2005 10 19 Evidence of Kenneth H. Tiedemann...Africa and the Philippines; Egypt planning studies for...

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British Columbia Hydro and Power Authority 1

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British Columbia Transmission Corporation Project No. 3698395

Certificate of Public Convenience and Necessity Application Vancouver Island Transmission Reinforcement Project

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7 TESTIMONY OF KENNETH H. TIEDEMANN 8

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Q1. Please introduce yourself to the British Columbia Utilities Commission.

A. My name is Ken Tiedemann. I hold the position of Manager, Power Smart Evaluation and was previously Manager, Market Forecast. I held the position of Manager, Market Forecast from April 2003 through September 2005.

Q2. What are the subjects of your evidence?

A. First, I will discuss my qualifications and experience.

Second, I will list the Information Requests (IRs) responses for which British Columbia Transmission Corporation (BCTC) requested information and for which I am responsible.

Third, I will update the load forecast for Vancouver Island.

QUALIFICATIONS AND EXPERIENCE19

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Q3. Please describe your professional qualifications and experience.

A. As the Manager, Market Forecast, I was responsible for energy and peak forecasts, small area forecasts, economic studies, and analysis in support of generation, transmission and distribution planning.

I held the following previous positions with BC Hydro: Manager, Market Research and Forecast from 1998 to 2000; Manager, Evaluation and Market Research from 1995 to 1998; and Manager, Residential Sector Evaluation, from 1993 to 1995. From 2001 to 2003, I was President, Applied Economics Consulting.

Prior to joining BC Hydro I held various positions as follows:

Manager, Strategic Planning, Consumer and Corporate Affairs Canada;

Manager, Program Evaluation, Consumer and Corporate Affairs Canada;

Manager, North-South Policies, Canadian International Development Agency (CIDA);

Senior Planning Officer, CIDA; and

Lecturer in Economics, University of Alberta.

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Testimony of Kenneth H. Tiedemann

I have a Bachelor of Arts (Honours) in Economics. I am a member of the following professional societies and organizations: International Association of Energy Economists, United States Association of Energy Economists; Econometric Society; and American Economists Association. My education, qualifications and experience are discussed in my Curriculum Vitae, which is attached hereto and marked Exhibit A.

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Q4. Have you previously testified before this Commission or its predecessors?

A. Yes. I testified in the 2005 Vancouver Island Call for Tenders (VICFT) hearing.

RESPONSES TO INFORMATION REQUESTS9

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Q5. Are there BC Hydro responses to IRs for which you had responsibility?

A. Yes. BCTC requested that BC Hydro provide information with respect to Island Residents Against High Voltage Overhead Lines (IRAHVOL) IRs 1.9.1 – 1.9.3. I am responsible for BC Hydro’s responses to IRAHVOL IRs 1.9.1 – 1.9.3.

VANCOUVER ISLAND ELECTRICITY DEMAND OUTLOOK14

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Q6. Please describe the Vancouver Island Demand/Supply Outlook.

A. BC Hydro’s most current demand/supply outlook for Vancouver Island is the December 2004 Load Forecast update, attached hereto and marked Exhibit B.

A summary comparison of the October 2004 and the December 2004 peak forecasts, in megawatts (MW) for selected years, for Vancouver Island is shown in the following table.

F2004 F2005 F2006 F2007 F2008 F2016

Oct 2004 - 2,256 2,260 2,275 2,279 2,556

Dec 2004 - 2,269 2,277 2,293 2,297 2,577

Actual peak 2,253 - - - - -

Weathernormalisedpeak

2,210 - - - - -

Difference - 13 17 18 18 21

The reason for the increase in forecast peak is that the October forecast assumed that the 8.7% rate increase sought by BC Hydro would be approved by the British Columbia Utilities Commission (BCUC). In fact, a smaller increase was approved by the BCUC.

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Testimony of Kenneth H. Tiedemann

Q7. When, and in what direction, is the current outlook expected to change? 1

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A. The BC Hydro electric load forecast is produced annually and is typically published in the fall. The December 2004 Load Forecast update is in the process of being updated.

A summary comparison of the forecasts with the preliminary October 2005 peak forecast, in MW for selected years, for Vancouver Island is shown in the following table.

F2004 F2005 F2006 F2007 F2008 F2016

Oct 2004 - 2,260 2,275 2,279 2,556

Dec 2004 - - 2,277 2,293 2,297 2,577

Oct 2005 Preliminary

- - 2,310 2,338 2,364 2,576

Differences

Oct ’05 - Oct ‘04 50 63 85 20

Oct ’05 - Dec ‘04 - - 33 45 67 (1)

The main reason for the increase is the forecast increase in the transmission load, particularly in the pulp and paper sector.

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Q8. Does that complete your testimony?

A. Yes.

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EXHIBIT A to the Testimony of Kenneth H. Tiedemann

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Kenneth H. Tiedemann, Economist 102-1205 West 14th Avenue Vancouver, BC, V6H 1P7

Phone: (604) 738-6027 E-mail: [email protected]

MEMBERSHIP IN PROFESSIONAL SOCIETIES

International Association of Energy Economists

United States Association of Energy Economists

Econometric Society

American Economics Association

KEY QUALIFICATIONS

(1) Research, statistical and program evaluation abilities. Excellent knowledge of applied evaluation research including multiple regression analysis, simultaneous equation models, limited dependent variables and time series models. Excellent knowledge of market research techniques including focus groups, survey design and implementation, conjoint analysis and market segmentation analysis. Prepared some two dozen consulting reports, most of them energy program evaluations and market studies, using advanced econometric techniques and cost benefit analysis for the World Bank, Global Environmental Facility, Natural Resources Canada, United Nations Development Program, Terasen Gas and BC Hydro.

(2) Ability to lead and manage a Department. Currently manager of the Market Forecast Department responsible for directing the activities of the department’s professionals in the preparation of the long-term forecasts, short-term forecasts, sector studies and analysis in support of generation planning, transmission planning, distribution planning and rate design. Previously successfully managed the Market Research and Evaluation Department for BC Hydro.

(3) Understanding of the energy sector and energy using industries. Supervised and prepared studies and reports on petroleum, natural gas, electricity, pulp and paper, forest products, chemicals, mining and commercial floor stock. Currently managing sector studies on the economic outlook for key sectors, the potential for self-generation and stepped rates.

(4) Presentation, communication and analytical skills. Made numerous presentations to clients and senior managers. Presented over fifty papers at economics, engineering and energy conferences.

(5) Knowledge of factors affecting electricity loads and sales. Undertaken detailed work on the drivers of demand for electricity and other energy commodities. Conducted numerous market studies and DSM evaluations on the impact of factors affecting energy demand. Published six papers dealing with natural gas,


(6) petroleum and electricity markets.

(7) Knowledge of research tools and methods. Excellent knowledge of research, statistical analysis and modelling and some familiarity with engineering methods gained through four years of engineering mathematics including elementary circuit theory, Fourier analysis, systems of partial differential equations and state space modelling. Presentation of five papers at engineering conferences including Probability Methods and Power Systems.

(8) Organisation and management skills. Successfully managed the Market Research and Forecast Department, the Strategic Planning and the Program Evaluation sections at Consumer and Corporate Affairs and the North-South Policies section at CIDA including organisation of the work, supervision and reporting and hiring.

(9) Knowledge of planning and budgeting. Successfully undertook planning, budgeting and evaluation for these operations. Undertook program planning for large CIDA programs in Guyana, Barbados, Malawi and Zimbabwe.

KEY EXPERIENCE

Manager of the Market Forecast Department, BC Hydro, May 2003-present.

Provide support for regulatory hearings, develop the load and sales forecast, review forecast needs with clients, develop new forecast tools and procedures, train staff, and develop and implement a number of recommendations for forecast process improvement.

Applied Economics Consulting, 2001 - present, President

Design and analysis for a number of energy program evaluations, market studies, electricity sector development projects and energy and peak forecasts. Evaluation of Natural Resources Canada’s EnerGuide for Equipment program, EnerGuide for Houses program, and Dollars to Sense Commercial and Industrial Workshops program. Evaluation of BC Gas’ Heating System Upgrade program, Efficient Boiler program, and Residential End Use Survey and Analysis, including impact of alternative rate designs and price and income elasticities. Evaluation of BC Hydro’s Home Energy Profile program and the Energy Star program. Developing country experience includes China Green Lights; the World Bank/GEF Efficient Lighting Initiative including market transformation evaluations for Argentina, Peru, Hungary, Latvia, Czech Republic, South Africa and the Philippines; Egypt planning studies for efficient motors, lighting and water heaters; EGAT Thailand air conditioner, CFL and T8 lamps evaluation.

Power Smart, BC Hydro, Canada, 1993 – 2000, Manager Market Research & Evaluation

Extensive experience in load and sales forecasting, market research and Demand Side Management program evaluation for BC Hydro. Specific experience includes undertaking, managing or directing studies dealing with the following programs and activities:

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load shape analysis

residential lighting (T8s, CFLs, electronic ballasts, controls)

commercial new construction (interior lighting, exterior lighting, air conditioning, HVAC auxiliaries, such as fans and pumps, envelope including glazing and insulation)

energy efficient appliances

furnaces and boiler systems

industrial processes (motors, ASDs, fans, pumps, compressors, process)

process studies dealing with customer satisfaction, reasons for program participation or non-participation, market effects such as free riders and spill over

market evaluations dealing with program penetration, size of the untapped market

logit and probit modelling of program participation, choice of technologies, customer demand

weather normalization of consumption demand using PRISM

simultaneous equation modelling of energy use

conjoint modelling of impact of product attributes and price on take-up

conditional demand analysis of end-use consumption

cost benefit studies of program impact

time of use rates, including a meta-analysis of residential time of use programs

green rates, including potential take-up with alternative rate designs.

cost of outages to customers

customer value of reliability.

International consulting experience with evaluation of residential air conditioner program for EGAT in Thailand, market transformation evaluation of the China Green Lights program, and seven market transformation evaluations of the Efficient Lighting Initiative.

EDUCATION

B.A. Economics (Hon), University of Alberta, 1968 Ph.D. candidate, Economics, University of Toronto

COUNTRIES OF WORK EXPERIENCE

Canada, United States, China, Thailand, Barbados, Guyana, Trinidad and Tobago, Botswana, Malawi, Zimbabwe, South Africa, Egypt, Peru, Argentina and Yugoslavia.

HISTORY OF EMPLOYMENT:

2003 - Present BC Hydro, Vancouver (April 2003-present)

Manager, Market Forecast Department

Responsible for management of the Market Forecast Department, energy and peak forecasts,

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small area forecasts, economic studies, and analysis in support of generation, transmission and distribution planning.

Worked as a consultant from April 2003 to October 2004 and a regular employee from November 2004 to present

2001 – 2002 (present) Applied Economics Consulting, Vancouver

President Lead consultant on a number of DSM program

evaluations, market research and pricing studies, economic impact studies and load research activities, many undertaken in conjunction with Habart and Associates.

Also Adjunct Professor, School of Resource and Environmental Management, Simon Fraser University, Burnaby, BC to the present

1998 - 2000 BC Hydro, Vancouver

Manager, Market Research and Forecast

Responsible for evaluation of Power Smart programs, operational analysis, customer needs and customer values assessment, load and sales forecast, customer behavior and satisfaction, service quality monitoring and market research.


Manager, Evaluation and Market Research

Responsible for evaluation of Power Smart programs, operational analysis, customer needs and customer values assessment, load and sales forecast, customer behavior and satisfaction, service quality monitoring and market research.


Residential Sector Evaluation Manager

Similar to above but restricted to residential sector programs, and excluding the Forecast function.

1989 -1993 Consumer and Corporate Affairs Canada (Federal Government), Ottawa, Ontario, Canada

Assistant Director for Strategic Planning

Managed the section, prepared an annual strategic plan, revised and maintained an environmental

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scanning system, advised on Departmental priorities and prepared policy studies.

Taught econometrics on a part-time basis at the University of Ottawa.

While on leave of absence taught public policy research at York University.

1983 – 1989 Consumer and Corporate Affairs Canada (Federal Government), Ottawa, Ontario, Canada

Senior Program Evaluation Manager

Evaluated regulatory processes and legislation, undertook cost-benefit studies and made recommendations for program changes to senior Department officials.

1975 - 1983 Canadian International Development Agency (CIDA), Ottawa, Ontario, Canada

Chief, North South Policies/ Senior Planning Officer

Work involved: preparing country programs; identifying, developing and appraising sectoral economic development strategies and specific projects; preparing reports and studies on aid and non-aid issues including market access; commodity stabilization, and preferential tariffs.

Represented Canada at multilateral negotiations within the OECD, the World Bank and the UN Conference on Trade and Development.

1973 - 1975 Department of Economics, University of Alberta, Edmonton, Alberta, Canada

Lecturer

Taught Introductory Economics, Intermediate Macroeconomics and Graduate Macroeconomics.

LANGUAGES Speak Read Write

English Excellent Excellent Excellent French Good Fair Fair

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PUBLICATIONS

Over forty energy and commodity market-related publications including:

Integrating end use metering, building modelling and statistical regression in the energy analysis of buildings. (2005). Computational and Experimental Methods in Electrical Engineering and Electromagnetics VII.

Environmental impacts of commercial energy efficiency. (2005). Ecosystems and Sustainable Development V.

Advertising to multicultural audiences: promoting energy efficiency in South Africa. (2004). Human Perspectives in the Internet Society I.

Online audits and energy using behaviour. (2004). Human Perspectives in the Internet Society I.

Learning from the market: efficient lighting in China. (2003). Data Mining IV.

Economic and environmental impacts of efficient air conditioning in Thailand. (2003). Energy and the Environment I.

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Electric Load Forecast2004/05 to 2024/25

Load ForecastingPower Planning and Portfolio ManagementDistribution Line of BusinessBC Hydro

December 2004 Forecast(update to October 2004 Forecast)

EXHIBIT Bto the Testimony of Kenneth H. Tiedemann

ELECTRIC LOAD FORECAST, 2004/05 – 2024/25 (DECEMBER 2004)

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Table of Contents

Executive Summary ....................................................................................................viiHighlights.............................................................................................................viiIntroduction...........................................................................................................ixSectors and Methodology.....................................................................................ixResidential Forecast............................................................................................. xCommercial Forecast ........................................................................................... xIndustrial Forecast................................................................................................xiPeak Demand......................................................................................................xiiEnergy and Peak Forecast Before Power Smart ................................................xiiSensitivity Analysis and Risks ............................................................................xiv

1 Introduction........................................................................................................... 11.1. Background and Context ...................................................................... 11.2. Role of Forecasting at BC Hydro .......................................................... 11.3. Overview............................................................................................... 2

2 Regulatory Background........................................................................................ 3

3 Forecast Process and Methodologies .................................................................. 43.1. Residential Forecast Methodology ....................................................... 43.2. Commercial Forecast Methodology...................................................... 53.3. Industrial Forecast Methodology........................................................... 63.4. Peak Forecast Methodology................................................................. 63.5. Validation.............................................................................................. 8

4 Forecast Drivers, Data Sources and Assumptions............................................... 94.1. Forecast Drivers ................................................................................... 94.2. Growth Rates........................................................................................ 94.3. Data Sources...................................................................................... 12

5 Reference Energy Forecast ............................................................................... 135.1. Comparison between October 2004 and December 2004 Forecasts. 135.2. Reference Forecast Before Power Smart........................................... 145.3. Reference Forecast With Power Smart .............................................. 17

6 Comparison Between 2003/04 and 2004/05 Forecasts ..................................... 216.1. Total Gross Requirements Forecast................................................... 216.2. Residential Forecast ........................................................................... 226.3. Commercial Forecast.......................................................................... 236.4 Industrial Forecast .............................................................................. 246.5. Peak Forecast .................................................................................... 25

7 Sensitivity Analysis............................................................................................. 277.1. Monte Carlo Analysis.......................................................................... 277.2. Uncertainty Assumptions .................................................................... 287.3 Temperature Sensitivity of Peak Demand .......................................... 28

8. Residential Forecast........................................................................................... 298.1. Summary ............................................................................................ 298.2. Forecast Methodology and Major Trends ........................................... 29

9 Commercial Forecast ......................................................................................... 359.1. Summary ............................................................................................ 359.2. Approach ............................................................................................ 359.3. Major Trends....................................................................................... 379.4. Lower Mainland .................................................................................. 38



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9.5. Vancouver Island................................................................................ 389.6. South Interior ...................................................................................... 399.7. Northern Region ................................................................................. 39

10 Industrial Forecast.............................................................................................. 4110.1. Summary ............................................................................................ 4110.2. Sector Outlooks ................................................................................. 4110.2.1. Medium-Term Forestry ....................................................................... 4210.2.2. Medium-Term Pulp and Paper Outlook .............................................. 4210.2.3. Medium-Term Mining Outlook ............................................................ 4310.3. Methodology ....................................................................................... 4310.4. Industrial Models ................................................................................ 4410.5. Forecast Sales by Sector.................................................................... 4710.6. Risks and Uncertainties ...................................................................... 49

11 Peak Forecast .................................................................................................... 5011.1 Introduction......................................................................................... 5011.1.2 Peak Forecast Method........................................................................ 5111.2.1. Distribution Peak Forecasts................................................................ 5411.2.2. Regional Transmission Peak Forecast ............................................... 5711.3 Weather Normalization ....................................................................... 5911.4 Total Regional Peak and System Peak Forecast ............................... 6111.4.1. 2004 Peak Forecast ........................................................................... 6511.4.2. Transmission Total Peak Forecast ..................................................... 6611.4.3. Distribution Total Peak Forecast......................................................... 67

12 Power Smart and the Conservation Potential Review Study ............................. 6812.1. Conservation Potential Review........................................................... 6812.2. Power Smart 10-Year Plan ................................................................. 6912.2.1. 10-Year Power Smart Plan ................................................................. 6912.2.2. Base Case Savings ............................................................................ 7012.2.3. Mitigation of Risks............................................................................... 7012.2.4. 10-Year Plan Allocation ...................................................................... 71

13 Glossary ............................................................................................................. 72

14 References ......................................................................................................... 75

Appendix 1. Price and Income Elasticities For Electricity Consumption .................... 76

Appendix 2. Price and Income Elasticities For Peak Demand ................................... 78

Appendix 3. Weather Normalization for Energy ......................................................... 80

Appendix 4. Weather Normalization for Peak ............................................................ 82

Appendix 5. Ordinary Least Squares-Based Forecasts ............................................. 84

Appendix 6. Maximum Likelihood-Based-Based Forecasts....................................... 88

Appendix 7. Industrial Sector Regressions ................................................................ 93

Appendix 8. Forecast Tables ..................................................................................... 96



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TablesTable 1. Energy and Peak Forecast Before Power Smart for Selected Years...........xiii

Table 2. Energy and Peak Forecast After Power Smart for Selected Years..............xiii

Table 2.1. B.C. Utilities Commission Comments and Actions...................................... 3

Table 4.1. Key Forecast Drivers................................................................................. 10

Table 4.2. Growth Assumptions ................................................................................. 11

Table 4.3. Data Sources and Uses for Growth Assumptions..................................... 12

Table 5.1 Comparison October 2004 v. December 2004 Integrated Syetem Forecast............................................................................................................................ 14

Table 5.2. Reference Forecast Before Power Smart ................................................. 16

Table 5.3. Reference Forecast With Power Smart..................................................... 18

Table 6.2. Comparison of Reference Energy Forecasts With Power Smart:Residential Sales ................................................................................................ 23

Table 6.3. Comparison of Reference Energy Forecasts With Power Smart:Commercial Sales............................................................................................... 24

Table 6.4. Comparison of Reference Energy Forecasts With Power Smart: IndustrialSales ................................................................................................................... 25

Table 6.5. Comparison of Reference Peak Forecasts With Power Smart ................. 26

Table 7.1 Monte Carlo Analysis – Energy and Peak Before Power Smart ................ 28

Table 8.1. Residential Sales Before Power Smart ..................................................... 32

Table 9.1. BC Hydro Commercial Sector Building Types .......................................... 36

Table 9.2. BC Hydro Regional Commercial Sales Forecast Before Power Smart ..... 40

Table 10.1. Forecast Industrial Sales Before Power Smart (GWh) Model 1 .............. 45

Table 10.2. Forecast Industrial Sales Before Power Smart (GWh) Model 2 .............. 46

Table 10.4. Industrial Sales by Sector Before Power Smart (GWh) Model 2............. 48

Table 11.2. Regional Non-Coincident and Coincident Transmission Peaks ForecastBefore Power Smart............................................................................................ 59

Table 11.3. Fiscal 2003/04 Regional and Total Actual and Weather-Adjusted Non-Coincident Distribution Peak............................................................................... 61

Table 11.4. Domestic System and Regional Peak Forecast Before Power Smart..... 63

Table 11.5. Domestic System and Regional Peak Forecast With Power Smart ........ 64

Table 11.6. Actual and Weather-Adjusted and Peak Forecasts Before Power Smart65

Table 11.7. Actual and Weather-Adjusted and Peak Forecasts With Power Smart... 66

Table 12.1 Forecast Summary – Total BC Hydro Service Area Annual ElectricityConsumption and Potential Savings*.................................................................. 68

Table 12.2. Forecast Summary – Total BC Hydro Service Area Demand Implicationsof Economic and Achievable Forecasts* ............................................................ 69

Table A1.1. Maximum Likelihood Estimates of Energy Elasticities ............................ 77

Table A2.1. Maximum Likelihood Estimates of Peak Elasticities ............................... 79



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Table A3.1. Actual and Weather-Normalized Sales for BC Hydro Service Territory.. 81

Table A4.1. Actual and Weather-Normalized Peak for BC Hydro Integrated System 83

Table A5.1. Ordinary Least Squares Regressions..................................................... 86

Table A5.2. Ordinary Least Squares Forecasts Before Power Smart........................ 87

Table A6.1. Maximum LIkelihood Regressions.......................................................... 90

Table A6.2. Maximum Likelihood Forecasts Before Power Smart ............................. 92

Table A7.1. Econometric Model of Industrial Transmission Sales (Model 1) ............. 93

Table A7.2. Econometric Model of Industrial Transmission Sales (Model 2) ............. 94

Table A7.3. Econometric Model of Industrial Distribution Sales (Model 1) ................ 94

Table A7.4. Econometric Model of Industrial Distribution Sales (Model 2) ................ 95

Table A8.1. 2004 BC Hydro, Reference Load Forecast Before Power Smart ........... 97

Table A8.2. 2004 BC Hydro, Reference Load Forecast With Power Smart............... 98

Table A8.3. 2004 BC Hydro, High Load Forecast Before Power Smart..................... 99

Table A8.4. 2004 BC Hydro, Low Load Forecast Before Power Smart ................... 100

Table A8.5. 2004 BC Hydro, High Load Forecast With Power Smart ...................... 101

Table A8.6. 2004 BC Hydro, Low Load Forecast With Power Smart....................... 102



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FiguresFigure 5.1. BC Hydro Load Forecast Build-up ........................................................... 13

Figure 5.2. Reference Forecast Before and With Power Smart – Total GrossRequirements (GWh) .......................................................................................... 19

Figure 5.3. Reference Forecast Before and With Power Smart – Integrated SystemPeak (GWh) ........................................................................................................ 20

Figure 8.1. Residential Consumption Before Power Smart by End Use for SelectedYears................................................................................................................... 34

Figure 11.1. Peak Forecast Methodology Overview .................................................. 52

Figure 11.2. Distribution Peak Forecast Guideline Before Power Smart – AverageAnnual MVA Growth (2003/04 to 2014/15) ......................................................... 55



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Executive SummaryBC Hydro’s December 2004 forecast is a re-release of the October 2004 forecast. Ithas been updated to reflect the BC Utilities Commission’s final approved rateincrease of 4.85% approved on November 24, 2004. The previously released(October) 2004 forecast had been based on an assumed rate increase of 8.9%effective April 2004.

This decline in the rate increase roughly translates into a 1% increase in electricityconsumption and a 0.8 % increase in peak demand compared to the October 2004load forecast (see section 5.1).

This change affects all forecast values in this document since all sales and peakforecasts have some dependency on the electricity price forecast

Highlights

Economic Outlook. Economic growth in British Columbia is forecast to be about2.8% in 2004 and to average about 3.0% per year for the five years from 2004 to2008. This compares with economic growth of 2.2% in 2003 and an average of2.2% per year for the five years from 1999 to 2003. The external economicenvironment continues to be positive, with consensus estimates of growth for2004 of 2.6% for Canada, 3.0% for the United States, 4.0% for Japan and 1.5%for the Euro zone. Over the first ten years of the forecast period, average forecastGDP growth is 2.8% per year compared to 2.6% per year for the 2004 forecast.Although this is small annual difference, the cumulative effects are significant overthe forecast period. The markets for BC’s main commodity exports including pulpand paper, wood and wood products, copper and coal all continue to be strong.This improved outlook for the external and domestic economic environments haspositive implications for electricity demand and sales in British Columbia.

Sales Forecast before Power Smart. Billed sales were approximately 50,300GWh in 2003/04. Billed sales, before incremental Power Smart impacts, areforecast to grow from about 50,300 GWh in 2003/04 to 54,900 GWh in 2008/09,to 59,600 GWh in 2014/15, and to 69,300 GWh in 2024/25. These increasesrepresent growth rates of 1.8% over the next five years (2003/04 to 2008/09),1.6% over the next 11 years (2003/04 to 2014/15), and 1.5% over the next 21years of the forecast (2003/04 to 2024/25).

Sales Forecast with Power Smart. Billed sales, with incremental Power Smartimpacts, are forecast to grow from about 50,300 GWh in 2003/04 to 52,600 GWhin 2008/09, to 56,700 GWh in 2014/15, and to 66,900 GWh in 2024/25. Theseincreases represent growth rates of 0.9% over the next five years (2003/04 to2008/09), 1.1% over the next 11 years (2003/04 to 2014/15), and 1.4% over thenext 21 years of the forecast (2003/04 to 2024/25).

Peak Demand. Peak demand is composed of the demand for electricity at thedistribution level (i.e. residential and commercial/light industrial loads),transmission customer loads (i.e. large commercial and industrial loads) plusinter-utility sales and transmission and distribution losses. Total integrated systempeak was approximately 10,100 MW in 2003/04. Forecast peak before PowerSmart, is expected to grow from about 10,100 MW in 2003/04 to 10,700 MW in2008/09, to 11,600 MW in 2014/15, and to 13,300 MW in 2024/25. Theseincreases represent growth rates of 1.2% over the next five years (2003/04 to2008/09), 1.3% over the next 11 years (2003/04 to 2014/15), and 1.3% over thenext 21 years of the forecast (2003/04 to 2024/25).



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Rate Impacts and Elasticities. The Reference Forecast includes the estimatedimpacts of the BCUC approved 4.85% for 2004 then assumes that rates will beconstant in real terms for the balance of the forecast period. Compared to thepreviously assumed rate increase of 8.9%, the final rate increase represents aneffective decrease of approximately 4% in real terms. With system elasticities inthe range of -0.25 the impact of this rate change is in the order of 1%.

Gross Requirements. Gross requirements include sales to BC Hydro’sresidential, commercial and industrial customers, BC Hydro’s own uses andtransmission and distribution losses. Gross requirements were approximately55,200 GWh in 2003/04. Gross requirements, before incremental Power Smartimpacts, are forecast to grow from about 55,200 GWh in 2003/04 to 60,300 GWhin 2008/09, to 65,500 GWh in 2014/15, and to 76,200 GWh in 2024/25. Theseincreases represent growth rates of 1.8% over the next five years (2003/04 to2008/09), 1.6% over the next 11 years (2003/04 to 2014/15), and 1.5% over thenext 21 years of the forecast (2003/04 to 2024/25). Based on evidence for thepast five years, forecast transmission losses have been reduced from 8% of billedsales to 7% of billed sales.

Forecast Validation. In addition to ongoing detailed analysis of inputs andoutputs, there are four primary methods of model validation. First, regressionmodels are built for energy and peak and the results for these models arecompared to those of the reference forecast. Second, the current year anchor iscompared to the short-term forecast for the current year and any major variancesare analyzed and corrected. Third, forecast of different recent vintages arecompared and the reasons for any major changes in the forecast from year toyear are examined. Fourth, drivers and growth factors are reviewed and validatedin light of implied trends.

Factors Leading to Lower than Forecast Sales. The main factors that couldlead to lower than forecast sales include the following:

rapid increase in the value of the Canadian dollar could to reduce growingcommodity exports from BC;

rising interest rates in the United States and Canada could reduce NorthAmerican housing demand and the demand for BC lumber; and,

a reduction in growth in China could lead to a slowing of commodity demandand commodity prices.

Factors Leading to Higher than Forecast Sales. The main non-economic risksthat could lead to higher than forecast sales to the forecast include assumptionsabout load reductions due to elasticity response to price increases applied to theforecast that do not materialize.

The main economic risks that could lead to higher sales are as follows:

strengthening world demand for market pulp and energy-intensive papergrades could increase electricity demand in the pulp and paper sector;

resolution of the softwood lumber dispute at a time when there is an amplesupply of fibre in the interior of BC could increase electricity demand in theforestry (saw milling) sector;

strengthening business confidence could increase investment in BC leading toincreased industrial and commercial demand for electricity; and,

economic spill-over of the Winter Olympics could increase investment andeconomic activity in British Columbia with consequent impact on electricitysales.



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Comparison With 2003 Forecast. The 2004 Energy Load Forecast is higherthan the F2003 Energy Load Forecast for all years of the forecast period. Thereasons for the increase in the forecast are as follows:

For residential sales, the 2004 forecast is below the 2003 forecast for 2004/05by 118 GWh because of the assumption of the impact of higher rates, butabove the 2003 forecast by 599 GWh in 2013/14 and above the 2003 forecastby 1,012 GWh in 2023/24 because of an uplift in the anchor point for 2003/04(i.e., 261 GWh) and an increase in the forecast number of accounts.

For commercial sales, the 2004 forecast is above the 2003 forecast by656 GWh for 2004/05, above the 2003 forecast by 657 GWh in 2013/14 andabove the 2003 forecast by 806 GWh in 2023/24. The main reasons for theincrease in forecast commercial sales are the uplift in the anchor point for2003/04 (i.e., 308 GWh) and the increase in forecast GDP which affects salesto the commercial sector.

For industrial sales, the 2004 forecast is above the 2003 forecast by1,002 GWh for 2004/05, above the 2003 forecast by 267 GWh in 2013/14 and1,294 GWh above the 2003 forecast in 2023/24. The main reasons for theincrease in industrial sales are the uplift in the anchor point for 2003/04 (i.e.,457 GWh) and the increase in forecast GDP and strong sales in severalresource sectors.

Introduction

BC Hydro is the third largest utility in Canada and generates nearly 80% of theelectricity produced in British Columbia. The company’s generating capacity is over11,000 MW and gross requirements including losses were over 55,000 GWh in2003/04. Sales to BC Hydro’s residential, commercial, industrial and wholesalecustomers were over 50,000 GWh in 2003/04.

Load forecasting is central to BC Hydro’s long-term planning, medium-terminvestment and short-term and real-time operational and forecasting activities. TheBC Hydro Electric Load Forecast is produced annually and published in the fall. Theforecast is based on several comprehensive engineering end-use and econometricmodels that use billed data up to March 31 of the relevant year as anchor information,combined with a wide variety of economic forecasts and inputs from internal,governmental and third party sources. The forecast outputs are validated throughadditional tests and information including time series econometric models. Theprimary purpose of the electric load forecast is to provide decision-making support onthe questions of “where, when, why and how much” electricity is expected to berequired on the BC Hydro system.

BC Hydro’s load forecasting activities centre on the production of a number of term-specific and location-specific forecasts of energy sales and peak demandrequirements to meet user needs for decision-support information. A variety of relatedproducts including quarterly forecast updates, monthly variance reports, inputs for therevenue forecast, load shape analysis and small area forecasts are produced tosupplement the base forecasts presented in this report.

Sectors and Methodology

BC Hydro’s load forecast is assembled from the following components: the residentialforecast; the commercial forecast (distribution voltage and transmission voltage), the



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industrial forecast (distribution voltage and transmission voltage) and the peakforecast. A variety of engineering and econometric models are used to produce theforecast. These specific forecast methods are based on their predictive value andtheir ability to most appropriately meet the needs of users.

Residential Forecast

Of the three customer classes, residential, commercial, and industrial, the residentialsector is the most stable. Growth in the number of residential accounts is about thesame as growth in population, which is currently about 1.2%. After many years ofgrowth in use per account, growth in use per account is forecast to moderate,growing at less than 1% over the entire forecast period. Key features of theresidential forecast include the following:

Electricity Use - BC Hydro’s residential sector currently consumes about 33% ofBC Hydro’s total annual billed sales. This electricity is used to provide a range ofservices including space heating, water heating, refrigeration, and miscellaneousplug-in load which includes computer equipment and home entertainmentsystems.

Drivers - The drivers of the residential forecast are number of accounts andaverage annual use per account. Number of accounts is driven by estimates ofhousing starts which in turn is driven by population growth and the trend in peopleper account. With growth expected in both number of accounts and use peraccount, growth in sales is forecast to range from 1.2% to 1.6% over the entireforecast period.

Trends - At the end of 2007/08, the new forecast for number of accounts is1,569,000, which is 6,400 or 0.4% above the previous forecast. At the end of2023/24, the new forecast for number of accounts is 2,006,000, which is 90,000or 4.7% above the previous forecast. Based on the application of elasticity of useassumptions, BC Hydro’s recent rate request is expected to lower use rate byabout 1% in the short term, but over the long term, use rate is not expected tochange significantly.

Forecast - Billed residential sales were approximately 15,900 GWh in 2003/04.Forecast sales to residential customers, before Power Smart, are expected togrow from about 15,900 GWh in 2003/04 to 17,400 GWh in 2008/09, to19,600 GWh in 2014/15, and to 23,000 GWh in 2024/25. These increasesrepresent growth rates of 1.8% over the next five years (2003/04 to 2008/09),1.9% over the next 11 years (2003/04 to 2014/15), and 1.8% over the next 21years of the forecast (2003/04 to 2024/25).

Commercial Forecast

BC Hydro’s commercial sector encompasses a wide a variety of commercial andpublicly provided services. It includes a very diverse set of BC Hydro customers whooperate a wide range of facilities such as office buildings, retail stores, institutions(i.e., hospitals and schools) and transportation infrastructure. The remainder of thesector is comprised of facilities (non-buildings) such as transportation infrastructureand public utilities. Key features of the commercial forecast include the following:

Electricity Use– BC Hydro’s commercial sector currently consumes 28% of BCHydro’s total annual billed sales This electricity is used to provide a range ofservices (often called end-uses) such as lighting, ventilation, heating, cooling,refrigeration, hot water, etc. These needs vary considerably between the differenttypes of buildings.



xi

Drivers – Within the building sector, floor stock and end use intensities areassumed to drive consumption. However because consumption in thecommercial sector is tied so closely with economic activity in the province, there isa strong relationship between consumption and economic indicators such as theprovincial GDP (Gross Domestic Product) and employment rates. As a result,future economic trends are a good indicator of future electricity consumption inthe commercial sector.

Trends – Electricity consumption of the commercial sector can vary considerablyfrom year to year reflecting the level of activity in BC’s service sector. Sales toBC Hydro’s commercial sector in 2003/04 grew by 3.1% reflecting the strongperformance of the BC economy. Growth consistent with a strong economy isexpected to continue however at a reduced level.

Forecast - Billed commercial sales were approximately 14,200 GWh in 2003/04.Commercial sales, before incremental Power Smart impacts, are forecast to growfrom about 14,200 GWh in 2003/04 to 15,900 GWh in 2008/09, to 17,700 GWh in2014/15, and to 21,100 GWh in 2024/25. These increases represent growth ratesof 2.3% over the next five years (2003/04 to 2008/09), 2.1% over the next 11years (2003/04 to 2014/15), and 1.9% over the next 21 years of the forecast(2003/04 to 2024/25).

Industrial Forecast

BC Hydro’s industrial sector is concentrated in a limited number of industries, themost important of which are pulp and paper, wood products, chemicals, metal miningand coal mining. The remaining industrial load is made up of a large number of smalland medium sized manufacturing establishments. Key features of the industrialforecast include the following:

Electricity Use - BC Hydro’s industrial sector currently consumes some 40% of BCHydro’s total annual billed sales. This electricity is used to provide a range ofservices including fans, pumps, compression, conveyance and processes such ascutting, grinding, stamping and welding and electrolysis.

Drivers – As in the case of the commercial sector, industrial electricityconsumption is tied closely with the level of economic activity in the province. Inother words, there is a strong relationship between industrial electricityconsumption and provincial Gross Domestic Product. Future economic trendsare a good indicator of future electricity consumption in the industrial sector.

Trends - Electricity consumption in the industrial sector is quite volatile, drivensubstantially by economic conditions in the United States, China and Japan thataffect commodity markets. Export sales to these three countries are a keydeterminant of domestic industrial output and of the industrial demand forelectricity. Trends for sales to these markets are positive.

Forecast - Billed industrial sales were approximately 18,700 GWh in 2003/04.Forecast sales to industrial customers, before Power Smart, are expected to growfrom about 18,700 GWh in 2003/04 to 20,100 GWh in 2008/09, to 20,600 GWh in2014/15, and to 23,300 GWh in 2024/25. These increases represent growth ratesof 1.4% over the next five years (2003/04 to 2008/09), 0.9% over the next 11years (2003/04 to 2014/15), and 1.0% over the next 21 years of the forecast(2003/04 to 2024/25).



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Gross Requirements

Gross requirements include sales to BC Hydro’s residential, commercial andindustrial customers, BC Hydro’s own uses and transmission and distribution losses.Gross requirements were approximately 55,200 GWh in 2003/04. Grossrequirements, before incremental Power Smart impacts, are forecast to grow fromabout 55,200 GWh in 2003/04 to 60,300 GWh in 2008/09, to 65,500 GWh in 2014/15,and to 76,200 GWh in 2024/25. These increases represent growth rates of 1.8% overthe next five years (2003/04 to 2008/09), 1.6% over the next 11 years (2003/04 to2014/15), and 1.5% over the next 21 years of the forecast (2003/04 to 2024/25).Transmission losses are significantly influenced by the origin and by the destinationof the load being transmitted. Based on evidence for the past five years, forecasttransmission losses have been reduced from 8% of billed sales to 7% of billed sales,reducing the growth in total gross requirements compared to the 2003 forecast.

Peak Demand

Peak demand is composed of the demand for electricity at the distribution level (i.e.residential and commercial/light industrial loads), transmission customer loads (i.e.large commercial and industrial loads) plus inter-utility sales and transmission anddistribution losses. Key features of the peak forecast include the following:

Electricity Use – Peak demand is forecast as the maximum expected one-hourdemand during the year. For BC Hydro’s load, this peak demand occurs in winterwith the peak driven in particular by the residential space heating load. BC Hydrodefines this one-hour peak event on the basis of expected demand on theaverage coldest day of the year. The average coldest day temperature (i.e.,design day temperature) has been changed from –6.8 degrees Celsius to –5.3degrees Celsius to reflect the most recent 30 years of historical temperatures.

Drivers – Key drivers of electricity peak at the regional level include the level ofeconomic activity, number of accounts, use rate per account, and the pattern ofwinter day cold temperatures. At the system level, the coincidence of regionalpeaks with overall system peak is also a determinant of the system peak.

Trends – Since peak demand is defined as an extreme event of expected onehour load on the average coldest day of the year, it is more volatile than electricitysales. This volatility can mask trends in peak demand, as peak can move up ordown substantially from year to year in response to a few days of extreme winterweather. Nevertheless, the long-term trend in peak is strongly upward.

Forecast – Total integrated system peak was approximately 10,100 MW in2003/04. Forecast peak before Power Smart, is expected to grow from about10,100 MW in 2003/04 to 10,700 MW in 2008/09, to 11,600 MW in 2014/15, andto 13,300 MW in 2024/25. These increases represent growth rates of 1.2% overthe next five years (2003/04 to 2008/09), 1.3% over the next 11 years (2003/04 to2014/15), and 1.3% over the next 21 years of the forecast (2003/04 to 2024/25).

Energy and Peak Forecast Before Power Smart

Table 1 and Table 2 provides a summary of historical and forecast sales and peak forselected years, both before and with Power Smart.



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Table 1. Energy and Peak Forecast Before Power Smart for Selected YearsResiden-

tial

(GWh)

Commer-

cial

(GWh)

Industrial

(GWh)

Total

Domestic

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

System

Peak *

(MW)

1998/99 13,972 12,814 18,077 45,513 50,897 9,026

2003/04 15,899 14,151 18,725 49,960 55,187 10,103(9,754)

2008/09 17,402 15,858 20,059 54,595 60,333 10,720

2014/15 19,564 17,710 20,588 59,260 65,507 11,587

2024/25 23,018 21,095 23,293 68,973 76,215 13,290

Growth Rates1

5 years:98/99 to 03/04 2.6% 2.0% 0.7% 1.9% 1.6% 2.3% (1.6%)

5 years:03/04 to 08/09 1.8% 2.3% 1.4% 1.8% 1.8% 1.2% (1.9%)

11 years:03/04 to 14/15 1.9% 2.1% 0.9% 1.6% 1.6% 1.3% (1.6%)

21 years:03/04 to 24/25 1.8% 1.9% 1.0% 1.5% 1.5% 1.3% (1.5%)

*Values shown in brackets are based on weather normalized actuals

Table 2. Energy and Peak Forecast After Power Smart for Selected YearsResiden-

tial

(GWh)

Commer-

-cial

(GWh)

Industrial

(GWh)

Total

Domestic

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

System

Peak*

(MW)

1998/99 13,972 12,814 18,077 45,513 50,897 9,026

2003/04 15,899 14,151 18,725 49,960 55,187 10,103(9,754)

2008/09 17,010 15,363 18,659 52,308 57,844 10,372

2014/15 18,999 17,070 18,957 56,424 62,416 11,153

2024/25 22,462 20,476 22,081 66,586 73,605 12,919

Growth Rates

5 years:98/99 to 03/04 2.6% 2.0% 0.7% 1.9% 1.6% 2.3% (1.6%)

5 years:03/04 to 08/09 1.4% 1.7% -0.1% 0.9% 0.9% 0.5% (1.2%)

11 years:03/04 to 14/15 1.6% 1.7% 0.1% 1.1% 1.1% 0.9% (1.2%)

21 years:03/04 to 24/25 1.7% 1.8% 0.8% 1.4% 1.4% 1.2% (1.3%)

* Values shown in brackets are based on weather normalized actuals

1 Unless otherwise noted, growth rates are calculated as annual compound growth rates.



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Sensitivity Analysis and Risks

BC Hydro’s load forecast is sensitive to a number of variables including weather,economic conditions, price, etc. BC Hydro’s analysis has looked specifically at thesensitivity of the load to changes in the economy (GDP) and price of electricity. Inaddition, a composite sensitivity analysis (Monte Carlo study) has been completed tolook at the sensitivity of the load to a combination of five non-weather causal factorsthat affect the forecast.

This composite sensitivity analysis is used to derive an uncertainty band around thereference forecast. Five major causal factors were analyzed to determine the range offorecasts that would have an 80 per cent confidence level encompassing thereference forecast. The high, reference and low growth rates over the forecast periodwere projected to be about 1.9 per cent, 1.5 per cent and 1.2 per cent respectively inTotal Gross Requirements, before Power Smart over the 21 year forecast period.

Beyond the general economic risk of higher or lower than economic growthassumptions materializing, a significant risk to the industrial forecast pertains todiscrete one time changes in sales to the base metal mining and pulp and papersectors. Specifically, risks not represented in the forecast relate to large one-timeadditions or contractions of load as a result of new investment, strikes or closure ofmajor facilities. Because it is difficult to assess the likelihood of these events, theapproach has been to relate the industrial load to GDP rather than attempt to forecastthese individual discrete events. However, as it relates to one large mining customer,based on public statements of intent to decommission beginning in 2007/08 madeearlier in the year, BC Hydro has reduced this load to reflect this discrete event.

The main downside risks to the forecast load include the following. First, rapidincrease in the value of the Canadian dollar could reduce growing commodity exportsfrom BC. Second, rising interest rates in the United States and Canada could reduceNorth American housing demand and the demand for BC lumber. Third, reduction ingrowth in China could lead to a slowing of commodity demand and commodity prices.

The main upside risks to the forecast load include the following. First, strengtheningworld demand for market pulp and energy-intensive paper grades could increaseelectricity demand in the pulp and paper sector. Second, resolution of the softwoodlumber dispute at a time when there is an ample supply of fibre in the interior of BCcould increase electricity demand in the sawmilling sector. Third, strengtheningbusiness confidence could increase investment in BC leading to increased industrialand commercial demand for electricity.

The main noneconomic risks to the forecast include assumptions about loadreductions due to elasticity response to price increases applied to the forecast that donot materialize.



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1 Introduction

1.1. Background and Context

BC Hydro is the third largest electric utility in Canada and generates nearly80 per cent of the electricity produced in British Columbia. Generating capacityis over 11,000 MW, with about 90 per cent of this capacity consisting of hydro-electric generation and the balance thermal generation. The remainder of theprovincial electric generation capacity includes Alcan’s Kemano facility, FortisBC’s plants, industry self-generation, particularly in the forest products sector,independent power producers, and small, off-grid installations, particularly in thenorthern part of British Columbia. Gross requirements, including losses weresome 55,187 GWh in 2003/04, while firm sales were some 50,273 GWh in2003/04.

The BC Hydro Electric Load Forecast is produced annually and published in thefall. The forecast is based on comprehensive model runs that use billed data upto March 31 of the relevant year as anchor information, combined with a widevariety of forecasts and inputs from internal, governmental and third partysources. In addition, because of the timing of forecast release, this year’sforecast has been adjusted to reflect actual sales through September 2004. Theprimary purpose of the electric load forecast is to provide decision-makingsupport on the questions of “where, when, why and how much” electricity isexpected to be required on the BC Hydro system.

The forecast includes only domestic load and firm exports. The forecast doesnot take into account the possibility of additional sales to other utilities in theevent of an excess water year generating surplus supply, nor does it reflect thenet effects of the time-shifting activities that are recorded as sales activities byPowerex.

1.2. Role of Forecasting at BC Hydro

Load forecasting is central to BC Hydro’s long-term planning, medium-terminvestment and short- and real-term operational and reporting activities. Assuch, BC Hydro’s load forecasting activities centre on the production of anumber of term-specific and location-specific forecasts of energy sales andpeak demand requirements to meet user needs for decision supportinformation. A variety of related products including quarterly forecast updates,monthly variance reports, inputs for the revenue forecast, load shape analysisand small area forecasts are produced to supplement the base forecasts.Additionally, analytical, statistical and modelling support for a number of specialor one time projects, including the Vancouver Island Call for Tender, the NITSApplication, Cost of New Energy Supply, Rate Design and the IntegratedElectricity Plan, are also provided.

Forecast requirements for electric utilities are changing in response to a numberof changes in the industry. These changes include:

• Increased risks as the system operates closer to capacity, increasingneed for more frequent, shorter-term and risk-based forecasting at theregional, area or district level;

• Future uncertainty, resulting in more need for stress testing, and a focuson risk/sensitivity analysis;



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• A shift from a previous focus on 20-year energy and peak forecasts foran integrated system, updated annually, to an increasing need for morefrequent and shorter-term forecasts at the regional, area or district level;

• Focus on understanding and meeting the needs of users; and

• Increased interest on the part of the regulator and other stakeholders,reinforcing a need to ensure methods are transparent, consistent anddefensible in regulatory context.

The main users and uses of forecast products include the following:

• Generation: real-time load forecast, generating facility station dispatchand system operations;

• Rates: rate design and rate structure;

• Distribution: revenue forecasting, portfolio forecasting, distributionplanning and investment;

• Transmission: transmission planning and investment;

• Powerex: resource availability for trade;

• Corporate:financial forecasts, service plan and budget reports; and

• BC Utilities Commission: demonstrating obligation to serve andprudence regarding expenditure and needs.

The key focus for the current year is to ensure that the forecast function isevolving appropriately in response to these trends and to completeimplementation of the Forecast Renewal Project. The Forecast Renewal Projectinvolves building new forecasting models that are more accurate, transparentand easier to use than the existing models.

1.3. Overview

The sections of the Electric Load Forecast are as follows.

• Section 1. Introduction

• Section 2. Regulatory Background

• Section 3. Forecast Process and Methodologies

• Section 4. Forecast Drivers, Data Sources and Assumptions

• Section 5. Reference Forecast

• Section 6. Comparison Between 2003/04 and 2004/05 Forecasts

• Section 7. Sensitivity Analysis

• Section 8. Residential Forecast

• Section 9. Commercial Forecast

• Section 10. Industrial Forecast

• Section 11. Peak Forecast

• Section 12. Power Smart and the Conservation Potential Review



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2 Regulatory BackgroundIn November 2002, the Government of British Columbia released its new energypolicy, Energy for our Future: A Plan for B.C. Following the release of theenergy policy, the Utilities Commission Act was amended, in part, to provide amandate consistent with the new energy policy. In particular, the amendmentsclarified the regulatory role of the British Columbia Utilities Commission (BCUC)with respect to the planning requirements of utilities.

In July 2003, the BCUC issued draft resource planning guidelines. Section III (2)of the draft guidelines considers the development of gross demand forecasts(before considering the effect of demand-side management programs) andstates:

“In making a demand forecast, it is necessary to distinguish betweendemographic, social, economic and technological factors unaffected byutility actions, and those actions that the utility can take to influencedemand, (e.g. rates, DSM programs). The latter actions should not bereflected in the utility’s gross demand forecasts. More than one forecastwould generally be required in order to reflect uncertainty about the future:probabilities or qualitative statements may be used to indicate that oneforecast is considered to be more likely than others…”

In its decision on the Vancouver Island Generation Project of September 8,2003, the BCUC provided a number of specific decisions relevant to the loadforecast. These decisions and the actions taken in response were summarizedin Table 2.1 of the 2003 Electricity Load Forecast.

In its decision on the Revenue Requirements Application of October 29, 2004,the BCUC found that the “Electric Load Forecast 2003/04 to 2023/24documenting BC Hydro’s forecasting approach informative and responsive tothe comments made in the September 2003 BCUC Decision on the VIGPCPCN application”. In addition in its decision, the BCUC provided a number ofcomments pertaining to the Load Forecast. These comments and actionscurrently underway in response to these comments are summarized in Table2.1.

Table 2.1. B.C. Utilities Commission Comments and ActionsComment Action

1. Streamline the load forecasting function. 1. New econometric based forecasting modelsare being built to simplify forecasting for theresidential and commercial sectors

2. Prepare more rigorous short-termforecasting

2. A series of short-term models are beingdeveloped to forecast quarterly electricityconsumption by rate class

3. Include impacts of the rate increase in theReference Load Forecast

3. Rate impacts have been included in the2004 Reference Load Forecast (Dec. 2004),based on the 4.85% approved rate increase.

4. Deal with seasonal bias in inputs 4. Use quarterly key drivers for the short-termforecast

5. Load Displacement Projects should beconsidered as supply side alternatives andnot included as part of the Power Smart 10-Year Plan

5. Revise the with Power Smart Load Forecastas revised Power Smart savings forecasts,excluding Load Displacement Projects, areavailable



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3 Forecast Process and MethodologiesThere are a number of key components to the load and sales forecast: theresidential forecast; the commercial forecast (distribution voltage andtransmission voltage); the industrial forecast (distribution voltage andtransmission voltage); and the peak forecast. This section briefly reviews thekey algorithms used for each of these components.

The December 2004 forecast is an update of the October 2004 forecast toreflect the final rate increase approved by the BC Utilities Commission. Themethodology used to update the forecast was to undertake a special run of theMonte Carlo Analysis as an expeditious means of calculating the change inconsumption based on the revised rate increase. This run calculated therelative rate impact between the 8.9% rate increase assumed in the October2004 forecast and the rate impact associated with the 4.85% approved rateincrease. The Monte Carlo model used the appropriate short and long run priceelasticities for each sector (residential, commercial and industrial) with all othercausal variables left unchanged (See Section 7.1 for details on the Monte CarloAnalysis). Section 11.1.2 provides details on the methodology used to calculatethe relative rate impact on peak and Section 5.1 provides an overview of thechange to both the peak and energy forecast.

3.1. Residential Forecast Methodology

The residential energy forecast is determined by forecasting the number ofaccounts times rate of use based on the following expression:

(3.1) RES = k j i Rijk*RURijk,

where:

• RES is residential consumption;

• R is the number of residential accounts;

• RUR is the residential use rate;

• i indexes 20 appliances (space heating, space cooling, water heater,refrigerator, freezer, clothes washer, clothes dryer, dishwasher,range, lighting and so on);

• j indexes four housing types (single/duplex, row, apartment andother); and

• k indexes four regions (Lower Mainland, Northern Region, SouthInterior and Vancouver Island).

The residential energy forecast is determined by forecasting the number ofaccounts multiplied by the rate of use. The forecast in the growth of the numberof residential accounts is based on a forecast of housing starts provided by athird party . The number of residential accounts is then the current number ofresidential accounts plus the additional accounts added each eyar.

Use rates are forecast from appliance saturation rates and unit energyconsumption per end use (as well as their trends) to determine the average userate by dwelling type and region and changes in these rates over time.Appliance saturation rates and unit energy consumption come from the



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Residential End-Use Energy Planning System model (REEPS) as updatedusing the Residential End Use Survey (REUS) and the Conservation PotentialReview (CPR).

3.2. Commercial Forecast Methodology

The commercial distribution energy forecast for buildings (about four-fifths of thecommercial load) is determined by forecasting floor stock times rate of usebased on the following expression:

(3.2) COMDB = k j iSTOCKijk*SHAREijk*EUIijk

where:

• COMDB is commercial distribution voltage building consumption;

• STOCK is segment floor stock;

• SHARE is the share of stock with a given end use (these areessentially fuel shares);

• EUI is end use intensity for a given end use;

• i indexes existing and new buildings;

• j indexes 10 end uses; and

• k indexes 13 building types.

Shares of end-use stock come from the Commercial End-Use Energy PlanningSystem (COMMEND) and are updated from the Conservation Potential Review(CPR).

The commercial distribution energy forecast for non-buildings, which includestransportation, communications and utilities (about one-fifth of the commercialload), is based on the following regressions:

(3.3) COMDNB = F(GDP, EMP, POP)

where:

• COMDNB is commercial distribution voltage non-buildingconsumption;

• GDP is real provincial GDP;

• EMP is provincial employment; and

• POP is provincial population.

The commercial transmission energy forecast is based on the followingexpression:

(3.4) COMT = j BASEj*(1 + EXPj)

where:



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• COMT is commercial transmission energy consumption;

• BASE is base year consumption for account j; and

• EXP is the expected impact of changes in facility size such asexpansions or changes in usage rates by reference to detailedcustomer-by-customer projections, which are validated against othergrowth indices, and combined with projected trends in energy use.

3.3. Industrial Forecast Methodology

The industrial distribution energy forecast is based on the following expression:

(3.5) INDC = j + j*GDP

where:

• INDC is industrial distribution energy;

• j and j are the regression coefficients from a time series regressionfor industry j of electricity sales on provincial GDP.

The industrial transmission voltage energy forecast is based on the followingexpression:

(3.6) INDD = j + j*GDP

where:

• INDD is industrial energy consumption for transmission voltagecustomers.

A modified version is also estimated that incorporates the impacts of industrialstrikes:

(3.7) INDDt = + *GDPt + *Dummy variable for strike years.

3.4. Peak Forecast Methodology

It is convenient to think of the peak or demand forecast as built up of fourstages, each with several steps. First, substation peak in MVA non-coincident2;second, area peak in MVA non-coincident; third, region peak in MW on a regioncoincident basis; and, fourth, system peak in MW on a system coincident basis.

The substation peak forecast is first built up in several steps: (a) actual andnormalized peak loads by substation/area; (b) area substation peak forecastguidelines are developed from an econometric model; (c) an 11-year substation

2 Non-coincident refers to use of a coincidence factor that is a ratio reflecting the relative

magnitude of a region’s (or customer’s or group of customers’) demand at the time of thesystem’s maximum peak demand to the region’s (or customer’s or group of customers’)maximum peak demand.



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forecast developed by the distribution group; and (d) the substation andguideline peak forecast are averaged together.

The first step is analysis of last year’s substation peak using the following:

(3.8) KVA = + *min

where:

• KVA is the weekly peak load; and

• min is the minimum temperature for the coldest day in the week.

Using the estimated regression coefficients, the weather-normalized peak isthen calculated based on the design day temperature for that substation:

(3.9) NKVA = + *designmin

where:

• NKVA is weather-normalized peak; and

• designmin is the design temperature for the substation.

The second step is the 11-year substation guideline (econometric model):

(3.10) SKit = [ 1SFDHTG + 2SFDNON + 3MULTHTG + 4MULNON +5U35E + 6O35E]

where:

• SFDHTG is the number of single-family electrically heated homes;

• SFDNON is the number of single-family non-electrically heatedhomes;

• MULTHTG is the number of multi-family electrically heated homes;

• MULTNON is the number of multi-family non-electrically heatedhomes;

• U35E is annual energy consumption under 35 kW;

• O35E is annual energy consumption over 35kW;

• the coefficients 1, 2, 3, and 4 are kW contribution to thedistribution peak per dwelling in area i, for the four dwelling typesunder normal temperature conditions; and

• the coefficients, 5 and 6 represent the increase in peak demanddue to a one-kWh increase in the General Under 35 and Over 35 kWenergy consumption.

As the third step, a longer-term (11-year) substation forecast is prepared basedon trends in substation growth, load transfers between substations and largesubstation load additions. The fourth step is calculation of the blend/average of



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the long-term substation peak forecast and the peak guideline forecast for area ias:

(3.11) PKit = it SKit

This calculation is done for 15 areas.

The regional peak is forecast using:

(3.12) RPKjt = j [PKit*DCFj*PFj *(1+DL) + TPj*TCFj*PFj + WPj*WCFj]

where:

• DCF is the distribution peak coincidence factor;

• PF is the power factor;

• DL is the distribution loss factor;

• TP is the transmission peak;

• TCF is the transmission coincident factor;

• WP is the wholesale peak;

• WCF is the wholesale coincident factor.

Finally, system peak is the sum of coincidence-adjusted regional peaks andincludes transmission losses:

(3.13) SPK = (1 + TL)* j RPKjt *SCFj

where:

• TL is the transmission loss factor; and

• SCF is the system coincidence factor for each of the four regions.

3.5. Validation

In addition to ongoing detailed analysis of inputs and outputs, there are fourprimary methods of model validation:

• First, regression models are built for energy and peak and the results for thesemodels are compared to those of the reference forecast.

• Second, the current year anchor is compared to the short-term forecast for thecurrent year and any major variances are analysed and corrected.

• Third, forecast of different recent vintages are compared and the reasons forany major changes in the forecast from year from year to year are examined.

• Fourth, drivers and growth factors are reviewed and validated in light ofimplied trends.



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4 Forecast Drivers, Data Sources and Assumptions

4.1. Forecast Drivers

Table 4.1 provides an overview of the key drivers for the reference forecast. Foreach forecast segment, this exhibit includes the activity variables, the use ratevariables and the summary data sources.

The activity variable is a variable that drives the scale of electricity use. The userate is a variable that measures the intensity of electricity use.

4.2. Growth Rates

Table 4.2 provides a summary of assumptions on key growth rates for thereference forecast. Growth rates are shown for account growth, real GDP,commercial buildings floor stock and employment. Unless otherwise noted, allgrowth rates are calculated as average annual compound growth rates.

For GDP, three sources have been used to generate the weighted averageshown in the table. The average is heavily weighted to the BC Ministry ofFinance Forecast in the first four years and then to third party sources for valuesfurther out (see Table 4.3). Actual data is shown for 1998 to 2003 and forecastdata is shown for 2004 to 2023. Three features of this data are worth noting.

• First, based on third party information modest rates of account growthare assumed for the forecast period, which acts as a constraint ongrowth of residential and commercial energy consumption.

• Second, although the forecast assumes reasonably smooth futuregrowth in GDP, the B.C. economy in recent years has been subject tosignificant external shocks that have led to quite uneven growth rates.This is a significant source of uncertainty for the load forecast.

Third, the growth assumptions used here do not take into account the possibilitythat improvements in the investment climate in British Columbia could lead tofurther increases in population, number of residential accounts and GDP abovethe levels forecast.



Page 10

Table 4.1. Key Forecast DriversActivity Use Rate Data Sources

1. ResidentialForecast

• Number ofresidentialaccounts byhousing type,heating type,region

• Consumption peraccount based onResidential End-Use EnergyPlanning System(REEPS)

• Current number of accountsas base

• Housing starts• Appliance saturation rates

from Residential End UseSurvey (REUS)

2. CommercialDistributionForecast

• Floor stock bybuilding type andby existing andnew buildings

• Fuel share• Consumption per

square foot basedon CommercialEnd-Use EnergyPlanning System(COMMEND)

• Floor stock forecasts• End use saturation rates and

intensities from CommercialEnd Use Survey (CEUS)with updates fromConservation PotentialReview (CPR).

3. CommercialTransmissionForecast

• Number of facilities • Currentconsumption

• Billing data• GDP forecast

4. IndustrialDistributionForecast

• GDP (based onregressionmodelling)

• Currentconsumption

• Billing data• GDP forecast

5. IndustrialTransmissionForecast

• GDP (based onregressionmodelling)

• Currentconsumptionadjusted forexpansions,contractions,closures

• Industrial billing data forindustrial transmissioncustomers

• GDP forecasts

6. Non-IntegratedForecast

• Number ofaccounts

• Consumption peraccount (based onREEPS)

• Current number of accountsas base

• Local conditions for shortterm (first four years)

• Population forecast forlonger term (next 17 years)

• Appliance saturation ratesfrom REUS

7. PeakForecast

• Number ofaccounts by type

• Sales to generalsector

• Industrial Activityand GDP

• Residential –kW/Account

• General– kW/kWh• Transmission –

peak demand (kW)from billing data

• Previous years peak bysubstation, region andweather data fornormalization

• Customer billing data• Economic and demographic

forecasts



Page 11

Table 4.2. Growth AssumptionsAccounts

(%)

Real GDP

Weighted

(%)

Commercial

Floor Stock

(%)

Employment

(%)

Actual

1998 0.8 1.3 2.2 0.1

1999 0.9 2.8 2.2 1.9

2000 0.9 4.3 2.1 2.3

2001 0.9 -0.2 2.2 -0.4

2002 1.3 1.8 1.9 1.6

2003 1.3 2.2 2.5 2.5

Forecast

2004 1.6 2.8 2.4 1.8

2005 1.7 3.1 2.4 1.8

2006 1.8 3.1 2.8 1.9

2007 1.8 3.0 2.8 1.9

2008 1.9 2.9 2.9 1.9

2009 1.9 2.8 2.9 2.5

2010 1.8 3.2 2.9 3.2

2011 1.8 2.4 2.6 1.7

2012 1.7 2.4 2.6 1.6

2013 1.6 2.4 2.5 1.7

2014 1.5 2.5 2.5 1.7

2015 1.5 2.5 2.5 1.7

2016 1.5 2.4 2.3 1.7

2017 1.4 2.4 2.3 1.7

2018 1.4 2.4 2.3 1.7

2019 1.4 2.3 2.3 1.7

2020 1.4 2.3 2.3 1.7

2021 1.3 2.3 2.3 1.7

2022 1.3 2.3 2.3 1.7

2023 1.3 2.3 2.3 1.7

2024 1.3 2.3 2.3 1.7

Notes: See Data Sources, Table 4.3.

Information on the sources and the uses of the growth assumptions is shown inTable 4.3. For each key driving variable, this exhibit shows those applicationswhere the variable is used, the time period(s) for which the variable is used andthe detailed data sources.



Page 12

4.3. Data Sources

Table 4.3. Data Sources and Uses for Growth AssumptionsVariable Application Period Source

Population • Used as acheck onaccount growth

• 2004-2024 • B.C. Statistics, B.C. Population Forecast,June 2004

GDP (Gov)* • Industrialenergy forecast

• 2004-2007 • B.C. Ministry of Finance, First QuarterlyReport 2004/05, September 2004

GDP (thirdparty)

• Industrialenergy forecast

• 2004-2024 • R A Malatest, July 2004

GDP (DOE) • Industrialenergy forecast

• 2008-2024 • B.C. share based on Canadian GDPforecast, USA Department of Energy,Annual Energy Outlook 2004

GDP(weighted)

• Industrialenergy forecast

• 2004-2024 • Weighted average of Malatest andgovernment sources

CommercialFloor Stock

• Commercialbuilding energy

• 2004-2024 • R A Malatest, July 2004

HousingStarts andAccountGrowth

• Residentialenergy forecast

• 2004-2024 • R A Malatest, July 2004

Employment • Number ofgeneral under35 kW accountsfor peak

• Number ofgeneral over35 kW accountsfor peak

• 2004-2024

• 2004-2024

• B.C. Ministry of Finance, First QuarterlyReport 2004/05, September 2004

• B.C. Ministry of Finance, First QuarterlyReport 2004/05, September 2004

*GDP forecast are available from the BC Provincial government for only the firstfive years of the forecast period thus requiring use of other sources.



Page 13

5 Reference Energy ForecastThe Reference Energy Forecast is built up of a number of elements. Figure 5.1summarizes the aggregation of the different elements of BC Hydro’s referenceenergy forecast.

Figure 5.1. BC Hydro Load Forecast Build-up

5.1. Comparison between October 2004 and December 2004 Forecasts

As discussed previously this December 2004 forecast is an update of theOctober 2004 forecast and includes the rate impact of BCUC approved rateincrease of 4.85%. This rate increase represents a decline from the assumedrate increase of 8.95% used in the October 2004 forecast. This relativedecrease in rates represents approximately a 1.0 % increase in BC Hydro’sintegrated system total gross requirements and a 0.8 % change in Integratedsystem peak. Table 5.1 summarizes the differences between the two forecasts.

-

IntegratedSystem

Total

Gross

Requirements

Non-integrated

Requirements

Total Gross

Requirements

Total

Firm

Sales

Losses

(Transmission

& Distribution)

Total

Domestic

Sales

Firm Exports

Seattle City Lights

Hyder Alaska

Alberta Power

Domestic Inter-Utility

City of New Westminster

Fortis BC

BCH Service Area Sales

Residential

Commercial

Industrial

+

+

+



Page 14

Table 5.1 Comparison October 2004 v. December 2004 Integrated SyetemForecast

Total Gross Requirements (GWh) Integrated System Peak (MW)

Oct 2004 Dec 2004 % Change Oct 2004 Dec 2004 % Change

2004/05 55,884 56,076 0.34% 10,044 10,098 0.54%

2005/06 56,673 57,118 0.79% 10,136 10,209 0.72%

2006/07 57,538 58,049 0.89% 10,297 10,372 0.73%

2007/08 58,463 58,997 0.91% 10,451 10,528 0.74%

2008/09 59,442 60,021 0.97% 10,645 10,720 0.70%

2009/10 59,917 60,511 0.99% 10,771 10,843 0.67%

2010/11 61,035 61,662 1.03% 10,952 11,027 0.68%

2011/12 61,532 62,146 1.00% 11,052 11,133 0.73%

2012/13 62,522 63,155 1.01% 11,215 11,296 0.72%

2013/14 63,491 64,099 0.96% 11,360 11,445 0.75%

2014/15 64,513 65,162 1.01% 11,499 11,587 0.77%

2015/16 65,525 66,168 0.98% 11,655 11,746 0.78%

2016/17 66,528 67,183 0.98% 11,814 11,904 0.76%

2017/18 67,543 68,189 0.96% 11,979 12,072 0.78%

2018/19 68,559 69,249 1.01% 12,146 12,240 0.77%

2019/20 69,632 70,289 0.94% 12,314 12,408 0.76%

2020/21 70,700 71,390 0.97% 12,475 12,568 0.75%

2021/22 71,785 72,443 0.92% 12,636 12,732 0.76%

2022/23 72,887 73,553 0.91% 12,819 12,914 0.74%

2023/24 74,001 74,664 0.90% 13,004 13,103 0.76%

2024/25 75,126 75,811 0.91% 13,193 13,290 0.74%

5.2. Reference Forecast Before Power Smart

Table 5.2 provides a summary of historical sales and peaks and the referenceforecast before Power Smart, that is, before considering the effects of BCHydro’s demand-side management program (See Sections 8, 9 and 10 for theindividual sector forecasts). BC Hydro’s total domestic sales before PowerSmart include residential, commercial and industrial sales for the BC Hydroservice area as well as sales to New Westminster and Fortis BC. BC Hydro’stotal domestic sales before Power Smart are expected to grow from49,960 GWh in 2003/04 to 68,973 GWh in 2024/25. BC Hydro’s total grossrequirements include total domestic sales, firm exports, losses and non-integrated areas. BC Hydro’s total gross requirements before Power Smart areexpected to grow from 55,187 GWh in 2003/4 to 76,215 GWh in 2024/25.

Growth rates of sales vary significantly by sector but within a given sector arefairly consistent over time. For the residential sector, the growth rates of salesbefore Power Smart are 1.8 per cent for the five years from 2003/04 to 2008/09;1.9 per cent for the 11 years from 2003/04 to 2014/15; and 1.8 per cent in the21 years of the forecast 2003/04 to 2024/25.

For the commercial sector, the growth rates of sales before Power Smart are2.3 per cent for the five years from 2003/04 to 2008/09; 2.1 per cent for the 11years from 2003/04 to 2014/15; and 1.9 per cent in the 21 years of the forecast,2003/04 to 2024/25.

For the industrial sector, the growth rates of sales before Power Smart are1.4 per cent for the five years from 2003/04 to 2008/09; 0.9 per cent for the 11



Page 15

years from 2003/04 to 2014/15; and 1.0 per cent in the 21 years of the forecast,2003/04 to 2024/25.

For total gross requirements, the growth rates of sales before Power Smart are1.8 per cent for the five years from 2003/04 to 2008/09; 1.6 per cent for the 11years from 2003/04 to 2014/15; and 1.5 per cent in the 21 years of the forecast,2003/04 to 2024/25.

BC Hydro’s total integrated system peak (system coincident basis excludingPowerex and related losses) before Power Smart is expected to grow from10,103 MW (9,754 MW weather normalized) in 2003/04 to 13,290 MW in2024/25. The five-year growth rate from 2003/04 to 2008/09 is 1.2 per cent (1.9per cent weather normalized). The 11-year growth rate from 2003/04 to 2014/15is 1.3 per cent (1.6 per cent weather normalized). The 21-year growth rate from2003/043 to 2024/05 is 1.3 per cent (1.5 per cent weather normalized).

Table A8.1 in Appendix 8 provides additional details of the 2004 forecast beforePower Smart.



Page 16

Table 5.2. Reference Forecast Before Power SmartResiden-

tial

(GWh)

Commer-

cial

(GWh)

Indust-

rial

(GWh)

Total

Domestic

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

Sys. Peak*

(MW)

Actual (not weather-normalized)

1997/98 13,802 12,466 16,339 43,072 48,342 8,566 (8,672)

1998/99 13,972 12,814 18,077 45,513 50,897 9,026

1999/00 14,572 13,176 17,890 46,376 51,534 8,646

2000/01 14,573 13,654 18,579 47,891 52,978 9,320

2001/02 15,090 13,583 17,739 47,473 52,567 9,003

2002/03 15,287 13,729 18,596 48,685 53,298 8,824

2003/04 15,899 14,151 18,725 49,960 55,187 10,103*(9,754)

Forecast (Residential Energy and System Peak forecasts assume “normal weather”)

2004/05 15,844 14,629 19,381 51,051 56,375 10,098

2005/06 16,366 14,898 19,563 51,954 57,418 10,209

2006/07 16,675 15,242 19,732 52,800 58,352 10,372

2007/08 17,033 15,518 19,903 53,660 59,303 10,528

2008/09 17,402 15,858 20,059 54,595 60,333 10,720

2009/10 17,770 16,207 19,757 55,028 60,828 10,843

2010/11 18,143 16,570 20,052 56,079 61,986 11,027

2011/12 18,495 16,815 19,865 56,509 62,476 11,133

2012/13 18,866 17,117 20,093 57,430 63,490 11,296

2013/14 19,195 17,386 20,336 58,291 64,439 11,445

2014/15 19,564 17,710 20,588 59,260 65,507 11,587

2015/16 19,885 18,025 20,847 60,176 66,519 11,746

2016/17 20,248 18,322 21,096 61,104 67,540 11,904

2017/18 20,571 18,638 21,358 62,022 68,552 12,072

2018/19 20,959 18,937 21,620 62,989 69,618 12,240

2019/20 21,265 19,280 21,902 63,937 70,665 12,408

2020/21 21,645 19,626 22,165 64,942 71,771 12,568

2021/22 21,961 19,983 22,437 65,904 72,831 12,732

2022/23 22,329 20,342 22,705 66,915 73,947 12,914

2023/24 22,664 20,722 22,987 67,926 75,063 13,103

2024/25 23,018 21,095 23,293 68,973 76,215 13,290

Growth Rates

5 years:98/99 to 03/04 2.6% 2.0% 0.7% 1.9% 1.6% 2.3% (1.6%)

5 years:03/04 to 08/09 1.8% 2.3% 1.4% 1.8% 1.8% 1.2% (1.9%)

11 years:03/04 to 14/15 1.9% 2.1% 0.9% 1.6% 1.6% 1.3% (1.6%)

last 21 years:03/04 to 24/25 1.8% 1.9% 1.0% 1.5% 1.5% 1.3% (1.5%)

*Values shown in brackets are based on weather normalized actual values.



Page 17

5.3. Reference Forecast With Power Smart

Table 5.3 provides a summary of historical sales and peaks and the referenceforecast with Power Smart, that is, including the effects of BC Hydro’s demand-side management program. BC Hydro’s total domestic sales with Power Smartinclude residential, commercial and industrial sales for the BC Hydro servicearea as well as sales to New Westminster and Fortis BC. BC Hydro’s totaldomestic sales with Power Smart are expected to grow from 49,960 GWh in2003/04 to 66,586 GWh in 2024/25. BC Hydro’s total gross requirementsinclude total domestic sales, firm exports and losses. BC Hydro’s total grossrequirements with Power Smart are expected to grow from 55,187 GWh in2003/04 to 73,605 GWh in 2024/25.

It should be noted that new Power Smart activities reflect the current 10-yearPower Smart Plan only.

Again, growth rates of sales vary significantly by sector but within a given sectorare fairly consistent over time. For the residential sector, the growth rates ofsales with Power Smart are 1.4 per cent for the five years from 2003/04 to2008/09; 1.6 per cent for the 11 years from 2003/04 to 2014/15; and 1.7 percent for the 21 years from 2003/04 to 2024/25.

For the commercial sector, the growth rates of sales with Power Smart are1.7 per cent for the five years from 2003/04 to 2008/09; 1.7 per cent for the 11years from 2003/04 to 2014/15; and 1.8 per cent for the 21 years from 2003/04to 2024/25.

For the industrial sector, the growth rates of sales with Power Smart are initiallynegative at -0.1 per cent for the five years from 2003/04 to 2008/09; 0.1 per centfor the 11 years from 2003/04 to 2014/15; and 0.8 per cent for the 21 years from2003/04 to 2024/25.

For total gross requirements, the growth rates of sales with Power Smart are0.9 per cent for the five years from 2003/04 to 2008/09; 1.1 per cent for the 11years from 2003/04 to 2014/153; and 1.4 per cent for the 21 years from 2003/04to 2024/25.

BC Hydro’s total integrated system peak (system coincident basis excludingPowerex and related losses) with Power Smart is expected to grow from 10,103MW (9,754MW weather normalized) in 2003/04 to 12,919 MW in 2023/24. Thefive-year growth rate from 2003/04 to 2008/09 is 0.5 per cent (1.2 per centweather normalized). The 11-year growth rate from 2003/04 to 2014/15 is0.9 per cent (1.2 per cent weather normalized). The 21-year growth rate from2003/04 to 2024/25 is 1.2 per cent (1.3% weather normalized).

The following points are worth noting with respect to the peak forecast:

• In the 2004 the distribution peak forecast was revised to reflect changesin the design temperature as well as changes in the 30 year historicalperiod used to determine the average coldest day.

• The transmission peak forecast has been updated to reflect restructuringoccurring among B.C.’s resource-based industries. The forecast alsoincludes the closing of Highland Valley Copper beginning in 2007/08 inthe South Interior.

• Capacity savings estimates related to Power Smart impacts for eachregion and the systems were provided by Power Smart.



Page 18

Table A8.2 in Appendix 8 provides additional details of the 2004 forecast withPower Smart.

Table 5.3. Reference Forecast With Power SmartResiden-

tial

(GWh)

Commer-

cial

(GWh)

Industrial

(GWh)

Total

Domestic

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

Sys. Peak*

(MW)

Actual (not weather normalized)

1997/98 13,802 12,466 16,339 43,072 48,342 8,566 (8.672)

1998/99 13,972 12,814 18,077 45,513 50,897 9,026

1999/00 14,572 13,176 17,890 46,376 51,534 8,646

2000/01 14,573 13,654 18,579 47,891 52,978 9,320

2001/02 15,090 13,583 17,739 47,473 52,567 9,003

2002/03 15,287 13,729 18,596 48,685 53,339 8,824

2003/04 15,899 14,151 18,725 49,960 55,184 10,103(9,754)

Forecast (Residential and System Peak forecasts assume “normal weather”)

2004/05 15,698 14,526 19,229 50,649 55,935 10,033

2005/06 16,145 14,757 19,015 51,044 56,427 10,069

2006/07 16,398 14,981 18,887 51,417 56,847 10,161

2007/08 16,696 15,108 18,701 51,712 57,183 10,232

2008/09 17,010 15,363 18,659 52,308 57,844 10,372

2009/10 17,330 15,683 18,296 52,604 58,188 10,473

2010/11 17,652 16,010 18,530 53,506 59,184 10,633

2011/12 17,946 16,221 18,269 53,770 59,492 10,713

2012/13 18,289 16,502 18,452 54,597 60,403 10,862

2013/14 18,622 16,758 18,693 55,448 61,341 11,009

2014/15 18,999 17,070 18,957 56,424 62,416 11,153

2015/16 19,329 17,385 19,253 57,386 63,478 11,318

2016/17 19,692 17,679 19,543 58,352 64,539 11,481

2017/18 20,015 17,992 19,826 59,289 65,571 11,651

2018/19 20,403 18,282 20,112 60,271 66,653 11,821

2019/20 20,709 18,621 20,439 61,259 67,742 11,995

2020/21 21,089 18,985 20,818 62,398 68,992 12,175

2021/22 21,405 19,366 21,225 63,519 70,224 12,361

2022/23 21,774 19,724 21,492 64,528 71,337 12,543

2023/24 22,108 20,103 21,776 65,540 72,455 12,731

2024/25 22,462 20,476 22,081 66,586 73,605 12,919

Growth Rates

5 years:98/99 to 03/04 2.6% 2.0% 0.7% 1.9% 1.6% 2.3% (1.6%)

5 years:03/04 to 08/09 1.4% 1.7% -0.1% 0.9% 0.9% 0.5% (1.2%)

11 years:03/04 to 13/14 1.6% 1.7% 0.1% 1.1% 1.1% 0.9% (1.2%)

21 years:03/04 to 24/25 1.7% 1.8% 0.8% 1.4% 1.4% 1.2% (1.3%)




Page 19

Comparing the growth rates in Tables 5.1 and 5.2 for energy and for peak bothbefore and with Power Smart, note that:

• First, energy before Power Smart tracks the key economic drivers realGDP and employment fairly closely. This largely reflects the importanceof real GDP and employment as forecast drivers and their stronghistorical relationships to energy consumption.

• Second, applying the full Power Smart targets reduces energy growthsubstantially for the first 11 years, but has less impact over latter part ofthe forecast period. This reflects the absence of new planned/approvedPower Smart activity after the current 10-year Power Smart plan.

• Third, peak is growing more quickly than energy on a weathernormalized basis. This is due to two main factors: increase in the relativeshare of residential energy compared to industrial energy; and increasein the relative share of commercial energy compared to industrialenergy.

Figure 5.2 and 5.3 represent the reference forecast before and with PowerSmart for total gross requirements and peak respectively.

Figure 5.2. Reference Forecast Before and With Power Smart – TotalGross Requirements (GWh)

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1998

/99

1999

/00

2000

/01

2001

/02

2

002/

03

2003

/04

2004

/05

2005

/06

2006

/07

2007

/08

2008

/09

2009

/10

2010

/11

2011

/12

2012

/13

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

2024

/25

Before PS

With PS



Page 20

Figure 5.3. Reference Forecast Before and With Power Smart – IntegratedSystem Peak (MW)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1

998/9

9

1999

/00

2000

/01

2001

/02

2

002/0

3

2

003/0

4

2004

/05

2005

/06

2006

/07

2

007/0

8

2

008/0

9

2009

/10

2010

/11

2

011/1

2

2

012/1

3

2

013/1

4

2014

/15

2015

/16

2

016/1

7

2

017/1

8

2

018/1

9

2019

/20

2020

/21

2

021/2

2

2

022/2

3

2

023/2

4

2024

/25

Before PS

With PS



Page 21

6 Comparison Between 2003/04 and 2004/05ForecastsThis section compares the December 2004 energy forecast with the October2003 forecast and discusses the differences between them. Much of thedifferences between the two forecast are directly a result of increases inconsumption due to stronger than expected economic performance in 2003/04and decreases in the forecast sales due to rate increases.

6.1. Total Gross Requirements Forecast

Table 6.1 compares the forecast for total gross requirements for the December2004 reference forecast including the effects of Power Smart with the October2003 forecast including Power Smart. For all years, the December 2004forecast is higher than the October 2003 forecast. The increase in the grossrequirements between the October 2003 and the December 2004 forecasts isshown in the table in GWh and as a percentage in parentheses.

For 2004/05, the 2004 forecast is above the 2003 forecast by 1,207 GWh. Muchof the increase in 2004/05 is a result of higher than expected sales to all sectorsin 2004/05 increasing the total gross requirements 2003/04 anchor by 624GWh. For 2014//15, the 2004 forecast is 1,328 GWh above the 2003 forecast,while for 2023/24 the 2004 forecast is 2,780 GWh above the 2003 forecast.



Page 22

Table 6.1. Comparison of Reference Energy Forecasts With Power Smart:Gross System Requirements

October

2003

Forecast

(GWh)

December

2004

Forecast

(GWh)

December

2004 minus

October 2003

(GWh)

1998/99 50,897* 50,897* -

1999/00 51,534* 51,354* -

2000/01 52,978* 52,978* -

2001/02 52,567* 52,567* -

2002/03 53,298* 53,298* -

2003/04 54,563 55,187* 624 (1.1%)

2004/05 54,728 55,935 1,207(2.2%)

2005/06 55,086 56,427 1,341 (2.4%)

2006/07 55,454 56,847 1,393 (2.5%)

2007/08 56,075 57,183 1,108 (2.0%)

2008/09 56,641 57,844 1,203 (2.1%)

2009/10 57,210 58,188 978 (1.7%)

2010/11 57,810 59,184 1,374 (2.4%)

2011/12 58,410 59,492 1,082 (1.9%)

2012/13 59,298 60,403 1,105 (1.9%)

2013/14 60,169 61,341 1,172 (1.9%)

2014/15 61,088 62,416 1,328 (2.2%)

2015/16 62,018 63,478 1,460 (2.4%)

2016/17 62,910 64,539 1,629 (2.6%)

2017/18 63,832 65,571 1,739 (2.7%)

2018/19 64,758 66,653 1,895 (2.9%)

2019/20 65,709 67,742 2,033 (3.1%)

2020/21 66,699 68,992 2,293 (3.4%)

2021/22 67,657 70,224 2,567 (3.8%)

2022/23 68,656 71,337 2,681 (3.9%)

2023/24 69,675 72,455 2,780 (4.0%)

NB. * = actuals

6.2. Residential Forecast

Table 6.2 compares the December 2004 residential reference forecast includingPower Smart with the October 2003 forecast including Power Smart. Forresidential sales, the 2004 forecast is below the 2003 forecast by 118 GWh for2004/05 (due mostly to the assumptions around increasing electricity rates),above the 2003 forecast by 665 GWh in 2014/15 and above the 2003 forecastby 1,012 GWh in 2023/24. The main reasons for the increase in forecastresidential sales for most of the forecast period are the uplift in the anchor pointfor 2003/04 and the increase in the forecast number of residential accounts.



Page 23

Table 6.2. Comparison of Reference Energy Forecasts With Power Smart:Residential Sales

October

2003

Forecast

(GWh)

December

2004

Forecast

(GWh)

December

2004 minus

October 2003

(GWh)

1998/99 13,972* 13,972* -

1999/00 14,572* 14,572* -

2000/01 14,573* 14,573* -

2001/02 15,090* 15,090* -

2002/03 15,287* 15,287* -2003/04 15,638 15,899* 261 (1.7%)

2004/05 15,816 15,698 -118 (-0.7%)

2005/06 16,042 16,145 103 (0.6%)

2006/07 16,280 16,398 118 (0.7%)

2007/08 16,526 16,696 170 (1.0%)

2008/09 16,730 17,010 280 (1.7%)

2009/10 16,950 17,330 380 (2.2%)

2010/11 17,171 17,652 481 (2.8%)

2011/12 17,410 17,946 536 (3.1%)

2012/13 17,716 18,289 573 (3.2%)

2013/14 18,023 18,622 599 (3.3%)

2014/15 18,334 18,999 665 (3.6%)

2015/16 18,646 19,329 683 (3.7%)

2016/17 18,957 19,692 735 (3.9%)

2017/18 19,267 20,015 748 (3.9%)

2018/19 19,576 20,403 827 (4.2%)

2019/20 19,882 20,709 827 (4.2%)

2020/21 20,188 21,089 901 (4.5%)

2021/22 20,492 21,405 913 (4.5%)

2022/23 20,794 21,774 980 (4.7%)

2023/24 21,096 22,108 1,012 (4.7%)

NB. * = actuals are not weather adjusted

6.3. Commercial Forecast

Table 6.3 compares the October 2004 commercial reference forecastincluding Power Smart with the 2003 forecast including Power Smart. Forcommercial sales, the 2004 forecast is above the 2003 forecast by 656GWh for 2004/05, above the 2003 forecast by 670 GWh in 2014/15 andabove the 2003 forecast by 806 GWh in 2023/24. The main reasons for theincrease in forecast commercial sales are the uplift in the anchor point for2003/04 and the increase in forecast GDP, which affects the commercialsector.



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Table 6.3. Comparison of Reference Energy Forecasts With Power Smart:Commercial Sales

October

2003

Forecast

(GWh)

December

2004

Forecast

(GWh)

December 2004

minus

October 2004

(GWh)

1998/99 12,814* 12,814* -

1999/00 13,176* 13,176* -

2000/01 13,654* 13,654* -

2001/02 13,583* 13,583* -

2002/03 13,729* 13,729* -2003/04 13,843 14,151* 308 (2.2%)

2004/05 13,870 14,526 656 (4.7%)

2005/06 14,011 14,757 746 (5.3%)

2006/07 14,208 14,981 773 (5.4%)

2007/08 14,467 15,108 641 (4.4%)

2008/09 14,718 15,363 645 (4.4%)

2009/10 14,993 15,683 690 (4.6%)

2010/11 15,280 16,010 730 (4.8%)

2011/12 15,537 16,221 684 (4.4%)

2012/13 15,834 16,502 668 (4.2%)

2013/14 16,101 16,758 657 (4.1%)

2014/15 16,400 17,070 670 (4.1%)

2015/16 16,706 17,385 679 (4.1%)

2016/17 16,979 17,679 700 (4.1%)

2017/18 17,273 17,992 719 (4.2%)

2018/19 17,577 18,282 705 (4.0%)

2019/20 17,900 18,621 721 (4.0%)

2020/21 18,253 18,985 732 (4.0%)

2021/22 18,576 19,366 790 (4.3%)

2022/23 18,930 19,724 794 (4.2%)

2023/24 19,297 20,103 806 (4.2%)

NB. * = actuals

6.4 Industrial Forecast

Table 6.4 compares the October 2004 industrial reference forecast includingPower Smart with the October 2003 forecast including Power Smart. Forindustrial sales, the 2004 forecast is above the 2003 forecast by 1,002 GWh for2004/05, above the 2003 forecast by 341 GWh in 2014/15 and 1,294 GWhabove the 2003 forecast in 2023/24. The main reasons for the increase inindustrial sales are the uplift in the anchor point for 2003/04 by and the increasein forecast GDP.



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Table 6.4. Comparison of Reference Energy Forecasts With Power Smart:Industrial Sales

October

2003

Forecast

(GWh)

December

2004

Forecast

(GWh)

December

2004 minus

October 2003

(GWh)

1998/99 18,077* 18,077* -

1999/00 17,890* 17,890* -

2000/01 18,579* 18,579* -

2001/02 17,739* 17,739* -

2002/03 18,596* 18,596* -

2003/04 18,268 18,725* 457 (2.5%)

2004/05 18,227 19,229 1,002 (5.5%)

2005/06 18,154 19,015 861 (4.7%)

2006/07 18,044 18,887 843 (4.7%)

2007/08 18,069 18,701 632 (3.5%)

2008/09 18,057 18,659 602 (3.3%)

2009/10 18,047 18,296 249 (1.4%)

2010/11 18,052 18,530 478 (2.6%)

2011/12 18,068 18,269 201 (1.1%)

2012/13 18,242 18,452 210 (1.2%)

2013/14 18,426 18,693 267 (1.4%)

2014/15 18,616 18,957 341 (1.8%)

2015/16 18,809 19,253 444 (2.4%)

2016/17 19,008 19,543 535 (2.8%)

2017/18 19,213 19,826 613 (3.2%)

2018/19 19,412 20,112 700 (3.6%)

2019/20 19,615 20,439 824 (4.2%)

2020/21 19,826 20,818 992 (5.0%)

2021/22 20,039 21,225 1,186 (5.9%)

2022/23 20,256 21,492 1,236 (6.1%)

2023/24 20,482 21,776 1,294 (6.3%)

NB. * = actuals

6.5. Peak Forecast

Compared to 2003, the peak forecast is increased in the short term to reflect theadjustment of the anchor point, current economic conditions and revisedforecast drivers. On an integrated system total basis, the 2004 forecastincluding Power Smart (9,620 MW) is 134 MW higher for 2004/05; 162 MWhigher for 2014/15; and 163 MW higher for 2023/24.

The following points are worth noting with respect to the peak forecast:

An adjustment to the anchor point to reflect the most recent 30 years oftemperature data and the incorporation of actual weather sensitivityexperience from the January 2004 peak resulting in a net increase.

• The distribution peak forecast was revised upward for 2003/04 to reflectchanges in the 2003 population and employment forecasts compared to2003. A gradual recovery is projected to occur over the next five years,



Page 26

resulting in higher growth over the longer term, using 2003/04 as a baseyear.

• The transmission peak forecast has been updated to reflect restructuringoccurring among BC’s resource-based industries. The forecast alsoincludes the closing of a large copper mine in 2007/08 in the SouthInterior.

Table 6.5. Comparison of Reference Peak Forecasts With Power SmartOctober

2003

Forecast

(MW)

October

2004

Forecast

(MW)

December 2004

minus

October 2003

(MW)

1998/99 9,077* 9,077* -

1999/00 8,646* 8,646* -

2000/01 9,320* 9,320* -

2001/02 9,003* 9,003* -

2002/03 8,824* 8,824* -

2003/04 9,620 10,103*(9,754**)

483 (5.0%)134 (1.4%)

2004/05 9,687 10,033 346 (3.6%)

2005/06 9,787 10,069 282 (2.9%)

2006/07 9,881 10,161 280 (2.8%)

2007/08 10,017 10,232 215 (2.1%)

2008/09 10,144 10,372 228 (2.2%)

2009/10 10,274 10,473 199 (1.9%)

2010/11 10,385 10,633 248 (2.4%)

2011/12 10,502 10,713 211 (2.0%)

2012/13 10,660 10,862 202 (1.9%)

2013/14 10,816 11,009 193 (1.8%)

2014/15 10,991 11,153 162 (1.5%)

2015/16 11,166 11,318 152 (1.4%)

2016/17 11,342 11,481 139 (1.2%)

2017/18 11,517 11,651 134 (1.2%)

2018/19 11,692 11,821 129 (1.1%)

2019/20 11,867 11,995 128 (1.1%)

2020/21 12,043 12,175 132 (1.1%)

2021/22 12,218 12,361 143 (1.2%)

2022/23 12,393 12,543 150 (1.2%)

2023/24 12,568 12,731 163 (1.3%)

* = actuals and ** = weather normalized actual for 2003/04



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7 Sensitivity AnalysisBC Hydro’s load forecast is sensitive to number of variables including weather,economic conditions, price, etc. BC Hydro’s analysis has looked specifically atthe sensitivity of the load to changes in the economy (GDP) and the price ofelectricity. In addition, a Monte Carlo analysis has been completed to look at thesensitivity of the load to a combination five causal factors that impact theforecast.

For peak load, a sensitivity for weather has also been developed thatestablishes the peak on the basis of a 1 day in 10 year coldest day, versus thecurrent method that assumes an average coldest day.

7.1. Monte Carlo Analysis

A Monte Carlo analysis was completed for the forecast to reflect a range ofuncertainties implicit in the load forecast that includes factors beyond GDP andprice. Monte Carlo analysis is a technique for estimating probabilities involvingthe construction of a model and the simulation of the outcome of an activity alarge number of times. Random sampling techniques are used to generate arange of outcomes. Probabilities are estimated from an analysis of this range ofoutcomes.

Five major non-weather causal factors were used to analyze the sensitivity ofthe forecast. These include: economic growth rate (reflected by GDP); theelectricity rate; the effective energy reduction achieved by demand-sidemanagement (DSM) programs; the response to electricity price changes (priceelasticity); and electricity intensity.

Probability distributions were assigned to each of these factors. Three values(low, probable and high) were established to reflect possible future levels ofeach of the factors, with a probability assigned to each.

An uncertainty model employing Monte Carlo simulation methods was used toquantify and combine the probability distributions, reflecting the relationshipsbetween the five causal factors and electricity consumption. A probabilitydistribution was thus obtained which showed the likelihood of various load levelsresulting from the combined effect of the five factors. This distribution is bandedby:

• The low scenario: There is a 10 per cent chance the outcome will bebelow this value.

• The high scenario: There is a 10 per cent chance that the outcome willexceed this value.

Table 7.1 summarizes the results of calibrating the results of the Monte Carlouncertainty analysis on the energy and peak forecast before Power Smart.



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Table 7.1 Monte Carlo Analysis – Energy and Peak Before Power SmartLow Scenario Reference Forecast High Scenario

Total Gross

Requirements

Integrated

System Peak

Total Gross

Requirements

Integrated

System Peak

Total Gross

Requirements

Integrated

System Peak

(GWh) (MW) (GWh) (MW) (GWh) (MW)

2003/04 55,187 10,103 55,187 10,103 55,187 10,103

2008/09 58,885 10,462 60,333 10,720 61,810 10,982

2014/15 62,619 11,077 65,507 11,587 68,557 12,127

2024/25 70,607 12,312 76,125 13,290 82,476 14,382

Growth Rates

5 years03/04 to 08/09

1.3% 0.7% 1.8% 1.2% 2.3% 1.7%

11 years03/04 to 14/15

1.2% 0.8% 1.6% 1.3% 2.0% 1.7%

21 years03/04 to 24/25

1.2% 0.9% 1.5% 1.3% 1.9% 1.7%

7.2. Uncertainty Assumptions

For each of the five major causal factors, a probability distribution defined bylow, probable and high scenarios was assigned.

(a) Long-Term Economic Growth (GDP)

The long-term growth scenarios used were based on average annual GDPincreases with a standard deviation of 0.5 per cent.

(b) Electricity Rates

The probable scenario assumes that electricity prices will increase at the rate ofinflation (i.e. no increase in real terms). The low and high scenarios assume thatannual electricity rate changes will be within a 2.5 per cent band of the probablerate changes.

(c) Effective Energy Reduction of Demand-Side Management (DSM) Programs

The annual low, probable and high reductions from DSM used were 50 per cent,100 per cent and 150 per cent, respectively, of the expected reductions.

(d) Response to Electricity Price Changes (Elasticity)

The elasticity of electricity demand measures the change in the consumption ofelectricity in response to changes in variables influencing such demand.However, estimates of elasticity are subject to considerable uncertainty.

(e) Electricity Intensity

Changes in overall electricity intensity are assumed to be within a 0.2 per centband of the annual change in electricity intensity for that year.

7.3 Temperature Sensitivity of Peak Demand

The forecasts and uncertainty bands described in Table 7.1 assume normalweather. They do not include the effect of colder or warmer than normal(30-year average) temperatures.



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8. Residential Forecast

8.1. Summary

Characteristics - Of the three customer classes, residential, commercial, andindustrial, the residential sector is the most stable. Growth in the number ofresidential accounts is about the same as growth in population, which iscurrently about 1.2%. After many years of strong growth in use per account,growth in use per account is forecast to be moderate growing at less than 1%over the entire forecast period. Of the 1.46 million residential accounts servedat the end of fiscal year 2003/04, 60% were single/duplex, 7% were row houses,25% were apartments, and 8% were mobile and miscellaneous.Geographically, 58% of the residential accounts are in the Lower Mainland, 10%are in the North Region, 11% are in the South Interior, and 21% are onVancouver Island. Vancouver Island has the highest percentage of electricallyheated accounts because of the limited availability of natural gas. On a salesbasis, 53% of residential sales were made in the Lower Mainland, 10% in thenorth interior, 11% in the south interior, and 26% in Vancouver Island.

Drivers – The drivers of the residential forecast are number of accounts andaverage annual use per account. Number of accounts is driven by housingstarts which is in turn driven by population growth and the trend in people peraccount. Since household size is gradually decreasing, account growth isexpected to be a little higher than population growth. Account growth can varyconsiderably from year to year in response to BC’s economy. In the mid 1990's,about 38,000 accounts were added annually, but by the early 2000's, thatnumber fell to about 13,000. As the economy pulls out of the slump of the early2000's, account growth is forecast to be about 25,000 - almost double thenumber of new accounts of a few years ago, but only about two thirds of thenumber of new accounts added 10 years ago. With growth expected in both thenumber of accounts and use per account, growth in sales is forecast to be about1.7% over the entire forecast period.

Trends – At the end of 2007/08, the new forecast for number of accounts is1,569,205, which is 6,399 or 0.4% above the previous forecast. At the end of2014/15, the new forecast for number of accounts is 1,771,935, which is 55,065or 3.2% above the previous forecast. At the end of 2023/24, the new forecast fornumber of accounts is 2,006,391, which is 89,755 or 4.7% above the previousforecast.

Electricity Use– BC Hydro’s residential sector currently consumes about 33%of BC Hydro’s total annual billed sales. This electricity is used to provide arange of services (often called end-uses). The largest end-uses in theresidential sector are space heating, water heating, refrigeration, andmiscellaneous plug-in loads which includes computer equipment and homeentertainment systems. Because space and water heating loads are dependenton the outside temperature, monthly residential sales can be strongly affectedby the weather, but sales variations due to the weather tend to average out overthe long term.

8.2. Forecast Methodology and Major Trends

The forecast for residential sales is calculated as forecast number of accountstimes forecast use per account (use rate). For the first year of the forecast,



Page 30

growth in number of accounts forecast is based on recent trends. For allsubsequent years, percentage growth in number of accounts is set equal topercentage growth in forecast housing starts, which are provided by aconsultant.

The use rate forecast is based on projections of factors such as housing mix(single family, row house, apartment, etc.), heating fuel choices (electric versusnon-electric), appliance penetration rates, appliance life span and changes inelectricity demands.

Ten years ago the average residential weather-normalized use rate was10,350 kWh per year, and was increasing by about 125 kWh per year. However,growth in use rate has declined since then, with use rate currently at10,800 kWh per year, and growing by only an average of 25 kWh per year forthe last five years. Improvements in building insulation and appliance efficiencyare the main reasons for the moderation of growth in the annual residential userate. Currently, an estimated 20 per cent of BC Hydro’s residential accounts areheated electrically, and on average they require about 14,700 kWh per year.Unless regulations or new laws are introduced, this share will likely increase,although the average usage may not change much for reasons stated below.

Over the longer term, use rate is not expected to change significantly becauseof the offsetting effects of the following residential trends.

First, increased electric space heating market share is expected to be offset bysmaller housing units. Due to limited availability of land for residentialdevelopment, the trend in major metropolitan centres is expected to be towardsdenser housing. Since row houses and apartments are more likely to be builtwith electric heat than single family homes, the market share for electricallyheated housing is expected to increase. Although new row houses andapartments tend to be larger than existing similar dwellings, they are generallysmaller in size than single family homes, and therefore have lower spaceheating load requirements. The increase in market share of electric spaceheating is also offset to some extent by improvements in building standards,and by the expansion of gas service on Vancouver Island. However, natural gasprices are projected to be higher for Vancouver Island compared to theMainland over the entire forecast period. As a result, the growth in thepenetration rate of gas heating is anticipated to be slower for Vancouver Islandthan it was for the Mainland.

Second, more efficient appliances versus higher penetration. Manufacturersthroughout Canada and the United States are expected to continue to improvethe energy efficiency of major electrical appliances. As older models wear outand are replaced by newer ones, electricity consumption for major appliancessuch as refrigerators, freezers, ovens and ranges is forecast to decrease.However, new models of these major appliances tend to be larger than modelscurrently in use. As a result, some of the reduction in electricity use resultingfrom improvements in electricity efficiency will be offset by an increase inappliance size.

Third, a projected decrease in the number of people per household would tendto reduce electricity use per account. However, this reduction is expected to beoffset by an increase in the penetration level of small appliances. An increase inelectricity use is also projected from lifestyle changes and technologicalimprovements. The latter are expected to cause an increase in demand forelectronic, entertainment and telecommunication devices in the home. A trendtowards home offices is also expected to produce a long-term increase inresidential electricity consumption. In the long term, the expected overall impact



Page 31

of these various trends is that the factors working to increase the use rate willbe offset by the factors working to decrease it, leading to the use rate levelling.

8.3 Forecast Overview

To develop the residential sales forecast for the entire BC Hydro service area,the total service area was divided into four customer service regions, and aforecast was prepared for each region. These regions are Lower Mainland,Northern Region, South Interior and Vancouver Island. For each region, housingstock forecast was prepared based on the number of residential accountsforecast in the region, and on other regional factors such as trends in housingmix and gas availability.

A use rate forecast was also developed for each region based on projections ofpenetration rates and individual consumption levels by end use (space heating,water heating, major appliances and small lifestyle appliances).

The residential sales forecast for a region is the sum of the requirements foreach end use. The requirements for each end use are the product of thenumber of accounts having that end use and the energy used by an averageaccount having that end use.

Table 8.1 forecasts residential sales before Power Smart, including sales byregion. Figure 8.1 summarizes residential consumption by end use for the years2003/04 and 2024/25.

Billed residential sales were 15,899 GWh in 2003/04 but on a weathernormalised basis were slightly higher at 15,902 GWh. Residential sales, beforeincremental Power Smart impacts, are forecast to grow from 15,899 GWh in2003/04 to 17,402 GWh in 2008/09, to 19,564 GWh in 2014/15, and to23,018 GWh in 2024/25. These increases represent growth rates of 1.8 per centover the next five years (2003/04 to 2008/09), 1.9 per cent over the next 11years (2003/04 to 2013/14), and 1.8 per cent over the next 21 years of theforecast (2003/04 to 2024/25).

The two main drivers of the residential forecast are the forecast of the numberof residential accounts, and the forecast of use rate (annual electricityconsumption per residential account). These are discussed in detail below.

Forecast growth rates for future residential energy sales are below the mostrecent five-year average. The reasons for slowing growth rates for energy salesinclude the impact of the rate increase and a slowing of forecast penetration ofenergy using appliances.



Page 32

Table 8.1. Residential Sales Before Power SmartBC Hydro

Total

Lower

Mainland

Vancouver

Island

South

Interior

Northern

Region

Sales Sales Sales Sales Sales

(GWh) (GWh) (GWh) (GWh) (GWh)

Actual

1998/99 13,972 7,355 3,718 1,541 1,357

1999/00 14,572 7,670 3,909 1,583 1,409

2000/01 14,573 7,695 3,863 1,617 1,397

2001/02 15,090 7,975 4,001 1,656 1,458

2002/03 15,287 8,120 3,981 1,729 1,457

2003/04 15,899 8,447 4,123 1,803 1,526

Forecast (Residential sales forecast based on “normal weather”)

2004/05 15,844 8,413 4,153 1,754 1,524

2005/06 16,366 8,750 4,257 1,808 1,550

2006/07 16,675 8,929 4,340 1,834 1,571

2007/08 17,033 9,137 4,437 1,865 1,595

2008/09 17,402 9,344 4,543 1,897 1,618

2009/10 17,770 9,553 4,647 1,929 1,641

2010/11 18,143 9,767 4,750 1,962 1,664

2011/12 18,495 9,968 4,849 1,992 1,686

2012/13 18,866 10,181 4,951 2,025 1,710

2013/14 19,195 10,373 5,040 2,053 1,729

2014/15 19,564 10,586 5,139 2,086 1,753

2015/16 19,885 10,774 5,226 2,113 1,772

2016/17 20,248 10,985 5,323 2,146 1,795

2017/18 20,571 11,175 5,408 2,174 1,814

2018/19 20,959 11,401 5,511 2,208 1,838

2019/20 21,265 11,583 5,591 2,235 1,856

2020/21 21,645 11,806 5,690 2,269 1,880

2021/22 21,961 11,993 5,772 2,297 1,899

2022/23 22,329 12,211 5,867 2,330 1,921

2023/24 22,664 12,410 5,953 2,360 1,941

2024/25 23,018 12,620 6,044 2,392 1,962

Growth Rates

5 years:98/99 to 03/04 2.6% 2.8% 2.1% 3.2% 2.4%

5 years:03/04 to 08/09 1.8% 2.0% 2.0% 1.0% 1.2%

11 years:03/04 to 14/15 1.9% 2.1% 2.0% 1.3% 1.3%

21 years:14/15 to 24/25 1.8% 1.9% 1.8% 1.4% 1.2%

The reasons for the difference between this forecast and last year’s forecastare: (a) higher anchor point; (b) higher number of accounts forecast for 2008/09forward; and (c) different forecast use rate.

(a) Anchor Point: In 2003, the forecast called for 2003/04 weather normalizedbilled sales with Power Smart impacts to be 15,638 GWh. Actual weather



Page 33

normalized billed sales for 2003/04 were 15,902 GWh, 264 GWh or 1.69%higher than forecast.

(b) Number of Accounts: Since forecast sales are calculated by multiplyingforecast use rate by forecast accounts, the number of accounts forecast canhave a significant impact on the sales forecast. However, for the first four yearsof the forecast, the number of accounts forecast in 2004 is very similar to theprevious forecast. The ending number of accounts for 2003/04 was 1,461,897which was 3,160 accounts or 0.2% above the forecast value of 1,465,057.

(c) Use Rate: The main reason that 2003/04 sales were above forecast isbecause the annual use per account was higher than expected. Use rate in2003/04 was 182 KWh or 1.69% higher than forecast. Weather had a verysmall impact on billed sales for 2003/04 - actual billed sales were 15,899 GW.hcompared to weather normalized billed sales of 15,902 GW.h - a difference of 3GW.h or 0.02%. Therefore the variance in use rate accounted for virtually all ofthe variance in residential billed sales for 2003/04.

The 2003 forecast called for use rate to show a steady increase over the entireforecast period. In comparison, the 2004 forecast starts with a higher anchorpoint for use rate, but slower growth over the longer term. The main reason forthe decline in use rate in the near term, and the slower growth in use rate overthe long term, is assumption of the load impact associated with the 4.85%increase in electricity rates applied in 2004.



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Figure 8.1. Residential Consumption Before Power Smart by End Use forSelected Years

2003/04Space

Heating16%

WaterHeating

10%

Refrigeration12%

Freezer4%

Cooking8%

Other51%

10,900 KW.h per account

11,400 KW.h per account

2024/25Space

Heating

16%

Other

53%

Refrigeration11%Freezer

3%

Cooking9%

WaterHeating

9%



Page 35

9 Commercial Forecast

9.1. Summary

Characteristics - BC Hydro’s commercial sector provides electricity to BritishColumbia’s service sector. It is a very diverse set of BC Hydro customers whooperate a wide range of facilities such as office buildings, retail stores,institutions (i.e., hospitals and schools) and transportation infrastructure. Thelargest portions of these facilities are buildings (approx. 85%), with theremaining “non-buildings” including facilities and infrastructure such astransportation systems and public utilities.

Electricity Use – BC Hydro’s commercial sector currently consumes about 28%of BC Hydro’s total annual billed sales. This electricity is used to provide arange of energy services (end-uses) such as lighting, ventilation, heating,cooling, refrigeration, hot water, etc. These needs vary considerably betweenthe different types of buildings. Unlike the residential sector, the diversity of usein the commercial sector means that consumption in the sector is not stronglycorrelated to weather.

Drivers – At an aggregate level, consumption in the commercial sector is tiedclosely with economic activity in the province - the stronger the economy themore services need and the greater the electricity consumption of thecommercial sector. As a result future economic trends (i.e., provincial GDP -Gross Domestic Product and Employment) are good indicators of futureelectricity consumption in the sector. At a more detailed level, the consumptionin the commercial sector is driven by the number of buildings and facilities andthe amount of electricity required to meet their needs.

Trends – Electricity consumption of the commercial sector can varyconsiderably from year to year reflecting the level of activity in BC’s servicesector. During periods where the economy is strong, electricity sales tend to behigh. Sales to BC Hydro’s commercial sector in 2003/04 grew by 3.1 per centreflecting the performance of the BC economy (GDP) which grew at 2.2 per centin 2003. This is higher that the 1.3% growth in commercial sales that wasforecast last year under the expectation that the GDP would grow at 1.5 percent. The BC Economy is anticipated to continue on a positive upswing andgrow at 2.8% in 2004 and 3.1% in 2005 contributing to sustained growth in thecommercial sector.

Forecast - Billed commercial sales were 14,151 GWh in 2003/04. Commercialsales, before incremental Power Smart impacts, are forecast to grow from14,151 GWh in 2003/04 to 15,858 GWh in 2008/09, to 17,710 GWh in 2014/15,and to 21,095 GWh in 2024/25. These increases represent growth rates of2.3 per cent over the next five years (2003/04 to 2008/09), 2.1 per cent over thenext 11 years (2003/04 to 2014/15), and 1.9 per cent over the next 21 years ofthe forecast (2003/04 to 2024/25).

9.2. Approach

The commercial sales forecast is based primarily on a bottom-up or end-useforecast. This methodology focuses on the “stock” of buildings or facilities andhow they consume energy in the sector. There are also top-down componentsto the forecast driven by general economic variables (such as GDP andemployment) that account for year-to-year fluctuations in electricity sales as aresult of changes in occupancy and use of the building stock.



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The building portion of the commercial sector accounts for 83% of thecommercial sector sales. Non-buildings, which account for the remainderinclude facilities such as transportation and communication infrastructure,pipeline transport, grain elevators and utilities.

As an end-use or bottom-up forecast, BC Hydro’s commercial sector forecastfocuses on the demand for energy consuming end uses (i.e. heat, light andrefrigeration) to meet the requirements for commercial buildings. In its simplestform, the forecast is the product of the commercial sector building floor stock(i.e. the floor area in square feet) and the intensity of end-use demand per unitof floor stock. BC Hydro’s forecast disaggregates commercial buildings in theprovince into 13 building types, listed in Table 9.1, and up to 10 different enduses (including space heating, lighting, hot water, ventilation, and so on).

Table 9.1. BC Hydro Commercial Sector Building TypesSmall Office Large OfficeNon-Food Retail GroceryRestaurants WarehouseSchools Colleges/UniversitiesHotel/Motel HospitalsNursing Homes ApartmentsOther Buildings

Notes: Apartments include apartment common areas only. Other Buildings includesamusement and recreation facilities, religious organizations and protective services.

The growth of commercial floor stock depends on many different factorsincluding economic trends, population growth, demographics and employment.For example, an aging population will require an increased number of healthcare facilities and growth in tourism will be reflected in the number of hotels,restaurants and recreation facilities.

The intensity of end-use demand will change with factors such as the turnoverof building stock and the evolution of the energy end-use technology. This canhave both positive and negative impacts on energy intensity. As an example,some of the trends in new buildings that act to reduce energy intensities include:

• change to T8 linear fluorescent lighting with electronic ballasts;

• improved thermal building characteristics with higher insulation levels;

• double pane with thermal break window glazing; and

• improved cooling equipment efficiencies.

In contrast, other loads will put an upward pressure on building energyconsumption, such as:

• greater lighting levels in retail stores;

• increased use of computers and other plug loads;

• new design practices that require higher ventilation rates in somebuildings, such as in schools and hospitals; and

• increased saturation of space cooling in selected segments.



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Electricity sales in the commercial sector building stock also depend on the levelof economic activity as a whole, regardless of the installed floor stock. Forexample, electricity sales to stores, warehouses or hotels will vary with factorssuch as retail sales, wholesale/retail trade and tourism. Significant fluctuationsin electricity sales growth in the commercial sector can result from changes inthe performance of the local economy. In addition, it is assumed the growth inthe non-building commercial facilities will vary with trends in the high-leveldrivers: GDP, employment and population.

As a result, the commercial forecast relies extensively on both short- and long-term population, demographic and economic forecasts. In the short term,variations in the economy on a year-to-year basis will effect electricity sales interms of occupancy rates and/or performance of the province’s commercialbuildings and facilities. In the long term, the size and structure of the economyas well as the size and age of the population will dictate what types ofcommercial buildings and facilities are constructed to meet the needs of theprovince’s service sector. Over the long term, commercial sales growth is likelyto be influenced by factors including:

• a growing population, which increases the demand for most generalservices;

• a gradual shift in the structure of British Columbia from a goods-based toa more service-based economy;

• an aging population, which will require increased heath care services;

• increases in electric intensity, a result of greater use of electronic andinformation end-use technologies;

• continued growth in the tourism sector;

• new electricity-using technologies becoming more common incommercial establishments;

• continued growth of Vancouver as an international finance centre;

• BC’s continued role as Canada’s link with Pacific markets; and,

• the potential for further development of a high tech sector within theprovince.

9.3. Major Trends

The BC economy continues to be strongly influenced by primary resourceindustries and their associated international markets. BC’s service sector hashowever been growing significantly in recent years and currently employs80 per cent of the total provincial population and is responsible forapproximately 76 per cent of the province’s GDP. This compares to 20 per centand 24 per cent for employment and GDP for the goods-producing sector. As aresult, the service sector has been the primary employment growth engine forthe province and this trend is expected to continue. In addition, the BC servicesector is also much less susceptible to fluctuations in international markets thanthe goods-producing sector, which contributes to its stronger and more stablegrowth.

This forecast accounts for changes in BC Hydro’s electricity rates which wereapproved by the BC Utilities Commission at 4.85%.



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The following discussion outlines some of the economic trends in BC fourregions that will impact commercial activity in the province and as a result theconsumption of electricity within the sectors buildings and facilities.

9.4. Lower Mainland

The Lower Mainland and particularly Greater Vancouver is the key economicregion of BC. Its economy is highly diversified with over 80% of employment inthe region within the services sector. This isolates the region from many of the“boom and bust” cycles experienced in other parts of the province. Thisdiversity contributed to the Lower Mainland experiencing growth of 3.8% incommercial sales in F2003/04 and a 5-year historic growth of 2.5%.

Sales to the commercial sector in the Lower Mainland vary across the region.Greater Vancouver’s commercial sector growth is relatively strong due to itsextensive service and high technology sectors. In other parts of the region, amixture of agriculture, resource development and tourism will have their ownimpacts on sales. Overall, it is expected that commercial sales growth beforePower Smart in the Lower Mainland will be 2.6 per cent, 2.3 per cent and2.1 per cent over the next five, 11, and for 21 years of the forecast respectively.

The outlook for the region includes impacts associated with the region hostingthe 2010 Winter Olympics resulting in a modest number of buildings, facilitiesand infrastructure projects that are being constructed to directly support Olympicevents in the region. There is however potential for many indirect impacts whichare much more difficult to quantify that may exist beyond the 2010 time frame.

9.5. Vancouver Island

Commercial Sales on Vancouver Island grew by 1.2% in 2003/04, lower thanthe historic 5-year average annual growth of 1.5%.

Vancouver Island benefits from some economic diversity in the south andgrowing diversity in the central part of the Island. There is a strong resourcecomponent to Island’s economy particularly in the central and northern parts ofthe region.

Sales to Vancouver Island’s service sector will be the result of a variety of mixedtrends over the next few years. Overall the region’s sector is expected to growat 1.5 per cent, 1.6 per cent and 1.7 per cent over the next five, 11, and 21years of the forecast, respectively, before Power Smart.

Factors contributing to this growth include the following:

possible increases in employment in the public sector following severalyears of provincial government reductions;

some improvements in the tourism but with uncertainty resulting from astrengthening Canadian dollar;

favourable rulings in the softwood lumber disputes;

an increasingly active aquaculture sector; and,

continued attractiveness of the region as a retirement destination particularlyin the south and central.



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9.6. South Interior

Commercial Sales in BC’s South Interior grew by 2.9% in 2003/04, much higherthan 5-year historical average annual growth of 2.0%.

BC’s South has also seen economic diversification though tourism (includinggolf courses, casinos and ski resorts), through diversification of the agriculturalindustry (wine and fruit) and as a retirement centre. This bodes well for theeconomic strength of the region and in turn, stability of commercial sales.Overall the service sector comprises about 75% of the total employment of theregion compared to 25% in the goods producing sectors.

The resource sector has rebounded in 2003 through strength in the forestrysector despite the softwood lumber dispute, and positive prices of many basemetals and for coal. However other factors such as the closure of HighlandValley Copper (expected to begin in 2007/08) and uncertainties in the resourcesector will likely have a moderating effect on this growth.

The forecast for electricity sales to the commercial sector in the South Interior isanticipated to grow at 2.3 per cent, 1.9 per cent and 1.9 per cent over the nextfive, 11, and 21 years of the forecast, respectively, before Power Smart.

9.7. Northern Region

Commercial Sales in BC’s Northern Region grew by 1.4% in 2003/04, muchhigher than the 5-year historical average annual growth of –0.4%.

The Northern Region is the most resource dependent of BC’s four regionscreating many single-industry communities who are susceptible to significantswings in economic activity. This includes a dependency on forestry throughoutthe region, coal mining and oil and gas in the north-east and aquaculture in thenorth-west.

Commercial electricity sales in the Northern Region are expected to grow,before Power Smart, at 1.3 per cent, 1.0 per cent and 1.0 per cent over the nextfive, 11, and for the last 21 years of the forecast, respectively. This moderatelylow growth is a result of the expectation that the region will continue to beheavily dependent of resource-based industries. Softness in the forestry sectorparticularly associated with the mountain pine beetle infestation has contributedto a more modest economic outlook. This is expected to be partially offset by animproving outlook in the mining industry.

Table 9.2 and summarize the total BC Hydro and regional commercial sectorforecast before Power Smart.



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Table 9.2. BC Hydro Regional Commercial Sales Forecast Before PowerSmart

BC Hydro

Total

Lower

Mainland

Northern

Region

South

Interior

Vancouver

Island

Sales(GWh)

Sales(GWh)

Sales(GWh)

Sales(GWh)

Sales(GWh)

Actual

1998/99 12,814 8,213 1,216 1,232 2,153

1999/00 13,176 8,478 1,249 1,208 2,241

2000/01 13,654 8,871 1,233 1,264 2,286

2001/02 13,583 8,828 1,180 1,298 2,277

2002/03 13,729 8,938 1,178 1,323 2,290

2003/04 14,151 9,280 1,194 1,361 2,317

Forecast

2004/05 14,629 9,627 1,227 1,414 2,361

2005/06 14,898 9,851 1,234 1,432 2,382

2006/07 15,242 10,119 1,245 1,463 2,415

2007/08 15,518 10,321 1,256 1,492 2,449

2008/09 15,858 10,573 1,270 1,521 2,494

2009/10 16,207 10,848 1,279 1,545 2,534

2010/11 16,570 11,160 1,282 1,563 2,565

2011/12 16,815 11,325 1,294 1,589 2,607

2012/13 17,117 11,530 1,309 1,621 2,657

2013/14 17,386 11,721 1,320 1,648 2,697

2014/15 17,710 11,950 1,330 1,679 2,751

2015/16 18,025 12,172 1,340 1,710 2,802

2016/17 18,322 12,382 1,347 1,739 2,853

2017/18 18,638 12,606 1,355 1,770 2,908

2018/19 18,937 12,817 1,362 1,800 2,958

2019/20 19,280 13,055 1,378 1,832 3,015

2020/21 19,626 13,291 1,395 1,868 3,072

2021/22 19,983 13,537 1,410 1,904 3,133

2022/23 20,342 13,783 1,428 1,940 3,191

2023/24 20,722 14,045 1,446 1,976 3,254

2024/25 21,095 14,303 1,463 2,014 3,316

Growth Rates

5 years:98/99 to 03/04 2.0% 2.5% -0.4% 2.0% 1.5%

5 years:03/04 to 08/09 2.3% 2.6% 1.3% 2.3% 1.5%

11 years :03/04 to 14/15 2.1% 2.3% 1.0% 1.9% 1.6%

21 years:03/04 to 24/25 1.9% 2.1% 1.0% 1.9% 1.7%



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10 Industrial Forecast

10.1.Summary

Characteristics - BC Hydro’s industrial sector is concentrated in a limitednumber of industries, the most important of which are pulp and paper, woodproducts, chemicals, metal mining and coal mining. The remaining industrialload is made up of a large number of small and medium sized manufacturingestablishments.

Electricity Use - BC Hydro’s industrial sector currently consumes some 40% ofBC Hydro’s total annual billed sales This electricity is used to provide a range ofservices including fans, pumps, compression, conveyance, and processes suchas cutting, grinding, stamping, welding and electrolysis. In comparison to thecommercial sector, space conditioning, lighting, refrigeration and freezing loadsare relatively less important. They can, however, be significant in small andmedium sized facilities.

Trends - Electricity consumption in the industrial sector is quite volatile, drivensubstantially by economic conditions in the United States and Asia that affectcommodity markets. Export sales to these three countries are a key determinantof domestic industrial output and of the industrial demand for electricity. Otherkey determinants of the industrial load include increasing levels of self-generation and of co-generation.

Drivers – As in the case of the commercial sector, industrial electricityconsumption is tied closely with the level of economic activity in the province,that is there is a strong relationship between industrial electricity consumptionand provincial Gross Domestic Product. Future economic trends are a goodindicator of future electricity consumption in the industrial sector. However, thisis mediated by the fact that the industrial share of provincial GDP has beendeclining. This is accounted for in the industrial forecast through the use ofregression model based forecast that incorporate the changing relationshipbetween GDP and industrial sales.

Forecast - Billed industrial sales were 18,725 GWh in 2003/04. Forecast salesto industrial customers before Power Smart, are expected to grow from18,725 GWh in 2003/04 to 20,059 GWh in 2008/09, to 20,588 GWh in 2014/15,and to 23,293 GWh in 2024/25. These increases represent growth rates of 1.4%over the next five years (2003/04 to 2008/09), 0.9% over the next 11 years(2003/04 to 2014/15), and 1.0% over the next 21 years of the forecast (2003/04to 2024/25).

10.2. Sector Outlooks

Resource extraction and processing form the basis of BC’s industrial economy.Key activities include metal mining, coal mining, ore processing and smelting,wood extraction, saw milling, pulp and paper and chemical production.Approximately 80 per cent of BC Hydro’s sales to the industrial sector are madeto large-scale customers involved in the extraction and processing of naturalresources and the remaining are smaller manufacturing companies.

Given the importance of the forestry and sawmilling, pulp and paper and miningsectors for BC Hydro’s electricity load, this section summarises the currentsituation and prospects in each of these sectors. It should be noted that all three



Page 42

of these sectors have experienced particularly large swings in the level ofeconomic activity over the past decade.

10.2.1. Medium-Term Forestry

Shipments of lumber from British Columbia, which have recently beenaveraging between 12 billion and 13 billion board feet per year, now go almostentirely to the United States, Japan and other parts of Canada. Shipments toEuropean and other markets, which peaked at about 1.5 billion board feet in thelate 1980s, have fallen to about 500 million board feet. This means that thehealth of the B.C. lumber market depends critically on the strength of theAmerican and Japanese economies as well as the degree of market access forB.C. lumber. The American lumber market has shown strength because ofstrong housing demand, but might fall back with anticipated rising interest rates.

Key issues for B.C. lumber sales in the medium term include:

• Changes in domestic timber supply. Move to smaller, poorer quality,second growth timber on Vancouver Island and destruction of largevolumes of wood by beetles are expected to eventually raise timbercosts. In the shorter term there could be a significant increase in theallowable cut to take advantage of dead and beetle-damaged timber.

• Changes in lumber demand. On-going oversupply in the North Americanmarket are likely to continue to provide downward pressure on pricesover the medium term and lead to more rationalisation of mills and millclosures. Some mills have substantially improved productivity in recentyears by improving product flow, replacing older equipment with newerand more efficient equipment, and improving use of labour on the millfloor.

• Impact of market access disputes. On-going disputes concerning accessto the United States market create uncertainty, reduce cash flow andlimit opportunities for upgrading B.C. mills. Some mills have reacted totariffs and anti-dumping duties by increasing production and exportsrather than reducing both.

10.2.2. Medium-Term Pulp and Paper Outlook

BC pulp capacity is about 9.3 million metric tons per year with about 58%bleached softwood kraft pulp, 28% thermal mechanical pulp (TMP) or chemicalthermal mechanical pulp (CTMP), 5% unbleached kraft and about 9% in othergrades. Consolidation in the pulp and paper industry, coupled with three yearsof relatively poor markets in both North America and overseas through 2003,have led to strong pressures to rationalise production and reduce costs. Anumber of BC pulp and paper mills are reasonably high cost producerscompared to competitors, particularly in emerging markets, and rising fibre costscombined with a strong Canadian dollar have furthered weakened profitability.For the near term, however, the pulp and paper outlook is positive, driven bystrong demand in South and South East Asia.

Key issues for BC pulp and paper sales in the medium term include:

• Ongoing decline in the North American newsprint market, which isexpected to foreshadow a slow decline in the world newsprint market.However, the use of recycled newsprint may have peaked which shouldstabilise the demand for new fibre.



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• Slowing demand growth for most printing and writing paper grades, withsome limited bright spots such as directory paper. Several BCcompanies appear to be making a successful transition away from kraftpulp and newsprint to higher value products.

• Growing demand for paper products on the part of China (a positivefactor for B.C.) combined with expected increased supply in China fromnew and very large mills (a negative factor for B.C.) as well as continuedexpansion of production of newer, low cost mills in developing countries.

• Rationalisation of the pulp and paper industry, leading to decisions oninvestment and upgrading made on a global basis, with negativeimplications for high cost paper machines and pulp and paper mills,including some B.C. facilities. Several BC pulp and paper companiesannounced that they had achieved major improvements in efficiency andcost reduction in 2003.

10.2.3. Medium-Term Mining Outlook

The mining sector had gross sales of some $3.5 billion in 2003. Some 20 minescurrently purchase power from BC Hydro, with more metal mines and coalmines currently in production. Most production is for export and there isrelatively little domestic processing or manufacturing based on mineralproduction. Coal is sold primarily to Japan and China with the demand formetallurgical coal decreasing in Japan but increasing in China. Precious andbase metals are exported to a number of countries. The domestic base metalindustry had been losing ground to foreign competitors for some years, althoughthere are some signs of domestic strength.

Key issues for B.C. mining sales in the medium term include:

• An increase in the Canadian dollar has tended to reduce profitabilitybecause most costs are in Canadian dollars. At the same time, copperand gold prices have been strong so that the overall impact on cash flowhas been positive.

• Exploration in BC has been limited for several reasons includingenvironmental regulations that are stronger than in many countries andunresolved native land claims.

• Few high quality ore deposits have been found in BC in recent years,while major finds have taken place in Latin America, Africa andIndonesia.

• Recently there have been some signs of renewed interest in the miningindustry but these have yet to be translated into a significant increase innew mines.

10.3.Methodology

The main determinant of industrial electricity sales is the level of forecast activityin the industrial sector. Since long-term forecasts for industrial GDP are notavailable, the forecast uses total provincial GDP as a proxy. At present, threesources of information on GDP are used for the load forecast. These sourcesare: B.C. Ministry of Finance, Second Quarterly Report 2004; Malatest andAssociates, June 2004; and a ratio based on U.S. Department of Energyforecast for Canadian GDP, Annual Energy Outlook 2004, December 2003.



Page 44

For the period 2004 through 2008, the GDP forecast is a weighted average ofthe B.C. Ministry of Finance and the Malatest and Associates Forecast. For2009 through 2024, the GDP forecast is a weighted average of the Malatest andAssociates and the DOE forecasts.

This forecast uses a weighted average for the following reasons:

• Since the three forecasts are based on somewhat different data sourcesand methodologies, pooling information through a weighted averageforecast reduces risk;

• The B.C. Ministry of Finance forecast is not available for the 20 yearsneeded for the load forecast so the B.C. Ministry of Finance forecastneeds to be supplemented by other forecasts; and

• The weighted average forecast appears to track future outcomes betterthan a single forecast.

10.4.Industrial Models

Table 10.1 compares the industrial forecasts based on the ordinary least squares(OLS) and maximum likelihood (ML) regression analyses for Model 1 before PowerSmart. The OLS industrial forecast rises from 18,725 GWh in 2003/04 to 24,807 GWhin 2024/25. The ML forecast is very similar, rising from 18,737 GWh in 2003/04 to24,789 GWh in 2024/25. These forecasts do not include the impact of electricity pricechanges. Model 1 excludes the impacts of industrial strikes while Model 2 includesthem. For details, see Appendix 7.



Page 45

Table 10.1. Forecast Industrial Sales Before Power Smart (GWh) Model 1Year Trans-

mission

OLS

Trans-

mission

ML

Distrib-

ution

OLS

Distrib-

ution

ML

Indus-

trial

OLS

Indus-

trial

ML

1994/95 13,347 13,347 3,740 3,740 17,087 17,087

1995/96 13,948 13,948 3,682 3,682 17,630 17,630

1996/97 13,613 13,613 3,834 3,834 17,447 17,447

1997/98 12,553 12,553 3,786 3,786 16,339 16,339

1998.99 14,257 14,257 3,820 3,820 18,077 18,077

1999/00 14,062 14,062 3,828 3,828 17,890 17,890

2000/01 15,052 15,052 3,627 3,627 18,679 18,679

2001/02 13,855 13,855 3,884 3,884 17,739 17,739

2002/03 14,550 14,550 4,046 4,046 18,596 18,596

2003/04 14,832 14,832 3,893 3,893 18,725 18,725

2004/05 14,993 14,982 3,930 3,935 18,923 18,917

2005/06 15,240 15,227 3,959 3,966 19,199 19,193

2006/07 15,495 15,480 3,988 3,997 19,483 19,477

2007/08 15,749 15,732 4,018 4,028 19,767 19,760

2008/09 16,002 15,983 4,048 4,059 20,050 20,042

2009/10 16,253 16,232 4,077 4,090 20,330 20,322

2010/11 16,548 16,525 4,112 4,126 20,660 20,651

2011/12 16,777 16,752 4,139 4,154 20,916 20,906

2012/13 17,011 16,984 4,166 4,183 21,177 21,167

2013/14 17,251 17,222 4,194 4,213 21,445 21,436

2014/15 17,506 17,475 4,224 4,244 21,730 21,719

2015/16 17,769 17,735 4,255 4,276 22,024 22,011

2016/17 18,026 17,991 4,285 4,308 22,311 22,299

2017/18 18,290 18,253 4,316 4,340 22,606 22,593

2018/19 18,561 18,521 4,348 4,373 22,909 22,894

2019/20 18,838 18,796 4,380 4,407 23,218 23,203

2020/21 19,109 19,065 4,412 4,441 22,521 23,506

2021/22 19,387 19,341 4,445 4,475 23,832 23,816

2022/23 19,672 19,623 4,478 4,510 24,150 24,133

2023/24 19,963 19,912 4,512 4,546 24,475 24,458

2024/25 20,260 20,207 4,547 4,582 24,807 24,789

Table 10.2 compares the forecasts based on the OLS and ML regressionanalyses for Model 2 before Power Smart. The OLS industrial forecast risesfrom 18,725 GWh in 2003/04 to 24,420 GWh in 2024/25. The ML forecast isvery similar, rising from 18,725 GWh in 2003/04 to 24,188 GWh in 2024/25.



Page 46

Table 10.2. Forecast Industrial Sales Before Power Smart (GWh) Model 2Year Trans-

mission

OLS

Trans-

mission

ML

Distrib-

ution

OLS

Distrib-

ution

ML

Indus-

trial

OLS

Indus-

trial

ML

1994/95 13,347 13,347 3,740 3,740 17,087 17,087

1995/96 13,948 13,948 3,682 3,682 17,630 17,630

1996/97 13,613 13,613 3,834 3,834 17,447 17,447

1997/98 12,553 12,553 3,786 3,786 16,339 16,339

1998.99 14,257 14,257 3,820 3,820 18,077 18,077

1999/00 14,062 14,062 3,828 3,828 17,890 17,890

2000/01 15,052 15,052 3,627 3,627 18,679 18,679

2001/02 13,855 13,855 3,884 3,884 17,725 17,739

2002/03 14,550 14,550 4,046 4,046 18,596 18,596

2003/04 14,832 14,832 3,893 3,893 18,725 18,725

2004/05 15,001 14,932 4,003 4,052 19,004 18,984

2005/06 15,214 15,126 4,044 4,103 19,258 19,229

2006/07 15,434 15,326 4,085 4,155 19,519 19,481

2007/08 15,654 15,525 4,127 4,206 19,781 19,731

2008/09 15,873 15,723 4,168 4,258 20,041 19,981

2009/10 16,090 15,921 4,210 4,309 20,300 20,230

2010/11 16,345 16,152 4,258 4,369 20,603 20,521

2011/12 16,543 16,331 4,296 4,416 20,839 20,747

2012/13 16,745 16,515 4,334 4,463 21,079 20,978

2013/14 16,953 16,703 4,373 4,512 21,326 21,215

2014/15 17,174 16,904 4,415 4,564 21,589 21,468

2015/16 17,400 17,109 4,458 4,617 21,858 21,726

2016/17 17,623 17,311 4,500 4,670 22,123 21,981

2017/18 17,851 17,518 4,544 4,724 22,395 22,242

2018/19 18,085 17,731 4,588 4,779 22,673 22,510

2019/20 18,324 17,948 4,633 4,835 22,957 22,783

2020/21 18,559 18,161 4,677 4,890 23,236 23,051

2021/22 18,800 18,379 4,723 4,947 23,523 23,326

2022/23 19,045 18,602 4,770 5,005 23,815 23,607

2023/24 19,297 18,830 4,818 5,064 24,115 23,694

2024/25 19,554 19,063 4,866 5,125 24,420 24,188

Table 10.3 provides the final industrial sector forecast before Power Smart withadjustments for assumed rate impacts and for the anticipated closure of theHigh Valley Copper operations over fiscal years 2008/09 and 2009/10. Inaddition, the forecast load has been adjusted to reflect actual sales for the firstsix months of 2004/05. This has increased industrial distribution sales by 71GWh, industrial transmission sales by 349 GWh and total industrial sales by 420GWh for 2004/05 above the regression model based estimate. This unexpectedincrease in the industrial load suggests the possibility that no rate impact hasbeen experienced for the industrial load for the first six months of 2004/05.

The rate impacts assumes a 4.85% nominal increase. A price elasticity of -0.28is used, based on an econometric analysis of BC Hydro’s industrial sales. TheHigh Valley Copper impact assumes that consumption will be reduced by aboutone-half for each of the two years of the closure but that there will be some



Page 47

remaining load for reclamation efforts. The maximum likelihood version of Model2 is used as the base for the reasons outlined in the appendix.

Table 10.3. Forecast Industrial Sales with Rate Impacts and HighlandValley Closure Before Power Smart (GWh) Model 2

Year Distrib-

ution

ML

Trans-

mission

ML

Indus-

trial

ML

1994/95 3,740 13,347 17,087

1995/96 3,682 13,948 17,630

1996/97 3,834 13,613 17,447

1997/98 3,786 12,553 16,339

1998/99 3,820 14,257 18,077

1999/00 3,828 14,062 17,890

2000/01 3,627 15,052 18,579

2001/02 3,884 13,855 17,738

2002/03 4,046 14,550 18,596

2003/04 3,918 14,819 18,725

2004/05 4,118 15,263 19,381

2005/06 4,169 15,394 19,563

2006/07 4,204 15,528 19,732

2007/08 4,240 15,663 19,903

2008/09 4,273 15,786 20,059

2009/10 4,306 15,451 19,757

2010/11 4,367 15,685 20,052

2011/12 4,414 15,451 19,865

2012/13 4,461 15,632 20,093

2013/14 4,510 15,826 20,336

2014/15 4,563 15,995 20,588

2015/16 4,617 16,230 20,847

2016/17 4,667 16,429 21,096

2017/18 4,722 16,636 21,358

2018/19 4,776 16,844 21,620

2019/20 4,834 17,068 21,902

2020/21 4,887 17,278 22,165

2021/22 4,944 17,493 22,437

2022/23* 4,998 17,707 22,705

2023/24 5,058 17,929 22,987

2024/25 5,120 18,173 23,293

10.5. Forecast Sales by Sector

Table 10.4 summarises the forecast load by industrial sector. The maximumlikelihood forecasts for transmission voltage and distribution voltage customerspresented above form the basis of the forecast. Strong medium term growth inmining is due, in part, to the relatively depressed level of sales in 2003/04. Asthese sectors recover to their medium-term growth trends, sales will increasesignificantly above their 2003/04 levels. The wood sector experienced stronggrowth in 2002/03 and 2003/04, but this is unlikely to be sustainable in themedium term as most facilities are working near capacity. Sales to pulp andpaper companies are expected to be strong through the current fiscal year, butthen are expected to be relatively flat, due to capacity constraints. Sales to the



Page 48

chemical producers and other transmission voltage customers are expected tobe essentially flat over the medium term. Distribution sales should bemoderately strong over the medium term.

Table 10.4. Industrial Sales by Sector Before Power Smart (GWh) Model 2Transmission Voltage Customers Distrib-

ution

Metal

Mines

Coal

Mines

Wood Paper Chem-

ical

Other Trans-

mision Rate

All

Sectors1

Total

Sales

Actual

1998/99 2,006 574 765 8,501 1,685 727 3,820 18,077

1999/00 1,619 558 826 8,685 1,710 663 3,828 17,890

2000/01 1,996 547 892 8,937 1,724 856 3,627 18,679

2001/02 1,952 554 885 7,957 1,626 880 3,884 17,7392002/03 1,873 516 928 8,534 1,798 902 4,046 18,596

2003/04 1,906 467 937 8,785 1,787 950 3,918 18,725

Forecast

2004/05 2,078 503 989 8,874 1,854 965 4,118 19,381

2005/06 2,356 534 938 8,797 1,813 956 4,169 19,563

2006/07 2,411 541 940 8,862 1,816 958 4,204 19,732

2007/08 2,592 559 926 8,828 1,809 949 4,240 19,903

2008/09 2,628 607 927 8,882 1,795 947 4,273 20,059

2009/10 2,450 596 917 8,768 1,782 938 4,306 19,757

2010/11 2,213 627 968 9,017 1,873 987 4,367 20,052

2011/12 2,238 637 959 8,780 1,862 975 4,414 19,865

2012/13 2,309 643 968 8,852 1,877 983 4,461 20,093

2013/14 2,430 646 976 8,898 1,888 988 4,510 20,336

2014/15 2,459 657 989 9,016 1,903 1,001 4,563 20,588

2015/16 2,490 669 1.002 9,137 1,918 1,014 4,617 20,847

2016/17 2,518 681 1,015 9,254 1,933 1,028 4,667 21,096

2017/18 2,549 693 1,028 9,377 1,948 1,041 4,722 21,358

2018/19 2,579 705 1,042 9,499 1,964 1,055 4,776 21,620

2019/20 2,612 719 1,056 9,632 1,981 1,068 4,834 21,902

2020/21 2,642 732 1,070 9,756 1,996 1,082 4,887 22,165

2021/22 2,673 745 1,084 9,884 2,011 1,096 4,944 22,437

2022/23 2,705 757 1,099 10,008 2,027 1,111 4,998 22,705

2023/24 2,737 770 1,113 10,141 2,043 1,125 5,058 22,987

2024/25 2,772 785 1,129 10,286 2,061 1,140 5,120 23,293

Growth Rates,

5 years:98/99 to 03/04 -1.0% -4.0% 4.1% 0.7% 1.2% 5.5% 0.5% 0.7%

5 years03/04 to 08/09 6.6% 5.4% -0.2% 0.2% 0.1% -0.1% 1.9% 1.4%

10 years03/04 to 14/15 2.5% 3.3% 0.4% 0.1% 0.6% 0.4% 1.5% 0.9%

21 years03/04 to 24/25 1.8% 2.5% 0.9% 0.8% 0.7% 0.9% 1.3% 1.1%

Notes: 1. Excluding distribution rate transmission voltage (DRTV)



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10.6. Risks and Uncertainties

Beyond the general economic risk of higher or lower than economic growthassumptions materializing, a significant risk to the industrial forecast pertains todiscrete one time changes in sales to the base metal mining and pulp and papersectors. Specifically, risks not represented in the forecast relate to large one-time additions or contractions of load as a result of new investment, strikes orclosure of major facilities. Because it is difficult to assess the likelihood of theseevents, the approach has been to relate the industrial load to GDP rather thanattempt to forecast these individual discrete events. However, as it relates toone large mining customer, based on public statements of intent todecommission beginning in 2007/08 made earlier in the year, BC Hydro hasreduced this load to reflect this discrete event.



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11 Peak ForecastThe peak forecast described in this chapter represents an update of the October2004 incorporating the final 4.85% rate increase approved by the BCUCNovember 24, 2004.

11.1 Introduction

Characteristics - BC Hydro’s peak demand is defined as the expectedmaximum amount of electricity consumed in a single hour under an averagecoldest day assumption established as the design temperature. BC Hydro is awinter peaking utility because the system has a larger share of heating loadthan summer air conditioning load. The distribution peak is the most sensitive totemperature. The transmission peak demand is considered to be responsive toexternal market conditions and changes in demands for BC’s key industrialcommodities such as wood, pulp and paper and mining.

Trends - The peak demand is temperature dependent. Last winter, BC Hydroreached an all time record peak, which was recorded at 9,619 MW for theDomestic System on January 5th 2004. The daily average temperature for thepeak day was -7.1OC. In recent winters, BC Hydro’s actual peak has beenoccurring at temperatures that were much milder. For example, in the winter of2002/03, the peak was recorded at 8,481MW on December 18th 2002 which hada daily average temperature of 5.3OC. The peak recorded in 2004 and eightyears ago were the last two times in the past 10 years that BC Hydro’s peakoccurred at cold temperatures close to its average coldest day designtemperature.

Peak Weather Adjustments - Since temperature creates most of the variabilityin the peak demand, the peak forecast is weather normalized to a designtemperature based on a historic series of winter temperatures. In addition, theactual peak is adjusted back to the design temperature in order to determine: (i)the growth in peak independent of weather impacts; (ii) the variance in theforecast independent of weather impacts, and (iii) the historical weatheradjusted peak anchor value. The anchor value is the previous year’s actualpeak adjusted for variations in temperature from the design temperature (i.e.,weather adjusted actual).

In this year’s forecast, BC Hydro has re-established its design temperaturebased on an average of the coldest daily average temperature using the mostrecent 30 years of weather data. This is consistent with British Columbia UtilitiesCommission direction in the VIGP decision and increases the designtemperature from -6.8 OC to -5.3 OC for the System. For VI, the designtemperature is increased from -4.4 OC to -3.6 OC. In addition to changing thedesign temperature, BC Hydro has also incorporated the impact of the increasein the peak sensitivity to temperature based on last winter’s cold temperatureand peak data. The impact of last year’s cold winter experience is reflected inthe regional and system weather-adjusted peak values for fiscal 2003/04.Historical information needs to be re-calibrated to the new design temperatures,so historical growth rates using the new design temperature are not yetavailable.

Drivers - BC Hydro’s peak forecast is based on the peak demands from itsdistribution, transmission and wholesales customers including other utilitiessuch as Fortis BC. The distribution peak forecast is prepared for 15 distributionplanning areas which are aggregated into four BC Hydro planning regions (i.e.



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Lower Mainland, Vancouver Island, South Interior and Northern Region). Thedistribution peak forecasts are based on local economic drivers includingemployment and housing starts.

Export market conditions, world demand for BC’s industrial commodities anddomestic supply and demand for BC’s raw industrial products impact BCHydro’s large transmission customers’ peak demand for electricity.

Forecast - The 2004 Domestic system peak forecast for 2004/05 with PowerSmart is 9,710 MW. This represents a 2.4% increase from the weather-adjusted2003/04 Domestic system peak of 9,431 MW for 2003/04. Compared to lastyear’s forecast for 2004/05, this year’s forecast with Power Smart is 346 MWhigher. The increase in the forecast peak reflects a re-calibration of loadsensitivities to the previous year’s cold winter temperature impact. As well, thisyear’s forecast reflects a strong recovery in some of BC Hydro’s large industrialcustomers.

With the record peak of 9,619 MW in January 2004, the peak increased in oneyear by over 1,100 MW compared to the fiscal 2003 peak recorded forDecember 2002 at 8,481 MW. After accounting for the weather adjustments tothe new design temperature of -5.3 OC, the level of the distribution peakforecast has increased due to a re-evaluation of the peak cold weathersensitivities resulting from last year’s cold temperatures on the peak.

The Domestic Peak forecast, including the impacts of Power Smart, is expectedto grow from 9,710 in 2004/05 to 10,049 MW in 2008/09 or an annual increaseby 1.3%, then to 10,814 MW in 2014/15 or an annual increase by 1.3%, andfinally increase to 12,581 MW in 2024/25. This is an annual increase of 1.5%over the 21 years of the forecast. These growth rates represent the increasesin the total system’s distribution and transmission peak demands includingtransmission losses. They also include the forecasted impacts of the revisedrate increase of 4.85% approved by the BCUC November 24,2004.

In the remainder of this chapter, the peak forecast methodology is described,the distribution peak forecast for the various distribution planning areas ispresented, the regional distribution and transmission peak forecasts aredescribed, the weather normalization for last year winter is discussed, the totalregional and system peak forecasts are presented and 2004 total system peakis provided.

11.1.2 Peak Forecast Method

December 2004 Forecast - 4.85% Rate Increase

The December 2004 forecast represents an update of the October 2004incorporating the final 4.85% rate increase approved by the BCUC November24, 2004.

The process used to identify the rate impacts on peak demand was slightlydifferent that was used to calculate the rate impacts on the energy forecastdiscussed in Section 3. The calculation of the peak rate impacts was based ontwo Monte Carlo simulations. The first established the total energy forecastunder the assumptions of no rate increase and the second established the totalenergy forecast under the assumption of the approved 4.85% rate increase.The difference between the two simulations was assumed to be the extent ofthe rate increase impacts on BC Hydro’s total energy forecast.



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Seventy-five percent of the energy rate impacts was applied to a total systembase peak forecast in order to determine the total system rate impact in MW.The total rate impact (MW) was then allocated to the rate classes (distributionand transmission) and to each region based on each classes and regions shareof the total base peak forecast. The assumption was that the overall peakdemand response was assumed to be 75% of the energy response, reflecting areduced price sensitivity of demand during average cold weather used toestablish the peak forecast.

After the allocation was completed, the rate impact was subtracted from thebase forecast to determine the reference peak forecast before Power Smart andincluding rate impacts.

Detailed Peak Forecasting Methodology

The peak forecast method is a bottom up driven process that has four mainsteps: (i) weather-normalised distribution peaks are prepared for four regions aswell as the system, (ii) a distribution peak forecast guideline is prepared for 15planning areas, (iii) a long-term substation peak forecasts is prepared for eachsubstation by BC Hydro’s distribution planning group, (iv) regional total peakforecasts and a system peak forecast are prepared. These steps are explainedin detail in the following sections

The method assembles information in a staged process, as users both internaland external to BC Hydro require disaggregated peak forecast information. Thefour main elements of the forecast process are outlined in figure 11.1. Thefigure also shows the various intermediate inputs used in developing variouspeak forecasts at the each stage. Each of these four steps is discussed below.

Figure 11.1. Peak Forecast Methodology Overview

Regional and System Peak Forecasts

4 RegionalDistribution

Peak +

4 RegionalTransmission

Peak

Other UtilityPeak SalesForecast

SystemLosses

System PeakForecast

+ + =

• Capacity Savings and Rate Impacts• Other Utilities’ Capacity Sales• Coincidence and Power Factors• System Loss Factor

• Distribution Energy Forecast• Transmission Peak Forecast• Average of Dist Peak Guideline Forecast and

Dist Substation Peak Forecast

Intermediate Inputs ofRegional and System

Peak Model

• Peak Intensity Coefficients by Dwelling & Heating Type• Electric Heating Share• Accounts Forecast• Regional Economic Outlook

• Employment Forecast• Weather Adjusted Peak

Intermediate Inputs ofDistribution PeakGuideline Model

• Historical Weather Adjusted Substation Peak Growth Rates

• Load Transfers

• Information On New Larger Load Additions

Distribution Peak GuidelineForecast

Substation Peak

Forecast



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(1) Weather Normalisation

From the actual recorded substation peaks, a weather-normalised peak isprepared for each substation in the various planning areas using a linearregression model and substation peak data and local weather stationtemperatures. The total weather-normalised substation peak in each planningarea is aggregated from bottom up starting from the15 planning areas into BCHydro’s four planning regions: Lower Mainland, Northern Region, South Interiorand Vancouver Island.

BC Hydro also uses a top down daily peak model and weather normalizationprocedure to produce weather normalized peaks for the historical recordedpeak. From the procedure, weather normalized peaks are prepared forVancouver Island and the total System. The daily peak model uses hourly loaddata and relates them to temperatures in a cubic equation. The model andprocedure is discussed in detail in Appendix 4.

After the results of the top down weather-adjusted peaks are determined, theSystem and distribution peak anchor points are compared to anchor pointsbased on the bottom up substation peak data. In the final analysis a blending oraveraging of the top down and bottom up weather-adjusted peaks are used toestablish the anchor points for each region and the total system.

Using weather-adjusted peaks from two approaches reduces the risk of relyingupon one method for weather adjustments.

(2) Distribution Peak Guideline Model

The peak forecast process starts by developing a non-coincident distributionpeak forecast for 15 distribution planning areas including: Vancouver IslandNorth, Central, South; Southern Interior Thompson/Shuswap andOkanagan/Thompson, Northern Region West, Central and East and sevenLower Mainland areas. The forecast is prepared using a model (see section11.2.1) that incorporates: (1) local area economic forecasts such asemployment forecast, (2) forecasts of housing starts and housing stock, (3)account forecasts by heating type and dwelling type, and (4) the total sum of theweather normalised substation peak of each substation serving the planningarea.

The distribution peak guideline forecast provides a guideline for the total growthfor the sum total of each substation that serves customers in that planning areaover an 11 year forecast period.

(3) Substation Peak Forecast

A discrete substation peak forecast is prepared for each of BC Hydro’s 220substations by BC Hydro’s distribution planning group. The forecasts reflecttrends in historic weather adjusted substation growth rate, load transfersbetween substations and information on new discrete load additions at thesubstation level in addition to the distribution peak guideline forecast.

(4) Regional Peak and System Peak Forecasts

For each of the four service regions, a total regional peak forecast is preparedas the sum of the region’s distribution peak, transmission peak and wholesalepeak sales to other utilities.

To prepare the regional distribution peak forecasts, the long-term substationpeak forecasts (Step 3) and the distribution peak guideline forecasts (Step 2)are averaged and aggregated from the 15 planning areas into four regionaldistribution peak forecasts. Averaging the substation and guideline forecast



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reduces the risk of relying upon one forecast method and incorporates theinformation on new larger developments that impact individual substations.

After the two forecasts are averaged and aggregated into four regionalforecasts, regional coincident factors and power factors are applied to convertthe non-coincident peak forecasts to regional distribution coincident forecasts.Coincidence factors are applied to account for load diversity (i.e. differences inthe timing of the total distribution substation peak relative to the region’sdistribution peak). Rate impacts and capacity savings from Power Smart areincorporated at the regional distribution peak level for each of the four serviceregions. Included in the regional distribution peak forecast are distributionlosses because the substation and guideline forecasts represent the total peaksat substation level.

To prepare the regional transmission peak forecasts, forecasts are prepared foreach transmission account over the short to medium-term on a non-coincidentbasis. The individual account forecasts are based on 3rd party industrial sectorstudies, customer information from BC Hydro’s key account managers and aneconometric time series model using historical metered peaks.

After the individual transmission account forecasts are finalized, they areaggregated into the four regions. Coincident factors and power factors areapplied at the regional total level to convert the total non-coincident peaksforecast to total regional coincident transmission peak forecasts. Rate impactsand capacity savings from Power Smart at the regional transmission level areincorporated into the transmission peak forecast for each service region.

For each of the four service regions, the total regional peak forecast is preparedas the sum of the regional distribution and transmission peak forecast andapplicable wholesale customer peak sales. The wholesale peak sales are basedon treaty arrangements as in the case of Seattle City Light or based onnominations for peak demand under approved rates such as in the case forFortis BC.

A total system peak forecast is prepared as the sum of the total distributionpeak, total transmission peak, total wholesale peak sales and total transmissionlosses.

The total distribution peak is the sum of the regional coincident distributionpeaks. The total transmission peak is the sum of the regional coincidenttransmission peak forecasts. Separate coincidence factors, for distribution andtransmission are applied in summing up the regional peaks to account for loaddiversity between the region and the total system peak. The coincidence factorsare based on analyses of historical data.

A system transmission loss factor of 8.7%, for the hour of the peak, is appliedto the domestic distribution and transmission peak forecasts. The loss factor isbased on transmission load flow studies.

11.2.1.Distribution Peak Forecasts

At the distribution level, electricity demand is closely linked to the forecast of thelocal economy, as well the historical trends of distribution load growth. As such,the regional economic outlook is one of the primary inputs into distribution peakdemand forecasts. BC Hydro obtains an economic forecast from B.C. Statistics,external consultants and the Greater Vancouver Regional District.

As described above, the distribution peak forecast is based on a distributionpeak guideline forecast for each of the 15 planning areas. The distribution peak



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guideline forecast is prepared using an econometric model. The forecastprovides a guideline for the total non-coincident (MVA) growth for all of thesubstations serving distribution customers in that area.

The basic framework of the distribution peak forecast guideline model is givenby:

(11.1) Peak = Stock X Electric Intensity per unit of Stock

The two main stock forecasts, used in the model, are the forecasts ofemployment and the number of residential customer accounts, which is drivenby housing starts. The residential account forecast is also used to develop theresidential sales forecast. The employment forecast is based on third partyinformation.

The electric intensity per unit of stock is in the form of the peak load per unit ofhousing (kW per account) for the residential sector. This intensity varies byheating type and dwelling type. For the general rate class, the peak per unit ofsales (kW per kWh) is the intensity factor.

Figure 11.2 shows the average annual growth rates, over the 11 year forecastperiod for the various planning areas, according to this year’s distribution peakguideline forecast.

In the short-term, this year’s total guideline forecast growth projection is 2.7%compared to last year’s growth projection of 1.9% for 2004/05 before rateimpacts. Areas that are expected to grow stronger in 2004/05 include the FraserValley East and the Coastal area, which includes the Whistler and Squamishdistricts.

The guideline forecast reflects changes in the anticipated growth in drivers ofthe forecast compared to last year’s forecast. This includes growth in theprojected number of residential accounts as account growth continues to befuelled by the low interest rate environment. Short-term employment forecast,another driver of the forecast, is projected to be relatively strong at 1.8%increase for 2004, but not as strong as the gains made in 2003 at 2.5%.

Over the 11-year period, the average growth rate in the total peak guidelineforecast is 1.8%, which is slightly below last year’s forecast average growth of2.0%.

Figure 11.2. Distribution Peak Forecast Guideline Before Power Smart –Average Annual MVA Growth (2003/04 to 2014/15)

Notes:

1. Coastal includes Squamish, Whistler, Sunshine Coast and Powell River. 2. Fraser Valley West includes

Richmond, Delta, Surrey and Langley. 3. VI-S is Vancouver Island South, VI - C is Vancouver Island

Central which includes Gulf Islands and VI - N is Vancouver Island North.

1.4% 1.4%

2.1%2.3% 2.2%

2.6%

1.7%

1.4% 1.5%

1.9%1.7%

1.9%

0%

1%

1%

2%

2%

3%

3%

Gro

wth

in M

VA

Van/ Bby Nort h Shore Coast al Coq/ Maple

Ridge

FVW FVE Richmond Nort hern

Region

Sout h

Int erior

VI-N VI-C VI-S



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Regional Distribution Peak Forecast

BC Hydro forecasts a distribution peak for each the four major regions ofVancouver Island, Lower Mainland, Northern Region and South Interior. Asdiscussed, the regional distribution peak forecast is based on an average of thedistribution guideline peak forecast and the long-term substation peak forecast.In the regional and system peak model, the forecasts are aggregated into fourregions from 15 planning areas. Regional power factors and coincidence factorsare applied to produce a coincident distribution peak forecast (MW) for each ofthe major regions.

Table 11.1 shows the non-coincident (MVA) and coincident distribution peak(MW) forecast before Power Smart for each region. The first 11 years of theforecast is based on the average of the two peak forecasts. The second 10years of the regional distribution peak forecast is derived using the growth ratein the distribution energy sales.

Compared to last year’s forecast for 2004/05, this year’s forecast by regionbefore Power Smart is above last year’s forecast. However, this year’s anchorpoints or weather-adjusted peaks are higher as they reflect the recalibration tothe cold temperature data. BC Hydro has re-calibrated its top down peak modelwith last year’s peak and cold temperature data to reflect changes to thedistribution sector’s sensitivity of temperature to peak. The results of theweather-adjusted peaks by region and for the System are discussed in section11.3.



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Table 11.1. Regional Non-Coincident and Coincident Distribution PeaksForecast Before Power Smart

Lower

Mainland

Vancouver

Island

South

Interior

Northern

Region

Non- Non- Non- Non-

Coinc. Coinc. Coinc. Coinc. Coinc. Coinc. Coinc. Coinc.

Peak Peak Peak Peak Peak Peak Peak Peak

(MVA) (MW) (MVA) (MW) (MVA) (MW) (MVA) (MW)

Actual

2003/04 4,318 3,935 1,849 1,703 932 841 723 645

Weather-Adjusted Actual

2003/04 4,258 3,826 1,803 1,660 913 824 725 647

Forecast (Weather-Adjusted)

2004/05 4,396 3,949 1,834 1,688 934 843 736 657

2005/06 4,492 4,036 1,856 1,709 945 852 743 664

2006/07 4,590 4,124 1,888 1,738 959 865 754 673

2007/08 4,694 4,217 1,927 1,774 974 879 766 684

2008/09 4,806 4,317 1,968 1,812 990 893 778 694

2009/10 4,903 4,405 2,009 1,850 1,006 908 789 704

2010/11 4,978 4,472 2,049 1,886 1,019 919 800 714

2011/12 5,054 4,541 2,086 1,921 1,032 931 811 724

2012/13 5,131 4,609 2,121 1,953 1,045 943 822 734

2013/14 5,213 4,684 2,155 1,984 1,058 955 834 745

2014/15 5,292 4,754 2,187 2,014 1,072 967 846 755

2015/16 5,386 4,838 2,225 2,048 1,088 982 855 764

2016/17 5,479 4,922 2,262 2,083 1,105 997 864 772

2017/18 5,577 5,010 2,301 2,118 1,122 1,012 874 780

2018/19 5,675 5,098 2,340 2,154 1,139 1,028 883 789

2019/20 5,775 5,188 2,380 2,191 1,157 1,044 893 797

2020/21 5,875 5,278 2,420 2,228 1,175 1,060 902 806

2021/22 5,980 5,372 2,461 2,266 1,193 1,076 912 815

2022/23 6,084 5,465 2,502 2,304 1,211 1,093 922 823

2023/24 6,192 5,563 2,545 2,344 1,230 1,110 932 832

2024/25 6,300 5,659 2,588 2,383 1,249 1,127 942 841Growth Rates (Weather Adjusted)

5 years03/04 to 08/09 2.4% 2.4% 1.8% 1.8% 1.6% 1.6% 1.4% 1.4%

11 years03/04 to 14/15 2.0% 2.0% 1.8% 1.8% 1.5% 1.5% 1.4% 1.4%

21years03/04 to 24/25 1.9% 1.9% 1.7% 1.7% 1.5% 1.5% 1.3% 1.3%

Notes:1. Distribution peak forecast based on average of Substation forecast and Distribution Peak

Guideline Forecast.2. Growth rates based on weather adjusted peaks.

11.2.2. Regional Transmission Peak Forecast

The transmission peak forecast is prepared on a customer-by-customer basis.Information from BC Hydro’s key account managers, historical billing data usedin an econometric model for the larger accounts, and 3rd party reports on BC’smajor industries are the key sources of information used to establish



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transmission forecast over the first 11 years. The individual customertransmission peak forecast is prepared on a non-coincident basis. The forecastsfor each account are then aggregated into four regional forecasts and regionalcoincidence and power factors are applied to establish the regional coincidenttransmission peak forecasts.

Over the first 11 years of the forecast period, the transmission forecasts arescaled to ensure the annual growth in the transmission peak forecast isconsistent with the annual growth in the total industrial energy transmissionforecast. This scaling preserves the load factor for each customer. Theindustrial energy transmission forecast is based on a regression analysis whereGDP is the key driver in the forecast.

For the last 10 years of the forecast period, the transmission peak forecast foreach region is derived using the growth rate in the transmission energy forecastin each region.

Table 11.2 shows the transmission peak forecast before Power Smart for eachregion.

The forecast reflects the anticipated closure of Highland Valley Copper Mine inwhich accounts for the negative annual growth rates in the Southern Interior. Inthe medium-term, the increase in the transmission peak requirements in theNorthern Region reflects the anticipated recovery in BC’s mining sector.

The non-coincident transmission peak forecast for Vancouver Island is expectedto decline in the short-term from 625 MW in 2003/04 to 603 MW to 2004/05. Thedecline reflects that expectation that higher exchange rate and softening oflumber prices for wood may impact some smaller transmission customers overthe short-term. The recovery in the pulp and paper sector is expected todevelop gradually.

On the other hand, the short-term transmission forecast for Vancouver Island,on a coincidence basis, shows an increase in from 440 MW in 2003/04 to 482MW in 2004/05. The increase in the forecast on a coincidence basis anddecrease in the forecast on non-coincidence basis reflects the fact that lastyear’s actual transmission coincidence factor of 0.70, (derived as the ratio ofcoincidence transmission peak (440 MW) to non-coincident peak (625 MW))was lower than expected. Historically, the five year average transmissioncoincidence factor is about 0.80, which is assumed in the forecast. BC Hydrohas maintained the assumption because this one-year change is not conclusiveevidence in the data to suggest a deviation from the historical trend.



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Table 11.2. Regional Non-Coincident and Coincident Transmission PeaksForecast Before Power Smart

Lower

Mainland

Vancouver

Island

South

Interior

Northern

Region

Non- Non- Non- Non-

Coinc. Coinc. Coinc. Coinc. Coinc. Coinc. Coinc. Coinc.

Peak Peak Peak Peak Peak Peak Peak Peak

(MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW)

Actual

2003/04 634 402 625 440 334 255 1,012 753

Forecast

2004/05 673 471 603 482 344 263 980 710

2005/06 657 460 595 476 336 257 984 713

2006/07 665 466 595 476 337 257 991 718

2007/08 660 462 588 470 333 255 995 721

2008/09 659 461 587 469 270 207 1,086 787

2009/10 654 458 579 463 267 204 1,046 758

2010/11 672 470 609 487 225 172 1,100 797

2011/12 650 455 602 482 223 171 1,088 788

2012/13 656 459 607 486 225 172 1,111 806

2013/14 659 461 611 489 226 173 1,117 810

2014/15 662 463 614 491 227 173 1,123 814

2015/16 663 464 616 493 227 174 1,125 816

2016/17 665 466 618 494 228 174 1,128 818

2017/18 667 467 621 496 229 175 1,133 821

2018/19 670 469 623 499 230 176 1,137 824

2019/20 672 470 625 500 230 176 1,140 826

2020/21 670 469 624 500 229 175 1,139 825

2021/22 668 468 623 498 228 174 1,136 824

2022/23 672 470 627 502 229 175 1,143 829

2023/24 676 473 631 505 231 177 1,150 834

2024/25 680 476 636 509 232 178 1,158 839

Growth Rates

5 years03/04 to 08/09 0.8% 2.8% -1.3% 1.3% -4.2% -4.1% 1.4% 0.9%

11 years03/04 to 14/15 0.4% 1.3% -0.2% 1.0% -3.5% -3.4% 1.0% 0.7%

21years03/04 to 24/25 0.3% 0.8% 0.1% 0.7% -1.7% -1.7% 0.6% 0.5%

11.3 Weather Normalization

The weather-normalized peak for each substation as produced by BC Hydrodistribution planning group, is based on a weather-normalization procedure thatuses a linear regression of winter-metered substation peak load on temperaturereadings from the weather station closest to each substation. After theregression is completed, a normalized peak for each substation is computed,based on an average coldest temperature, which is defined as the average ofthe lowest daily average temperature over 30 year period. The temperature datais based on the most recent daily temperatures as provided by Environment



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Canada for each local weather station. The weather adjusted substation peaksis aggregated from the bottom up from the 15 planning areas into four regions.

Difficulties, however, may hamper a weather-normalized peak from beingdetermined for each substation. These difficulties are related to the recentmilder weather conditions, load transfers between stations and load fluctuationsin industrial distribution loads, which impact the recorded peaks. Last year’scold temperatures provided good data which were close to average coldesttemperatures used to estimate the weather adjustments.

BC Hydro also uses a top-down approach to determine the weather-adjustedpeak. This approach uses hourly load data and a weather-normalizationprocedure based on a cubic regression equation as a means to determine theweather-adjusted peak for the total system and for Vancouver Island only. Theprocedure for weather-normalized total system and Vancouver Island peakusing hourly load is described in Appendix 4.

The weather-adjusted peak for Vancouver Island and the system peak, asdetermined by the top-down approach, is used as a means to validate andreconcile the anchor point for the distribution peak and total system peak basedon the bottom up substation weather normalized peak data. The process ofreconciling the anchor point from the two approaches to weather normalizationoccurs in the regional and system peak model. This involves a blending of bothapproaches to weather normalization. Having both approaches reduces the riskof relying upon any single approach.

Table 11.3 shows the total weather-adjusted peak as determined by the bottom-up approach by aggregating each substation’s weather-adjusted peak. Thetable also presents the weather-adjusted peak used to develop the 2004forecast and the weather adjusted peaks based on the top down weathernormalization procedure.

As a result of this past winter experience of cold day temperature there was anopportunity to recalibrate the weather adjustment for most of the substations.



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Table 11.3. Fiscal 2003/04 Regional Weather-Adjusted Distribution PeakTop Down

Weather

Adjusted Peak

Bottom-Up

Substation

Weather-Adjusted

Peak

Weather-Adjusted

Base Year for

This Year’s Forecast

MVA MW MVA MW MVA MW

Non-Coin.

RegionalCoin.

Non-Coin.

RegionalCoin.

Non-Coin

RegionalCoin.

Lower Mainland 4,276 3,841 4,258 3,825 4,258 3,825

Vancouver Island 1,803 1,660 1,760 1,620 1,803 1,660

South Interior 923 833 905 816 913 824

Northern Region 716 639 725 647 725 647

System Distribution Peak Total(1)

7,718 6,856 7,648 6,792 7,699 6,838

System Transmission Peak Total(2)

1,838 1,838 1,838

Losses (3)

756 751 755

Domestic System Peak 9,450 9,381 9,431

Notes:

1. Total Regional distribution peak is less than the sum of each Regional peak because regional

distribution peaks occur at different time relative to system’s distribution peak.

2. Transmission Peak includes peak requirements for the City of New Westminster.

3. Losses are not computed on a non-coincident MVA basis but are computed on a coincident

basis, as such only the Domestic system coincident peak is provided.

11.4 Total Regional Peak and System Peak Forecast

As previously stated, BC Hydro calculates a regional peak forecast for theregions of Lower Mainland, Vancouver Island, Northern Region and SouthInterior. The peak forecast in each region is equal to the coincident sum of theregional distribution and transmission peak and applicable wholesale peakloads. In any region, the total regional peak is:

(11.2) Regional Peak(t) = Regional Transmission Peak t + RegionalDistribution Peakt + Wholesale Peakt

The regional peak forecast and system peak forecast are developed using BCHydro’s regional and system peak forecast model. The model also incorporatescapacity savings, as provided by Power Smart and estimated rate impacts fordistribution and transmission peaks.

Tables 11.4 and 11.5 present the total regional and System peak forecastsbefore and with Power Smart.

Since the total regional peak forecast is computed as a sum, differencesbetween this year’s total regional peak forecast growth rates for 2004/05 andlast year’s are primarily due to changes in the peak demands for the distributionand transmission customers which were discussed previously.

This year’s total regional forecasts are also higher than last year’s because ofthe re-calibration of the distribution anchor points to last year’s peak and coldtemperature data.

In addition to the System reaching a record peak, the peak on Vancouver Islandreached an all time record peak at 2,193 MW on January 4th 2004 (excluding



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Gulf Island Loads and including transmission losses) and 2,253 MW (includingGulf Island Load and including transmission losses). The daily averagetemperature for the day of the peak was -4.7 OC which is only 0.3 OC colder thanVI peak design temperature used in last year’s forecast.3

For 2004/05, the VI peak forecast before Power Smart (excluding transmissionlosses) for 2004/05 is 2,171 MW is 105 MW above last year’s forecast of 2,066MW for 2004/05. This increase in the forecast reflects a 43 MW increase in thecoincidence transmission peak forecast and 62 MW increase in the coincidencedistribution peak forecast. This increase reflects the re-calibration of theweather adjusted peak to the increase in the sensitivity of peak compared to lastyear’s forecast as well as the change in the expected drivers of the distributionin last year’s forecast compared to this year’s forecast.

Tables 11.4 and 11.5 also provide the Domestic Peak forecast before and withPower Smart. BC Hydro’s Domestic system peak forecast is the coincident sumof the four region’s distribution and transmission peak forecasts, pluswholesales peak loads, plus the total transmission losses. The total Systempeak forecast is examined in the next section.

3

The 2003/04 weather adjusted peak or anchor point for this year’s forecast is estimated to be 2,240 MW

(including transmission losses) or 2,129 MW (excluding transmission losses). Using the most recent 30 years

of temperature, the weather adjusted peak is reduced to 2,210 MW and 2,100 MW with and without losses

respectively.



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Table 11.4. Domestic System and Regional Peak Forecast Before PowerSmart

Lower

Mainland

Vancouver

Island

Southern

Interior

Northern

Region

Transmission

Losses

Domestic

System

(MW) (MW) (MW) (MW) (MW) (MW)

Actual

2003/04 4,528 2,143 1,468 1,399 757 9,591 (8)


2003/04 4,429 2,100 1,260 1,401 754 9,431

Forecast

2004/05 4,621 2,171 1,287 1,368 761 9,775

2005/06 4,697 2,185 1,291 1,377 769 9,886

2006/07 4,792 2,214 1,304 1,392 782 10,049

2007/08 4,883 2,245 1,315 1,405 793 10,205

2008/09 4,983 2,281 1,281 1,482 808 10,397

2009/10 5,068 2,314 1,297 1,463 817 10,516

2010/11 5,148 2,374 1,280 1,512 831 10,696

2011/12 5,203 2,403 1,294 1,513 839 10,798

2012/13 5,276 2,439 1,310 1,541 851 10,958

2013/14 5,354 2,473 1,323 1,556 863 11,107

2014/15 5,427 2,505 1,336 1,570 874 11,249

2015/16 5,513 2,541 1,352 1,580 886 11,408

2016/17 5,600 2,577 1,367 1,590 898 11,566

2017/18 5,690 2,615 1,383 1,602 910 11,734

2018/19 5,781 2,653 1,400 1,614 923 11,901

2019/20 5,873 2,691 1,416 1,625 936 12,070

2020/21 5,963 2,727 1,431 1,632 948 12,230

2021/22 6,056 2,764 1,447 1,639 960 12,394

2022/23 6,154 2,806 1,464 1,653 974 12,576

2023/24 6,254 2,849 1,482 1,667 989 12,764

2024/25 6,355 2,892 1,500 1,681 1,003 12,952Growth Rates (Weather Adjusted)

5 years03/04 to 08/09 2.4% 1.7% 0.3% 1.1% 1.4% 2.0%

11 years03/04 to 14/15 1.9% 1.6% 0.5% 1.0% 1.3% 1.6%

21years03/04 to 24/25 1.7% 1.5% 0.8% 0.9% 1.4% 1.5%

Notes:

1. Regional peak includes distribution losses but not transmission losses.

2. The domestic system peak is less than the sum of the regional peaks plus transmission losses

because regional peaks occur at different times.

3. Lower Mainland peak includes sales to City of New Westminster and firm exports to Seattle

City Light.

4. Southern Interior includes sales to Fortis BC Canada.

5. Northern Peak includes integrated system only.

6. Actual peaks are not weather-normalized and peak forecast values are weather-normalized.

7. Growth rates are based on weather-adjusted peaks.

8. The recorded peak for 2003/04 was 9,619 MW on January 5, 2004. The value in the table has

been reduced for account for the estimated losses associated with firm export sales.



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Table 11.5. Domestic System and Regional Peak Forecast With PowerSmart

Lower

Mainland

Vancouver

Island

Southern

Interior

Northern

Region

Transmission

Losses

Domestic

System

(MW) (MW) (MW) (MW) (MW) (MW)

Actual

2003/04 4,528 2,143 1,468 1,399 757 9, 591 (8)


2003/04 4,429 2,100 1,260 1,401 754 9,431

Forecast

2004/05 4,591 2,159 1,282 1,355 756 9,710

2005/06 4,657 2,166 1,283 1,313 759 9,746

2006/07 4,726 2,181 1,287 1,309 766 9,838

2007/08 4,788 2,194 1,289 1,300 771 9,909

2008/09 4,869 2,220 1,248 1,363 782 10,049

2009/10 4,945 2,248 1,262 1,340 789 10,146

2010/11 5,016 2,303 1,242 1,384 801 10,303

2011/12 5,061 2,327 1,253 1,380 807 10,379

2012/13 5,128 2,360 1,268 1,404 818 10,524

2013/14 5,204 2,393 1,280 1,419 830 10,671

2014/15 5,277 2,426 1,294 1,434 840 10,814

2015/16 5,365 2,463 1,310 1,446 853 10,979

2016/17 5,452 2,500 1,326 1,459 865 11,143

2017/18 5,542 2,539 1,343 1,472 878 11,313

2018/19 5,633 2,578 1,359 1,485 891 11,483

2019/20 5,725 2,617 1,376 1,498 905 11,657

2020/21 5,821 2,657 1,394 1,513 918 11,836

2021/22 5,919 2,699 1,412 1,528 932 12,022

2022/23 6,016 2,741 1,430 1,542 946 12,204

2023/24 6,117 2,784 1,448 1,556 960 12,393

2024/25 6,218 2,826 1,466 1,570 975 12,581Growth Rates (Weather Adjusted)

5 years03/04 to 08/09 1.9% 1.1% -0.2% -0.5% 0.7% 1.3%

11 years03/04 to 14/15 1.6% 1.3% 0.2% 0.2% 1.0% 1.3%

21years03/04 to 24/25 1.6% 1.4% 0.7% 0.5% 1.2% 1.4%

Notes:

1. Regional peak includes distribution losses but not transmission losses.

2. The domestic system peak is less than the sum of the regional peaks plus transmission losses

because regional peaks occur at different times.

3. Lower Mainland peak includes sales to City of New Westminster and firm exports to Seattle

City Light.

4. Southern Interior includes sales to Fortis BC Canada.

5. Northern Peak includes integrated system only.

6. Actual peaks are not weather-normalized and peak forecast values are weather-normalized.

7. Growth rates are based on weather-adjusted peaks.

8. The recorded peak for 2003/04 was 9,619 MW on January 5, 2004. The value in the table has

been reduced for account for the estimated losses associated with firm export sales.



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11.4.1. 2004 Peak Forecast

Table 11.6 summarizes the BC Hydro domestic system peak forecast beforePower Smart. This year’s peak forecast for 2004/05 is 275 MW higher than lastyear’s forecast before Power Smart. Distribution Peak is 169 MW higher for thecurrent forecast and Transmission Peak is 80 MW higher. Table 11.7summarizes the BC Hydro domestic system peak forecast with Power Smart

Table 11.6. Actual and Weather-Adjusted and Peak Forecasts BeforePower Smart

Distribution Transmission Domestic System Peak

Actual

Peak

Weather-

Adjusted

Peak

PeakActual

Peak

Weather-

Adjusted

Peak

MW MW MW MW MW

Actual

2003/04 7,004 6,838 1,760(1)

9,591 9,431

Forecast

2004/05 7,016 1,898 9,775

2005/06 7,139 1,877 9,886

2006/07 7,277 1,888 10,049

2007/08 7,428 1,879 10,205

2008/09 7,588 1,895 10,397

2009/10 7,736 1,855 10,516

2010/11 7,859 1,898 10,696

2011/12 7,982 1,867 10,798

2012/13 8,102 1,893 10,958

2013/14 8,229 1,903 11,107

2014/15 8,350 1,912 11,249

2015/16 8,489 1,917 11,408

2016/17 8,629 1,922 11,566

2017/18 8,774 1,930 11,734

2018/19 8,920 1,937 11,901

2019/20 9,069 1,943 12,070

2020/21 9,218 1,939 12,230

2021/22 9,373 1,934 12,394

2022/23 9,527 1,946 12,576

2023/24 9,688 1,958 12,764

2024/25 9,847 1,971 12,952

Growth Rates

5 years03/04 to 08/09 2.1% 1.5% 2.0%

11 years03/04 to 14/15 1.8% 0.8% 1.6%

21years03/04 to 24/25 1.8% 0.5% 1.5%

Notes:

1. On the day of the system peak, there was 20 MW of curtailment in the transmission peak. As

such, the actual transmission peak is 1,740 MW but reported as 1,760 MW.

2. Growth rates for the total distribution peak and Domestic peak are reported on a weather-

adjusted basis.



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Table 11.7. Actual and Weather-Adjusted and Peak Forecasts With PowerSmart

Distribution Transmission Domestic System Peak

Actual

Peak

Weather-

Adjusted

Peak

PeakActual

Peak

Weather-

Adjusted

Peak

MW MW MW MW MW

Actual

2003/04 7,004 6,838 1,760(1)

9,591 9,431

Forecast

2004/05 6,974 1,881 9,710

2005/06 7,077 1,810 9,746

2006/07 7,185 1,785 9,838

2007/08 7,301 1,734 9,909

2008/09 7,438 1,725 10,049

2009/10 7,573 1,678 10,146

2010/11 7,681 1,713 10,303

2011/12 7,790 1,673 10,379

2012/13 7,902 1,694 10,524

2013/14 8,026 1,704 10,671

2014/15 8,147 1,715 10,814

2015/16 8,287 1,725 10,979

2016/17 8,426 1,736 11,143

2017/18 8,570 1,747 11,313

2018/19 8,714 1,758 11,483

2019/20 8,862 1,769 11,657

2020/21 9,015 1,781 11,836

2021/22 9,173 1,793 12,022

2022/23 9,327 1,805 12,204

2023/24 9,488 1,817 12,393

2024/25 9,647 1,829 12,581

Growth Rates

5 years03/04 to 08/09 1.7% -0.4% 1.3%

11 years03/04 to 14/15 1.6% -0.2% 1.3%

21years03/04 to 24/25 1.7% 0.2% 1.4%

Notes:

1. On the day of the system peak, there was 20 MW of curtailment in the transmission peak. As

such, the actual transmission peak is 1,740 MW but reported as 1,760 MW.

2. Growth rates for the total distribution peak and Domestic peak are reported on a weather-

adjusted basis.

11.4.2. Transmission Total Peak Forecast

The 2004 transmission peak forecast is summarized in Figures 11.6 and 11.7,above. The forecast of the transmission peak for the winter of 2004/05 is 113MW above last year’s forecasts with Power Smart and 80 MW before PowerSmart, on a system coincident basis.

The increase in the 2004 forecast is attributed to the increase in the industrialactivity last year. The increase in demand for commodities and recovery of



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prices for copper and pulp and paper led to modest gains in 2003. As such thisyear’s peak forecast has increased compared to last year forecasts.

In addition, this year’s forecast has been adjusted by a load factor to maintainthe growth in the industrial transmission energy forecast. The industrialtransmission energy forecast is higher this year compared to last year becausethe increase in the GDP forecast. This has increased the level of the forecastcompared to last year.

11.4.3. Distribution Total Peak Forecast

The forecast of the distribution peak for the winter of 2004/05 is 203 MW abovelast year’s forecasts with Power Smart and 169 MW before Power Smart on asystem coincident basis. This increase reflects the impact of the higher peaksensitivity to temperature and changes in the anticipated drivers of the peakforecast for this year’s forecast compared to last year’s forecast for 2004/05.This year’s distribution peak forecast is above last years forecast. This is due tothe continued anticipated strong growth in the residential accounts forecast, theincrease in the employment forecast in the medium-term due to the Olympics,and an increase in the overall distribution energy forecast in the long-term.



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12 Power Smart and the Conservation PotentialReview Study

12.1.Conservation Potential Review

BC Hydro made a significant investment in conservation through its PowerSmart program in the 1989 to 1997 period. Investments of this type are made byutilities to defer capital investments in distribution and transmissioninfrastructure, and to defer the need to build generation or purchase power.Investments by the utility and its customers are made in energy-efficientequipment and process improvements where both the utility ratepayers and thecustomers are better off in the long term. The first phase of Power Smartresulted in energy savings by 1998/99 of approximately 2,500 GWh per year.

During the early years of Power Smart, a Conservation Potential Review (CPR,1994) was undertaken to assess the potential for electricity savings undervarious scenarios. As the current phase of Power Smart was ramping up, planswere based on outputs from the first CPR, and the experience and results of thefirst decade of investment. A second CPR was commissioned and wascompleted in 2003. The study had the following objectives:

• To provide BC Hydro’s Power Smart program planners with an updatedassessment of the remaining electricity efficiency potential in B.C. as abasis for designing new initiatives or rates;

• To estimate the potential contribution of Power Smart efficiencyprograms to the reduction of BC Hydro’s peak capacity requirements;and

• To identify additional technologies that could be “fast tracked” to providefurther savings over the study period.

The Review confirms that significant cost-effective electricity efficiencyimprovements do exist in every sector in BC Hydro’s service area. Table 12.1summarizes the total energy savings potential under the conditions defined inthe study as “Economic” and “Achievable,” as compared with the ReferenceCase. Table 12.2 summarizes the demand implications of the projections.

Table 12.1 Forecast Summary – Total BC Hydro Service AreaAnnual Electricity Consumption and Potential Savings*

Annual Electricity Consumption (GWh per year)

All Sectors

Potential Annual Savings

(GWh per year)

Achievable AchievableBaseYear

ReferenceCase

Economic

MostLikely

Upper

Economic

MostLikely

Upper

2000/01 47,521 47,521

2005/06 49,739 42,513 48,509 47,652 7,226 1,231 2,087

2010/11 52,663 41,856 49,208 47,406 10,807 3,455 5,257

2015/16 54,729 42,267 48,894 46,507 12,462 5,835 8,222

* Line losses are not included.



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Table 12.2. Forecast Summary – Total BC Hydro Service AreaDemand Implications of Economic and Achievable Forecasts*(High-demand period, winter weekdays from 6 AM to 10 PM, December andJanuary)

Average On-peak Demand (MW)

All Sectors

Potential On-peak

Demand Savings (MW)

Achievable AchievableBaseYear

ReferenceCase

Economic

MostLikely

Upper

Economic

MostLikely

Upper

2000/01 4,912 7,146

2005/06 7,522 6,400 7,345 7,220 1,122 177 302

2010/11 7,982 6,349 7,491 7,228 1,633 491 754

2015/16 8,342 6,462 7,501 7,138 1,880 841 1,204

* Includes line losses at seven per cent. (This includes distribution losses of four per cent andarea transmission losses of three per cent).

12.2.Power Smart 10-Year Plan

Power Smart was launched by BC Hydro in 1989/90 with the primary aim ofachieving significant energy savings from existing and new customers, thusdeferring the need for new generation supply.

By 2000, Power Smart yielded 2,500 GWh per year in energy savings at a costof $338.4 million. These savings are equivalent to meeting the energy needs ofa community the size of Surrey (250,000 homes), and deferring the need for500 MW of generation. Over 700,000 customers have participated in PowerSmart thus far, resulting in savings of more than $1.1 billion on their electricitybills. Savings have also resulted in the reduction and/or avoidance of more than1.25 million tonnes of greenhouse gases per year.

The provincial government in its 2002 energy policy clearly elevates theimportance of conservation and energy efficiency in the context of meetingB.C.’s future energy needs. Power Smart is again well positioned to play astrategic role in BC Hydro’s continuing efforts to deliver competitive integratedenergy solutions to its customers in an environmentally and socially responsiblemanner.

12.2.1. 10-Year Power Smart Plan

Based on the process and the outputs from the 2002 Conservation PotentialReview, a Power Smart 10-Year Plan was developed, refining the investmentstrategy both in the area of sector focus and timing of the savings. The recentfirst 18 months of experience in operating current programs, combined with theexperience in the previous programs, also contributed to the plan, which detailsthe investment strategy from 2001/02 to 2011/12.

The savings targets/forecast in the 10-year plan are somewhat lower than theachievable forecast from the CPR. This is due to the desire to balanceinvestment dollars and timing, along with the fact that the resultant programlevel plans add more knowledge to the estimates from the CPR process. The10-Year target for Power Smart is to save 3,618 GWh per year by the year2011/12. The 2003 Electric Load Forecast started with year-end billing data for



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2002/03, which includes the results of all Power Smart investments prior to thatdate. As a result, Power Smart savings estimates now show future effects lessthan the 10-year target as more and more of the effects are built into theforecast starting point.

The plan also addresses some fuel switching initiatives that are cost-effective.Vancouver Island has an increasing peak capacity issue, which is exacerbatedby the fact that a high proportion of residential customers are choosing electricspace heating, even when natural gas is available. A number of small fuel-switching initiatives are being examined and are included in the plan. Acomplete inventory of the fuel-switching potential is not available, but somepreliminary estimates are included as a component of the plan. Fuel switchinginitiatives will be broken out by application, type of fuel and efficiency ofconversion, and will identify the net impact on greenhouse gas emissions, butthey only represent about five per cent of the total Power Smart portfolio.

Load displacement initiatives are considered “Clean” (as defined in the B.C.Energy Plan) as they are expected to use biomass fuel. Fuel switchinginitiatives included in the plan meet all appropriate cost effectiveness tests andtake into account the full life-cycle electric and gas costs.

12.2.2. Base Case Savings

This 10-year plan has been developed by drawing on the market intelligencecontained in the CPR, plus other opportunities that were outside of the scope ofthe CPR. The 10-year plan’s base case energy savings target (at thecustomers’ meter, net of free riders, free drivers, and measurement andverification allowances) is 3,618 GWh per year by 2011/12, and requires aninvestment of $690.6 million. This yields a cost-effective levelized utility cost anda total resource cost of $0.021/kWh and $0.044/kWh respectively. Thecustomer bill savings resulting from the electricity savings identified in this planis calculated at $2.28 billion at current electricity rates.

Approximately 59 per cent of the 10-year energy savings target comes from theindustrial sector, compared to 21 per cent from government and othercommercial, and 20 per cent from residential.

Several factors outside of BC Hydro’s control could have an impact on theactual energy savings that are achieved. For example, with 59 per cent of thetotal 10-year energy savings coming from the industrial sector, the plan is verydependent on stability in that sector.

Issues such as softwood lumber tariffs or the general health of the economycould have significant impacts on the industrial sector’s participation, and henceon the overall 10-year target. Further, a heavy dependence on markettransformation in terms of its percentage of the 10-year target would make theplan more risky. Finally, overall market penetration assumptions surroundingdirect energy acquisition are by no means certain.

12.2.3. Mitigation of Risks

Because there are risks in preparing this plan, a number of steps have beentaken to mitigate these risks and lessen their impacts. First, the plan has beenassembled to minimize dependence on market transformation. Only seven percent of the 3,618 GWh per year target is related to market transformation.

Second, in order to reflect the risks and uncertainties characteristic of the basecase target, a range has been developed within which the actual 10-year energy



Page 71

savings are expected to result. The upper end of the range is approximately4,225 GWh per year (at an investment of $739.5 million) whereas the lower endis approximately 3,011 GWh per year (at an investment of $618.6 million).Importantly, the plan passes all cost-effectiveness tests under both the low andhigh scenarios.

Third, it is important to emphasize that the upper and lower ends of the rangedo not represent a “best case” / “worst case” view. Given that the marketpenetration assumptions characteristic of this range are largely linked to the“most likely” achievable potential scenario from the Conservation PotentialReview, there is additional upside potential which could generate a “best case”that is higher than the upper scenario. Conversely, should major eventstranspire during the planning period which adversely affect consumerconfidence or the overall economy, thus triggering a major recession, then thetrue “worst case” could be significantly worse than the lower scenario.

12.2.4. 10-Year Plan Allocation

In order to achieve the base case energy savings target of 3,618 GWh per year,a 10-year portfolio investment of $690.6 million is required, and allocated asfollows:

• 39 per cent, or $265.6 million, is invested in the industrial sector tocapture load displacement and various demand-side managmentopportunities, largely through the Power Smart Partners program.

• 25 per cent, or $171.9 million, is invested in the government and othercommercial sectors in pursuit of opportunities related to lighting andother end uses through existing and new programs such as PowerSmart Partners, SUCH, Power Smart Express and New Construction.

• 16 per cent, or $112.4 million, is invested in the residential sector, inpursuit of opportunities related to lighting, appliances and homeenvelope through new and existing programs such as compactfluorescent lighting (CFL), Refrigerator Buy-back, Home EnergyUpgrade, Power Smart New Home and various fuel switching initiatives.

• Eight per cent, or $52.4 million, is invested in public education and non-program specific communications with customers

• 12 per cent, or approximately $88.3 million, is invested in variousenabling costs, overheads, administration and management.



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13 GlossaryCoincidence Factor A ratio reflecting the relative magnitude of a region’s (or

customer’s or group of customers’) demand at the time of the system’smaximum peak demand to the region’s (or customer’s or group of customers’)maximum peak demand.

Consumer Price Index (CPI) An inflation index calculated by comparing the price ofa typical bundle of goods in the year in question to the price of the same goodsin a set reference year.

Demand-Side Management (DSM) The influencing of energy demand to achievesocially economic efficiency improvements in the end use of electricity, and toshift electricity demand to reduce utility capacity costs.

Region A geographical sub-division of the BC Hydro service area. Four regions exist:Lower Mainland, Vancouver Island, South Interior and the Northern Region.

Distribution voltage customer A BC Hydro customer who receives electricity viadistribution lines that operate at relatively low voltage (34 kV and less).

Diversity That quality or characteristic by which individual maximum demands occurat different times. Diversity may be examined on an hourly, daily, monthly oryearly basis.

Econometric modelling The use of statistical techniques, typically regressionanalysis of time-series and/or cross-sectional data, to detect statisticallyverifiable relationships, coherent with economic theory, between an explainedvariable (e.g. electricity consumption) and explanatory variables (e.g. industryoutput, prices of alternative energy inputs and GDP).

Elasticity The proportionate change in a dependent variable, (e.g. electricityconsumption, divided by the proportionate change in a specified independentvariable; electricity price). A dependent variable is highly elastic with respect toa given independent variable if the calculated elasticity is much greater thanone. The dependent variable is inelastic if the elasticity is less than one.

End-use model A model used to analyze and forecast energy demand, whichfocuses on the end uses or services provided by energy. Typical end uses arelighting, process heat and motor drive. For a given industry, the modelestimates the influence of prices and technological change on the evolution ofthe secondary energy inputs required to satisfy the industry's end uses overtime.

Energy The amount of electricity delivered or consumed over a certain time period,measured in multiples of watt-hours. A 100-watt bulb consumes 200 watt-hoursin two hours. A typical BC Hydro residential account consumes about10,000 kWh (10 million watt-hours) annually.

Intensity A unitized measure of energy consumption, typically in kilowatt-hours perunit of stock. For example, kWh per account in the residential sector or kWh perunit of production in the industrial sector.

Gross Domestic Product (GDP) A measure of the total flow of goods and servicesproduced by the economy over a specified time period, normally a year orquarter. It is obtained by valuing outputs of goods and services at market prices(alternatively at factor cost), and then aggregating the total of all goods andservices.



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Gigawatt-hour (GWh) A measure of electrical energy, equivalent to one millionkilowatt-hours. (See Units of Measure.)

Integrated system That portion of the BC Hydro system which is connected as onewhole. Non-integrated facilities refer to generating facilities that are notconnected to the system, located in remote areas of the province.

kilowatt-hour (kWh) A measure of electrical energy, equivalent to the energyconsumed by a 100-watt bulb in 10 hours. (See Units of Measure.)

Load The total amount of electrical power demanded by the utility's customers at anygiven time, typically measured in megawatts (MW).

Megawatt (MW) A unit used to measure the capacity or potential to generate orconsume electricity. One MW equals one million watts. (See Units of Measure.)

Monte Carlo method A technique for estimating probabilities involving theconstruction of a model and the simulation of the outcome of an activity a largenumber of times. Random sampling techniques are used to generate a range ofoutcomes. Probabilities are estimated from an analysis of this range ofoutcomes.

MVA Megavolt-Amps – a unit of apparent power. Apparent power is real power inMW divided by power factor.

Natural conservation The increase in energy efficiency that would occur in theabsence of any utility-induced demand-side management program, all otherthings being equal.

Normalization The correction of actual customer sales and peak demand for factorssuch as unusually warm or cold weather.

Price elasticity of demand The percentage change in quantity demanded, dividedby the percentage change in price that caused the change in quantitydemanded.

Real price increases that have been adjusted for changes in prices of all goods. Thenominal price of an item may rise by 10 per cent over a year, but inflation (andassumed wages) may have risen by seven per cent over the same time period.Therefore the effective price increase faced by the consumer is three per cent. Itis necessary to deflate current prices by an appropriate inflation index (the CPIin Canada) to convert money values to constant prices or real terms.

Stock A quantity representing a number of energy consuming units. For example, inthe residential sector, stock is the number of accounts or housing units; in thecommercial sector, stock is represented by the floor area of commercial buildingspace.

System peak demand The greatest combined demand of all BC Hydro customersfaced by the generation system during a given fiscal year.

Transmission voltage customer A BC Hydro customer that is supplied its electricityvia high-voltage transmission lines (60 kV or above).

Units of measure The large amounts of electricity generated and consumed on asystem-wide basis are discussed in multiples of the basic units of watt and watt-hours. Kilowatts and megawatts are used to measure power, and kilowatt-hours, megawatt-hours, and gigawatt-hours are used to measure energy. Theequivalence are:



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1 kilowatt (kW) = 1000 watts1 megawatt (MW) = 1000 kilowatts or

1 million watts1 kilowatt-hour (kWh) = 1000 watt-hours1 megawatt-hour (MWh) = 1000 kilowatt-hours or

1 million watt-hours1 gigawatt-hour (GWh) = 1000 megawatt-hours or

1 billion watt-hours



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14 ReferencesBC Hydro, 1995 Integrated Electricity Plan, 1995.

BC Hydro, Integrated Electricity Plan: An Update to the 1995 IEP, January2000.

BC Hydro, Conservation Potential Review, 1994, 2003.

B.C. Ministry of Energy and Mines, Energy for our Future: A Plan for B.C.,November 2002.

B.C. Ministry of Finance, First Quarterly Report 2004/05. September2004.

B.C. Statistics, B.C. Population Forecast, June 2004.

Gellings, C.W. ed., Demand Forecasting in the Electric Utility Industry, SecondEdition, 1996.Malatest, R.A., British Columbia Regional Economic Outlook 2004 to 2023, July2004.

U.S. Department of Energy, Annual Energy Outlook 2004.



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Appendix 1. Price and Income Elasticities ForElectricity Consumption

The own price elasticity of electricity consumption is a measure of theresponsiveness of the quantity of electricity demanded to a small change inelectricity price. Formally, the definition of the own price elasticity ofconsumption is shown in (A1.1).

(A1.1) = ( C/C)/( P/P)

Here:

• refers to a small change in the following variable;

• C refers to consumption in GWh; and

• P refers to an electricity price index.

The own price elasticity of consumption measures the percentage change inconsumption caused by a one per cent change in electricity price.

The cross price elasticity of electricity consumption is a measure of theresponsiveness of the quantity of electricity demanded to a small change in gasprice. Formally, the definition of the cross price elasticity of consumption isshown in (A1.2).

(A1.2) = ( C/C)/( G/G)

Here:



• G refers to a gas price index.

The cross price elasticity of consumption measures the percentage change inconsumption caused by a one per cent change in gas price.

The income elasticity of electricity consumption is a measure of theresponsiveness of the quantity of electricity demanded to a small change inincome. Formally, the definition of the income elasticity of consumption is shownin (A1.3).

(A1.3) = ( C/C)/( Y/Y)

Here:



• Y refers to a provincial GDP in billions of constant 1997 dollars.

The income elasticity of consumption measures the percentage change inconsumption caused by a one per cent change in income.



Page 77

Maximum likelihood methods are used to estimate the price and incomeelasticities of electricity consumption for the total domestic load. The outcomevariable is the log of the total domestic load in GWh (on a weather-adjustedbasis). The independent variables are the log of real GDP in billions of 1997dollars, the log of the price of electricity to a base of 1997 = 100, and the log ofthe real price of natural gas to a base of 1997 = 100.

The Cobb-Douglas specifications are used, and the model is estimated bothwith (A1.4) and without (A1.5) gas prices as an explanatory variable, where e isthe error term.

(A1.4) lnCt = lnconstant + *lnYt + *lnPt + et

(A1.5) lnCt = lnconstant + *lnYt + *lnPt + *lnGt + et

In both cases, the error terms are modeled as first-order auto-regressivescheme (A1.6)

(A1.6) et = et-1 + ut, t =1, 2, …, T

Assuming that the absolute value of the parameter is less than one, the ut areindependently and identically distributed with variance u

2, and et are generatedby a stationary stochastic process beginning in the indefinite past. Roughlyspeaking, a stochastic process is stationary if the mean, variance andcovariance’s for given lags are constant.

Table A1.1 shows that according to Model 1, the “without gas” price model, aone per cent increase in GDP increases domestic electricity sales by 0.42 percent while a one per cent increase in the price of electricity reduces domesticelectricity sales by 0.30 per cent. According to Model 2, the “with gas” pricemodel, a one per cent increase in GDP increases domestic electricity sales by0.28 per cent, a one per cent increase in the price of electricity sales reduceselectricity sales by 0.60 per cent, and a one per cent increase in price of gasreduces electricity demand by 0.034 per cent. The negative sign for the effect ofgas prices is counter-intuitive. Given this anomaly in the impact of natural gasprice, the “without gas” price estimates are more appropriate for the analysis ofenergy consumption sensitivities.

Table A1.1. Maximum Likelihood Estimates of Energy ElasticitiesVariable Model 1

(Without Gas)

Model 2

(With Gas)

Constant 7.21 (3.63) 10.3 (4.33)

Log GDP 0.42 (0.20) 0.28 (0.23)

Log electricity price -0.30 (0.28) -0.60 (0.37)

Log gas price - -0.034 (0.028)

Log likelihood 30.8 31.8

Durbin-Watson 2.24 2.14



Page 78

Appendix 2. Price and Income Elasticities For PeakDemand

The own price elasticity of electricity peak demand is a measure of theresponsiveness of peak demand to a small change in price. Formally, thedefinition of the own price elasticity of peak demand is shown in (A2.1).

(A2.1) = ( D/D)/( P/P)

As before:


• D refers to peak demand in MW; and

• P refers to an electricity price index.

The own price elasticity of peak demand measures the percentage change inpeak demand caused by a one per cent change in electricity price.

The cross price elasticity of electricity consumption is a measure of theresponsiveness of peak demand to a small change in gas price. Formally, thedefinition of the cross price elasticity of consumption is shown in (A2.2).

(A2.2) = ( D/D)/( G/G)

Here:


• D refers to demand MW; and

• G refers to a gas price index.

The cross price elasticity of consumption measures the percentage change inconsumption caused by a one per cent change in gas price.

The income elasticity of electricity peak demand is a measure of theresponsiveness of peak demand to a small change in income. Formally, thedefinition of the income elasticity of peak demand is shown in (A2.3).

(A2.3) = ( D/D)/( Y/Y)

Here:

• refers to a small change in the following variable,

• D refers to peak demand in MW; and

• Y refers to a provincial GDP in billions of constant 1997 dollars.

The income elasticity of peak demand measures the percentage change in peakdemand consumption caused by a one per cent change in income.

Maximum likelihood methods are used to estimate the price and incomeelasticities of peak demand for the total domestic load. The outcome variable isthe log of the total peak load in MW (on a weather-adjusted basis), and theindependent variables are the log of real GDP in billions of 1997 dollars, the log



Page 79

of the price of electricity to a base of 1997 = 100, and the log of the real price ofnatural gas to a base of 1997 = 100.

The Cobb-Douglas specifications are used and the model is estimated both with(A2.4) and without (A2.5) gas prices as an explanatory variable, where e is theerror term.

(A2.4) lnCt = lnconstant + *lnYt + *lnPt + et

(A2.5) lnCt = lnconstant + *lnYt + *lnPt + *lnGt+ et

In both cases the error terms are modelled as first-order auto-regressivescheme (A2.6)

(A2.6) et = et-1 + ut, t =1, 2, …, T


2, and et are generatedby a stationary stochastic process beginning in the indefinite past. Roughlyspeaking, a stochastic process is stationary if the mean, variance andcovariance for given lags are constant.

Table A2.1 shows that according to Model 1, the “without gas” price model, aone per cent increase in GDP increases domestic peak demand by 0.25 percent while a one per cent increase in the price of electricity reduces domesticpeak demand by 0.32 per cent. According to Model 2, the “with gas” pricemodel, a one per cent increase in GDP increases domestic electricity sales by0.24 per cent, a one per cent increase in the price of electricity sales reduceselectricity sales by 0.36 per cent, and a one per cent increase in price of gasreduces electricity demand by 0.0085 per cent. Again, the negative sign for theeffect of gas prices is counter-intuitive. Given this anomaly in the impact ofnatural gas price, the “without gas” price estimates are more appropriate for theanalysis of peak demand sensitivities.

Table A2.1. Maximum Likelihood Estimates of Peak ElasticitiesVariable Model 1

(Without Gas)

Model 2

(With Gas)

Constant 7.63 (4.41) 8.00 (4.93)

Log GDP 0.25 (0.25) 0.24 (0.27)

Log electricity price -0.32 (0.35) -0.36 (0.41)

Log gas price - -0.0085 (0.031)

Log likelihood 29.9 30.0




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Appendix 3. Weather Normalization for EnergyWeather-normalized sales are an estimate of the sales that would have beenmade if normal weather had been experienced. Sales are adjusted usingheating degree-days (a standard approach used by the utility industry). Adegree-day is measure of coldness, defined by the number of degrees below18 degrees Celsius in (A3.1), for the average daily temperature. For example, ifthe average temperature on day t is 12 degrees Celsius then that day has 18-12= 6 heating degree-days. The heating degree-days for a month are the sum ofthe heating degree-days for the days in that month.

Formally, for day t heating degree-days is defined in (A3.1) where max is themaximum function.

(A3.1) heating degree-dayt = max (18 C – average daily temperature, zero)

Note that degree-days are never negative because the heating system will notbe required to produce heat at temperatures above 18 C.

We assume that the monthly residential use rate for a given class of residentialaccounts can be modelled using the following cubic polynomial (A3.2.).

(A3.2) use ratet = + *HDDt + *HDDt2 + *HDDt

3 + t

The most recent 36 months of data available is used to estimate eachregression, which is modelled using ordinary least squares. To calculate theweather-adjusted use rate for a particular period, the heating degree-days forthe period are substituted into the estimated regression equation (A3.2).

It is important to note the following points:

• First, weather normalization is undertaken for the residential sector onlysince only limited evidence exists of weather response for thecommercial and industrial sectors. This means that when weather-normalized totals are reported, only the residential part of the total isactually weather-adjusted. Although this is not viewed as a major sourceof error, research is being conducted to determine if and how thecommercial and industrial loads should be weather normalized.

• Second, the model actually normalizes the use per account or the userate rather than sales per se. Normalized sales are then calculated asnormalized use rate multiplied by the average number of accounts forthe class. Eight classes are used in these calculations, namely a heatingand non-heating class in each of the four regions.

• Third, because this forecast uses billed sales rather than the unknownactual consumption by class, monthly heating degree-days are allocatedusing a 25/50/25 per cent adjustment to match the assumed pattern ofmeter reading.

Table A3.1 compares the actual and weather-normalized sales for BC Hydro’sservice territory for the fiscal years 1993/94 to 2003/04.



Page 81

Table A3.1. Actual and Weather-Normalized Sales for BC Hydro ServiceTerritory

Year Actual

(GWh)

Weather

Normalized

(GWh)

1993/94 40,979 41,367

1994/95 41,616 41,992

1995/96 42,851 43,055

1996/97 43,598 43,095

1997/98 42,607 43,115

1998/99 44,863 45,418

1999/00 45,638 45,542

2000/01 46,806 46,628

2001/02 46,412 46,252

2002/03 47,612 47,789

2003/04 48,774 48,776



Page 82

Appendix 4. Weather Normalization for PeakThe domestic generation load is made up of the transmission load plus thedistribution load plus losses. The transmission load is assumed to be insensitiveto weather. The distribution load is sensitive to weather primarily through theresidential heating load. Using appropriate data, the transmission load is nettedout and the distribution load is then weather normalized separately.

A daily peak model was estimated for each year to be weather normalized.Daily peaks are modelled as a function of trend, day type, and weathervariables. The daily peak model is specified as (A4.1).

(A4.1) peakt = + * trendt + * weekendt + * xmast + * daylightt +

* tempt + * tempt*daylightt + *tempt2 + * tempt

3 + t

The variables are as follows.

• Peak is the daily peak in MW;

• Trend is a linear trend indexed by day t of the year;

• Weekend takes the value one for weekends and holidays and zerofor weekdays;

• Xmas takes the value one for days between Christmas and NewYear and zero otherwise;

• Daylight is the number of daylight hours;

• Temp is the daily average temperature; and

• is the random error.

Once the model is estimated, the estimated daily peak model for each givenyear is loaded with the preceding 30 historical weather years (a set of 365 dailytemperatures), one year at a time. The simulated annual peaks are found foreach of the 30 annual simulations. A histogram can be generated, showing theprobability distribution of the annual simulated peaks. The weather-normalizedpeak for each given year is computed as the average of the thirty simulatedannual peaks.

Table A4.1 compares the actual and weather-normalized sales for BC Hydro’sservice territory for the fiscal years 1993/94 to 2002/03. Note that elsewhere thepeak is for the domestic system and is somewhat higher.



Page 83

Table A4.1. Actual and Weather-Normalized Peak for BC Hydro DomesticSystem

Note: 1. As released in VIGP hearing.

2. Weather adjusted base year for the forecast prior to fiscal 2004 are based on the

design temperature of –6.8 deg C.

Year Actual

(MW)

Bottom-up Approach

Substation Weather-

Normalized1

(MW)

Top-Down

Procedure

Weather-

Normalized

(MW)

Weather-

Adjusted

Base Year for

Forecast

(MW)2

1994/95 8,168 8,253 - 8,253

1995/96 8,451 8,301 - 8,301

1996/97 8,267 8,271 - 8,271

1997/98 8,243, 8,385 - 8,385

1998/99 8,777 8,772 9,076 8,772

1999/00 8,423 8,835 9,053 8,835

2000/01 8,995 8,986 9,154 8,986

2001/02 8,692 9,016 9,339 9,016

2002/03 8,481 8,972 9,127 9,082

2003/04 9,619 9,382 9,450 9,431



Page 84

Appendix 5. Ordinary Least Squares-Based ForecastsMost economic analysis deals with situations where the outcome variables canbe assumed to be continuous and normally distributed. These include decisionsabout how much of a product to purchase, how much of a product to produceand what price to charge for a product. In each of these cases, explaining thedeterminants of the variable typically involves modelling the outcome variableas a continuous function of a set of k explanatory or independent variables, aset of k associated parameters, plus an error term assumed to be normallydistributed with mean zero, constant variance 2 and covariance’s equal to zero(there is no correlation between errors for different observations). The basicidea of least squares regression is to choose the parameters to minimize thesum of squares of the errors. The assumption of normally distributed errors isnot necessary to apply ordinary least squares regression, but some assumptionon the distribution of errors is needed to generate test statistics for theparameters.

The rationale for using minimum least squared error as the criterion forchoosing parameter values makes intuitive sense since large errors are moreimportant than are small errors. Equally important is the fact that ordinary leastsquares estimators have desirable properties in the classical regression context.In particular, ordinary least squares estimates are unbiased and have minimumvariance in the class of linear unbiased estimators. In other words, they are thebest estimator for this class of regression problem.

In the typical set-up, then, the regression model is given by:

(A5.1) yt = x t + t , where t N(0, 2) and t = 1,2, … T

Here:

• yt is the dependent variable at observation;

• t is a k 1 vector of independent variables at observation t;

• is a k 1 vector of parameters assumed constant for allobservations; and

• T is the number of observations.

In other words, (A5.1) is a set of T equations where the value of yt at time t is alinear function of k variables, x1t, x2t, … , xkt.

It is convenient for what follows to write equation (A5.1) in matrix form as follows

(A5.2) y = X + ,

where:

• y is a T 1 vector;

• X is a k T matrix;

• is a T 1 vector; and

• is a T 1 vector.



Page 85

Assuming that X is a non-stochastic matrix of full rank k T that satisfies theregularity condition limT (X X/T) = Q, where Q is a finite and non-singularmatrix, and using E for the expectation operator, note that the E( ) is zero sincethe expectation of each of its components is zero.

Defining the sum of the squared errors as S, note that the variance of the errorsis the expectation of S:

(A5.3) S = (y – X ) (y – X ) and E ’ = 2I

The ordinary least squares estimators of the vector of parameters * and thevariance of the errors 2* are found by minimizing the sum of the squared errors

(A5.4) S/ = -2X y + 2X X * = 0

Solving (A6.5) for * the estimated value of yields the following expression

(A5.5) * = (X X)-1 X y

The ordinary least squares estimate of the variance of the errors 2 is given bythe following expression:

(A5.6) 2* = * * / (T – k) where * = y - X *

The estimate of 2 is used to estimate confidence intervals and to conducthypotheses tests for the parameters. Further, the least squares estimates of theparameters can be shown to be unbiased and consistent estimates of thepopulation parameters.

It may be useful to find the effect of a change in an independent variable on theoutcome variable. This partial effect is found by calculating the relevant partialderivative and in the linear model is just the value of the regression coefficientfor that variable.

(A5.7) y/ x = (X + ) x = , since the derivative of is zero.

Finally, the measure of goodness of fit for an ordinary least squares regressionis R-squared adjusted for degrees of freedom, which is calculated as theexplained sum of square divided by the total sum of squares times T – k dividedby T.

Table A5.1 presents ordinary least squares regression models for energy bysector, total sales, gross requirements and peak based on weather normaliseddata for 1994/95 to 2003/04. The dummy variable takes on the value 1 for1997/98 and 2001/02 and zero otherwise and is designed to capture the effectsof strike activity.

The models do not account for the impacts of price changes or the anticipatedphase out of Highland Valley Cooper.



Page 86

Table A5.1. Ordinary Least Squares RegressionsResiden-

tial

(GWh)

Commer-

cial

(GWh)

Indust-

rial

(GWh)

Total

Firm

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

Sys. Peak*

(MW)


Constant 3,588(1,724)

840(413)

9,993(1,263)

11,352(1,858)

16,290(2115)

2.748(545)

GDP 0.091(0.015)

0.10(0.0035)

0.068(0.011)

0.30(0.016)

0.30(0.018)

0.052(0.0046)

Dummy - - -1,082(212)

-1,106(311)

-1277(354)

-136(91)

Adjusted R-squared

0.81 0.99 0.87 0.96 0.97 0.93

Durbin-Watson

1.42 1.18 2.38 2.31 3.20 2.31

Table A5.2 presents forecasts for energy by sector, total sales, grossrequirements and peak based on weather normalised data for 1994/95 to2003/04. The dummy variable takes on the value 1 for 1997/98 and 2001/02and zero otherwise and is designed to capture the effects of strike activity.




Page 87

Table A5.2. Ordinary Least Squares Forecasts Before Power SmartResiden-

tial

(GWh)

Commer-

cial

(GWh)

Indust-

rial

(GWh)

Total

Firm

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

Sys. Peak*

(MW)

Actual (weather-normalized)

1994/95 13,292 11,611 17,087 43,029 47,610 8,253

1995/96 13,504 11,920 17,630 43,795 48,389 8,301

1996/97 13,480 12,226 17,549 44,019 49,292 8,271

1997/98 14,310 12,465 16,338 43,890 48,480 8,385

1998/99 14,527 12,814 18,077 46,360 51,519 8,772

1999/00 14,477 13,176 17,890 46,596 51,428 8,835

2000/01 14,395 13,654 18,579 48,026 52,840 8,986

2001/02 14,930 13,583 17,739 47,627 52,330 9,016

2002/03 15,464 13,729 18,596 49,176 53,563 9,082

2003/04 15,901 14,151 18,724 50,275 55,186 9,564

Forecast (Residential energy and System Peak forecasts assume “normal weather”)

2004/05 15,897 14,641 19,173 51,464 56,266 9,624

2005/06 16,279 15,068 19,458 52,708 57,505 9,838

2006/07 16,672 15,509 19,751 53,990 58,783 10,057

2007/08 17,065 15,949 20,004 55,269 60,057 10,277

2008/09 17,456 16,388 20,336 56,452 61,327 10,495

2009/10 17,844 16,823 20,625 57,808 62,588 10,712

2010/11 18,300 17,334 20,965 59,294 64,069 10,967

2011/12 18,653 17,730 21,229 60,445 65,216 11,164

2012/13 19,015 18,136 21,498 61,623 66,390 11,366

2013/14 19,385 18,551 21,775 62,830 67,593 11,573

2014/15 19,780 18,994 22,069 64,117 68,875 11,794

2015/16 20,185 19,447 22,371 65,436 70,190 12,020

2016/17 20,583 19,894 22,668 66,734 71,484 12,242

2017/18 20,991 20,351 22,972 68,063 72,808 12,470

2018/19 21,409 20,819 23,284 69,242 74,165 12,703

2019/20 21,836 21,299 23,603 70,818 75,554 12,942

2020/21 22,256 21,770 23,916 72,185 76,917 13,177

2021/22 22,685 22,251 24,236 73,585 78,311 13,417

2022/23 23,125 22,743 24,563 75,016 79,737 13,662

2023/24 23,574 23,247 24,899 76,480 81,197 13,913

2024/25 24,034 23,762 25,241 77,978 82,690 14,170

Growth Rates

5 years:98/99 - 03/04 1.8% 2.0% 0.7% 1.6% 1.4% 1.7%

5 years:03/04 - 08/09 1.9% 3.0% 1.7% 2.3% 2.1% 1.9%

10 years:03/04 - 13/14 2.0% 2.7% 1.5% 2.3% 2.0% 1.9%

Last 11 years:13/14 - 24/25 2.0% 2.3% 1.4% 2.0% 1.8% 1.9%



Page 88

Appendix 6. Maximum Likelihood-Based-BasedForecasts

The main alternative to least squares estimation is maximum likelihoodestimation. It is normally used in circumstances where the underlyingassumptions of the standard linear model are not met, but it is convenient to firstreview maximum likelihood estimation of the standard linear model (described inAppendix 5) before considering the more complicated case of auto-correlatedresiduals. The basic idea of maximum likelihood estimation is to chooseestimates for the parameter values that maximize the probability that thedistribution represented by the estimated parameters generated the observedsample.

Formally, consider the normal linear regression model considered in Appendix5, the joint likelihood for the T observations is the product of T normal densitiesas follows:

(A6.1) L = f(y1, y2, …, yT) = (2 2)-T/2 exp{-(2 2)-1(y - X ) (y - X )}

Taking the log of this expression yields:

(A6.2) ln L = -T/2 ln(2 ) – T/2 ln( 2) – (2 2)-1(y - X ) (y - X )

Maximizing the log likelihood function with respect to the parameters yields thefirst order conditions given by expressions (A6.3) and (A6.4):

(A6.3) L/ = - -2 (-X y + X X ) = 0

(A6.4) L/ 2 = -T (2 2)-1 + (2 4)-1 (y - X ) (y - X ) = 0

Solving these equations for the unknown parameters yields the estimators(A6.5) and (A 6.6):

(A6.5) ** = (X X)-1X y

(A6.6) 2** = ** **/T = (T – k)/T 2*

The maximum likelihood estimate of is the same as the ordinary least squaresestimate for this model and is unbiased and consistent. The maximum likelihoodestimate of 2 is different from the ordinary least squares estimate by the factorT/(t – k), and is therefore a biased estimator, but it is a consistent estimate sinceas T the bias goes to zero. In fact, the strength of maximum likelihoodestimators is that under fairly general conditions they are consistent,asymptotically normal and asymptotically efficient. These features account fortheir widespread use in econometrics in situations where least squaresestimates are inappropriate because the requirements of the classical linearregression model are not met.



Page 89

Up to now we have assumed that covariance’s of the errors are zero or thatthere is no auto-correlation. However in many cases, errors are correlated overtime, often due to persistent shocks reflecting the inertia of economic processesor due to omitted variables that are hopefully uncorrelated to variables in themodel.

Consider the linear model:

(A6.7) y = X + ,

where:

• y is a T 1 vector;

• X is a k T matrix;

• is a T 1 vector; and

• is a T 1 vector;

but where:

(A6.8) t = t-1 + ut, t = 1,2, …,T


2, and t are generatedby a stationary stochastic process beginning in the infinite past. Roughlyspeaking, a stochastic process is stationary if the mean, variance andcovariance’s for given lags are constant over time.

The form of the errors is awkward to work with and the calculations can besimplified by expanding the previous expression by making successivesubstitutions for t to yield:

(A6.9) t =iut-1 , where the sum runs over i = 0,1,…,

Using the assumptions on ut and the formula for the sum of a converging seriesgives the variance of t as follows:

(A6.10) E( t2) = 0E(ut

2) + 2(ut2) + 4E(uu

4) + … = u2/(1 - 2) = 2

Finally, the covariance of t with t-i is needed, which is:

(A6.11) E( t t-i) = E([ut + ut-1 + 2ut-2 + …]*[ut + ut-1 + 2ut-2 +…]) = i 2

This gives all the variances and covariance’s in the variance-covariance matrixfor t. Noting that every term contains u

2, this common term can be extractedand the variance-covariance matrix can be written as follows:

(A6.12) E ’ = u2



Page 90

If the value of were known, the value of could be found that minimizes thissum of squares as with for the ordinary least squares estimator to yield thegeneralized least squares estimator:

(A6.13) * = (X’ -1X)-1X’ -1y

But since the value of is not known, a maximum likelihood estimator can beused, which gives us consistent and asymptotically efficient estimates of theparameters. Starting by formulating the likelihood function in the usual way andtaking its log that yields:

(A6.14) ln L(y, X, , u2, ) = -T/2 ln(2 ) – 1/2 ln u

2 – (2 u2)-1(y - X )

-1(y - X )

This expression can be simplified by partially maximizing with respect to ( )and u

2( ) which, noting that these expressions are functions of , yields thesimpler concentrated likelihood function:

(A6.15) ln L*( , y, X) = -T/2 ln(2 ) + 1 -T/2 ln [ u2( )][(1 - 2)-1/T]

Maximizing this function with respect to is then a relatively straightforwardnumerical estimation problem. The method of Beach-MacKinnon (1978) can beused to maximize this function.

Table A6.1 presents maximum likelihood regression models for energy bysector, total sales, gross requirements and peak based on weather normaliseddata for 1994/95 to 2003/04. The dummy variable takes on the value 1 for1997/98 and 2001/02 and zero otherwise and is designed to capture the effectsof strike activity.


Table A6.1. Maximum LIkelihood RegressionsResiden-

tial

(GWh)

Commer-

cial

(GWh)

Indust-

rial

(GWh)

Total

Firm

Sales

(GWh)

Total Gross

Require-

ments

(GWh)

Total

Integrated

Sys. Peak

(MW)


Constant 3440(2057)

953(529)

9897(1033)

11092(1629)

16176(1206)

2725(361)

GDP 0.093(0.017)

0.10(0.0045)

0.069(0.0089)

0.30(0.014)

0.30(0.010)

0.051(0.0031)

Dummy - - -1048(213)

-1111(318)

-1163(283)

-197(84)

Log likelihood -71.8 -57.3 -67.9 -72.0 -71.3 -58.9

Durbin-Watson

1.63 1.82 2.17 2.32 3.25 2.63



Page 91

Table A6.2 presents forecasts for energy by sector, total sales, grossrequirements and peak based on weather normalised data for 1994/95 to2003/04. The dummy variable takes on the value 1 for 1997/98 and 2001/02and zero otherwise and is designed to capture the effects of strike activity.




Page 92

Table A6.2. Maximum Likelihood Forecasts Before Power SmartResiden-

tial

(GWh)

Commer-

cial

(GWh)

Industrial

(GWh)

Total

Firm

Sales

(GWh)

Total

Gross

Require-

ments

(GWh)

Total

Integrate

d Sys.

Peak*

(MW)

Actual (weather-normalized)

1994/95 13,292 11,611 17,087 43,029 47,610 8,253

1995/96 13,504 11,920 17,630 43,795 48,389 8,301

1996/97 13,480 12,226 17,549 44,019 49,292 8,271

1997/98 14,310 12,465 16,338 43,890 48,480 8,385

1998/99 14,527 12,814 18,077 46,360 51,519 8,772

1999/00 14,477 13,176 17,890 46,596 51,428 8,835

2000/01 14,395 13,654 18,579 48,026 52,840 8,986

2001/02 14,930 13,583 17,739 47,627 52,330 9,016

2002/03 15,464 13,729 18,596 49,176 53,563 9,082

2003/04 15,901 14,151 18,724 50,275 55,186 9,564

Forecast (Residential energy and System Peak forecasts assume “normal weather”)

2004/05 15,935 14,612 19,187 51,501 56,257 9,633

2005/06 16,322 15,035 19,475 52,753 57,499 9,847

2006/07 16,721 15,472 19,771 54,045 58,780 10,068

2007/08 17,120 15,907 20,068 55,333 60,058 10,288

2008/09 17,516 16,341 20,363 56,616 61,331 10,508

2009/10 17,911 16,772 20,656 57,891 62,595 10,726

2010/11 18,374 17,278 21,000 59,389 64,081 10,982

2011/12 18,732 17,670 21,266 60,548 65,230 11,180

2012/13 19,099 18,071 21,539 61,735 66,408 11,383

2013/14 19,475 18,482 21,819 62,950 67,613 11,590

2014/15 19,876 18,920 22,117 64,247 68,899 11,812

2015/16 20,827 19,369 22,422 65,575 70,217 12,039

2016/17 20,691 19,811 22,723 66,883 71,514 12,263

2017/18 21,105 20,264 23,031 68,222 72,842 12,492

2018/19 21,529 20,727 23,346 69,593 74,202 12,726

2019/20 21,963 21,202 23,669 70,997 75,595 12,966

2020/21 22,389 21,668 23,985 72,375 76,962 13,202

2021/22 22,825 22,144 24,309 73,784 78,360 13,442

2022/23 23,271 22,631 24,641 75,226 79,790 13,689

2023/24 23,727 23,130 24,980 76,702 81,253 13,941

2024/25 24,193 23,640 25,327 78,211 82,750 14,199

Growth Rates

5 years:98/99 - 03/04 1.8% 2.0% 0.7% 1.6% 1.4% 1.7%

5 years:03/04 - 08/09 2.0% 2.9% 1.7% 2.4% 2.1% 1.9%

10 years:03/04 - 13/14 2.0% 2.7% 1.5% 2.3% 2.1% 1.9%

Last 11 years:13/14 - 24/25 2.0% 2.3% 1.4% 2.0% 1.9% 1.9%




Page 93

Appendix 7. Industrial Sector RegressionsTables A7.1 through A7.4 summarise alternative econometric estimates of thedeterminants of electricity consumption for the industrial sector and comparesthem to the reference forecast developed with the above methodology. Theregressions are as follows:

• OLS (Transmission). Ordinary least squares regression of transmissionindustrial sales on GDP and with/without a dummy variable for 1997 and2001 because of work stoppages (Model 1 and Model 2 respectively).Ordinary least squares regression is a method of choosing parametersto minimise the sum of squares of errors produced as a function of a setof variables (See Appendix 5) ;

• ML (Transmission). Maximum likelihood regression of industrial sales onGDP and with/without a dummy variable for 1997 because of workstoppages (Model 1 and Model 2 respectively). Maximum likelihoodregression is a method to choose estimates for parameter values thatmaximise the probability that estimated parameters will represent anobserved sample (See Appendix 6);

• OLS (Distribution). Ordinary least squares regression of distributionindustrial sales on GDP and with/without a dummy variable for 2000because of low economic activity with the recession (Model 1 and Model2 respectively);

• ML (Distribution). Maximum likelihood regression of distribution industrialsales on GDP and with/without a dummy variable for 2000 (Model 1 andModel 2 respectively).

The OLS industrial transmission sales equation for Model 1 has an adequate fitwith an adjusted R-squared values of 0.38, although the Durbin-Watson statisticsuggests the possible presence of auto-correlation. (The Durbin-Watson is ameasure of auto-correlation, which means that the errors are correlated overtime rather than being independent as assumed in the ordinary least squaresmodel. If the errors are auto-correlated, then use of a maximum likelihoodestimation procedure may lead to statistically superior estimates.) The MLindustrial transmission sales equation looks reasonable with coefficients havingthe anticipated signs. The Durbin-Watson statistic is better (closer to the desiredvalue of 2.0) suggesting that auto-correlation has been reduced.

Table A7.1. Econometric Model of Industrial Transmission Sales (Model 1)

Variable OLS ML

Constant 7024 (2736) 7078 (2183)

GDP 0.0592 (0.0231) 0.0587 (0.0185)

Dummytran - -

Adjusted R-sq 0.38 -

Log likelihood - -76.2


Estimated Auto -0.31 -0.13



Page 94

The OLS industrial transmission sales equation for Model 2 has a good fit withan adjusted R-squared values of 0.80, although the Durbin-Watson statisticsuggests the possible presence of auto-correlation. (The Durbin-Watson is ameasure of auto-correlation, which means that the errors are correlated overtime rather than being independent as assumed in the ordinary least squaresmodel. If the errors are auto-correlated, then use of a maximum likelihoodestimation procedure may lead to statistically superior estimates.) The MLindustrial transmission sales equation looks reasonable with coefficients havingthe anticipated signs. The Durbin-Watson statistic is better (very close to thedesired value of 2.0) suggesting that auto-correlation has been reduced.According to this model, a one billion dollar increase in provincial GDPincreases the industrial transmission demand for electricity by 64 MWh.

Table A7.2. Econometric Model of Industrial Transmission Sales (Model 2)Variable OLS ML

Constant 8112 (1737) 8682 (970)

GDP 0.0512 (0.015) 0.0464 (0.0081)

Dummytran -1414 (366) -1452 (275)





The OLS industrial distribution sales equation for Model 1 has a poor fit with anadjusted R-squared values of 0.11, although the Durbin-Watson statistic is closeto 2.0. The maximum likelihood equation for distribution sales again looksreasonable. The maximum likelihood equation has a Durbin-Watson statisticthat is better (very close to the desired value of 2.0) suggesting that auto-correlation has been reduced. According to this model, a one billion dollarincrease in provincial GDP increases the industrial distribution voltagetransmission demand for electricity by 7 MWh.

Table A7.3. Econometric Model of Industrial Distribution Sales (Model 1)Variable OLS ML

Constant 2995 (513) 2956 (476)

GDP 0.0069(0.0043) 0.0073 (0.0040)

Dummydist - -





The OLS industrial distribution sales equation for Model 2 has a good fit with anadjusted R-squared values of 0.68, although the Durbin-Watson statisticsuggests the presence of auto-correlation. The maximum likelihood equationfor distribution sales again looks reasonable. The maximum likelihood equationhas a Durbin-Watson statistic that is much better (close to the desired value of



Page 95

2.0) suggesting that auto-correlation has been reduced. According to thismodel, a one billion dollar increase in provincial GDP increases the industrialdistribution voltage transmission demand for electricity by 12 MWh

Table A7.4. Econometric Model of Industrial Distribution Sales (Model 2)Variable OLS ML

Constant 2697 (323) 2430 (116)

GDP 0.0097 (0.0028) 0.0121 (0.00099)

Rate

Dummydist -275 (72) -339 (33)







Page 96

Appendix 8. Forecast TablesTables A8.1 and A8.2 summarize BC Hydro’s reference load forecast includingthe effects of Power Smart and before Power Smart. Table A8.3 to A8.6summarize BC Hydro’s high and low scenarios resulting from the Monte Carlouncertainty analysis including the effects of Power Smart and before PowerSmart.


EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

9

7

Tabl

e A

8.1.

200

4 B

C H

ydro

, Ref

eren

ce L

oad

Fore

cast

Bef

ore

Pow

er S

mar

tB

C H

ydro

Serv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Exp

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

515,8

44

14,6

29

19,3

81

49,8

55

1,1

96

51,0

51

306

51,3

57

5,0

18

56,3

75

10,1

54

56,0

76

10,0

98

2005/0

616,3

66

14,8

98

19,5

63

50,8

27

1,1

27

51,9

54

311

52,2

65

5,1

53

57,4

18

10,2

65

57,1

18

10,2

09

2006/0

716,6

75

15,2

42

19,7

32

51,6

49

1,1

51

52,8

00

311

53,1

11

5,2

41

58,3

52

10,4

29

58,0

49

10,3

72

2007/0

817,0

33

15,5

18

19,9

03

52,4

53

1,2

07

53,6

60

313

53,9

73

5,3

30

59,3

03

10,5

85

58,9

97

10,5

28

2008/0

917,4

02

15,8

58

20,0

59

53,3

19

1,2

76

54,5

95

311

54,9

06

5,4

27

60,3

33

10,7

78

60,0

21

10,7

20

2009/1

017,7

70

16,2

07

19,7

57

53,7

33

1,2

95

55,0

28

311

55,3

39

5,4

89

60,8

28

10,9

03

60,5

11

10,8

43

2010/1

118,1

43

16,5

70

20,0

52

54,7

65

1,3

14

56,0

79

311

56,3

90

5,5

96

61,9

86

11,0

88

61,6

62

11,0

27

2011/1

218,4

95

16,8

15

19,8

65

55,1

75

1,3

34

56,5

09

313

56,8

22

5,6

54

62,4

76

11,1

95

62,1

46

11,1

33

2012/1

318,8

66

17,1

17

20,0

93

56,0

76

1,3

54

57,4

30

311

57,7

41

5,7

49

63,4

90

11,3

60

63,1

55

11,2

96

2013/1

419,1

95

17,3

86

20,3

36

56,9

16

1,3

75

58,2

91

311

58,6

02

5,8

37

64,4

39

11,5

10

64,0

99

11,4

45

2014/1

519,5

64

17,7

10

20,5

88

57,8

62

1,3

98

59,2

60

311

59,5

71

5,9

36

65,5

07

11,6

53

65,1

62

11,5

87

2015/1

619,8

85

18,0

25

20,8

47

58,7

57

1,4

19

60,1

76

313

60,4

89

6,0

30

66,5

19

11,8

13

66,1

68

11,7

46

2016/1

720,2

48

18,3

22

21,0

96

59,6

66

1,4

38

61,1

04

311

61,4

15

6,1

25

67,5

40

11,9

73

67,1

83

11,9

04

2017/1

820,5

71

18,6

38

21,3

58

60,5

66

1,4

56

62,0

22

311

62,3

33

6,2

19

68,5

52

12,1

41

68,1

89

12,0

72

2018/1

920,9

59

18,9

37

21,6

20

61,5

15

1,4

74

62,9

89

311

63,3

00

6,3

18

69,6

18

12,3

11

69,2

49

12,2

40

2019/2

021,2

65

19,2

80

21,9

02

62,4

47

1,4

90

63,9

37

313

64,2

50

6,4

15

70,6

65

12,4

81

70,2

89

12,4

08

2020/2

121,6

45

19,6

26

22,1

65

63,4

36

1,5

06

64,9

42

311

65,2

53

6,5

18

71,7

71

12,6

42

71,3

90

12,5

68

2021/2

221,9

61

19,9

83

22,4

37

64,3

81

1,5

23

65,9

04

311

66,2

15

6,6

16

72,8

31

12,8

07

72,4

43

12,7

32

2022/2

322,3

29

20,3

42

22,7

05

65,3

77

1,5

38

66,9

15

311

67,2

26

6,7

21

73,9

47

12,9

90

73,5

53

12,9

14

2023/2

422,6

64

20,7

22

22,9

87

66,3

73

1,5

53

67,9

26

313

68,2

39

6,8

24

75,0

63

13,1

80

74,6

64

13,1

03

2024/2

523,0

18

21,0

95

23,2

93

67,4

06

1,5

67

68,9

73

311

69,2

84

6,9

31

76,2

15

13,3

69

75,8

11

13,2

90

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

91.8

%2.3

%1.4

%1.8

%1.5

%1.8

%-0

.1%

1.8

%2.0

%1.8

%1.2

%1.8

%1.2

%

11 y

rs 0

3/0

4-

14/1

51.9

%2.1

%0.9

%1.6

%1.5

%1.6

%-0

.1%

1.6

%1.7

%1.6

%1.3

%1.6

%1.3

%

21 y

rs 0

3/0

4-

24/2

51.8

%1.9

%1.0

%1.6

%1.3

%1.5

%0.0

%1.5

%1.7

%1.5

%1.3

%1.5

%1.3

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

9

8

Tabl

e A

8.2.

200

4 B

C H

ydro

, Ref

eren

ce L

oad

Fore

cast

With

Pow

er S

mar

tB

C H

ydro

Serv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Ex p

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

515,6

98

14,5

26

19,2

29

49,4

53

1,1

96

50,6

49

306

50,9

55

4,9

80

55,9

35

10,0

89

55,6

36

10,0

33

2005/0

616,1

45

14,7

57

19,0

15

49,9

17

1,1

27

51,0

44

311

51,3

55

5,0

72

56,4

27

10,1

25

56,1

27

10,0

69

2006/0

716,3

98

14,9

81

18,8

87

50,2

66

1,1

51

51,4

17

311

51,7

28

5,1

19

56,8

47

10,2

17

56,5

44

10,1

61

2007/0

816,6

96

15,1

08

18,7

01

50,5

05

1,2

07

51,7

12

313

52,0

25

5,1

58

57,1

83

10,2

89

56,8

77

10,2

32

2008/0

917,0

10

15,3

63

18,6

59

51,0

32

1,2

76

52,3

08

311

52,6

19

5,2

25

57,8

44

10,4

31

57,5

32

10,3

72

2009/1

017,3

30

15,6

83

18,2

96

51,3

09

1,2

95

52,6

04

311

52,9

15

5,2

73

58,1

88

10,5

33

57,8

70

10,4

73

2010/1

117,6

52

16,0

10

18,5

30

52,1

92

1,3

14

53,5

06

311

53,8

17

5,3

67

59,1

84

10,6

95

58,8

60

10,6

33

2011/1

217,9

46

16,2

21

18,2

69

52,4

36

1,3

34

53,7

70

313

54,0

83

5,4

09

59,4

92

10,7

76

59,1

62

10,7

13

2012/1

318,2

89

16,5

02

18,4

52

53,2

43

1,3

54

54,5

97

311

54,9

08

5,4

95

60,4

03

11,9

25

60,0

68

10,8

62

2013/1

418,6

22

16,7

58

18,6

93

54,0

73

1,3

75

55,4

48

311

55,7

59

5,5

82

61,3

41

11,0

74

61,0

01

11,0

09

2014/1

518,9

99

17,0

70

18,9

57

55,0

26

1,3

98

56,4

24

311

56,7

35

5,6

81

62,4

16

11,2

18

62,0

71

11,1

53

2015/1

619,3

29

17,3

85

19,2

53

55,9

67

1,4

19

57,3

86

313

57,6

99

5,7

79

63,4

78

11,3

85

63,1

27

11,3

18

2016/1

719,6

92

17,6

79

19,5

43

56,9

14

1,4

38

58,3

52

311

58,6

63

5,8

76

64,5

39

11,5

50

64,1

82

11,4

81

2017/1

820,0

15

17,9

92

19,8

26

57,8

33

1,4

56

59,2

89

311

59,6

00

5,9

71

65,5

71

11,7

21

65,2

08

11,6

51

2018/1

920,4

03

18,2

82

20,1

12

58,7

97

1,4

74

60,2

71

311

60,5

82

6,0

71

66,6

53

11,8

92

66,2

83

11,8

21

2019/2

020,7

09

18,6

21

20,4

39

59,7

69

1,4

90

61,2

59

313

61,5

72

6,1

70

67,7

42

12,0

67

67,3

66

11,9

95

2020/2

121,0

89

18,9

85

20,8

18

60,8

92

1,5

06

62,3

98

311

62,7

09

6,2

83

68,9

92

12,2

48

68,6

10

12,1

75

2021/2

221,4

05

19,3

66

21,2

25

61,9

96

1,5

23

63,5

19

311

63,8

30

6,3

94

70,2

24

12,4

36

69,8

36

12,3

61

2022/2

321,7

74

19,7

24

21,4

92

62,9

90

1,5

38

64,5

28

311

64,8

39

6,4

98

71,3

37

12,6

19

70,9

44

12,5

43

2023/2

422,1

08

20,1

03

21,7

76

63,9

87

1,5

53

65,5

40

313

65,8

53

6,6

02

72,4

55

12,8

09

72,0

56

12,7

31

2024/2

522,4

62

20,4

76

22,0

81

65,0

19

1,5

67

66,5

86

311

66,8

97

6,7

08

73,6

05

12,9

98

73,2

01

12,9

19

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

91.4

%1.7

%-0

.1%

0.9

%1.5

%0.9

%-0

.1%

0.9

%1.2

%0.9

%0.5

%0.9

%0.5

%

11 y

rs 0

3/0

4-

14/1

51.6

%1.7

%0.1

%1.1

%1.5

%1.1

%-0

.1%

1.1

%1.3

%1.1

%0.9

%1.1

%0.9

%

21 y

rs 0

3/0

4-

24/2

51.7

%1.8

%0.8

%1.4

%1.3

%1.4

%0.0

%1.4

%1.5

%1.4

%1.2

%1.4

%1.2

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

9

9

Tabl

e A

8.3.

200

4 B

C H

ydro

, Hig

h Lo

ad F

orec

ast B

efor

e Po

wer

Sm

art

BC

Hyd

ro S

erv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Ex p

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

516,1

34

14,6

83

19,4

85

50,3

02

1,1

96

51,4

98

306

51,8

04

5,1

00

56,9

04

10,2

50

56,5

99

10,1

93

2005/0

616,6

91

15,0

38

19,7

37

51,4

66

1,1

27

52,5

93

311

52,9

04

5,2

19

58,1

23

10,3

92

57,8

14

10,3

34

2006/0

717,0

69

15,4

81

19,9

85

52,5

35

1,1

51

53,6

86

311

53,9

97

5,3

32

59,3

29

10,6

04

59,0

15

10,5

45

2007/0

817,4

71

15,8

57

20,2

20

53,5

48

1,2

07

54,7

55

313

55,0

68

5,4

42

60,5

10

10,8

02

60,1

91

10,7

42

2008/0

917,9

21

16,2

86

20,4

52

54,6

59

1,2

76

55,9

35

311

56,2

46

5,5

64

61,8

10

11,0

43

61,4

85

10,9

82

2009/1

018,3

27

16,7

12

20,2

24

55,2

63

1,2

95

56,5

58

311

56,8

69

5,6

44

62,5

13

11,2

05

62,1

82

11,1

43

2010/1

118,7

87

17,1

64

20,6

03

56,5

54

1,3

14

57,8

68

311

58,1

79

5,7

78

63,9

57

11,4

41

63,6

20

11,3

78

2011/1

219,1

79

17,4

92

20,4

71

57,1

42

1,3

34

58,4

76

313

58,7

89

5,8

55

64,6

44

11,5

84

64,3

00

11,5

19

2012/1

319,6

46

17,8

88

20,7

91

58,3

25

1,3

54

59,6

79

311

59,9

90

5,9

78

65,9

68

11,8

03

65,6

19

11,7

37

2013/1

420,0

22

18,2

42

21,0

91

59,3

55

1,3

75

60,7

30

311

61,0

41

6,0

85

67,1

26

11,9

90

66,7

71

11,9

23

2014/1

520,5

01

18,6

59

21,4

70

60,6

30

1,3

98

62,0

28

311

62,3

39

6,2

18

68,5

57

12,1

95

68,1

97

12,1

27

2015/1

620,8

45

19,0

37

21,8

04

61,6

86

1,4

19

63,1

05

313

63,4

18

6,3

27

69,7

45

12,3

85

69,3

79

12,3

16

2016/1

721,3

37

19,4

37

22,1

98

62,9

72

1,4

38

64,4

10

311

64,7

21

6,4

59

71,1

80

12,6

17

70,8

08

12,5

46

2017/1

821,6

96

19,8

24

22,5

38

64,0

58

1,4

56

65,5

14

311

65,8

25

6,5

71

72,3

96

12,8

21

72,0

17

12,7

49

2018/1

922,2

08

20,2

02

22,9

14

65,3

24

1,4

74

66,7

98

311

67,1

09

6,7

02

73,8

11

13,0

51

73,4

25

12,9

77

2019/2

022,5

77

20,6

38

23,2

72

66,4

87

1,4

90

67,9

77

313

68,2

90

6,8

23

75,1

13

13,2

65

74,7

21

13,1

89

2020/2

123,0

50

21,1

18

23,6

65

67,8

33

1,5

06

69,3

39

311

69,6

50

6,9

62

76,6

12

13,4

93

76,2

14

13,4

16

2021/2

223,4

06

21,5

60

24,0

44

69,0

10

1,5

23

70,5

33

311

70,8

44

7,0

82

77,9

26

13,7

00

77,5

21

13,6

22

2022/2

323,8

96

22,0

57

24,4

59

70,4

12

1,5

38

71,9

50

311

72,2

61

7,2

27

79,4

88

13,9

61

79,0

77

13,8

82

2023/2

424,2

50

22,5

10

24,8

09

71,5

69

1,5

53

73,1

22

313

73,4

35

7,3

47

80,7

82

14,1

81

80,3

66

14,1

01

2024/2

524,7

61

23,0

50

25,2

84

73,0

95

1,5

67

74,6

62

311

74,9

73

7,5

03

82,4

76

14,4

64

82,0

54

14,3

82

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

92.4

%2.9

%1.8

%2.3

%1.5

%2.3

%-0

.1%

2.3

%2.5

%2.3

%1.7

%2.3

%1.7

%

11 y

rs 0

3/0

4-

14/1

52.3

%2.5

%1.3

%2.0

%1.5

%2.0

%-0

.1%

2.0

%2.2

%2.0

%1.7

%2.0

%1.7

%

21 y

rs 0

3/0

4-

24/2

52.1

%2.4

%1.4

%1.9

%1.3

%1.9

%0.0

%1.9

%2.0

%1.9

%1.7

%1.9

%1.7

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

1

00

Tabl

e A

8.4.

200

4 B

C H

ydro

, Low

Loa

d Fo

reca

st B

efor

e Po

wer

Sm

art

BC

Hyd

ro S

erv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Ex p

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

515,5

83

14,6

03

19,2

94

49,4

80

1,1

96

50,6

76

306

50,9

82

5,0

14

55,9

96

10,0

83

55,7

12

10,0

30

2005/0

616,0

18

14,7

65

19,3

91

50,1

74

1,1

27

51,3

01

311

51,6

12

5,0

85

56,6

97

10,1

34

56,4

13

10,0

81

2006/0

716,2

93

15,0

05

19,4

87

50,7

85

1,1

51

51,9

36

311

52,2

47

5,1

52

57,3

99

10,2

56

57,1

14

10,2

02

2007/0

816,5

76

15,1

87

19,5

83

51,3

46

1,2

07

52,5

53

313

52,8

66

5,2

17

58,0

83

10,3

65

57,7

97

10,3

11

2008/0

916,9

09

15,4

32

19,6

64

52,0

05

1,2

76

53,2

81

311

53,5

92

5,2

93

58,8

85

10,5

17

58,5

94

10,4

62

2009/1

017,1

89

15,6

87

19,2

94

52,1

70

1,2

95

53,4

65

311

53,7

76

5,3

29

59,1

05

10,5

91

58,8

08

10,5

35

2010/1

117,5

28

15,9

62

19,5

13

53,0

03

1,3

14

54,3

17

311

54,6

28

5,4

17

60,0

45

10,7

39

59,7

43

10,6

82

2011/1

217,7

97

16,1

42

19,2

62

53,2

01

1,3

34

54,5

35

313

54,8

48

5,4

53

60,3

01

10,8

03

59,9

93

10,7

45

2012/1

318,1

33

16,3

69

19,4

15

53,9

17

1,3

54

55,2

71

311

55,5

82

5,5

29

61,1

11

10,9

32

60,7

98

10,8

73

2013/1

418,3

75

16,5

52

19,5

77

54,5

04

1,3

75

55,8

79

311

56,1

90

5,5

92

61,7

82

11,0

34

61,4

64

10,9

73

2014/1

518,7

11

16,7

83

19,7

46

55,2

40

1,3

98

56,6

38

311

56,9

49

5,6

70

62,6

19

11,1

38

62,2

97

11,0

77

2015/1

618,9

41

17,0

13

19,9

22

55,8

76

1,4

19

57,2

95

313

57,6

08

5,7

38

63,3

46

11,2

48

63,0

18

11,1

86

2016/1

719,2

81

17,2

45

20,0

95

56,6

21

1,4

38

58,0

59

311

58,3

70

5,8

17

64,1

87

11,3

77

63,8

54

11,3

13

2017/1

819,4

56

17,4

91

20,2

41

57,1

88

1,4

56

58,6

44

311

58,9

55

5,8

77

64,8

32

11,4

82

64,4

93

11,4

17

2018/1

919,8

34

17,7

26

20,4

19

57,9

79

1,4

74

59,4

53

311

59,7

64

5,9

61

65,7

25

11,6

21

65,3

80

11,5

55

2019/2

020,0

17

17,9

74

20,5

76

58,5

67

1,4

90

60,0

57

313

60,3

70

6,0

23

66,3

93

11,7

26

66,0

42

11,6

58

2020/2

120,3

60

18,2

33

20,7

56

59,3

49

1,5

06

60,8

55

311

61,1

66

6,1

06

67,2

72

11,8

49

66,9

16

11,7

80

2021/2

220,5

66

18,4

90

20,9

16

59,9

72

1,5

23

61,4

95

311

61,8

06

6,1

72

67,9

78

11,9

53

67,6

16

11,8

83

2022/2

320,9

23

18,7

91

21,1

21

60,8

35

1,5

38

62,3

73

311

62,6

84

6,2

64

68,9

48

12,1

12

68,5

81

12,0

41

2023/2

421,0

80

19,0

59

21,2

90

61,4

29

1,5

53

62,9

82

313

63,2

95

6,3

26

69,6

21

12,2

25

69,2

49

12,1

53

2024/2

521,4

54

19,3

72

21,4

84

62,3

10

1,5

67

63,8

77

311

64,1

88

6,4

19

70,6

07

12,3

86

70,2

30

12,3

12

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

91.2

%1.7

%1.0

%1.3

%1.5

%1.3

%-0

.1%

1.3

%1.5

%1.3

%0.7

%1.3

%0.7

%

11 y

rs 0

3/0

4-

14/1

51.5

%1.6

%0.5

%1.1

%1.5

%1.1

%-0

.1%

1.1

%1.3

%1.2

%0.8

%1.2

%0.8

%

21 y

rs 0

3/0

4-

24/2

51.4

%1.5

%0.7

%1.2

%1.3

%1.2

%0.0

%1.2

%1.3

%1.2

%0.9

%1.2

%0.9

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

1

01

Tabl

e A

8.5.

200

4 B

C H

ydro

, Hig

h Lo

ad F

orec

ast W

ith P

ower

Sm

art

BC

Hyd

ro S

erv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Ex p

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

515,9

88

14,5

80

19,3

33

49,9

00

1,1

96

51,0

96

306

51,4

02

5,0

60

56,4

62

10,1

85

56,1

57

10,1

28

2005/0

616,4

70

14,8

97

19,1

89

50,5

56

1,1

27

51,6

83

311

51,9

94

5,1

38

57,1

32

10,2

53

56,8

23

10,1

95

2006/0

716,7

92

15,2

20

19,1

40

51,1

52

1,1

51

52,3

03

311

52,6

14

5,2

10

57,8

24

10,3

94

57,5

11

10,3

35

2007/0

817,1

34

15,4

47

19,0

18

51,6

00

1,2

07

52,8

07

313

53,1

20

5,2

70

58,3

90

10,5

08

58,0

71

10,4

48

2008/0

917,5

29

15,7

91

19,0

52

52,3

72

1,2

76

53,6

48

311

53,9

59

5,3

62

59,3

21

10,6

98

58,9

96

10,6

37

2009/1

017,8

87

16,1

88

18,7

63

52,8

39

1,2

95

54,1

34

311

54,4

45

5,4

29

59,8

74

10,8

38

59,5

42

10,7

73

2010/1

118,2

96

16,6

04

19,0

81

53,9

81

1,3

14

55,2

95

311

55,6

06

5,5

49

61,1

55

11,0

51

60,8

18

10,9

88

2011/1

218,6

30

16,8

98

18,8

75

54,4

03

1,3

34

55,7

37

313

56,0

50

5,6

09

61,6

59

11,1

68

61,3

16

11,1

03

2012/1

319,0

69

17,2

73

19,1

50

55,4

92

1,3

54

56,8

46

311

57,1

57

5,7

24

62,8

81

11,3

73

62,5

31

11,3

07

2013/1

419,4

49

17,6

14

19,4

48

56,5

12

1,3

75

57,8

87

311

58,1

98

5,8

30

64,0

28

11,5

58

63,6

73

11,4

91

2014/1

519,9

36

18,0

19

19,8

39

57,7

94

1,3

98

59,1

92

311

59,5

03

5,9

63

65,4

66

11,7

66

65,1

06

11,6

98

2015/1

620,2

89

18,3

97

20,2

10

58,8

96

1,4

19

60,3

15

313

60,6

28

6,0

75

66,7

03

11,9

62

66,3

36

11,8

93

2016/1

720,7

81

18,7

94

20,6

45

60,2

21

1,4

38

61,6

59

311

61,9

70

6,2

10

68,1

80

12,2

00

67,8

07

12,1

29

2017/1

821,1

40

19,1

78

21,0

06

61,3

25

1,4

56

62,7

81

311

63,0

92

6,3

23

69,4

15

12,4

06

69,0

35

12,3

34

2018/1

921,6

52

19,5

47

21,4

06

62,6

06

1,4

74

64,0

80

311

64,3

91

6,4

55

70,8

46

12,6

38

70,4

60

12,5

64

2019/2

022,0

21

19,9

79

21,8

09

63,8

09

1,4

90

65,2

99

313

65,6

12

6,5

78

72,1

90

12,8

58

71,7

98

12,7

83

2020/2

122,4

94

20,4

77

22,3

18

65,2

89

1,5

06

66,7

95

311

67,1

06

6,7

27

73,8

33

13,1

05

73,4

35

13,0

29

2021/2

222,8

50

20,9

43

22,8

32

66,6

25

1,5

23

68,1

48

311

68,4

59

6,8

60

75,3

19

13,3

35

74,9

14

13,2

57

2022/2

323,3

41

21,4

39

23,2

46

68,0

25

1,5

38

69,5

63

311

69,8

74

7,0

04

76,8

78

13,5

96

76,4

68

13,5

17

2023/2

423,6

94

21,8

91

23,5

98

69,1

83

1,5

53

70,7

36

313

71,0

49

7,1

24

78,1

73

13,8

17

77,7

57

13,7

36

2024/2

524,2

05

22,4

31

24,0

72

70,7

08

1,5

67

72,2

75

311

72,5

86

7,2

81

79,8

67

14,1

00

79,4

45

14,0

18

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

92.0

%2.2

%0.3

%1.4

%1.5

%1.4

%-0

.1%

1.4

%1.8

%1.5

%1.0

%1.5

%1.0

%

11 y

rs 0

3/0

4-

14/1

52.1

%2.2

%0.5

%1.6

%1.5

%1.6

%-0

.1%

1.5

%1.8

%1.6

%1.3

%1.6

%1.3

%

21 y

rs 0

3/0

4-

24/2

52.0

%2.2

%1.2

%1.8

%1.3

%1.8

%0.0

%1.8

%1.9

%1.8

%1.6

%1.8

%1.6

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

EL

EC

TR

IC

L

OA

D F

OR

EC

AS

T, 2

00

4/0

5 –

2

02

4/2

5 (

De

ce

mb

er

2

00

4)

PA

GE

1

02

Tabl

e A

8.6.

200

4 B

C H

ydro

, Low

Loa

d Fo

reca

st W

ith P

ower

Sm

art

BC

Hyd

ro S

erv

ice A

rea S

ale

sIn

tegra

ted S

yste

mR

esid

entia

lC

om

merc

ial

Industr

ial

Tota

l BC

HN

ew

West

Aquila

Tota

lD

om

estic

Sale

s

Firm

Ex p

ort

Tota

l F

irm

Sale

sLosses

Tota

l Gro

ss

Require-

ments

Tota

lS

yste

mP

eak

Tota

l G

ross

Require-

ments

Peak

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(GW

h)

(MW

)(G

Wh)

(MW

)A

ctu

al

1999/0

014,5

72

13,1

76

17,8

90

45,6

39

738

46,3

76

314

46,6

91

4,8

43

51,5

34

8,6

94

51,2

79

8,6

46

2000/0

114,5

73

13,6

54

18,5

79

46,8

05

1,0

85

47,8

91

314

48,2

04

4,7

74

52,9

78

9,3

69

52,7

18

9,3

20

2001/0

215,0

90

13,5

83

17,7

39

46,4

12

1,0

62

47,4

73

314

47,7

87

4,7

80

52,5

67

9,0

54

52,2

92

9,0

03

2002/0

315,2

87

13,7

29

18,5

96

47,6

12

1,0

72

48,6

85

314

48,9

99

4,2

99

53,2

98

8,8

76

53,0

10

8,8

24

2003/0

415,8

99

14,1

51

18,7

25

48,7

75

1,1

85

49,9

60

313

50,2

73

4,9

14

55,1

87

10,1

59

54,8

92

10,1

03

Fore

cast

2004/0

515,4

37

14,5

00

19,1

42

49,0

78

1,1

96

50,2

79

306

50,5

80

4,9

74

55,5

54

10,0

18

55,2

70

9,9

65

2005/0

615,7

97

14,6

24

18,8

43

49,2

64

1,1

27

50,3

91

311

50,7

02

5,0

04

55,7

06

9,9

94

55,4

22

9,9

41

2006/0

716,0

16

14,7

44

18,6

42

49,4

02

1,1

51

50,5

53

311

50,8

64

5,0

30

55,8

94

10,0

44

55,6

10

9,9

90

2007/0

816,2

39

14,7

77

18,3

81

49,3

98

1,2

07

50,6

05

313

50,9

18

5,0

45

55,9

63

10,0

67

55,6

77

10,0

13

2008/0

916,5

17

14,9

37

18,2

64

49,7

18

1,2

76

50,9

94

311

51,3

05

5,0

91

56,3

96

10,1

67

56,1

05

10,1

12

2009/1

016,7

49

15,1

63

17,8

33

49,7

46

1,2

95

51,0

41

311

51,3

52

5,1

13

56,4

65

10,2

18

56,1

68

10,1

62

2010/1

117,0

37

15,4

02

17,9

91

50,4

30

1,3

14

51,7

44

311

52,0

55

5,1

87

57,2

42

10,3

42

56,9

40

10,2

85

2011/1

217,2

48

15,5

48

17,6

66

50,4

62

1,3

34

51,7

96

313

52,1

09

5,2

07

57,3

16

10,3

80

57,0

09

10,3

21

2012/1

317,5

56

15,7

54

17,7

74

51,0

84

1,3

54

52,4

38

311

52,7

49

5,2

75

58,0

24

10,4

93

57,7

11

10,4

34

2013/1

417,8

02

15,9

24

17,9

34

51,6

61

1,3

75

53,0

36

311

53,3

47

5,3

36

58,6

83

10,5

92

58,3

65

10,5

32

2014/1

518,1

46

16,1

43

18,1

15

52,4

04

1,3

98

53,8

02

311

54,1

13

5,4

15

59,5

28

10,6

98

59,2

06

10,6

37

2015/1

618,3

85

16,3

73

18,3

28

53,0

86

1,4

19

54,5

05

313

54,8

18

5,4

86

60,3

04

10,8

14

59,9

76

10,7

52

2016/1

718,7

25

16,6

02

18,5

42

53,8

70

1,4

38

55,3

08

311

55,6

19

5,5

68

61,1

87

10,9

49

60,8

53

10,8

85

2017/1

818,9

00

16,8

45

18,7

09

54,4

55

1,4

56

55,9

11

311

56,2

22

5,6

29

61,8

51

11,0

55

61,5

11

11,9

90

2018/1

919,2

78

17,0

71

18,9

11

55,2

61

1,4

74

56,7

35

311

57,0

46

5,7

14

62,7

60

11,1

97

62,4

15

11,1

30

2019/2

019,4

61

17,3

15

19,1

13

55,8

89

1,4

90

57,3

79

313

57,6

92

5,7

78

63,4

70

11,3

06

63,1

20

11,2

39

2020/2

119,8

04

17,5

92

19,4

09

56,8

05

1,5

06

58,3

11

311

58,6

22

5,8

71

64,4

93

11,4

49

64,1

37

11,3

81

2021/2

220,0

10

17,8

73

19,7

04

57,5

87

1,5

23

59,1

10

311

59,4

21

5,9

50

65,3

71

11,5

76

65,0

09

11,5

06

2022/2

320,3

68

18,1

73

19,9

08

58,4

48

1,5

38

59,9

86

311

60,2

97

6,0

41

66,3

38

11,7

35

65,9

71

11,6

64

2023/2

420,5

24

18,4

40

20,0

79

59,0

43

1,5

53

60,5

96

313

60,9

09

6,1

03

67,0

12

11,8

47

66,6

40

11,7

75

2024/2

520,8

98

18,7

53

20,2

72

59,9

23

1,5

67

61,4

90

311

61,8

01

6,1

96

67,9

97

12,0

08

67,6

20

11,9

35

Gro

wth

Rate

s:

5 y

rs 0

3/0

4-

08/0

90.8

%1.1

%-0

.5%

0.4

%1.5

%0.4

%-0

.1%

0.4

%0.7

%0.4

%0.0

%0.4

%0.0

%

11 y

rs 0

3/0

4-

14/1

51.2

%1.2

%-0

.3%

0.7

%1.5

%0.7

%-0

.1%

0.7

%0.9

%0.7

%0.5

%0.7

%0.5

%

21 y

rs 0

3/0

4-

24/2

51.3

%1.3

%0.4

%1.0

%1.3

%1.0

%0.0

%1.0

%1.1

%1.0

%0.8

%1.0

%0.8

%

Note

: Losses a

re a

ssum

ed to b

e 4

% w

ithin

th

e D

istr

ibutio

n s

yste

m a

nd 7

% w

ithin

the T

ransm

issio

n s

yste

m

EX

HIB

IT B

to t

he T

esti

mon

y of

Ken

neth

H. T

iede

man

n

2005 10 19 Evidence of Kenneth H. Tiedemann...Africa and the Philippines; Egypt planning studies for...

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Transcript of 2005 10 19 Evidence of Kenneth H. Tiedemann...Africa and the Philippines; Egypt planning studies for...