DIRECT PRE-FILED TESTIMONY OF THE SALES AND REVENUE FORECASTING PANEL

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1 BEFORE THE LONG ISLAND POWER AUTHORITY IN THE MATTER of a Three-Year Rate Plan Case - DIRECT PRE-FILED TESTIMONY OF THE SALES AND REVENUE FORECASTING PANEL Date: January 0, 0

2 TABLE OF CONTENTS I. WITNESS QUALIFICATIONS AND DESCRIPTION OF TESTIMONY II. ELECTRIC SALES FORECAST III. RESIDENTIAL ELECTRIC SALES FORECAST IV. COMMERCIAL & INDUSTRIAL ELECTRIC SALES FORECAST V. OTHER ELECTRIC SALES FORECAST VI. ELECTRIC CUSTOMER FORECAST VII. RISKS TO THE ELECTRIC SALES FORECAST VIII. ELECTRIC REVENUE FORECAST

3 0 I. WITNESS QUALIFICATIONS AND DESCRIPTION OF TESTIMONY Q. Please state the names of the members of this Sales and Revenue Forecasting Panel (the Panel ). A. We are Bryan Irrgang and Robert Karol. Q. Mr. Irrgang, please state your employer and business address. A. I am employed by PSEG Long Island LLC ( PSEG LI or the Company ) and my business address is East Old Country Road, Hicksville, NY. Q. In what capacity are you employed by the Company? A. I am employed by the Company as Manager of Electric Load Forecasting. Q. Please summarize your educational background and professional experience. A. I have been employed in the energy industry for over years. I was previously employed by MacLeod & Steward for ten years, then by The Long Island Lighting Company ( LILCO ) for eight years, then by KeySpan for nine years and then by National Grid for six years. In 0 I assumed my current position with PSEG LI. I have been performing electric load forecasting on Long Island for years under LILCO, KeySpan, National Grid and PSEG LI. Additionally I am currently serving in my th consecutive year as chair of the New York Independent System Operator s ( NYISO ) Joint Load Forecasting Task Force and have been a task force member for years. I received an Associate of Science degree in Engineering Science from the College at Farmingdale, SUNY; a Bachelor of Science degree in Mathematics from the SUNY College at Old Westbury and a Master of Science degree in Applied Mathematics and Statistics from Stony Brook University. - -

4 0 Q. Robert Karol, please state your employer and business address. A. I am employed by PSEG LI and my business address is E Old Country Road, Hicksville, New York. Q. In what capacity are you employed by the Company? A. I am employed by the Company as Lead Analyst, Revenue Analytics Regulation and Pricing. Q. Please summarize your educational background and professional experience. A. In, I joined LILCO and spent six years as an Industrial Engineer in the Gas Operations Department. Before the Brooklyn Union Gas Company LILCO merger that formed KeySpan, I moved to Corporate Planning where I worked on various mergers and acquisitions ( M&A ) activities and performed financial analysis for diversified projects. Subsequently, I became Manager of Financial Analysis in KeySpan s unregulated Energy Development subsidiary. In 00, I accepted a position as Lead Analyst in the Forecasting group in KeySpan s electric Business Unit. KeySpan subsequently was acquired by National Grid. This group was responsible for the Revenue Analysis function on behalf of the Long Island Power Authority ( LIPA ). I was responsible for maintaining the models that forecast LIPA s revenues and for analyzing monthly variances. Essentially, this same position was reorganized into my current position in the Regulation and Pricing group when PSEG LI became the service provider for the LIPA contract as of January,

5 I hold a Master in Business Administration degree from Pace University and a Bachelor of Science degree in Industrial Engineering from the Pennsylvania State University. Q. What is the purpose of your testimony in this proceeding? A. The purpose of our testimony is to present the Company s electric sales and customer forecasts used to support the revenue requirement presented in this filing. Q. Are you sponsoring any exhibits in support of your testimony? A. Yes. We are sponsoring the following exhibits, which were prepared by us or under our direction and supervision: 0 Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) Exhibit (SRFP-) - Annual Residential and Commercial & Industrial Sales per Customer Models: Statistical Results - Residential and Commercial & Industrial Sales Forecast - Other Sales Forecast - Sales Forecast Reductions for Energy Efficiency & Renewables and Cogeneration - System Sales Forecast - Sales Forecast Assumptions - Residential and Commercial & Industrial Customer Forecast - Sales Forecast Input to Revenue Model - Sector Sales Forecast - Forecast Revenues by Rate Categories II. ELECTRIC SALES FORECAST Q. Please give a high level description of your electric sales forecast. A. We are projecting modest average annual growth in electricity sales for LIPA of 0.% during the years 0 through 0 resulting from the combination of moderate - -

6 0 forecast growth for the Long Island economy, slow projected population growth and aggressive energy efficiency and renewable programs. Please allow me to give some context. LIPA sales achieved average annual growth of.% for the ten years ending in 00, which was a period of robust expansion for the Long Island economy characterized by advances in employment, household income and home prices. Conversely, LIPA sales declined at an average annual rate of 0.% for the five years ending in 0, coinciding with a contraction in the Long Island economy characterized by flat employment and household income and falling home prices. For the period 0 through 0, our sales projections assumed mixed results characterized by growth in employment and household income but continuing weakness in home prices, which would produce a moderate expansion of the Long Island economy. We have based our underlying economic assumptions on data provided by Moody s analysts. Additionally, the continuation of the recent trend on Long Island toward slower growth in population and new household formations, with correspondingly slow growth in residential and commercial industrial customers, anticipated for the years 0 through 0 would further constrain growth in electricity sales. Finally, aggressive energy efficiency and renewable programs which have contributed to the reductions in electricity use per customer experienced recently a phenomenon particularly noticeable in the residential sector are likely to constrain sales growth in 0 through

7 0 Q. Please explain how the Panel forecasted electric sales? A. The Panel forecasted residential and commercial & industrial electric sales using econometric modeling. Q. Did the Panel use econometric modeling to forecast all of its electric sales? A. No. As will be explained later in this testimony, econometric modeling was used to forecast residential and commercial & industrial electric sales only, which together comprise about percent of LIPA s total annual sales. PSEG LI employs other methodologies to forecast the remaining three percent of electric sales relating to other public authorities, street lighting and electric vehicles. Q. What is econometric modeling? A. Econometric modeling is a technique used to estimate economic relationships based on historical data which are then used to make predictions under a set of assumed economic conditions. Econometric models are empirically derived mathematical equations that specify the statistical relationship between independent (or explanatory) variables and the dependent variable. Q. Are econometric models frequently used to forecast electric sales? A. Yes, all of New York s major electric utilities employ some form of econometric modeling to forecast all or a portion of their electric sales. Q. Did you use computer software to calculate the relationship between electric use and the explanatory variables? A. Yes, PSEG LI utilizes Statistical Analysis System ( SAS ) software to run its econometric models. SAS is a software suite developed by the SAS Institute for - -

8 advanced analytics, business intelligence, data management, and predictive analytics. The SAS software is widely used for advanced analytics. III. RESIDENTIAL ELECTRIC SALES FORECAST 0 Q. Mr. Irrgang, please discuss the residential model. A. The model development process began with the identification of those explanatory variables considered relevant in explaining the dependent variable, e.g., residential sales per residential customer (use per customer). Next, multiple combinations of the independent variables were tested using regression analysis to arrive at a satisfactory model. As seen in rows through on Exhibit (SRFP-), the statistical results show that the equation fits the data well. For the independent variables all of the t- values are at least., except one, which is only slightly below. A t-value of. indicates that the particular variable is statistically significant with % confidence. The residential model specification resulted in an Adjusted R of.%, which indicates the percentage of variation in the dependent variable that is explained by the independent variables is considerable. Q. Why did the Company specify electric use per customer instead of sales as the dependent variable for the residential model? A. The Company specified use per customer because it more accurately accounts for growth in the market. A simple example is to consider a regression model that uses residential electricity sales for the past 0 years as the dependent variable and includes annual cooling degree days ( CDD ) among the independent variables. Using such a model, one can estimate the impact that an extra 0 CDDs (out of - -

9 0 about, CDDs in an average year) will have on sales and it will be the same for each year modeled. This is an obvious problem since the number of LIPA s residential customers has increased by nearly 0% over the past 0 years, meaning the sales impact should be relatively larger for the more recent years. However, if the dependent variable is electricity use per residential customer, then the estimate of the impact of an extra 0 CDD from the model will again be the same for each year modeled; however this value will be multiplied by the number of customers for each year and so would be 0% greater for the most recent year compared to the earliest year. This approach gives a more accurate estimate of the sales impact for any given year. Q. What is the source of the data used to construct the use per customer variable for the residential model? A. The electric sales and customer levels for the residential sector were obtained from the customer billing system. Q. Please describe the explanatory or independent variables the Company used to develop its residential electric sales forecast. A. As shown in rows through on Exhibit (SRFP-), PSEG LI s current model specification utilized six independent or explanatory variables to forecast its residential electric sales per customer: ) cooling degree days; ) the ratio of employees to residential customers; ) median real home price; ) annual average real price of electricity; ) real regional income per customer; and ) real gross metro product per customer. Again the dependent variable is use per customer. - -

10 0 Q. What was the historical data set you used to construct the residential electric sales per customer model? A. Annual historical data from the past 0 years was used. Q. Please describe the variable cooling degree day. A. CDD is a weather variable that is used to measure conditions above a fixed reference level, called the base. For example, the National Weather Service calculates CDDs as the number of degrees ( F) that the average temperature for a day (the average of the daily maximum plus minimum temperatures) exceeds a base of F. However, there are alternative definitions of CDDs that are commonly used in the utility industry. PSEG LI calculates cooling degree days as the number of degrees that the average Temperature-Humidity-Index ( THI ) for a day (the average of the hourly THI values) exceeds a base of 0 degrees. CDDs are used during warm weather to estimate the energy needed to cool indoor air to a comfortable temperature. Higher values indicate warm weather and the need for higher energy demands for cooling. The residential electric sales forecast is based on normal weather conditions where the normal weather is determined by a 0-year average of annual CDDs. Q. Where did the CDD variable come from? A. CCDs were prepared internally based on information purchased initially from the National Weather Service and more recently from a commercial vendor (Schnieder Electric) for the Central Park Weather Station. - -

11 0 Q. Could data from a weather station on Long Island be used? A. National Weather Service data is currently available for several Long Island airport weather stations but the available history was insufficient to develop 0-year normal weather. Q. Why didn t the Panel also use heating degree days ( HDD ) as a variable in the residential model? A. We tested HDD in the model, but it was determined not to be a significant variable and thus was excluded. There simply are not enough customers with electric heat in our service territory to make HDD a significant variable. Q. What is the ratio of employees to residential customers variable? A. The ratio of employees to customers variable is the number of people employed on Long Island divided by the number of residential customers served by the Company. What we have found is that as the ratio increases, it indicates fewer people are remaining at home and therefore electricity use in the home decreases. Q. What is your source for the employment data? A. We obtain the employment statistics from the U.S. Department of Labor, Bureau of Labor Statistics. The Bureau of Labor Statistics is the principal Federal agency responsible for measuring labor market activity, working conditions, and price changes in the economy. Its mission is to collect, analyze, and disseminate essential economic information to support public and private decision-making. PSEG LI is able to obtain employment information specific to its service territory from the Bureau of Labor Statistics. - -

12 0 Q. What is the median real home price variable? A. The median real home price variable is the median selling price of existing single family homes in our service territory adjusted for inflation using a local Consumer Price Index ( CPI ). Q. What is your data source for the median home price in the Company s service territory? A. All of our economic data, including median home price, is provided to us by our consultant, Moody s Analytics. Q. What is Moody s Analytics? A. Through its team of economists, Moody s Analytics is a leading independent provider of data, analysis, modeling and forecasts on national and regional economies, financial markets, and credit risk. Moody s Analytics tracks and analyzes trends in consumer credit and spending, output and income, mortgage activity, population, central bank behavior, and prices. It provides concise and timely reports and one of the largest assembled financial, economic and demographic databases, which supports firms and policymakers in strategic planning, product and sales forecasting, credit risk and sensitivity management, and investment research. Its products are used by more than 00 major corporations worldwide, representing a broad range of industries including banking, government, asset management, real estate, utilities, and retail. Major New York utilities, the NYISO, ISO New England, and numerous federal government bodies all use data from Moody s Analytics. - -

13 0 Q. What is the real income per customer variable? A. This variable refers to the regional income for Long Island divided by the number of our residential customers, as adjusted for inflation using the local CPI. We found that for this variable the two year average value of the current year and one year prior works best in the model. The regional income series is obtained from Moody s Analytics. Q. What is the real gross metro product per customer variable? A. This variable refers to value of all the goods and services produced on Long Island divided by the number of our residential customers, as adjusted for inflation using the GDP implicit price deflator. The gross metro product series is obtained from Moody s Analytics. Q. What did you mean when you referred to the annual average real price of electricity variable? A. This refers to the annual average price of electricity that our customers actually paid, adjusted for inflation using the local CPI. We obtain this information directly from the Company billing system. Again we have found that for this variable the two-year average value of the current year and one year prior works best in the model. Q. Why did you use annual data for the residential model? A. Simply put, it is to minimize the degree of estimation and to maximize the degree of uniformity in the data used to develop the residential model, which I will explain. The dependent variable, use per customer, could be constructed using the residential sales reported in the billing system each month. However, there is some disadvantage with that approach. About % of the residential customers are billed for 0 days of - -

14 0 electric consumption calculated from one actual meter read and one estimated meter read, introducing a mean absolute percent error of 0.% of the total billed sales reported each month. The growth in residential sales has only averaged 0.% per month for the past decade and so is easily overwhelmed by the 0.% error introduced through estimated meter reads. Furthermore, of the total customers represented in the billed sales for any given month, % are from that half of the customers that are in the current month meter read group while the remaining % are from the other half of customers that are in the prior month meter read group and those proportions alternate in subsequent months, meaning the two mutually exclusive customer groups are not uniformly represented in the monthly observations. If the period under observation is increased from monthly to quarterly the error introduced through estimated meter reads is only 0.%, smaller than the 0.% average growth for the past forty quarters. However, % of the total customers represented in the billed sales reported quarterly have their meters read during the first and third months while the remaining % have their meters read during the second month and again those proportions alternate in subsequent quarters so the lack of uniformly represented in the observations remains an issue. Additionally, there are calendar differences that further reduce uniformity in quarterly observations. If the period under observation is increased to annual, the error introduced through estimated meter reads is only 0.0%, which is more than an order of magnitude smaller than the.0% average growth for the past ten years. Also, the two mutually exclusive customer groups are equally represented in the billed sales reported - -

15 0 annually, establishing uniformity in the observations. Finally, the calendars are the same for annual observations (with the exception of Leap Days which are adjusted manually) so uniformity is maintained. Q. Mr. Irrgang, please discuss the sources for the assumptions used in the residential electric sales forecast. A. The assumptions represent the projected values of the independent variables for 0 through 0 as used in the residential use per customer model. Most of the assumptions were provided by Moody s Analytics with the exception of normal cooling degree days, residential customers and the residential price of electricity which were developed internally. In particular, the electricity price assumptions represent preliminary values since the sales forecast is developed at an early stage in the overall process, before sales forecast results are available to establish more refined price values. The use of preliminary price projections in econometric modeling is acceptable because of the relative price inelasticity of electric consumption. All of the variables used to create the assumptions for the residential sales forecast are shown on Exhibit (SRFP-) except for the residential customer forecast which is shown in rows through for column on Exhibit (SRFP-). Q. Mr. Irrgang, did you make any out-of-model adjustments to the residential electric sales forecast? A. Yes. Q. Why are out-of-model adjustments necessary? A. Out-of-model adjustments are necessary because certain factors or variables will impact projected sales but cannot be adequately accounted for in the model. - -

16 0 Q. What out-of-model adjustment was made to your residential electric sales forecast? A. We adjusted the residential sales forecast to account for demand side management ( DSM ) initiatives. Reductions in load due to DSM are not a function of local economic conditions (as sales are) but rather represent PSEG LI s deliberate efforts to constrain load growth for purposes of system reliability, operational efficiency and to further New York State public policy goals. Thus an out-of-model adjustment is needed to account for the anticipated reductions due to DSM. Q. What is DSM? A. DSM involves reducing electricity use through activities or programs that promote electric energy efficiency or conservation, or more efficient management of electric energy loads. Q. What programs were considered by PSEG LI when calculating its DSM reduction to the residential sales forecast? A. The DSM reduction is composed of PSEG LI s existing Energy Efficiency and Renewable Energy programs. These programs are discussed in detail in the direct pre-filed testimony of the Utility.0 and Energy Efficiency Panel. Q. What were the overall forecasted reductions to the residential electric sales forecast resulting from DSM? A. Exhibit (SRFP-), among other things, summarizes the total DSM reductions to the residential electric sales forecast. As set forth in column of the exhibit, the DSM reductions to the residential electric sales forecast are: 0.0 gigawatt hours ( GWh ) for 0;. GWh for 0;.0 GWh for 0; and. GWh for

17 Q. Mr. Irrgang, please summarize the total residential electric sales forecast. A. Referring to Exhibit (SRFP-), column, the projected customers in rows through are multiplied by the model predicted use per customer values in rows through, resulting in the calculated sales shown in rows through. Next, the calculated sales are calibrated to the projected year-end sales for the current year as shown in rows through. Finally the sales reductions shown in rows through are subtracted from the calibrated sales, resulting in the sales forecast shown in rows through. In summary, as shown in rows through of columns and on Exhibit (SRFP-), the Panel is forecasting residential electric sales growth rates (after accounting for reductions due to the DSM out-of-model adjustment) of: 0.% (. GWh) for 0; -0.% (-. GWh) for 0; -0.% (-. GWh) for 0; and -0.% (-. GWh) for 0. After adjusting for leap years as shown in rows through of columns and on the exhibit, the growth rates are: 0.% (. GWh) for 0; -0.% (-. GWh) for 0; -0.% (-. GWh) for 0; and -0.% (-. GWh) for 0. IV. COMMERCIAL & INDUSTRIAL ELECTRIC SALES FORECAST 0 Q. Mr. Irrgang, did the Panel use econometric modeling to forecast the Company s commercial & industrial electric sales? A. Yes. The commercial & industrial electric sales forecast was developed using econometric models very similar to the one used to forecast the Company s residential electric sales. - -

18 Q. Please describe the econometric models used to develop the commercial & industrial electric sales forecast. A. The Panel modeled the following eight distinct segments or sectors for Long Island to forecast its commercial & industrial electric sales: manufacturing ( MFG ); trade, transportation and utilities ( TTU ); leisure and hospitality ( LEI ); financial activities ( FIN ); information ( INFO ); business services ( SER ); education and health services ( EHS ); and government ( GOV ). The Panel developed 0 econometric models for each of these sectors to produce the overall commercial & industrial electric sales forecast. Q. Please discuss the eight commercial & industrial models. A. As shown in rows through on Exhibit (SRFP-), the statistical results show that the equations fit the data well. Specifically, except for two intercept terms and two of the independent variables, the t-values are all at least. indicating statistical significance with % confidence. The model specifications resulted in Adjusted R that indicate the percentage of variation in the dependent variables explained by the independent variables is again considerable: three models are above 0% and all the rest are at least % except one, the FIN which is an acceptable value of.%. Q. Were the variables the same for each sector? A. The dependent variable for each sector model was electricity use per customer. The explanatory or independent variables, however, tended to differ for each sector model as shown in rows through of the specifications on Exhibit (SRFP-). - -

19 0 Q. Please describe the independent variables for the MFG sector. A. The independent variables for the MFG sector were MFG employment per MFG customer until and MFG employment per MFG customer after. Q. Explain the MFG employment per MFG customer variables used in the model? A. We found that the change in electricity use in response to changes in the ratio of MFG employment to MFG customers was different for the periods up to and then after it had increased over time. We isolated the response by using two variables. The first, MFG employment per MFG customer until has a value of 0 after while the second, MFG employment per MFG customer after has a value of 0 before. Q. What were the variables for the TTU sector? A. There were two variables: real regional income per TTU customer until 00 and real regional income per TTU customer after 00. Q. Please describe the explanatory variables for the LEI sector. A. There were seven explanatory variables for this sector: HDD; CDD; real LEI GMP per LEI employee; a category or dummy variable for the years -; real electric price; real regional income per LEI customer; and the ratio of households in the service territory to LEI customers. Q. What is a category or dummy variable? A. In statistics and econometrics, a dummy variable is one that takes the value 0 when the condition is not present and a fixed value when the condition is present. Dummy variables do not represent any underlying trends and are used to account for - -

20 anomalies in the historic data set. Dummy variables therefore accommodate a specific set of data points to reduce model error. Q. What is the LEI GMP? A. GMP is one of several measures of the size of the economy of a metropolitan area. Similar to gross domestic product, GMP is the market value of all final goods and services produced within a metropolitan area in a given period. LEI GMP is simply a further refinement of the GMP for the Long Island metropolitan area that only applies to the LEI sector. Q. What were the independent variables for the FIN sector? A. There were five: CDD; real Long Island GMP per FIN customer; a dummy variable for years -; another dummy variable for the years 00-0 and real income per household (two-year average). Q. Please describe the independent variables for the INFO sector. A. The econometric model for the INFO sector included three independent variables: INFO employment per INFO customer; real electric price (two-year average) and a dummy variable for the years -. Q. What were the independent variables for the SER category? A. The independent variables for this category were SER employment per SER customer; real electric price (two-year average) and a before dummy variable. - -

21 0 Q. Please describe the independent variables for the EHS sector. A. The EHS sector econometric model included three variables: Real Income per household (two-year average); the difference in rates between the ten-year Treasury Note and the three-month Treasury Bill (two-year average) and a before dummy variable. Q. Finally, what were the explanatory variables for the GOV sector? A. The independent variables for the GOV sector included: GOV employment per GOV customer until ; GOV employment per GOV customer after ; real electric price; and a before dummy variable. Q. Mr. Irrgang, please discuss the sources of the assumptions used in the commercial & industrial electric sales forecast. A. Most of the assumptions were provided by Moody s Analytics with the exception of normal cooling and heating degree days, commercial & industrial customers and the commercial & industrial price of electricity which were developed internally. All of the variables used to create the assumptions for the commercial & industrial sales forecast are shown on Exhibit (SRFP-) except for the commercial & industrial customer forecast which is shown in rows through on Exhibit (SRFP-). Q. Mr. Irrgang, were any out-of-model adjustments made to the commercial & industrial electric sales forecast? A. Yes, we reduced the commercial & industrial forecast to account for DSM programs. Q. What programs were considered by PSEG LI when calculating its DSM reduction to its commercial & industrial electric sales forecast? A. As was the case for the residential forecast, the DSM reduction for the commercial & industrial sales forecast is composed of PSEG LI s existing energy efficiency, - -

22 0 renewables and demand response programs. These programs are also discussed in detail in the direct pre-filed testimony of the Utility.0 and Energy Efficiency Panel. Q. What were the overall forecasted reductions to the commercial & industrial electric sales forecast resulting from DSM? A. Please refer to Exhibit (SRFP-). As set forth therein, the DSM reductions to the commercial & industrial electric sales forecast are:. GWh for 0;. GWh for 0;.0 GWh for 0; and. GWh for 0. Q. Were there any other out-of-model adjustments made to the commercial & industrial electric sales forecast? A. Yes. We made an adjustment for reductions related to cogeneration (which also includes a small amount of reductions due to fuel cells, energy storage and microturbines). In other words, the forecast was adjusted to reflect the projected loss in delivery for customers who plan to supply a portion, or all, of their existing load using on-site generation. Q. What were the forecasted reductions to the commercial & industrial electric sales forecast resulting from cogeneration? A. As set forth in column on Exhibit (SRFP-), the cogeneration reductions to the commercial & industrial electric sales forecast are:. GWh for 0;. GWh for 0;. GWh for 0; and. GWh for 0. Q. Mr. Irrgang, please summarize the total commercial & industrial electric sales forecast. A. Once again referring to Exhibit (SRFP-), in columns through, the projected customers in rows through are multiplied by the model predicted use per customer values in rows through, resulting in the calculated sales shown in rows - 0 -

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