Integrated Hourly Electricity Demand Forecast For Ontario Andrzej Zerek The Itron User s s Conference September 2009 Ontario Power Generation, Energy Markets Planning & Analysis 1
ENERGY MARKETS Objective of the Presentation and Agenda Objective of the Presentation: Describe the process of generating hourly demand forecast on a weather normal basis from the current month to a long-term forecast (next 5-155 years) Agenda: Overview of OPG Demand Forecasting System/Definitions Process # 1: Producing Basic Hourly Forecast starting from actual hourly demand data to Weather Normal Hourly Profile before adjustments Process # 2: Producing Final Hourly Primary Demand Forecast including adjustments for Conservation & Demand Management, Smart Meters (TOU), Power Cost Escalation and Electric Vehicles (PHEV) Disclaimer: Some assumptions underlying the presented herein trends and analysis were primarily made for the purpose of this presentation and therefore the results dod o not represent official Ontario Power Generation views. 2
OPG Demand Forecasting System/Definitions 3 Ontario Primary Demand (PD) represents the total energy that was supplied from the IESO-Administered Market for the sake of supplying load within Ontario. It is measured by the Independent Market Operator (IESO) Total Market Demand = Generation in Ontario + Imports. PD = TMD - Exports Basic Demand is Primary Demand before the impact of Conservation, PHEV and Power Cost Escalation. The result of market forces and so includes the impact of natural conservation, technology improvements or the impact of existing standards and codes At the Customer Level Includes Self-Generation At the Generator Level excludes Self-Generation, includes Line Losses Conservation (called Conservation and Demand Management - CDM) is the incremental reduction in Basic Demand due to government programs, customer rate/price changes and enhanced standards and codes that t exceeds what otherwise would have happened as a result of market forces Energy Efficiency (EE) Demand Management (DM) Fuel Switching (FS)
OPG Demand Forecasting System/Definitions Self-Generation/Distributed Generation: CustomerC based generation also called Load Displacement Non-Utility Generation (LD NUG) Reduces demand at the customer level; part of Basic Demand Power Cost Escalation : Impact of possible strong wholesale price increases as a result of the future generation mix/contracts and/or driven by government s s policy changes e.g. carbon tax Short-term term forecast (next 14-days) is primarily driven by weather (MetrixND). Used for generation scheduling, offers to the Market and trading Long-term forecast is weather-normal (WN). Used for: Business Planning (5 years) and monthly updates. Annual energy forecast is produced once a year for BP and updated during the yeary as required Special long-term studies (up to 2027) to evaluate resource requirements and impact of various government decisions. Used as input for fundamental models: Prosym and CROP (a model developed internally) Consistency and transparency of the forecasting methodology 4
OPG Demand Forecasting System/Definitions Long-term forecast requirements: Demand forecast input required in hourly resolution (8760 for next 20 years) New challenges for the forecasters Incorporate assumptions related to: Economic drivers and sectoral forecasts (incl. natural efficiency improvements), Conservation & Demand Management (CDM), Self-Generation (distributed generation), Electric Vehicles (PHEV), Customer Rate Changes (Smart Meters/Time-of-Use rates) Power Cost Escalation Impact (price response/price elasticity of demand) Create alternative cases combining the above factors 5
Process # 1: Producing Basic Hourly Forecast Step 1: Actual Energy/OPG Weather Correction Procedure Step 2: Annual WN Energy Forecast Step 3: Monthly WN Energy & Peak Forecasts/Patterns (using MetixND) Step 4: Hourly WN Forecast/Patterns (using MetrixND) Actual Hourly Energy from IESO OPG Weather Correction Procedure Actual Weather -Dry Bulb -Dew Point -Wind Speed -Illumination Weather Corrected Energy and Peak End-Use Forecasting System for Ontario (Residential, Commercial, Industrial, Agriculture, Transportation) Base-Year Demand/End- Use Model Calibration Econometric Modeling Annual Energy Forecast derived from Monthly Forecasts Economic Data (Hstarts/H hold, GDP/GSP, Employme nt) Monthly Regression Energy Models Monthly Regression Peak Models Calendar Variables Monthly WRF (heat and cool slopes) WN Monthly Energy Forecast (Patterns) WN Monthly Peak Forecast (Patterns) WN Seasonal Peak Forecast MetrixND (Daily Energy Models, Daily Peak Models, Hourly Models) Historic Hourly Energy Data WN Hourly Load Pattern (2005 weather pattern) Weather Data Ranking & Mapping Output into the 2005 Hourly Weather Pattern Basic WN Hourly Forecast (reconciled to WN energy & peak forecast) Hourly Load Reconciliation 6
Step 1: Actual Energy/ Weather Correction Step 1: Actual Energy/OPG Weather Correction Procedure Actual Hourly Energy from IESO OPG Weather Correction Procedure Actual Weather -Dry Bulb -Dew Point -Wind Speed -Illumination Weather Corrected Energy and Peak Actual hourly demand data from IESO Weather Correction is a statistical process designed to remove the impact of abnormal weather conditions from observed load data. OPG uses it s s own WC methodology to capture impact of typically volatile weather in all seasons in Ontario. A multi-step process for weather correcting based on daily weather and energy modeling. OPG uses the last 10 years to derive normal weather Weather response coefficients are estimated using the last 2 years of data Weather Normal Peak in an interval (week, month or season) is the expected value of the maximum daily peak in that interval Summer vs. Winter WN Peak 7
Ontario Primary Demand 14750 14,619 Ontario Actual (Observed) Primary Demand (GWh) 2005 2006 2007 2008 2009 14500 14000 Ontario Weather-Corrected Primary Demand (GWh) 14,423 2005 2006 2007 2008 2009 14250 13750 13250 12750 12250 11750 11250 13,730 12,732 11,698 13,494 12,168 11,828 11,745 13,499 14,095 14,032 12,226 11,316 12,553 12,187 12,441 13,746 13500 13000 12500 12000 11500 13,201 12,729 11,763 13,239 12,237 12,066 11,885 12,739 11,455 13,657 13,407 12,368 12,358 12,318 12,092 12,538 13,477 10750 10250 10,903 10,748 10,504 JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC 11000 10500 10,727 10,660 JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC 25000 24000 23000 22000 24,532 22,436 Ontario Weather-Corrected Peak Demand (MW) 2005 2006 2007 2008 2009 24,672 24,624 24,448 23,427 22,888 22,629 22,375 21,984 22,048 22,067 22,380 23,116 5% 4% 3% 2% 1% 0% Ontario Primary Demand, Y/Y Monthly % Changes (Weather-Corrected) 21000 20000 20,574 20,480 20,050 20,552-1% -2% -3% -4% Jan-05 Mar-05 Jul-05 May-05 Sep-05 Jan-06 Nov-05 Mar-06 Jul-06 May-06 Sep-06 Jan-07 Nov-06 Mar-07 Jul-07 May-07 Sep-07 Jan-08 Nov-07 Mar-08 Jul-08 May-08 Sep-08 Jan-09 Nov-08 Mar-09 Jul-09 May-09 19000 18,730 18,535-5% -6% 18000 JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC -7% -8% -9% 8
Step 2: Annual Energy Forecast Step 2: Annual WN Energy Forecast End-Use Forecasting System for Ontario (Residential, Commercial, Industrial, Agriculture, Transportation) Base-Year Demand/End- Use Model Calibration Econometric Modeling Annual Energy Forecast derived from Monthly Forecasts End-Use Forecasting System for Ontario consists of detailed end-use models for residential and commercial sectors Residential segments modelled are: space cooling & heating by technology, water heating, base load (appliances plus lighting), miscellaneous (office and entertainment equipment, small appliances, etc.) Key drivers: housing stock, equipment penetration and replacement rates, equipment efficiency (old & new), efficiency improvements Consumption measured by Unit Energy Consumption UEC (kwh per annum) Estimating Program Driven Energy Efficiency Improvements 9
Step 2: Annual Energy Forecast Step 2: Annual WN Energy Forecast End-Use Forecasting System for Ontario (Residential, Commercial, Industrial, Agriculture, Transportation) Base-Year Demand/End- Use Model Calibration Econometric Modeling Annual Energy Forecast derived from Monthly Forecasts Commercial demand forecast for lighting, heating, cooling, ventilation, office equipment and smaller end-uses: End-uses forecasted for several building types: office, retail, health, schools etc. Consumption measured by Energy Utilization Index (kwh/sq.f./year), old & new buildings, subject to natural rate of efficiency improvements Industrial is broken down by segment and updated based on market intelligence Econometric modeling 10
Step 2: Annual Energy Forecast Base-Year Actual Energy from IESO Weather Corrected Energy @ Generator Weather Corrected Energy @ Customer Level before CDM Long-Term Forecast Sectoral Forecasts by End-Use/Segment Weather Correction Procedure Adjust for Estimated CDM Calibration of End-Use Model to Weather Corrected Base-Year Residential Commercial Agriculture Final Annual Primary Energy Demand Forecast @ Generator Transportation CDM Self- Generation Line Losses PHEV s Price Responses 11
Ontario Demand by Sector 60.00 Ontario Sectoral Demand (incl. CDM) (TWh) 39.0% Ontario Market Share by Sector (%) 55.00 37.0% 50.00 45.00 35.0% 33.0% 40.00 35.00 30.00 1990 1991 1992 Residential (Incl. MR) Commercial (Excl. MR) Industrial 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Industrial demand declining since 2005, strong drop in 2009 (-13%)( Commercial demand growing but expected to decline in 2009 (-4%)( Residential demand flat; strong construction activity in Ontario 31.0% 29.0% 27.0% 25.0% Residential (Incl MR) Commercial (Excl. MR) Industrial 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 12
Ontario Basic Electricity Demand (2006 2030) Weather Normal (TWh) Final Demand Residential Industrial Transport Line Losses Commercial Agricultural Non-Utility Generation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Annual average growth rates by Sector 2010-2027: 2027: Residential: 0.5% Commercial: 1.5% Industrial: 0.6% Total Basic Demand: 0.7% 13
Step 3: Monthly WN Energy & Peak Step 3: Monthly WN Energy & Peak Forecasts/Patterns (using MetixND) Economic Data (Hstarts/H hold, GDP/GSP, Employme nt) Monthly Regression Energy Models Monthly Regression Peak Models Calendar Variables Monthly WRF (heat and cool slopes) WN Monthly Energy Forecast (Patterns) WN Monthly Peak Forecast (Patterns) WN Seasonal Peak Forecast Balance of the year and next year monthly forecast uses economic intelligence and monthly patterns Monthly energy and peak regression models driven by: various economic drivers, calendar variables and a monthly WRF Monthly energy pattern calibrated to annual energy W/C peak derived from W/C energy WN Seasonal Peak forecast based on historical relationship between WC Monthly and Seasonal peaks 14
Step 4: Hourly WN Patterns/Forecasts Step 4: Hourly WN Forecast/Patterns (using MetrixND) MetrixND (Daily Energy Models, Daily Peak Models, Hourly Models) Historic Hourly Energy Data WN Hourly Load Pattern (2005 weather pattern) Weather Data Ranking & Mapping Output into the 2005 Hourly Weather Pattern Hourly profile based on weather normal (last 10 years average with hourly pattern derived from the year 2005) HELM-based process of ranking weather elements (1996-2005) and mapping averages to the 2005 hourly weather pattern. Output: WN forecast for four weather elements based on 2005 weather pattern. Input to MetrixND MetrixND generates WN Hrly load patterns Output reconciled and scaled to peak and monthly energy Basic WN Hourly Forecast (reconciled to WN energy & peak forecast) Hourly Load Reconciliation The forecasted is scaled to Seasonal WN Summer Peak. 15
Step 4: Hourly WN Patterns/Forecasts Flat WN Flat WN redistributed into the 2005 Weather Pattern WN (fromranking the daily avg) in the 2005 Weather Pattern Hourly PD for July 2015 (MW) 0.00016 0.00014 0.00012 0.0001 0.00008 0.00006 0.00004 0.00002 Density of Hourly PDinJuly 2015 Flat WN Flat WNredistributedintothe 2005Weather Pattern WN(fromRankingthe daily avg) inthe 2005Weather Pattern act 05PD(warmest inlast 10yrs) 7/1/2015 0:00 7/6/2015 0:00 7/11/2015 0:00 7/16/2015 0:00 7/21/2015 0:00 7/26/2015 0:00 7/31/2015 0:00 0 10000 12000 14000 16000 18000 20000 22000 24000 26000 28000 Weather Pattern Year: dictates when the Peak and Min Energy will occur in the forecast. In Ontario, we expect the peak on a Tuesday or Wednesday in mid July. The 2005 weather pattern: warmest day on a Tuesday, and coldest on a Saturday. For 2015, this results in the Peak on Tuesday July 14 and the Min n on Saturday July 5. The Flat WN results in the unlikely days: Peak on Friday July 17,, Minimum on Monday July 12. Flat WN vs WN obtained by ranking the daily average weather: Taking averages at the same days of the month produces a smaller variation in daily values than would typically occur over the month, consequently, a flatter load shape. Reconciling a flat shape with Peak and Min Energy forecast targets pushes many values towards either of these extremes. The WN weather pattern from ranking captures both the average monthlym levels and the expected average daily variation within the month. Produces a more realistic hourly load pattern after reconciling: fewer extremes and more of the typical mid-range values. 16
Process # 2: Producing Final Hourly Forecast Step 5: Hourly Forecast Adjusted for Energy Efficiency (EE) Step 6: Hourly Forecast Adjusted for Price Response/Elasticity Basic WN Hourly Forecast (reconciled to WN energy & peak forecast) Actual Savings (EE Reports from LDC s, Conservation Bureau Reports) Total Basic Annual Energy after EE Estimating Annual EE Savings by Selected End-Uses (Lighting, AC, Cooking, Washing & Drying, TV) End-Use Model Simulations Annual Price Response Short & Long Term Price Elasticities Hourly Load Profiles by End- Uses End-Use Load Profiles (Hourly) Generate Hourly EE Impact Subtract from Basic Hourly Forecast Generate Hourly Price Response Impact Subtract from Basic Hourly Forecast 17
Step 5: Basic WN Hrly Forecast Adjusted for EE Step 5: Hourly Forecast Adjusted for Energy Efficiency (EE) Basic WN Hourly Forecast (reconciled to WN energy & peak forecast) 18 Estimating Annual EE Savings by Selected End-Uses (Lighting, AC, Cooking, Washing & Drying, TV) Generate Hourly EE Impact Subtract from Basic Hourly Forecast Actual Savings (EE Reports from LDC s, Conservation Bureau Reports) End-Use Model Simulations Hourly Load Profiles by End- Uses Program-driven/enhanced Energy Efficiency (EE) improvements Natural vs. Enhanced Annual EE savings are derived by simulating the rate of efficiency improvements in the selected end-uses (e.g. lighting, AC, cooking, washing & drying, TV) Accounting approach for residential lighting Other Conservation Programs are appliance retirement, improvements in building HVAC, installation of energy saving devices The hourly load profiles by end-use combined to determine their coincident hourly impact Hourly load profiles from New England adjusted to Ontario weather
Step 5: Basic WN Hrly Forecast Adjusted for EE Annual CDM Energy Savings (TWh) Res 10/10 Other_EE CommLight CommAC Refrig ResLight Res AC Total CDM Res AC Res Lighting Refrig Comm AC Comm Lighting Other EE Res 10-10 Ontario PD reductions by CDM (TWh) before CDM after CDM 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 CDM programs introduced in Ontario in 2006; Basic Demand starting g in 2006 Various estimates of future impact of CDMs and Energy Efficiency Improvements; OPG uses End-Use Models to simulate potential impact plus various studies/judgment 19
Step 5: Basic WN Hrly Forecast Adjusted for EE 28000 26000 CDM reduction of Hourly PD on Summer Peak Day aftercdm before CDM (excl PHEV) 3600 3200 25000 CDM Reduction of PD on Winter Peak Day aftercdm before CDM (excl PHEV) 2500 Hourly Prim ary Dem and (M W ) 24000 22000 20000 18000 16000 14000 12000 Comm AC Comm Lighting Refrig Res 10-10 Other EE Res AC Res Lighting 2800 2400 2000 1600 1200 800 400 CDM Hourly Buildup (MW) Hourly Prim ary Dem and (MW) 22000 19000 16000 13000 Comm Lighting Refrig Other EE Res Lighting 2000 1500 1000 500 CDM Hourly Buildup (MW) 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 Savings driven by End-Use consumption and load shapes; higher impact during on-peak for both seasons Lighting provides most savings in the Winter AC, Lighting and Other EE provide most savings in the Summer 20
Step 6: Basic WN Hrly Forecast Adjusted for Price Response Step 6: Hourly Forecast Adjusted for Price Response/Elasticity Total Basic Annual Energy after EE Annual Price Response Generate Hourly Price Response Impact Subtract from Basic Hourly Forecast Short & Long Term Price Elasticities End-Use Load Profiles (Hourly) Simulate impact of possible strong wholesale power cost increases in the future Load reduction impact of prices estimated using own price elasticity: Short-run run price elasticity reflects the rate of appliance utilization as immediate response to price signals; In this case, -0.1 S-RS R Elasticity was used Long-run price elasticity reflects changes in appliance efficiency and stock change; In this case, - 0.25 L-TT Elasticity was used Hourly price response impact generated annual energy after EE, price elasticities and end-use load profiles 21
Ontario PD Reductions Ontario PD reductions by CDM and Price Elasticity (TWh) Res AC Res Lighting Refrig Comm AC Comm Lighting Other EE Res 10-10 before CDM after CDM after Price Elast aftertou Price Shifting 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 22
Process # 2: Producing Final Hourly Forecast Step 7: Hourly Profile Adjusted for TOU/Smart Meters Annual Consumption by Res. End-Use Shiftable Loads End-Use Shapes Smart Meters/Timing & Penetration Rate Projected Vehicle Sales PHEV Penetration Rate Step 8: Hourly Profile Adjusted for PHEV s Final Hourly PD Forecast Elasticity Estimating Hourly Impact by Shiftable Loads Annual Electricity Consumption by Car Load Profile Annual Electricity Consumption Generate Hourly TOU Impact Subtract from Basic Hourly Forecast Generate Hourly PHEV Impact Subtract from Basic Hourly Forecast 23
Step 7: Hourly Profile Adjusted for TOU/Smart Meters Annual Consumption by Res. End-Use Shiftable Loads End-Use Shapes Elasticity Smart Meters/Timing & Penetration Rate Estimating Hourly Impact by Shiftable Loads Generate Hourly TOU Impact Subtract from Basic Hourly Forecast Regulated Price Plan, Ontario Energy Board, March 2005 Step 5: Basic WN Hrly Forecast Adjusted for TOU/Smart Meters Ontario plans to install Smart Meters in all households by 2010/2011 Installation rate at 20% from 2007 to 2011;full participation by 2014 Ontario Energy Board TOU rates TOU shifting of consumption estimated using elasticity of substitution, a measure of the degree to which a consumer shifts consumption from on-peak (when price is high) to off-peak periods Various estimates of elasticity. In this case, a value of 0.11 was used 24 Off-peak Mid-peak On-peak 2.7 c/kwh 7.3 c/kwh 9.3 c/kwh Weekends 24 Winter 7 4 6 3 2 2 Weekdays Summer 7 4 6 5 2 Weekdays 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 12 a.m. 7 a. m. 11 a.m. 5 p.m. 8 p.m. 10 p.m. Time of Day Elasticity is used to shift the hourly residential profile derived from the forecast of the individual end-use consumption (shiftable loads) and their load shapes.
Step 5: Basic WN Hrly Forecast Adjusted for TOU/Smart Meters Residential End Use Consumptions (GWh) 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 2009 2010 2011 2012 2013 2014 TV Cooking Res AC 2015 2016 Washing & Drying Res Lighting 2017 2018 2019 2020 2021 2022 2023 2024 2025 before CDM after CDM 2026 2027 2028 2029 2030 400 350 300 250 200 150 100 50 0 Savings in PD Peak from TOU Shifting in Residential Consumption (MW) Shifting After CDM Residential Consumption Shifting Before CDM Residential Consumption 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Residential demand for shiftable loads are declining over time dued to impact of both natural efficiency improvements and CDM In this case, impact from TOU shifting on peak declining from 340 0 MW in the 2014-2017 2017 period to 200 MW by 2025. Higher impact at 4 pm peak Higher Price Elasticity would increase the impact of TOU 25
Step 5: Basic WN Hrly Forecast Adjusted for TOU/Smart Meters 0.12 Residential Medium-Term Price Elasticity of Substitution 7000 Sum of the Residential End Use Loads on day of Summer Peak 2020 (Tuesday, 14 Jul) 0.1 0.08 0.06 0.04 Full Installation in 5 years (20% from 2007 to 2011) Full Customer participation by 2014 6000 5000 4000 3000 2000 250 MW reduction in the Total "shiftable" end use load at 4 p.m. (Lighting, AC, Cooking, Washing & Drying and TV) before CDM off-peak mid-peak on-peak shifted 0.02 1000 0 2005 2010 2015 2020 2025 2030 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Price Elasticity of Substitution (0.11) is used reaching full impact by 2014 and stays at this level thereafter. Could continue to grow. Elasticity is used to shift the hourly residential profile derived ed from the forecast of the individual shiftable end-use consumption and their load shapes: Residential lighting, AC, cooking, washing & drying, TV No impact on energy but stronger impact on on-peak reduction 26
Step 5: Basic WN Hrly Forecast Adjusted for PHEVs Projected Vehicle Sales PHEV Penetration Rate Annual Electricity Consumption by Car 1600 1400 1200 Load Profile Step 8: Hourly Profile Adjusted for PHEV s Annual Electricity Consumption Generate Hourly EE Impact Subtract from Basic Hourly Forecast Final Hourly PD Forecast Projected # of PHEVs and Peak Impacts Peak Impact (MW) Number of PHEV 1,600,000 1,400,000 1,200,000 First Plug-In Electric Vehicles (PHEV) in Ontario in 2012 PHEV penetration of sales /share: 2020: 10%/2.9% 2025: 17%/7.5% Annual consumption 3285 kwh per vehicle (9 kwh per charge) About 690,000 PHEVs with an annual consumption of 2.8 TWh by 2025 Battery charging 100% off-peak (11 pm-7 7 am) Possible some charging in on- peak as well Peak Impact (MW) 1000 800 600 1,000,000 800,000 600,000 Number of PHEV 400 400,000 200 0 27 2012201320142015201620172018201920202021202220232024202520262027202820292030 200,000 0
Ontario Basis & Primary Demand 2006-2030 Weather Normal (TWh) Basic Demand Basic Demand - CDM Basic Demand - CDM + Price Response Basic Demand - CDM + Price Response + PHEV 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 28
Final Primary Demand Forecast Ontario PD reductions by CDM and Price Elasticity (TWh) Res AC Res Lighting Refrig Comm AC Comm Lighting Other EE Res 10-10 before CDM after CDM after Price Elast aftertou Price Shifting including PHEV 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 29
Final Primary Demand Forecast Annual PD Peak Reductions (MW) Res AC Res Lighting Refrig Comm AC Comm Lighting Other EE Res 10-10 beforecdm after CDM (EE + Res 10-10) after Price Elast aftertou Price Shifting including PHEV ` 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 30
Ontario Hourly Electricity Demand 2020 Basic Demand Basic Demand - CDM Basic Demand - CDM + Price Response Basic Demand - CDM + Price Response + PHEV* Weather Normal (MW) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 31
Ontario Hourly Electricity Demand (July 2020) Basic Demand Basic Demand - CDM Basic Demand - CDM + Price Response Basic Demand - CDM + Price Response + TOU Basic Demand - CDM + Price Response + TOU + PHEV Weather Normal (MW) 1 51 101 151 201 251 301 351 401 451 501 551 601 651 701 751 32
Ontario Hourly Electricity Demand (July 14, 2020) Weather Normal (MW) Basic Demand Basic Demand - CDM Basic Demand - CDM + Price Response Basic Demand - CDM + Price Response + TOU Basic Demand - CDM + Price Response + TOU + PHEV 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 33
Thanks for Your Attention Andrzej 34