Load Forecast

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1 Load Forecast October 2014

2 LOAD FORECAST Prepared: July 2014 October 2014 Issued: October 2014

3 LOAD FORECAST TABLE OF CONTENTS Page 1.0 SUMMARY INTRODUCTION RESIDENTIAL SALES FORECAST End Use Model Residential Forecast Results GENERAL SERVICE AND STREET LIGHTING SALES FORECAST Econometric Model General Service Forecast Results Street Lighting Forecast Results INDUSTRIAL SALES FORECAST Pulp and Paper Industry Mining and Smelting Industry LIREPP Industrial Forecast Models Industrial Forecast Results PEAK DEMAND FORECAST Econometric Model Peak Demand Forecast Results SUMMARY OF MAJOR FORECAST ADJUSTMENTS Natural Gas Reduce and Shift Demand (RASD) System Losses OVERALL FORECAST RESULTS Annual Requirements Monthly Energy Supply Monthly Peak Hour and Non-Coincident Demands FORECAST VARIATION Sensitivities Historical Forecast Variations METHODOLOGY UPDATES Load Forecasting Audit Residential Customer Forecast and Household size APPENDICES Appendix 1 Key Assumptions Appendix 2 Load Forecasting Audit Status Report Appendix 3 General Service Econometric Model Appendix 4 Industrial Distribution Econometric Model Appendix 5 Peak Demand Econometric Model Appendix 6 Monthly Energy and Peak Demand Tables... 63

4 LOAD FORECAST LIST OF TABLES Page Table 1: Forecast Summary... 3 Table 2: Total Energy Sales By Sector... 6 Table 3: 2013/14 Major Appliance Assumptions Table 4: Residential Energy Sales Table 5: General Service Energy Sales Table 6: Street Light Sales Table 7: Industrial Energy Requirements Table 8: Industrial Transmission Energy Sales Table 9: Industrial Distribution Energy Sales Table 10: Peak Demand Requirements Table 11: RASD Program Savings by Sector Table 12: Annual Energy, Demand and Load Factor Forecast Table 13: In-Province Monthly Energy Requirements Table 14: In-Province Monthly Peak Hour Demand Table 15: Sensitivities of Major Forecast Inputs Table 16: Forecast Variance Table 17: Non-Industrial Forecast Variance Table 18: Forecast Bias Statistics Table 19: Load Forecasting Audit Recommendation Summary... 45

5 LOAD FORECAST LIST OF FIGURES Page Figure 1: Annual Energy Requirements... 4 Figure 2: Annual Demand Forecast... 5 Figure 3: Energy Sales by Customer Classification... 8 Figure 4: Average End Use Breakdown of Residential Electricity Sales... 9 Figure 5: 2013/14 Average Appliance Load per Customer Figure 6: Primary Heating System Conversions Figure 7: Residential Sales Per Household Figure 8: General Service Sales Figure 9: Industrial Transmission Sales Figure 10: Industrial Distribution Sales Figure 11: Pulp and Paper Sales History Figure 12: High & Low Energy Requirement Forecast Scenarios Figure 13: High & Low Peak-Hour Demand Forecast Scenarios Figure 14: Non-industrial Actual and Weather Adjusted Actual Variance Figure 15: Industrial Forecast Variance With and Without Bowater/UPM Adjustments Figure 16: NB Population History and Foercast Figure 17: Adults per Houshold History and Forecast... 47

6 This report documents the forecast of the electricity requirements of the in-province customers of NB Power Corporation for the ten-year period from fiscal year 2015/16 to 2024/25. A load forecast is prepared based on a cause and effect analysis of past loads and trends. The analysis uses data gathered through customer surveys along with assessments of economic, demographic, technological and other factors that will affect the utilization of electrical energy. Appendix 1 summarizes the key assumptions used in this forecast In addition to the forecast requirements of each sales classification and total energy supply by month, this document includes a forecast of the annual and monthly peak hour demands (the total amount of energy required in a one-hour period). Finally, the document includes the forecasted annual system load factor (the average demand as a percentage of the peak hour demand). A management discussion of the forecast is included that compares the forecast to history and highlights the reasons for abnormalities. The forecast results are used for the financial, facilities and supply planning activities of NB Power, specifically: To provide energy sales and requirements to NB Power s short and long term business plans; To support the Transmission System Operator's province-wide forecasting and assessment activities; To provide NB Power s generation and transmission planning groups with a forecast of in-province requirements; To provide NB Power s strategic planning group with long-term energy & demand requirements. Load Forecast

7 Energy requirements and the peak hour demand are affected by weather conditions; most significantly by temperature. The energy forecast is based on 30-year rolling average temperatures (1984/ /14). The annual demand forecast is based on the historical peak demands which occur at an average temperature of -24 degrees Celcius. Actual experience is likely to differ from the forecast and such differences are usually larger in later years. Variations from the forecast can affect future financial and facilities requirements. This document includes a discussion of potential sources of variance and sensitivities, as well as a discussion of forecast accuracy over various timeframes. Load Forecast

8 Electrical energy required to meet the in-province load is forecasted to increase from 14,001 GWh in fiscal year 2014/15 to 14,839 GWh in fiscal year 2024/25 as shown in Table 1. During the same period, the maximum one-hour peak demand is forecasted to decrease from 3,000 MW to 2,880 MW. Energy Forecast The annual energy forecast and historical requirements are shown in Figure 1. Overall, energy is forecast to grow at an average of 0.6 per cent per year during the 2014/15 to 2024/25 period, compared to annual growth of 0.2 per cent over the last 20 years. Load Forecast

9 In 2004/05, in-province energy requirements crested and began a period of decline due to a number of industrial operations being closed in the pulp & paper, chemical and mining sectors. Numerous industrial distribution customers in the forestry sector also closed or reduced operational levels as market conditions for their products deteriorated. In the 11 years prior (1994/95 to 2004/05), the annual average growth rate was 1.3 per cent. A decrease in load from 2013/14 to 2014/15 is the result of the closure of Brunswick Mine and the Penobsquis mine at Potash of Saskatchewan. Annual forecasted growth is not uniform over the forecast period and growth in the early years of the forecast deviates from long-term historical growth. Higher growth in 2016/ /19 is being driven by the addition of new load at large industrial customers (Sisson Mine, Oxford Blueberries, and the new Picadilly mine at Potash of Saskatchewan). In all years, energy reductions from Reduce and Shift Demand (RASD) programs decrease growth by approximately 0.5 per cent per year. Load Forecast

10 Peak Hour Demand Forecast The annual peak hour demand forecast and historical peaks are shown in Figure 2. The peak hour demand averaged annual growth of 0.4 per cent over the last 20 years. However, it is forecasted to decrease at an average rate of -0.6 per cent per year during the forecast period. This decrease is the result of RASD programs focusing on both energy efficiency and demand response. Without any RASD programs, growth in peak demand would have been +0.7 per cent per year. Energy Sales Total energy sales by sector are shown in Table 2. Load Forecast

11 Annual sales growth is forecasted to be the strongest in the industrial sector over the forecast period. The ramp-up of the new potash mine, opening of Sisson Mine and overall growth of small industrial customers are driving sales increases to industry. Growth in the residential sector is mainly due to an increase in the number of customers, driven by decreasing average household size (persons per household). The general service sector experiences natural growth as a result of consistent GDP growth. Load Forecast

12 For forecasting purposes, NB Power s in-province electrical requirements are divided into three main groups: residential, general service, and industrial. The grouping reflects similarity in end uses of electricity; that is, the electrical requirements of customers in each group are similar and the customers within each group are to some extent homogenous. As a result electricity requirements within each group are affected by similar factors. The residential classification includes year-round and seasonal households, churches, and farms. The general service classification comprises mostly commercial and institutional establishments. The industrial classification is for customers involved in the extraction of raw materials or the manufacturing and processing of goods. The residential, general service and industrial forecasts are then separated into seven customer classifications: residential, general service, street lighting, industrial distribution, industrial transmission and wholesale (includes the sales to the preceding classifications by the municipal utilities in the cities of Saint John and Edmundston). Forecasts by customer classification are required for facilities and financial planning. The relative proportions of in-province energy sales in fiscal year 2013/14 to each of the seven customer classifications are shown in Figure 3. Load Forecast

13 The following sections outline the methodology, inputs and results of the annual energy sales forecast. Also included is the monthly distribution of the annual energy requirements and the associated peak hour demands. In fiscal year 2013/14, residential customers accounted for 45 per cent of the total inprovince electrical energy sales (40 per cent directly by NB Power and five per cent by Wholesale utilities). The residential classification is made up mostly of year-round domestic (household) customers. It also includes some non-domestic customers such as farms and churches, which account for less than three per cent of the total residential energy requirements. Seasonal customers, accounting for approximately one per cent of the residential requirements, are also included. The electrical requirements for seasonal customers are small and were forecasted by extrapolating historical trends. Load Forecast

14 Growth in the residential forecast is driven by the addition of new customers. Approximately 75 per cent of new residential customers are the result of decreasing average household size while the remaining 25 per cent are the result of provincial adult population growth. Increasing annual household usage is offset by naturally occurring energy efficiency and RASD programs. Average household energy is comprised of electric space heating, water heating and other uses. In 2013/14 the average household energy was comprised of 46 per cent electric space heating, 18 per cent water heating and 36 per cent other uses. The breakdown of usage by end use is shown in Figure 4. The mixture of household energy can change over time as a result of a number of factors including; future penetration levels of electric space and water heating, as well as Load Forecast

15 appliance trends and efficiency standards. To account for such trends, the forecast for the total residential class is based upon an end use model that requires identification of the various applications of electricity. These applications include space heating, water heating and other household appliances. The penetration (saturation) level and the average use for each household application provide the basis for average use per customer. The number of customers is based on an analysis of population trends. The model can be simply stated as: where: Energy = Year round Customers Average Use per Customer Average Use per Customer = (Appliance Average Use) In 2013/14, there were 340,400 year-round residential customers in New Brunswick. Of those, 301,500 were served directly by NB Power and 40,000 were served by the municipal utilities in the cities of Saint John and Edmundston. The adult population of New Brunswick is forecast to increase by 6,700 adults over the forecast period, which represents a 0.1 per cent average annual increase. In New Brunswick, the average number of persons per household is expected to decline from 2.21 in 2013/14 to 2.14 in 2024/25. These factors result in an increase of 15,300 new year-round customers over the forecast period. Details on the customer forecast can be found in Section An appliance efficiency model was used to estimate the changes in per unit consumption for major household appliances: refrigerators, freezers, dishwashers, clothes washers and clothes dryers. Data on penetration of appliances, the probability of a particular customer acquiring an appliance and the expected life were used to determine the Load Forecast

16 expected average use for each appliance. All new appliances are assumed to meet existing energy efficiency standards so as older appliances are replaced, the energy efficiency of stock appliances will increase over time. The saturation levels of major household electrical appliances were based on data from Natural Resources Canada and compared to results from NB Power s 2013 Energy Planning Survey of residential customers for verification. The 2013/14 appliance data is shown in Table 3. Saturation rate (units/houeshold) Annual Energy Consumption (kwh/unit) Refrigerators 120% 569 Electric Ranges 97% 697 Freezers 72% 418 Dishwashers 55% 122 Clothes Washers 91% 88 Cothes Dryers 89% 990 The average major appliance usage per customer per year is 2,695 kwh. The breakdown of each end use on a per customer basis is shown in Figure 5. Load Forecast

17 The penetration of electric heat has increased slightly from 62 per cent in 2008 to 63 per cent in 2013 based on the latest Energy Planning Survey results. This is because most new homes have opted for electric space heating (75 per cent) and electric water heating (90 per cent). Existing homes have also shown a trend of moving toward electric space heating. Between 2010 and 2013, approximately 6 per cent of customers converted their primary home heating system. Of those, an estimated 22 per cent more moved away from fossil fuels (oil, natural gas and propane) than converted to fossil fuels. Electricity saw a net gain of approximately 23 per cent of the conversions, while other heating fuels (including wood, pellets, solar, etc.) were largely unchanged. See Figure 6 below. Load Forecast

18 To Wood/Other 34% From Wood/Other 35% From Electricity 31% To Electricity 54% To Oil/Gas 12% From Oil/Gas 33% The penetration of electric water heating has also increased slightly from 92 per cent in 2008 to 93 per cent in Similar to space heating, more customers are opting to heat their water with electricity rather than using fossil fuels. As indicated previously, the annual energy requirement of the year-round customers is a function of the stock of electrical appliances and the extent to which these appliances are used. The annual energy required for space heating was derived by analyzing data from the previous year's sales along with historical and forecasted heating degree day effects. Average energy required for water heating was based on historical trending of kilowatthours per person. For the remaining appliances, estimates of average annual usage were based on an appliance efficiency model. Since 1994/95, the average use for year-round residential customers has decreased from 17,000 kwh/year to 16,600 kwh/year in 2013/14; an average annual decrease of 0.1 per cent. As shown in Figure 7, usage per customer peaked in 2005/06 at 17,700 kwh/year. Since then the average has declined by approximately 0.5 per cent per year. This decrease was the result of declining household size and improvements in construction standards. Also, the establishment of Efficiency NB in November 2005 granted Load Forecast

19 customers access to programs to help them save energy. With continued investment in RASD programs by NB Power, this trend is expected to continue through the forecast period. Further detail on RASD programs can be found in Section 7.2. The total New Brunswick residential electrical energy requirements are forecasted to increase from 5,740 GWh in fiscal year 2014/15 to 5,888 GWh in fiscal year 2024/25. The net increase in the forecast period is 148 GWh, an annual average increase of 0.3 per cent. Year over year growth is higher in early years of the forecast as a result of population growth trends. Table 4 summarizes the history and forecast of residential sales. Past large year over year variations were the result of weather variances. Load Forecast

20 In the 2013/14 fiscal year, general service energy requirements accounted for 21 per cent of the total in-province energy sales (17 per cent directly by NB Power and four per cent by Wholesale utilities). Street lighting sales, which include unmetered services, accounted for less than one per cent of total provincial sales. Load Forecast

21 Sales to the general service classification include commercial (retail/wholesale, hotel/motel/restaurants, offices) and institutional customers (hospitals, schools, universities). At the end of March 2014 there were 25,500 general service customers served by Disco and an additional 5,000 served by the wholesale utilities. The proportions of total general service sales to each of the major customer groups are shown in Figure 8. Approximately 70 per cent of general service sales are commercial in nature and, therefore, considered to be directly related to the level of provincial economic activity. The remaining 30 per cent of general service sales are to the institutional sector, which is indirectly related to the economic activity in the province. As the economic activity of the province increases, the activity in the institutional sector is also likely to increase. Load Forecast

22 General Service sales in New Brunswick reflect the level of commercial activity and are closely related to the provincial gross domestic product (GDP). In addition, weather affects the level of sales. The general service model relates changes in the level of sales to changes in the provincial GDP, the real price of electricity, the number of heating degree days, the previous year's sales, and the previous year s number of heating degree days. An average annual real GDP growth of 1.8 per cent is forecasted between 2015/16 and 2024/25. This GDP growth forecast was based on the June 2014 Province of New Brunswick Economic Outlook and consultation with staff at the Department of Finance on the prospects of the provincial economy. Based on NB Power s current RASD plan, energy savings are assumed to increase during the course of the forecast, suppressing growth through all years. Normal heating degree days were based on a weighted average provincial total for the 30-year period 1984/85 to 2013/14. Detailed model parameters and the fit of the model, which illustrates confidence in the performance of the model, are included in Appendix 3. Table 5 summarizes the history and forecast of general service sales. The total New Brunswick general service energy requirements are forecasted to increase from 2,929 GWh in fiscal year 2014/15 to 3,053 GWh in fiscal year 2024/25. The net increase in the forecast period is 124 GWh. Growth is steady through the forecast period, based on consistent GDP growth and no change in real prices. The wholesale portion of the general service sales is expected to increase by 28 GWh over the forecast. The general service forecast also accounts for decreases from RASD programs which are estimated to reduce sales by 4 GWh in 2015/16, increasing to 85 GWh in 2024/25. Note that the RASD program energy efficiency estimates are based solely on future programs and would be in addition to any past or current energy efficiency savings. Load Forecast

23 Street lighting sales include unmetered energy sold for street and area lighting plus other services such as sign lighting and traffic signals. The key factors affecting growth in street light and unmetered installations are new residential and commercial development. The total New Brunswick street light energy requirements are forecast to decrease from 76 GWh in fiscal year 2014/15 to 65 GWh in fiscal year 2024/25. The shift to light Load Forecast

24 emitting diode (LED) technology is driving energy decreases. Table 6 summarizes history and forecast of street lighting sales. In 2014/15 New Brunswick's industrial customers will consume about 33 per cent of the total in-province electrical energy. Industrial sales are divided into two groups: industrial Load Forecast

25 transmission sales (to customers who are served at transmission voltages of 69 kv and above) and industrial distribution sales (to customers who are served at distribution voltages of 25 kv or less). Sales as part of the Large Industrial Renewable Energy Purchase Program (LIREPP sales) are included within the industrial transmission sales unless noted otherwise. There are 36 customers served at transmission voltages, which constitute the majority of industrial sales. The portions of total industrial transmission sales for fiscal year 2013/14 to each of the main industry groups are shown in Figure 9. The industrial transmission forecast includes the opening of a major mining operation and food processing plant within the forecast period. NB Power serves some 1,700 industrial customers at distribution voltages, while the wholesale utilities serve approximately another 70. Together they account for 16 per cent of the total industrial sales. The major industrial distribution groups are wood industries, food and beverage, manufacturing, and other operations. The portions of NB Load Forecast

26 Power's industrial distribution sales to the main industry groups for fiscal year 2013/14 are shown in Figure 10. An overview of the key industrial groups and the factors that can affect their future electrical requirements is presented in the following sections. In fiscal year 2003/04 and the years prior, sales to the pulp and paper industrial customers were approximately 3,600 GWh compared to 2,200 GWh in 2013/14. This 1,400 GWh per year sales reduction resulted from the permanent closure of two paper mills, Bowater Maritimes and UPM Kymmene due to world market and economic conditions. The effect of this closure on sales is shown in Figure 11. Load Forecast

27 Since 2008/09, the pulp and paper industry in New Brunswick has stabilized and additional mill closures are not assumed to occur during the forecast period. The electricity intensive mineral industries in New Brunswick are related to base metal and potash mines. The base metal industry includes mining and concentration of ore bodies with zinc, copper, lead and silver. Lead concentrate is refined into lead ingots in Belledune. The potash mines in southeast New Brunswick export essentially their entire product. Production is tied to the worldwide demand for potash, used in fertilizer. The size and quality of the ore body limit the useful life of mines. Brunswick Mine closed operations in 2013, while Potash of Saskatchewan has recently shut down their existing mine at Penobsquis and is focusing on an expansion at Picadilly. The impacts of both announcements have been included in the forecast. Additionally, North Cliff Resources Load Forecast

28 is expected to begin mining Molybdenum and Tungsten at Sisson Mine in the western part of the province. Under the Large Industrial Renewable Energy Purchase Program (LIREPP), NB Power purchases electricity from qualifying large industrial customers who have renewable electricity generating facilities located in New Brunswick. LIREPP renewable energy purchases contribute to meeting the overall renewable energy target set out in the Electricity from Renewable Regulation Electricity Act. The revenue from renewable energy sales assist these qualifying customers in reducing their net electricity costs and thereby increase their competitiveness in the global market. The LIREPP program results in an increase in sales and revenue and a corresponding increase in generation requirements and cost. LIREPP sales differ from typical sales, as they are generated exclusively at customer owned facilities. The energy for LIREPP is produced at the site of the end use and therefore does not impact NB Power s capacity planning requirements, system losses or transmission system requirements. Because of this, LIREPP sales are sometimes reported separately from the system energy requirements. The relationship between increases in the total electricity requirements and provincial gross domestic product (GDP) for goods producing industries is used to forecast industrial load growth. Industrial transmission and industrial distribution forecasts are modeled separately. The industrial distribution forecast is based on historical trends and GDP growth. The firm and non-firm industrial transmission forecast is modeled on a customer-by-customer basis with adjustments for unallocated growth and RASD programs. Unallocated growth is added beginning in 2018/19 and is based on historical trends and GDP growth. Load Forecast

29 Each industrial transmission customer was modeled individually based on historical load levels and input from NB Power s account management team. The forecast was adjusted to include known industrial transmission closures and expansions. Typically, new large industrial projects or expansions are made known a few years ahead of opening. Therefore, unallocated growth begins in 2018/19 to capture unannounced new customers or expansions that would otherwise not be included. Customer-owned generation was also modeled in aggregate based on historical data. An econometric model was used to forecast the total industrial distribution electric energy sales. The model was based on a forecast of provincial goods producing gross domestic product and its historical relationship with industrial distribution sales. During the forecast period, average 1.8 per cent increases in GDP increase the industrial distribution sales by 84 GWh (1.1% per cent per year) after the effects of RASD programs. Statistical details on the industrial distribution econometric model are provided in Appendix 4. The forecasted industrial distribution electrical requirements are split between the amount to be supplied by NB Power and municipalities based on a historical factor. The industrial distribution forecast is based on growth in the overall GDP in New Brunswick. The key input assumption is an average annual GDP growth of 1.8 per cent over the forecast period. The industrial transmission forecast is based on individual customer forecasts and the key input assumptions are announced new customers and expansions. Load Forecast

30 Energy efficiency savings are expected as a result of NB Power s RASD industrial programs. Energy savings applied to the industrial transmission sector total 31 GWh annually in 2024/25. Energy savings applied to the industrial distribution sector total 7 GWh annually in 2024/25. The history and forecast of the total industrial electricity requirements are shown in Table 7. Load Forecast

31 Industrial Transmission Sales to transmission voltage customers are shown in Table 8. Non-firm sales to transmission customers make-up part of the overall energy requirement; some customers can accommodate infrequent and short duration interruptions so they take a portion of their energy needs as non-firm supply. While non-firm energy and demand are included in the forecast of in-province requirements, they are excluded for capacity planning purposes. The forecast decreased in 2013/14 and 2014/15 as a result of the closures of operations at Brunswick Mining. The increases in 2015/16 to 2018/19 result from the Potash of Saskatchewan expansion, and the addition of two new customers: Sisson Mine and Oxford Blueberries. Gradual decreases in later years result from NB Power s RASD programs. Load Forecast

32 Industrial Distribution Sales to distribution voltage industrial customers (by both NB Power or municipal utilities) are shown in Table 9. The net increase over the forecast period is 84 GWh. Economic and market conditions have resulted in significant reductions in industrial distribution sales since 2004/05. A number of sawmills and forestry-related customers have closed or reduced operations. Historical industrial distribution sales tend to be cyclical in nature. Industrial distribution sales are expected to grow but sales are not expected to surpass 2004/05 levels (950 GWh) within the forecast period. Load Forecast

33 NB Power is a winter peaking system driven by electric space heating in homes and businesses, with the peak normally occurring in late January or early February. In addition to the total annual energy, the maximum energy requirement in a one-hour period is also critical for planning system operations and new supply sources. The maximum energy required in a one-hour period is referred to as peak hour demand. Load Forecast

34 Peak demand is driven by weather. The system peak typically occurs on one of the coldest days of the year, on average at -24 o C. Weather sensitive loads are subject to the same weather patterns, and therefore peak at roughly the same time. Industrial loads tend to be less affected by weather and remain relatively flat. The econometric model relates peak demand to the level of monthly industrial and non-industrial sales and changes in temperature. The average monthly load levels within industrial and non-industrial classes prior to the effects of any RASD programs are used, along with monthly normal peak temperatures to forecast the unadjusted peak demand to 2024/25. Based on NB Power s current RASD plan, peak demands are then adjusted for both energy efficiency and demand response programs. Normal peak temperatures days were based on a weighted average provincial total for the 30-year period 1984/85 to 2013/14. Detailed model parameters and the fit of the model, which illustrates confidence in the performance of the model, are included in Appendix 5. Table 10 summarizes the history and forecast of system peak demand. The total New Brunswick system peak demand requirements are forecasted to decrease from 3,000 MW in fiscal year 2014/15 to 2,880 MW in fiscal year 2024/25. The net decrease in the forecast period is 120 MW. Through the forecast period, peak demand requirements slowly decline, and decrease more sharply in the final three years due to more aggressive RASD programs. The LIREPP portion of the peak demand is expected to remain relatively flat. Load Forecast

35 Prior to 2013 the load forecast was adjusted to account for residential customers converting to natural gas from electricity. Based on the results of the Energy Planning Survey completed in 2013, this adjustment was removed. The survey showed that the number of customers converting to natural gas is minimal. The number of customers Load Forecast

36 converting from oil to electricity is more than enough to cancel out those converting from electricity to natural gas. Refer to Figure 6 in Section Natural Gas has been widely available in many urban areas of New Brunswick since The effect natural gas has on the general service sector is therefore captured within the general service forecasting model, making an adjustment unnecessary. Natural gas is not expected to reduce industrial sales. An important part of the load forecasting process is recognizing the effects of conservation, energy efficiency and load demand management, also referred to as demand-side management. Demand management is any attempt to change or influence the demand placed upon the system by the customer. It encompasses a broad range of techniques from the direct control of customer equipment to educating customers about conserving electricity. Reducing Demand Energy efficiency and conservation is an integral part of NB Power s Reduce and Shift Demand (RASD) program. This part of the program provides benefit to the participating customers through direct savings on their power bills. It also provides benefit to NB Power through immediate fuel cost savings and through lower capital requirements in the long term by reducing the need for new supply in the future. This provides indirect benefit to all customers by ensuring low and stable rates. Shifting Demand These programs will be enabled through smart grid technology that will allow the systematic control of participating customers load. The shifting of demand will benefit the utility by improving the utilization and operation of generating plants and through an overall improved efficiency in operating the transmission and distribution systems. This also provides indirect benefit to all customers by ensuring low and stable rates. Load Forecast

37 Forecast Impacts Estimates of NB Power s RASD program were factored into the forecasts for the residential, general service, street lights and industrial sectors. These estimates were based on a 10-year projection of NB Power s Triennial RASD Plan. Program savings lower the forecast by 820 GWh and 390 MW in 2024/25. Table 11 shows the energy efficiency estimates by sector and peak demand savings for the system. Efficiency NB s program savings are also included. The forecast also includes estimates of energy efficiency measures that consumers are expected to naturally implement. The impact of improving construction standards in the residential sector is expected to increase the thermal shell efficiency of homes in the province, reducing average heating requirements by 0.25 per cent per year. This equates to 77 GWh and 20 MW in the final year of the forecast. The delivery of electricity from generation source to the end user involves three stages: high voltage transmission, transformation to lower voltages and ultimate distribution to Load Forecast

38 the customers at standard service voltages. There are losses associated with each of these stages. The amount of losses is a function of the load levels, technical characteristics of the transmission and distribution system and the distance between the generation sources and the customers. The basis of forecast energy losses on the transmission system is the Open Access Transmission Tariff loss factor, currently 3.3 per cent. Loss factors or percentages were multiplied by the amount of energy delivered over the system to meet NB Power s total energy requirements. LIREPP generation and sales occur behind the customer s transformer and are therefore not subject to transmission losses. Distribution losses were forecasted based on an analysis of the energy supplied over the distribution system compared to the billed distribution sales. Energy losses on the distribution system are estimated at 4.0 per cent of the total distribution sales over the forecast period. In addition, a substation transformer loss factor of 0.6 per cent is applied to all distribution level sales. The total energy supply requirements for NB Power are the combined total of the sales to the seven customer classifications plus transmission and distribution losses related to those sales. In order for the forecast to be used effectively in the overall utility planning process, it is necessary to spread the total sales and associated system losses over the year. The monthly spread is particularly useful for planning system operations and estimating the peak hour and non-coincident demands. The following sections outline the overall forecast results of annual requirements by customer class, monthly energy supply and peak hour and non-coincident demands. Load Forecast

39 Results of the residential, general service and industrial forecasts are allocated to the sales classifications and, where applicable, further allocated to the municipal wholesale utilities in the cities of Saint John and Edmundston. Table 12 summarizes the forecast sales to each of the seven customer classifications: Residential, General Service, Street Lighting, Industrial Distribution, Industrial Transmission, Wholesale and LIREPP. Also included in the table are the forecasted transmission and distribution losses, total energy supply (total sales plus losses), associated peak hour and non-coincident demands, and annual load factors. LIREPP sales are shown separately as many applications of the data require it to be removed. Load Forecast

40 The annual energy sales are apportioned by month for revenue and cash flow projections. The monthly spread of the annual forecast requirements is also required for scheduling the operation and maintenance of facilities. The monthly energy supply forecast is shown in Table 13. Load Forecast

41 The peak hour demand for each month of the forecast is shown in Table 14. As with the monthly energy spread, the peak demands were estimated separately for the base energy and any adjustments (e.g. RASD) on a month by month basis. The base energyassociated demand was combined with the adjustment-associated demand to give the final peak demand for each month. LIREPP sales are not always required for system planning or generation adequacy purposes. Therefore, the monthly energy and demand without LIREPP are included in Appendix 6. Additionally, non-coincident peak (NCP) demand is also included. NCP is the sum of the individual distribution, wholesale and industrial transmission peak loads Load Forecast

42 not necessarily occurring at the same time interval. It is used to calculate transmission tariff and ancillary service costs. The actual electric energy requirements and peak demands are expected to differ from the forecast. Furthermore, these differences are usually larger in the later years of the forecast. In view of the significance of the forecast for facility and financial planning, it is useful to understand the causes and effects of significant variances. In a specific year, the actual sales can be significantly affected by fluctuations in weather and the operational levels of large industrial customers. The forecast is based on past results that have been adjusted for such temporary fluctuations. In the long-term, the forecast will differ from actual experience as the factors that contribute to load do not materialize as forecasted or do not have the expected impact. For example, provincial economic growth may exceed (or fall short of) the forecast value or the impact of economic growth on electricity requirements may in the future be less (or more) than expected. Also, the take-up of energy efficiency programs may be more (or less) rapid than expected, and displace more (or less) existing load than forecast. Weather adjustments to historical energy supply are made based on a 30-year average of the heating degree-days in each month. Adjustments to historical peak demand are made based on the difference between average temperature in the eight hours leading up to the peak hour and -24ºC (the average temperature for peak demands since 1985). Major factors that impact the actual requirements for electricity and sensitivities to variations in forecast inputs have been estimated and are presented in Table 15. Load Forecast

43 ± Figures 12 and 13 illustrate the combined impact a number of these sensitivities could have on the forecast. The high and low cases include the following assumptions. new/closure of 50 MW industrial customer RASD program savings +/- 25 per cent of estimate Change of +/- 0.5 per cent to GDP growth rate in all years of the forecast The likelihood of all three sensitivities occurring at the same time is unknown, but they were chosen to provide a reasonable upper and lower limit to the forecast. Forecasted annual average growth is 0.6 per cent, compared to 1.1 per cent and 0.1 per cent in the high and low cases respectfully. Load Forecast

44 The impact of heating-degree variations from the 30 year average has not been included in the high and low cases, but can be significant in a particular year. Historical variations greater than 300 GWh have occurred due to abnormally warm or cold temperatures in a fiscal year. The impact of the average temperature at the time of the peak demand has also not been included. In the years 2002/03 to 2004/05, the energy requirements were very similar yet the peak demands were very different. The peak demand during fiscal year 2003/04 occurred at a temperature of -30 degrees Celsius, while the adjacent years were very close to expected at -24 and -25 degrees Celsius. This five degree difference resulted in a 200 MW increase in peak demand. In 2006/07 the demand drops back down again due to both a drop in the energy requirements as well as a warmer peak temperature (-18 degrees Celsius). Load Forecast

45 A comparison of forecasts prepared from 1990/91 to 2012/13 with actual results is shown in Table 16. This table compares the forecast of energy supply and peak hour demand to actual and weather adjusted results. The short-term forecasts tend to be fairly close, with most of the variance in any year being related to a combination of weather and industrial operating conditions. As the forecast period increases, the accuracy of the forecast decreases. On a weather adjusted basis, the energy forecasts tend to be accurate within about one per cent per forecast year. Demand forecasts tend to be slightly less accurate than energy. Though not shown, there are more occurrences in which the forecast is higher than actual. This reflects the increased likelihood of industrial closures than a new unplanned industrial load and warmer than normal temperatures in recent years. Load Forecast

46 Since 2005 poor world market conditions have caused several large industrial customers to close operations. Past forecasts were based on continued operation of existing customers resulting in higher than expected variances. The variances in Table 17 show the accuracy of the energy requirements less industrial sales, which give a better representation of the accuracy that would be expected in the future. Load Forecast

47 Budget forecasts are based on forecasting one year in advance. Because of the significance of the budget, the accuracy and bias (if any) of the one-year forecast is of particular importance. Figure 14 shows the non-industrial energy sales forecasted one year in advance. Weather adjusting the actual improves the accuracy of the one year forecast greatly from 2.7 per cent to 1.0 per cent. For industrial energy sales, a similar analysis shows a five year period (2005/06 through 2009/10) where forecast had large varinaces in excess of five per cent. The years 2005/06 to 2008/09 forecast errors were the result of two major industrial closures: Bowater Maritimes and UPM Kymmene. Both the forecast and actual were adjusted to exclude the Bowater and UPM loads, and the variance plotted in Figure 15. Load Forecast

48 The variance in 2009/10 was caused by the significant downturn in the economy leading to production shutdowns and closures of other pulp and paper customers and mining operations. Forecast accuracy is measured in two ways. The first goal of forecasting in any individual year is to minimize forecast error (or variance), and on a longer term scale, forecast bias should be minimized. Bias is the tendency of a forecast to vary in one direction more than the other. It is important to test that the energy forecasts are not systematically (though inadvertently) being over or under forecast. The simplest way to test this for bias is to analyze the forecast error in each year with a one sample t-test. The t-test is a statistical tool used to measure whether a sample s mean is different from a specific value, in this case zero. Where: = the sample mean (in this case the average forecast error) 0 = the null hypothesis (in this case, zero) = the sample standard deviation n 1 = the degrees of freedom (in this case, 15 1 = 14) Load Forecast

49 If the magnitude of t is greater than 1.96, bias is said to be present within the sample. The statistics for the weather adjusted non-industrial and adjusted industrial load groups are shown in Table 18. Because in neither instance the magnitude of t exceeds 1.96, bias is not present in the industrial nor non-industrial forecasts. NB Power strives to continuously improve its forecasting models. In 2007, an independent consultant GDS Associates reviewed NB Power s Load Forecasting process. In the auditor s opinion, the forecast meets industry standards and best practices. Nine recommendations were made to improve the results and transparency. A summary of the recommendations and their statuses is shown below in Table 19. A detailed update on each measure can be found in Appendix 2. Load Forecast

50 In addition to the nine recommendations, the 2007 Load Forecast audit discussed other areas for possible improvement. Special mention was made of the residential customer forecast, outlining its importance and the need to review the methodology. The greatest impact on changes in residential sales is growth in the number of customers; therefore, considerable attention must be devoted to the development of a number of households forecast, which drives the customer forecast. -GDS Associates, Load Forecasting Audit. (2007) New Brunswick is facing significant demographic challenges, as the population ages and birth rate declines drawing more attention to the relationship between population and number of residential customers. Since 1994, the total population of NB is largely unchanged but there has been a shift in the demographics, as a larger percentage of the population is now over the age of 20. This trend is important, as the number of residential customers is directly related to the adult population more so than the total population. The latest population forecasts from the provincial government show a Load Forecast

51 decline in general population in the next ten years, yet adult population increases in the same period as shown in Figure 16. Household size is calculated as the average provincial population divided by the number of year round residential customers. Household size in the traditional sense is declining because of the declining youth population as well as cultural factors leading to more people choosing to live alone. Basing the household size on the number of adults only, isolates the two phenomena to give a better forecast. The number of adults per household is declining slowly as shown in Figure 17. A logarithmic regression was used to represent the facts that household size is decreasing, yet decelerating and it cannot decrease indefinitely to zero, but will rather trend to a value between 1 and 2 adults per household in the long run. The final equation for adults per household (HHS A ) is given by: Load Forecast

52 Where: t = the fiscal year ending (for 2015, t = 0) and is the long term, theoretical minimum for adults per household The household size and population forecasts are combined to give the final year round customer forecast, as denoted below: Year-round Customers = Adult Population Adults per Household When this change was first implemented in 2012, the impact to the forecast was a decrease in customer growth of 16,000 customers over 10 years. The current forecast shows customer growth down slightly, as the population forecast has been reduced since the 2012 outlook. Load Forecast

53 Load Forecast

54 The GDP forecast is based on the June 2014 Province of New Brunswick Economic Outlook and consultation with staff at the Department of Finance on the prospects of the Provincial economy. Low growth in 2014/15 is boosting 2015/16. Growth in 2016/17 and beyond is assumed at slightly reduced historical levels. Real GDP % Fiscal Year Change 2015/ % 2016/ % 2017/ % 2018/ % 2019/ % 2020/ % 2021/ % 2022/ % 2023/ % 2024/ % Sensitivity: +0.1% change in annual GDP growth 2015/16: 0 GWh, 0 MW 2024/25: 40 GWh, 5 MW Continued operation of existing large industrial customers through the forecast period. Individual loads modeled based on historical sales levels and known information about customers future operational plans. No industrial shutdowns are included within the forecast period. New customers and load additions include: Potash of Saskatchewan second mine (sinking of shaft and compaction load until full production in 2017/18). Sisson Mine assumed to be operational in 2016/17 Oxford Blueberries assumed to be operational in 2016/17 Unallocated growth averaging 0.8%/year is assumed beginning in year 4 (2018/19). All existing transmission customers (including Wholesale customers) assumed to continue on standard service supply. Load Forecast

55 (LIREPP) will increase sales to some Industrial Transmission customers currently self supplying. Separate forecasts are prepared with and without LIREPP sales. Continued participation of current customers assumed. No new customers are assumed to join the program. Canadian average rates are assumed to increase at 3%/year. NB Power rates are assumed to average 2%/year through the forecast period, resulting in a decrease in the LIREPP discount: Fiscal Year LIREPP Discount % 2015/ % 2016/ % 2017/ % 2018/ % 2019/ % 2020/ % 2021/ % 2022/ % 2023/24 9.2% 2024/25 8.3% An econometric model was used to forecast industrial distribution sales. The model is based on a forecast of provincial goods producing gross domestic product and its historical relationship with industrial distribution sales. A 2.0 per cent average rate increase is assumed in July 2015, followed by and 2.0 per cent average rate increases (no real increase in the price of electricity) in remaining years. Consumer Price Index (CPI) assumed to be 1.8 per cent in 2015/16, and 2.0 per cent in all other years. Sensitivity: 1% increase in 2015/16 rates 2015/16: -24 GWh, -5 MW 2024/25: -42 GWh, -10 MW Load Forecast

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