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1 Load Forecast and Scenarios David Bailey Customer Energy & Forecasting Manager Soyean Kim Rate Design Manager

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LTERP Forecast 3 step process: Base Forecast As used for the 2016 PBR Update Provides a common starting point Monte Carlo Business as usual but incorporates recent volatility for several measures Scenarios All the new factors not part of business as usual 2

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STEP 1: BASE FORECAST 3 All information presented is before incremental DSM and other savings

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2016 Load Forecast by Rate Group (GWh) 4

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2016 Customers by Rate Group 5

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Wholesale Customers Load % 6

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7 Annual Load Forecast

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2016 Peak Demand Forecast 8

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STEP 2: MONTE CARLO 9 All information presented is before incremental DSM and other savings

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Long-Term Load Forecast Applies to the business as usual scenario Large degree of uncertainty inherent in the long term forecast Rapidly changing market conditions and technology options introduce additional uncertainty Monte Carlo simulation allows a quantitative assessment of the long term uncertainty Upper range (P90) tied to 90% probability Lower range (P10) tied to 10% probability 10

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Monte Carlo Process 1.Identify major influencing factors 2.Assign probability distribution 3.Apply random sampling using @Risk 11

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Major Influencing Factors In the model as random variables: Population GDP Weather 12

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Residential Forecast Probability Distribution 13 Uncertainty increases with time

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Annual Gross Load Forecast 14 Maximum range from base is +/-5% Biggest uncertainty from Industrial, then Wholesale Commercial forecast to be most stable Residential variation +/-6% Commercial +/- 4% Wholesale +/- 13% Industrial +/- 24%

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Peak Forecast 15

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STEP 3: SCENARIOS 16

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Scenarios We will add scenarios to the Monte Carlo (MC) results Some future scenarios will increase load and some will reduce load Additions will be added to the high MC case while deductions will be removed from the low MC case Hybrid scenarios (eg. some EV and some DG) will land somewhere in the middle 17

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18 High Load Forecast Scenario Continued low DG growth High EV growth FBC promotes charging stations and EV range improves Higher gasoline prices High gas-to-electricity switching (e.g. gas to ASHP) Government policy focused on environment, electrification and GHG emission reductions with higher carbon tax and subsidies for green technologies like EV Natural gas rates rise more than electricity rates (partially due to increasing carbon tax) driving fuel switching High climate change scenario

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19 Low Load Forecast Scenario High DG growth (includes rooftop solar, wind, home batteries, CHP) Low EV growth due to other technology like fuel cell vehicles and low gasoline prices Low gas-to-electricity switching Government policy less focused on environment so no increases to carbon tax and no subsidies for green technology Government policies favour positive role for natural gas in BC for domestic use Natural gas rates remain low relative to electricity rates Low climate change scenario

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20 Questions? Feedback on scenarios?

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21 Backup Slides

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22 Definitions Load the annual load measured in GWh Demand the peak measured in MW MWh A typical single family home uses 12 MWh per year. A typical restaurant uses 65 MWh per year A typical 24 hr convenience store uses 200-300 MWh per year A typical grocery store uses 1,200 MWh per year GWh 1,000 MWh Larger industrial/commercial customers typically use over 10 GWh A large shopping mall can use 10 GWh A large hospital can use 20 GWh PV Photovoltaic or solar panel DG Distributed generation EV Electric Vehicle Monte Carlo - A modeling technique that uses experienced volatility in different measures to forecast future volatility. ASHP Air source heat pumps CHP Combined heat and power

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Electrical End Use Shares of Annual KWh Consumption FBC (Direct) Residential Customers 23

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Base Methodology Overview Load ClassCustomersUPCLoad% of Total ResidentialBC STATS regression 3 year average of normalized actuals Calculated UPC X Customers 39.4% CommercialCBOC GDP regression Calculated Load/Customers Regression using CBOC GDP forecast 22.8% WholesaleSurvey28.1% IndustrialSurvey + Sector GDP 9.1% LightingTrend Analysis0.4% Irrigation5 Year Average1.2%

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Residential UPC 25 Before-savings forecast Forecast Methodology: 3-year average of normalized loads

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Residential Customer Count 26 Forecast Forecast Methodology: BC stats regression

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Residential Load Forecast 27 Before-savings forecast Forecast Methodology: Calculated UPC x Customers

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Commercial Load Forecast 28 Before-savings forecast Forecast Methodology: Regression using CBOC GDP forecast

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Commercial Customer Count 29 Forecast Methodology: CBOC GDP regression Forecast

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Industrial Load Forecast 30 Before-savings forecast Forecast Methodology: Survey and CBOC Sector GDP

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Wholesale Load Forecast 31 Before-savings forecast Forecast Methodology: Survey

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Irrigation Load Forecast 32 Before-savings forecast Forecast Methodology: 5-year average

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Lighting Load Forecast 33 Before-savings forecast Forecast Methodology: Trend Analysis

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Peak Forecast 34

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Residential 35

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Commercial 36

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Wholesale 37

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Industrial 38

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Peak Monthly Variation 39

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Comparison of 2012 and 2016 LTERP Gross Load 40

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2016 Total Direct and Indirect (Wholesale) Customers 41