NEW YORK STATE ELECTRIC & GAS CORPORATION DIRECT TESTIMONY OF THE SALES AND REVENUE PANEL

Transcription

1 Case No. 0-E- NEW YORK STATE ELECTRIC & GAS CORPORATION DIRECT TESTIMONY OF THE SALES AND REVENUE PANEL September 0, 00 Patricia J. Clune Michael J. Purtell

2 0 Q. Please state the names of the members on this Sales and Revenue Panel (the "Panel"). A. Our names are Patricia J. Clune and Michael J. Purtell. Q. Mrs. Clune, please state your current position and business address. A. My title is Lead Analyst in New York State Electric & Gas Corporation's ("NYSEG's" or the "Company's") Rates and Regulatory Economics department. My business address is NYSEG, Link Drive, P.O. Box, Binghamton, New York -. Q. Please summarize your educational background. A. I received an Associates degree in Accounting from Broome Community College in. Q. Please describe your work experience. A. I joined NYSEG in April and held support positions in several departments. In April, I was promoted to Project Analyst in the Electric Rates & Tariffs Department. In that position, I provided analysis and support for the Company s revenue and forecast model, and tariff development and interpretation. In July, I was promoted to Senior Analyst. In May 00, I was promoted to Principal Analyst (Lead Analyst) in the Pricing and Analysis Section. My duties, among other things, include revenue forecast modeling, rate design, and offsystem billing protocols. Q. Have you previously testified in other proceedings before the New York State Public Service Commission ("PSC" or the "Commission")?

3 0 A. Yes. I testified in June 00, as a member of the Delivery Rate Panel, in Case 0- E-0. In May 00, I testified as a member of the rate design and tariff development panel (in support of the Joint Proposal) relative to the implementation of standby rates in Case 0-E-0. Q. Mr. Purtell, please state your current position and business address. A. My title is Lead Analyst in NYSEG's Rates and Regulatory Economics department. My business address is NYSEG, Link Drive, P.O. Box, Binghamton, New York -. Q. Please summarize your educational background. A. I received a Bachelor of Science degree in Mathematics from Franciscan University in and a Master of Science degree in Systems Science from Binghamton University's Watson School of Engineering in 00. Q. Please describe your work experience. A. From the time that I was hired by NYSEG in until 000, I held various positions with progressively increasing responsibilities in the customer service department. In 000, I was promoted to Principal Analyst in the Load Forecasting and Reporting department, through which I had responsibility for NYSEG's electric forecasts. When that department merged with NYSEG's Performance Management department in 00, I took on the additional responsibility for NYSEG's natural gas forecasts. In 00, I was promoted to Lead Analyst in the Rates and Regulatory Economics group where I assumed the additional responsibilities for RG&E's electric and gas long-term forecasts. Q. Have you previously testified in other proceedings before the Commission?

4 0 A. Yes, I testified in Cases 0-E-0 and 0-G-0. Q. What is the purpose of your testimony? A. In our testimony we address two separate topics. First, we will present NYSEG's forecast of monthly electric sales, customers, and retail access migration for the rate year [i.e., the Twelve Months Ending ( TME ) December, 00] and the corporate total forecast for the outer years, The second topic of our testimony is electric delivery revenues. Specifically, the Panel will present the electric delivery revenue forecast for the rate year under the current rates, which corresponds with the Delivery Rate Design Panel s Exhibit (DRD-), Schedule A, which presents existing revenues. Q. Is this Panel sponsoring any exhibits? A. Yes. NYSEG s electric sales customer and retail access customer forecasts are illustrated in Exhibit (SRP-). Exhibit (SRP-), contains the results of the Company's electric sales and retail access forecasts. The sales and customer schedules include both actual and weather normalized historical sales data from January 00 through June 00 and forecasted data through December 00. Exhibit (SRP-), shows the electric model specifications and the results of NYSEG's validation tests of the models used to develop the forecasts of monthly electric sales and customers. Exhibit (SRP-) presents the projected electric revenue data for the rate year at current rates. Exhibit (SRP-) shows electric customer and billed unit forecasts for the years 00 through 0.

5 0 ELECTRIC CUSTOMERS, SALES AND RETAIL ACCESS MIGRATION Forecast of Electric Customers Q. Please describe the development of the electric customer forecast for the Residential Regular without Heat customer class. A. The number of Residential Regular without Heat customers is forecasted with an econometric model that uses Economy.com's forecast of the number of households in Upstate New York as the main explanatory variable. Q. What is econometric modeling? A. Econometric modeling is the art of applying statistical techniques, such as linear regression, to estimate the relationship between certain explanatory, or independent, variables and the dependent variable, which, in this case, is monthly Residential Regular without Heat customers. An econometric model, also known as a linear regression model, is an estimate of a best-fit line between one dependent variable and one or more explanatory variables. The term "best-fit" refers to the line with the lowest sum of squared errors. Q. How did you develop the econometric models? A. We used a computer program called MetrixND to develop the econometric models. Itron, Inc., created this forecasting software specifically for utilities. More than 0 utilities and independent system operators use this software, including the New York Independent System Operator ("NYISO"). Q. What is the growth of Residential Regular without Heat customers over the forecast period?

6 0 A. The average annual growth rate for the period TME June 00 through TME December 00 is 0.% per year. Q. How did the Panel develop the electric customer forecasts for the Residential with Heat, Residential Regular Farm, and Residential Farm with Heat customer classes? A. We developed those forecasts using exponential smoothing models for each customer class. Q. How did the Panel develop the electric customer forecasts for the non-residential customer classes, which include the Commercial, Public Authority, and Industrial classes? A. We developed those forecasts using exponential smoothing models for each customer class. Q. What are the results of your electric customer forecast? A. The electric customer forecast is illustrated in Exhibit (SRP-). It covers the actual number of customers from January 00 through June 00 and the forecasted number of customers from July 00 through December 00. The forecast projects an increase in the average monthly number of electric customers of, for the rate year compared with the test year. Electric Sales Forecast Q. How did you forecast monthly electric sales? A. We used an econometric modeling methodology. Q. What types of explanatory variables did the Panel use?

7 0 A. We used five categories of explanatory variables: economic variables, price variables, weather variables, calendar binary (or dummy) variables and a demographic variable. Q. Why did you use these five categories of explanatory variables? A. We concluded that monthly electric sales are a function of these types of variables. Q. How much history was used in the database? A. At least 0 years of monthly historical data were used for each of the econometric models, with two exceptions. In particular, the Company used reliable data for the Borderline class from January, and for the Street Lighting class from January. All historical data used by the Company ran through June 00, which is the end of the test year. This amount of history allowed for the maximum number of degrees of freedom and efficient estimates of econometric model coefficients. Monthly data also were used to improve model stability and to better model seasonality. For the exponential smoothing models, which were used for Interdepartmental and Company Use electric unit forecasts, data from January 000 onward were used. Older data were no longer applicable to these classes because of the closing of numerous local satellite offices. Q. Please describe the category of economic variables. A. This category represents the fiscal health of the economy of the Upstate New York area. Among the economic variables used by NYSEG are variables for Income, Upstate New York Manufacturing and Non-Manufacturing Gross Regional Product ("GRP") and Upstate New York Manufacturing Employment.

8 0 Q. How did you obtain the economic variable data? A. Economy.com provided all the data. Q. What is Economy.com? A. Economy.com is a nationally recognized independent provider of economic, financial and industry research. It has over 00 clients in 0 countries, including governments at all levels, utilities, commercial and investment banks, insurance companies, financial services firms, manufacturers, money managers, and industrial and technology constituents. Among its vast clientele are: the State of New York and at least nine other states and commonwealths, the major New York utilities, the NYISO, ISO New England Inc. and numerous federal government bodies. Q. What is the release date of the economic forecasts provided by Economy.com? A. The data are from Economy.com s Spring 00 forecast and were released in May 00. Q. What are price variables? A. Price variables are electric prices. The electric prices used by this Panel are the actual average prices (revenues divided by sales) for each customer class during the period January through June 00 adjusted by specific price indices or deflators. In particular, electric prices are deflated by the Consumer Price Index for the residential classes, adjusted by the GDP implicit price deflator for the commercial and public authority classes, and adjusted by the Producer Price Index for the industrial class. Q. What are weather variables?

9 0 A. Weather variables measure billing month heating and cooling degree days and variations from normal billing month heating and cooling degree days. The term "normal degree days" is defined as the official 0-year normal (-000) of degree days for any given calendar day as published by the National Oceanic and Atmospheric Administration ("NOAA"). Q. How did the Panel obtain the weather variables? A. The Binghamton NY, Buffalo NY, Rochester NY, Syracuse NY, Albany NY, and Burlington VT stations of the National Weather Service provided the weather data. A composite weather variable was constructed by using a weighted sum that reflects sales in each area. Q. What are binary variables? A. Binary variables, also known as dummy variables, take a value of "" when a condition is present and assume a value of "0" when the condition is not present. For example, a variable called "January" takes a value of "" in January and "0" in any other month. Binary variables are merely shape variables and do not represent any underlying trends. Consequently, the Panel used these variables to model seasonality. Q. What demographic variable did the Panel use? A. The demographic variable was the number of Residential Regular without Heat customers per month. Q. Please identify the explanatory variables that you used from these five categories to develop the total electric sales forecast for the Residential Regular without Heat class.

10 0 A. For the Residential Regular without Heat class, we used variables representing the number of Residential Regular without Heat customers, Real Disposable Personal Income, Real Residential Price, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month. Q. Please identify the explanatory variables that you used from these five categories to develop the total electric sales forecast for the Residential with Heat class. A. For the Residential with Heat class, we used variables representing the number of Residential with Heat customers, Real Disposable Personal Income, Real Residential with Heat Price, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month. Q. Please identify the explanatory variables that you used from these five categories to develop the total electric sales forecast for the Residential Farm without Heat class. A. For the Residential Farm without Heat class, we used variables representing the number of Residential Farm without Heat customers, Real Farm Income, Real Residential Farm without Heat Price, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month. Q. Please identify the explanatory variables that you used from these five categories to develop the total electric sales forecast for the Residential Farm with Heat class.

11 0 A. For the Residential Farm with Heat class, we used variables representing the number of Residential Farm with Heat customers, Real Farm Income, Real Residential Farm with Heat Price, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month. Q. What is the total Residential class sales growth rate for the forecast period? A. The average annual growth rate for Residential Total Billed Sales for the period TME June 00 (weather normalized) through TME December 00 is.% per year. Q. What was the next step after the total Residential sales forecast was created? A. The total Residential sales were then allocated among service classifications ( SCs ),, and. The allocation among SCs is based on the last months of billing data. As discussed later in this testimony, the SC units are then used to calculate the forecasted delivery revenues. Q. What specific variables are used in your historical monthly database to develop the total electric sales forecast for the Commercial without Heat class? A. The database contained variables for Real Non-Manufacturing Gross Regional Product ( GRP ), Real Commercial without Heat Price, Total Residential Regular customers, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly dummy variables and the number of billing days in the month. Q. Why did you include a variable for Total Residential Regular customers as a driver of Commercial Sales?

12 0 A. NYSEG s service territory is primarily rural in nature. As such, most of NYSEG s Commercial customers are relatively small. The size of these customers is confirmed by the fact that more than % of NYSEG s commercial customers take secondary service (i.e. voltage requirement of to 0 volts), and more than % of those customers have demand requirements of kilowatts ( kw ) or less. The success of small, rural Commercial businesses is largely determined by the economic well-being of their clientele. Their clientele is comprised, for the most part, of NYSEG s Residential customer class. For these reasons, simply using the Upstate Non-Manufacturing GRP variable, which largely reflects the financial strength of NYSEG s large Commercial customers, without the number of Total Residential Regular customers would not accurately represent NYSEG s service territory. Instead, the combination of the Upstate Non-Manufacturing GRP and the number of Total Residential Regular customers, a demographic variable specific to NYSEG s service territory, is the better collective indicator of the economic health of the Company s Commercial customers. Q. Does the inclusion of Upstate New York Non-Manufacturing GRP and Total Residential Regular Customers variables in the same econometric model specification yield multicollinearity? A. No. If the economic/demographic variables were representing the same underlying trend, which would lead to one or both variables being statistically insignificant in the model, then multicollinearity would be present. That is not the case here. Both variables, Upstate Non-Manufacturing GRP and Total

13 0 Residential Regular customers, yield very significant t-statistics, as shown in Exhibit (SRP-), pages and. This means that each variable is representing a different underlying trend. Their t-statistics, along with the model s high R- squared statistic support NYSEG s assumption that including both variables in the model specification is appropriate. Q. What specific variables are used in your historical monthly database to develop the total electric sales forecast for the Commercial with Heat class? A. The database contained variables for Real Non-Manufacturing GRP, Real Commercial with Heat Price, Total Residential Regular customers, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly dummy variables, the number of billing days in the month, and a generic trend variable. Q. What specific variables are used in your historical monthly database to develop the total electric sales forecast for the Commercial Farm class? A. The database contained variables for Real Farm Income, Real Commercial Farm Price, Commercial Farm customers, Billing month Heating and Cooling Degree Day variations from normal weather, and the number of billing days in the month. Q. What is the total Commercial class sales growth rate for the forecast period? A. The average annual growth rate for the period TME June 00 (weather normalized sales) through TME December 00 is.% per year. Q. What did you do after determining the Commercial class sales forecast?

14 0 A. We then allocated the Commercial sales among the SCs using last months of billing data. The SCs are SC-, SC-, SC-, SC-, SC- (subclasses,, and ) and SC-. Q. What particular variables did you include in your historical monthly database to develop the total electric sales forecast for the Industrial class? A. The database consisted of variables for Upstate New York Manufacturing Employment, Upstate New York Real Manufacturing GRP, Real Industrial Price, Billing month Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month. Q. What is the Industrial class sales growth rate over the forecast period? A. The average annual growth rate for the period TME June 00 (weather normalized sales) through TME December 00 is 0.% per year. Q. How did the Panel allocate sales once you determined the Industrial class sales forecast? A. We allocated Industrial sales among the SCs using the last months of billing data. The SCs are SC-, SC-, SC-, SC-, SC- (subclasses,, and ) and SC-. Q. What specific explanatory variables did you use to develop the total electric sales forecast for the Public Authority class? A. We used the following variables: number of Residential Regular without Heat Customers, Real Public Authority Price, Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and the number of billing days in the month.

15 0 Q. What is the Public Authority class sales growth rate over the forecast period? A. The average annual growth rate for the period TME June 00 (weather normalized sales) through TME December 00 is.% per year. Q. What was the next step after determining the Public Authority class sales forecast? A. The Public Authority sales were then allocated among the SCs based on the last months of billing data. The SCs are SC-, SC-, SC-, SC-, SC-, SC- (subclasses,, and ), SC-, SC- and SC-. Q. Please describe the explanatory variables that the Panel used to develop the total electric sales forecast for the Street Lighting class. A. The Street Lighting total sales forecast is based upon an econometric model that utilizes the average monthly burning hours, outlined in the Company s tariff, as the main explanatory variable. Additionally, a generic trend variable was used in the model specification. Q. What explanatory variables did the Panel use to develop the total electric sales forecast for the Borderline class? A. We used the following variables: Billing month Heating and Cooling Degree Day variations from normal weather, Monthly binary variables and a generic trend variable. Q. Please summarize the electric sales forecast for the NYSEG service territory. A. Based on the forecasts that we have described, NYSEG expects that the overall electric sales volume will increase, on average, by 0.% annually during the period from TME June 00 (weather normalized sales) through TME December

16 0 00. The electric sales forecast is illustrated in Exhibit (SRP-). It covers actual and weather normalized sales from January 00 through June 00 and forecasted sales from July 00 through December 00. This electric sales forecast is based heavily on economic variables, and the forecast may be subject to update to reflect changes in economic conditions. Retail Access Migration Forecast Q. Please explain how the Panel developed its forecast of the number of electric customers migrating to retail access [i.e., ESCO Price Option ( EPO ) and ESCO Option with Supply Adjustment ( EOSA )] for electric service. A. This forecast predicts the number of customers in NYSEG's service territory, by customer class, obtaining electric service from an ESCO (i.e., EPO and EOSA). Understanding that NYSEG offers an open enrollment to retail access options every two years, and having experienced enrollments in January 00 and January 00, we utilized a - and -month moving average algorithm for each revenue class. This approach effectively captured the step-function increase in customer migration that occurred every two years during enrollment. Once the retail access forecast for each revenue class was complete, individual retail access rate option (i.e., EPO and EOSA) customer forecasts were computed based on the latest enrollment customer distributions. For the remaining non-retail access customers, the distribution from the test year was used to allocate between the Fixed Price Option ( FPO ) and Variable Price Option ( VPO ) rate options. Q. Do you have any comments regarding this forecast?

17 0 A. Yes. The overall uncertainty of retail access migration forecasts will inevitably be great enough that decisions in this case will need to reflect that uncertainty. The accuracy of the forecast will be heavily dependent on State policies regarding retail access, the overall status of the energy market and pricing volatility in the market. Q. How did the Panel develop the forecasted electric usage for retail access customers? A. We developed this forecast by performing an average use trend analysis for each rate option, in each revenue class. Model Validation Q. Did the Panel validate the models used to develop the forecasts of monthly electric sales and customers? A. Yes. Models constructed to "memorize" a dataset are not useful if those models are not checked to make sure they both () fit the dataset, and () generalize well. Test processes have been developed to ensure that both these objectives are satisfied. Q. What test processes are used to determine whether a model fits a dataset? A. The Panel analyzed "Goodness of Fit" tests to determine what percentage of the variation in the dependent variable can be explained by the explanatory variables that we selected. Stated in another way, these tests check a model's summary statistics that can explain how well the model fits, or explains, a dataset. Q. What is the first test statistic that you analyzed?

18 0 A. The first test statistic is called the Coefficient of Determination, better known as the R-squared. An R-squared value of means that the dependent variable is, on average, completely explained by the explanatory variables, while a value of 0 means that there is no explanatory relationship between the dependent variable and the estimated model. Q. Please explain the second test statistic. A. The second test statistic is called the Mean Absolute Percent Error ("MAPE"). The MAPE is the ratio of the absolute value difference between a modeled value and an actual value over the entire dataset of actual values. The smaller the MAPE, the better. A model that results in a high R-squared value and a small MAPE is said to have "strong summary statistics." Q. Please describe the test process employed to ascertain whether a model generalizes well. A. The process is called an "Ex Post Forecast" test. Under this test, a model is first estimated. The ex-post test dataset, in this case the 0-month period from January 00 through June 00, is then withheld and the model is re-estimated. A 0- month dataset was selected because the time between the end of the test year (June 00) and the end of the rate year (December 00) is 0 months. By withholding the test data, the summary statistics can be examined to determine how the model performs on the test data and how well the model forecasts 0 months into the future. The stronger the summary statistics of the test data, the better the model's ability to generalize. Q. What are the results of your validation tests?

19 0 A. The test results and model specifications are shown in Exhibit (SRP-). They establish that the econometric models used by NYSEG for electric forecasting have very strong summary statistics and generalize well. The models prove to be robust and consistent enough to forecast 0 months into the future. ELECTRIC REVENUES Q. Please describe how the electric department delivery revenues are calculated for the rate year in Exhibit (SRP-). A. A three-step process is used to calculate the forecast monthly electric delivery revenues for each revenue class (Residential, Commercial, Industrial, Public Authority, Borderline and Interdepartmental) of customers and SC, as set forth in Exhibit (SRP-). Q. What is the first step? A. The first step of the electric revenue forecast process is to incorporate the forecasted energy sales and the number of electric service customers for each of the four rate options (identified above) by revenue class, and by month, into the NYSEG revenue model. The allocation of the sales and customer forecast from revenue class into SC is based on the last months of billing data. Separately, the monthly kilowatt-hour ( kwh ), kw, reactive kilovolt-ampere hour ( rkvah ) and SC customer counts (i.e., collectively, the billing determinants) are forecasted, by month, for each SC, for each rate option. Q. Please describe the second step. A. Once the kwh sales and customers have been allocated to the appropriate SC by month, the current tariff delivery rates as of January, 00, are applied to the

20 0 forecasted monthly billing determinants to develop the tariff revenue amounts by SC. Q. What is the third step? A. The resulting tariff revenue amounts are then multiplied by a blended revenue-tax to determine the total revenue amounts, per month, per SC. Q. Please describe Exhibit (SRP-). A. Exhibit (SRP-), shows the forecasted delivery revenues, MWh, and number of customers for 00. This information is shown by revenue class - Residential, Commercial, Industrial, Public Authority, Borderline (Sales for Resale), Interdepartmental and Total Revenues. Pages - of this exhibit show the total of all four rate options. Pages - of this exhibit present a monthly forecast for each rate option. Q. What is RPS Revenue as shown under each revenue class? A. RPS revenue is the Renewables Portfolio Standard charge. The RPS, which begins October, 00, mandates that NYSEG collect a specified amount through a volumetric (kwh) charge. The RPS charge changes each October, and the forecast for 00 reflects the required RPS payment amount for that rate year. Actual kwh will be used to reconcile over- or under-collections, which will be added into the RPS charge each successive year. Q. Will NYSEG collect the System Benefits Charge ( SBC )? A. No. This program is scheduled to end June 0, 00. It is not included, therefore, in the forecast for 00. Q. Please describe Surcharge Rev on each sheet.

21 0 A. These are Surcharge Revenues that are economic development program (e.g., Empire Zone Incentive) rate incentive discounts forecasted for each SC. For 00, the forecasted rate incentive discounts for all four rate options total $,0,000. Also included are the Power Partner Program Customer Charge discounts, which total $,,000. Total Revenues, as shown on page of Exhibit (SRP-), are reduced by a total of $,0,000 for economic rate incentives and Power Partner Program Customer Charge discounts. Q. Please describe how delivery revenues are addressed in the forecast for customers with special contracts? A. NYSEG delivers energy to customers who purchase supply from the New York Power Authority ( NYPA ), and also to customers who have negotiated contract rates with NYSEG under SC and SC ( Flex Contracts ). The delivery revenues for NYPA and for Flex Contract customers are calculated looking at the individual billing determinants for the last months of billing data. A forecast is then developed taking into consideration the contract end date for each customer, and also any forecasted changes during the rate year. Once delivery revenues are forecasted, revenues for NYPA and Flex Contract customers are then entered into the revenue model by its applicable revenue class. NYSEG s standby SC customer revenues are forecasted using a similar methodology. A forecast is developed for these customers taking into consideration any changes leading up to the rate year. The delivery revenues for SC customers are entered into the model based on its applicable revenue class. Q. What NYPA programs are included in the forecast? 0

22 0 A. The Economic Development Power ( EDP ) and NYPA Expansion Power are both included in the 00 forecast. The Power For Jobs ( PFJ ) program is scheduled to end December, 00, as amended by Article of Section of the Economic Development Law. The NYPA PFJ program, therefore, was not included in the 00 forecast. Such sales are forecasted at the otherwise applicable SC. Q. After you have developed the forecast rate year billing determinants and revenues, did you provide these results to other Company panels? A. Yes. Once the forecast was finalized, the billing determinants are provided to the Delivery Rate Design Panel to be used in designing delivery rates. The Delivery Rate Design Panel will discuss the design process in further detail. Moreover, the rate year revenues at existing rates are provided to the Revenue Requirements Panel to determine the rate year revenue requirement. The Outer Years: 00-0 Q. Did the Panel forecast billed sales and customers for the years 00 through 0? A. Yes. The results of the forecasts are shown in Exhibit (SRP-). Q. Were the same models that were used for the rate year forecast used to forecast the years through 0? A. Yes. The same models were used to forecast through 0 based on the same long-term economic trends predicted by Economy.com. Q. What is the Total Corporate Billed Sales growth rate for the forecast period?

23 A. The annual average compound growth rate of Total Corporate Billed Sales for the years 00 through 0 is expected to be 0.% per year. Q. What is the Total Corporate customer growth rate for the forecast period? A. The annual average compound growth rate of Total Corporate customers for the years 00 through 0 is expected to be 0.0% per year. Q. Does this complete your direct testimony at this time? A. Yes, it does.