Residential MWh Change ~, I I 1.99%1 Table C Interpolated Long-Term Sales Forecast (Annualized) Year I MWh!Change I f ,065, % 1,083, % ,, 1.55% 2008 I 2009) I 1.53% ,133,079 I 1.45% ,149,048 I 1.41% U ,103, % 1,118, % - 1,133, % 1,149, % Table D: Regression Results Model: MODEL1 Dependent Variable: mwh Analysis of Variance Sum of Mean Source DF Squares Square FValue Pr> F Model Error 0 0 Corrected Total ;!901 Root MSE. R-Square Dependent Mean Adj R-Sq. Coeff Var Parameter Estimates Parameter Siandard Variable DF Estimate Error t Value Pr =. Jtl Intercept vear

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