LT Load Forecast and Scenarios

Transcription

1 LT Load Forecast and Scenarios Mike Hopkins Senior Manager, Price Risk & Resource Planning David Bailey Customer Energy & Forecasting Manager Peter Steele-Mosey Associate Director, Energy - Navigant

2 Agenda LT load forecast and Monte Carlo range What are load scenarios? Why load scenarios? Navigant load scenarios modelling and results Load scenarios tool Next steps 2

3 LT Load Forecast + Monte Carlo Range Base Forecast As used for the PBR Update Provides a common starting point Monte Carlo Business as usual but incorporates recent volatility for several measures Status Completing the 2017 PBR Update forecast now. Will re-run the Monte Carlo analysis once the update is complete. 3

4 Annual Gross Load Forecast Status: Will be updated as part of the 2017 PBR Update forecast cycle. 4

5 Peak Demand Forecast by Year Status: Will be updated as part of the 2017 PBR Update forecast cycle. 5

6 What are Load Scenarios? They define possible future states of the world e.g. electrification They help to determine future load boundaries (e.g. high and low scenarios) They are long-term load sensitivities ( what if ) We don t assign probabilities to them They can be updated again in future LTERPs with the latest information 6

7 Why Load Scenarios? Prudent part of long term resource planning We are on the verge of some potentially significant changes in energy use and technology - e.g. distributed generation, electric vehicles Framework for any future infrastructure requests, initiatives, resources Determines flexibility and contingency planning requirements BCUC Resource Planning Guidelines include developing a range of gross (pre-dsm) demand forecasts Consistency with inclusion of scenarios in FEI 2014 Long Term Resource Plan and other utilities IRPs 7

8 LOAD SCENARIO MODELING RPAG CONSULTATION APRIL 27,

9 TABLE OF CONTENTS SECTION 1: SECTION 2: SECTION 3: SECTION 4: SECTION 5: SECTION 6: Introduction Load Drivers Load Scenarios Summary of Boundary Scenario Impacts Scenario 1 (Upper Boundary Scenario) Scenario 5 (Lower Boundary Scenario) APPENDICES SECTION 7: Tabular Summary of all Scenario Impacts 9

10 INTRODUCTION THIS STUDY STUDY PURPOSE: Quantify the potential impact of major structural changes in FortisBC s electricity load drivers through scenario analysis. STUDY FOCUS: Boundary scenarios that define major deviations from existing empirical forecasts. Anticipating the cumulative effects of emerging technologies and structural shifts in load behaviour that are unaccounted for in FortisBC s current base forecast. FOR EXAMPLE, what is the impact on system load if, by Half of all new cars being sold are EVs? A quarter of all single-family homes have rooftop photovoltaic solar? Half of those replacing their electric space-heating systems switch fuels? Etc. 10

11 INTRODUCTION THIS STUDY AND THE CPR How does this study relate to the CPR? STUDY PURPOSE: STUDY FOCUS: Conservation Potential Review Estimate baseline technical and economic DSM potential in B.C. consistent with the B.C. utilities reference load forecasts. The CPR itself is not a load forecast. Identifying DSM opportunities for further investigation, both in reference case and in sensitivity (economic/policy) cases. FOR EXAMPLE, what is the economic DSM potential in 2035 of. The entire residential sector? The office segment of the commercial sector? Heat pump water heaters belonging to residential customers? Etc. FortisBC load scenarios may be used by FortisBC to define some of the CPR DSM potential scenarios, to provide consistent scenarios across studies. 11

12 LOAD DRIVERS DESCRIPTION OF DRIVERS Rooftop Solar. Residential rooftop solar photovoltaic (PV) generation and integrated photovoltaic storage systems (IPSS) Electric Vehicles Plug-in and battery (fully electric) electric vehicles (PHEV & BEV) supported by level 1 (120V), level 2 (240V) and fast DC charging Fuel Switching Gas to Electric Residential customers converting from natural gas to electric space (mostly heat pumps) and water heating. 4 Fuel Switching Electric to Gas Residential customers converting from electric to natural gas space and water heating. 12

13 LOAD DRIVERS DESCRIPTION OF DRIVERS Consistent & Persistent Weather Changes Gradual increases in average monthly temperatures as predicted by models of climate change. Large Load Sector Transformation Unanticipated growth of large load customers not associated with traditional energy intensive industries (forestry/manufacturing). Internet of Things Connected devices, information feedback and residential control systems working together to reduce consumption. 8 Combined Heat and Power Very large C&I customers investing in cogeneration facilities. 13

14 LOAD DRIVERS DRIVER DIRECTIONAL IMPACTS Load Driver Short Form Effect on System Load (+/-) Rooftop Solar Electric Vehicles Fuel Switching Gas to Electric Fuel Switching Electric to Gas Consistent & Persistent Weather Changes Large Load Sector Transformation Internet of Things Combined Heat and Power PV EV FS G2E FS E2G Weather LLST IoT CHP 14

15 SCENARIOS BOUNDARY SCENARIOS Five scenarios explored two boundary scenarios, & three intermediate/offsetting scenarios. Boundary Scenarios 15 Impact arrows are directional only not to scale.

16 SCENARIOS INTERMEDIATE & OFFSETTING SCENARIOS Boundary scenarios define the potential extremes of load driver impacts. Intermediate scenarios define the impacts in scenarios where load drivers are offsetting and the potential consequences of such interactions. Impact arrows are directional only not to scale. 16

17 SCENARIOS SCENARIO IMPACTS (ENERGY) By 2035, the energy impacts of the boundary scenarios are substantial. - Scenario 1: Increase in consumption by more than 800 GWh/year (largely due to EVs and fuel-switching) - Scenario 5: Decrease in consumption by nearly 900 GWh/year (largely due to PVs and fuel-switching). 17

18 SCENARIOS SCENARIO IMPACTS (DEMAND) Correlation between impacts on energy and demand in HE18 varies by scenario. - Scenario 3 & 4: increase in HE18 demand despite net decrease in energy consumption, principally as a result of PV/EV interactions. 18

19 19 SCENARIO 1: ALL DRIVERS INCREASE LOAD

20 SCENARIO 1: INPUT ASSUMPTIONS Electric Vehicles By 2035, 50% of automobile purchases will be EVs (scenario assumption) When 50% of automobile purchases are EVs, 75% of home charging will be Level 2 charging (scenario assumption) When 50% of automobile purchases are EVs, sufficient DC fast charging stations are deployed to allow BEVs to increase average daily travel distance by 20% (scenario assumption) PHEV10 and PHEV20 sales are displaced by PHEV40 sales entirely by 2025 (current sales are 3% and 7% of EVs, respectively). Vehicle stock turn-over: approximately 7% per year (CANSIM) Approximately 1.4 vehicles per household/customer (NRCan) PHEV/BEV proportions stay consistent w/ 2015 sales levels:~ 67% of EVs sold in 2015 were BEV (GreenCarReports) 20

21 SCENARIO 1: INPUT ASSUMPTIONS Fuel Switching Gas to Electric By % of purchasing decisions made by residential customers that would otherwise use gas select electricity for space and water heating. Space heating stock turnover is 5% per year. Water heating stock turnover is ~8% per year (in line with CPR assumptions). Large Load Sector Transformation Two new breweries (each 25% size of current average breweries in Fortis territory) One new data centre (10 times the size of current average data centres in Fortis territory). Two new community colleges (each the same size as current average community colleges in Fortis territory). One new hospital (the same size as current average hospitals in Fortis territory). The scenario input assumptions dictate driver penetrations (next slide). 21

22 SCENARIO 1: DRIVER PENETRATION Total number of EVs by year (Scenario 1) 22

23 SCENARIO 1: DRIVER PENETRATIONS Proportion of total average annual EV charging (all cars) by charge type. 23

24 SCENARIO 1: DRIVER PENETRATIONS Number of customers switching from gas to electricity for space or water heating. Water Heating Equipment Space Heating Equipment 24

25 SCENARIO 1: IMPACTS - ENERGY Under Scenario 1, total annual system load increases by ~800 GWh by

26 SCENARIO 1: IMPACTS DEMAND Under Scenario 1, demand from 5pm 6pm in January increases by nearly 200 MW. Seasonal variation in impacts is driven principally by fuel-switching. Total Scenario Demand Impacts by Year Driver Demand Impacts in

27 27 SCENARIO 5: ALL DRIVERS DECREASE LOAD

28 SCENARIO 5: INPUT ASSUMPTIONS Rooftop Solar By 2035, 33% of residential customers in single-family detached (SFD) homes will have rooftop PV (scenario assumption) When 33% of residential SFD customers have rooftop PV, half of those homes will have energy storage integrated photovoltaic solar storage (IPSS). (scenario assumption) 64% of FortisBC residential customers live in SFD (as per FortisBC) Internet of Things By 2035, the equivalent of 50% of residential customers will live in homes fully connected with the IoT. 28

29 SCENARIO 5: INPUT ASSUMPTIONS Fuel Switching Electric to Gas By % of purchasing decisions made by residential customers that would otherwise use electricity, and are within 50m of a gas main, select gas for space and water heating. Space heating stock turnover is 5% per year. Water heating stock turnover is ~8% per year (in line with CPR assumptions). Combined Heat & Power By 2035, all existing very large FortisBC C&I meeting the criteria defined for this driver will deploy cogeneration capacity of up to 5MW each. Deployment is assumed to occur smoothly incremental capacity coming online every 3 years. 29

30 SCENARIO 5: INPUT ASSUMPTIONS Consistent & Persistent Weather Changes Change from current temperatures to assumed 2035 temperatures is linear (annual) Persistent weather may be accompanied by increased short-term volatility and extreme events with ambiguous peak demand impacts, therefore, as per FortisBC, no peak demand impacts are estimated for this driver. The scenario input assumptions dictate driver penetrations (next slide). 30

31 SCENARIO 5: DRIVER PENETRATIONS Total number of FortisBC customers with PV or IPSS by year, compared with non- PV/non-IPSS customers 31

32 SCENARIO 5: DRIVER PENETRATIONS Number of customers switching fuels from electricity to gas Water Heating Equipment Space Heating Equipment 32

33 SCENARIO 5: IMPACTS - ENERGY Under Scenario 5, total annual system load decreases by ~900 GWh by

34 SCENARIO 5: IMPACTS DEMAND Under Scenario 5, demand from 5pm 6pm in January decreases by ~ 80 MW. Seasonal variation in impacts is driven by fuel-switching, PV and IPSS. Total Scenario Demand Impacts by Year Driver Demand Impacts in

35 CONTACTS TODD WILLIAMS Managing Director PETER STEELE-MOSEY Managing Consultant navigant.com 35

36 Load Scenarios Tool There are an infinite number of combinations of the 8 load drivers FBC developed a simple tool in Excel to let anyone explore the combinations and sensitivities. The Slider tool: Interactively allows users to slide each driver as well as the timing of each load Presents the aggregate results in charts 36

37 Slider Demo Slider demo in Excel 37

38 Next Steps FBC and Navigant to consider any feedback today please back the results from the load scenarios tool on your flash drives Navigant to finalize scenarios and report FBC to apply load scenario results to 2017 Base + Monte Carlo forecast to test pre-dsm load forecast sensitivities DSM scenarios to be applied for net-of-dsm impacts Contingency planning, as part of portfolio analysis, will consider load scenarios impacts 38