LOADS, CUSTOMERS AND REVENUE

Transcription

1 EB-00-0 Exhibit K Tab Schedule Page of 0 0 LOADS, CUSTOMERS AND REVENUE The purpose of this evidence is to present the Company s load, customer and distribution revenue forecast for the test year. The detailed load forecasts by rate class are shown at Exhibit K, Tab, Schedules to. Forecasts of customers by rate class are shown in Exhibit K, Tab, Schedules to. Forecast of distribution revenues by rate class are shown at Exhibit K, Tab, Schedules to. Table below provides a summary of the loads, revenues, and customer forecasts. The revenue forecast is calculated based on proposed distribution rates, excluding commodity, and excluding rate riders. Table : Total Load, Revenues and Customers Year Total GWh Total MVA Total Distribution Revenue ($M) Total Customers 00 Actual,, $., 00 Actual,, $.,0 00 Actual,,0 $., 00 Bridge,0, $.,00 00 Test,, $0.,0 Notes:. Total GWh are purchased GWh, and are weather normalized to Test Year heating and cooling assumptions.. Total kva are weather normalized kva. Distribution Revenue is weather normalized and does not include adjustment for Transformer allowance.. Total Customers are as of year-end and exclude streetlighting and unmetered load connections.

2 EB-00-0 Exhibit K Tab Schedule Page of 0 HISTORICAL LOADS Historical and total system load (actual and weather-normalized) for THESL is illustrated in Figure below. Annual Historic Purchased Energy, kwh,00,000,000,0,000,000,000,000,000,0,000,000,00,000,000,0,000,000,000,000,000,0,000,000,00,000,000,0,000,000,000,000,000,0,000,000,00,000, Actual Weather Normalized Figure : Historical Purchased Energy Since 00, there has been a significant decrease in consumption. Table below shows normalized loads and annual growth. Essentially flat growth over the period has been replaced by declining loads over the period. While it is difficult to precisely attribute this decline to any particular event, THESL believes that the impact of conservation activities both program driven and naturally occurring conservation is playing a role. More recently, economic conditions are also likely having an impact, and perhaps even reinforcing conservation activities.

3 EB-00-0 Exhibit K Tab Schedule Page of 0 0 Table : Historical Annual Load Year Normalized GWh Growth GWh Percent Change (%) 00,0 00,0.% 00, 0.% 00, 0.% 00, () (.)% 00, () (0.)% Table below shows THESL s Board-approved load forecast for 00 and 00 compared to 00 actuals and 00 forecast. The 00 forecast includes four months of actual loads. Over the two years, loads have been and are expected to be about. percent lower than previously forecast. Because the trend in lower loads is relatively recent, THESL s previous load forecasts did not have an opportunity to incorporate these negative trends. Table : Board-Approved vs. Actual Purchased Energy Forecast Year Board-Approved GWh Actual GWh Variance (%) 00,.,0. (.)% 00,.,. (.)% LOAD FORECAST METHODOLOGY The Company s revenue and load forecast is developed using multifactor regression techniques that incorporate historical load, weather, and economic data. Energy forecasts are developed for each rate class separately. Total system load is summed from the individual rate class loads. Demand at the system and rate class level is based on historical relationships between energy and demand. The forecast of customers by rate class is determined using time-series econometric methodologies. Revenues are determined by applying the proposed distribution rates to the rate class billing

4 EB-00-0 Exhibit K Tab Schedule Page of determinants for the forecast period. KWh Load Forecast The process of developing a model of energy usage involves estimating multifactor models using different input variables to determine the best fit. Based on a priori assumptions about which input variables will impact energy use, different models were fit. Using stepwise regression techniques different explanatory variables were tested with the ultimate model being determined based on model statistics and judgement. The kwh load forecast is developed using multifactor regression models for each rate class. Previously, THESL forecasted system load at an aggregate level, and then allocated loads to each rate class based on historical load shares. The updated methodology allows for greater detail in modelling loads, and allows for different variables and coefficients to be modelled for different rate classes. For example, while heating and cooling degree days impact both Residential and Large User loads, the degree to which they impact these rate classes is different. In modelling total system loads, this difference is averaged in the determination of the coefficients. Modelling the rate classes separately allows for the different interactions to be modelled independently. The structures of the models for each rate class are generally the same, however different independent variables have been used depending on which variables best fit the models. The following table summarizes the variables included in each of the rate class energy models. All of the regression models use monthly kwh per day as the dependent variable, and monthly values of independent variables from July 00 through to the latest actual values (April 00) to determine the monthly regression coefficients.

5 EB-00-0 Exhibit K Tab Schedule Page of 0 Table : Regression Variables by Rate Class Residential GS<0 GS 0-kW GS 000-kW Large Users Unmetered Load Street lighting HDD0 per day HDD0 per day HDD0 per day HDD0 per day HDD0 per day monthly dummy variables: (excluding March) Extrapolation model January to used December CDD per day CDD per day CDD per day CDD per day CDD per day Intercept term Toronto City Population Dew Point Temperature Dew Point Temperature Dew Point Temperature Dew Point Temperature Linear Trend (July 00) Business Days Percentage Business Days Percentage Business Days Percentage Business Days Percentage Blackout dummy Toronto City Number of GS 0- Number of GS - Linear Trend Population 000 kw customers MW customers (January 00) Intercept term Number of GS<0 Linear Trend Blackout dummy kw customers (January 00) Blackout dummy Linear Trend (July 00) Intercept term Blackout dummy Intercept term Blackout dummy Intercept term Intercept term Note: For USL, relatively stable loads suggested extrapolation model was best for forecasting loads.

6 EB-00-0 Exhibit K Tab Schedule Page of The main drivers of load growth over time are economic conditions, while the primary driver of year-over-year changes is weather. Both of these effects are captured within the multifactor regression model. Economic conditions are captured in the model by the customer, population, and time trend variables. Population and customer variables capture overall levels of economic activity, and were found to be statistically significant in the Residential, GS <0 kw, GS 0- kw and GS 000- kw class models. The time trend variable, which is used in the Residential, GS <0 kw, GS 000- kw and Large Users models, is intended to capture the impacts which are being seen in the decline in loads for those sectors. One of the significant drivers of these decreases is believed to be the impact of conservation natural and program delivered, within THESL s territory. Weather impacts on load are apparent in both the winter heating season, and in the summer cooling season. For that reason, both Heating Degree Days ( HDD a measure of coldness in winter) and Cooling Degree Days ( CDD measure of summer heat) are modelled. In analysing load patterns against temperature data, THESL determined that the standard definition of HDD which uses degree Celsius as the point at which loads start to be impacted by temperature was not as effective as a measure which uses 0 degree Celsius as the balance point. The following figure shows the relationship between temperatures and loads.

7 EB-00-0 Exhibit K Tab Schedule Page of 0 Purchased Energy (GWh) vs Average Temperature (ºC),00,00,00,00,00,00,00,000,00, Average monthly temperature, ºC Figure : Purchased Energy vs Average Temperature In addition to the Degree Day/Load historical analysis, HDD were calculated based on various base temperatures (,, 0,, and degree Celsius) to test their performance in the regression models. The models containing HDD based on 0-degree Celsius demonstrated the best statistical results. To better explain weather impacts, dew point temperature was also included as an additional variable for almost all customer classes (excluding Residential, Street Lighting and Unmetered Scattered Loads). This variable captures the impact of humidity on consumption, and shows a positive impact of temperature on loads during summer months and negative during winter months.

8 EB-00-0 Exhibit K Tab Schedule Page of The third main factor determining energy use in the monthly model can be classified as calendar factors. For example, the number of business days in a month will impact monthly load. To capture different number of days in the calendar months the modelling of purchased energy was performed on per-day basis. To reflect different number of business days in the month and, consequently, different number of peak hours, business days percentage was used in the class models. One dummy variable was included to reflect the impact of the 00 August blackout on energy use in that month. Exhibit K, Tab, Schedule contains the historical and forecast load and input variable details. The model statistics are shown in Exhibit K, Tab, Schedule. From the regression models, the forecast of energy usage is determined by applying the model coefficients to forecasts of the input variables. The forecast for heating, cooling degree-days and dew-point temperature inputs is based on a ten-year historical average of HDD, CDD and Dew Point. A ten-year average was chosen over the 0-year average based on analysis of the annual HDD and CDD data that shows a definite trend (see Figure below). The forecast of Toronto City population and customer numbers were derived using various extrapolation techniques (Holt-Winters model and historic linear trend extrapolation). The forecasts of the calendar variables are based on the calendars.

9 EB-00-0 Exhibit K Tab Schedule Page of 0, , HDD,00.0, CDD Figure : Historic CDD and HDD HDD CDD 0 0 Peak Demand Forecast The forecast of peak demand by customer class, which is used to determine revenue for those customers billed on a demand basis, is established using historical relationships between energy and demand. CDM Impact on kwh and kw Forecast The load forecast as described above does not explicitly take into account any load impacts arising from CDM programs undertaken by THESL. However, the inclusion of the time trend variables does capture the impacts of conservation both natural conservation and CDM program conservation. No additional adjustments for CDM are thus required. Customer Forecast Customer additions in the company s operating area have been fairly flat over recent history, with about,00 to,00 new customers (excluding Unmetered loads and streetlighting) added annually. The forecast of new customers is primarily based on extrapolation models for each rate class.

10 EB-00-0 Exhibit K Tab Schedule Page 0 of 0 The forecast of customers for the residential sector in 00 through 00 includes an estimate for new individually-metered condominium suites, as well as the conversion of some condominiums from bulk-metered to individual suite-metering. The following table provides the detail on the number of new suite metered customers expected over the 00/00 period. These numbers are included in the total residential customer forecast. Table : Individually-Metered Suites Year Individually-Metered Suites (cumulative) 00 Actual, 00 Actual,0 00, 00, 0 The detailed forecast of customers by rate class is found in Exhibit K, Tab, Schedule.