California Energy Commission Hourly Load Forecasts and Peak

California Energy Commission Hourly Load Forecasts and Peak Shift IEPR Workshop December 15, 2017 Chris Kavalec and Ravinderpal Vaid Energy Assessments Division Chris. Kavalec@energy. ca. gov / 916 -654 -5184 1

California Energy Commission Background • Energy Commission long-term demand forecasts currently done at annual level • Long-term projections at hourly level increasingly important for demand analysis and resource planning – In addition to peak hour, analysis of “ramp-up” period and mid-day loads important – Demand-side factors, including PV and EVs, likely to shift the peak hour 2

California Energy Commission Goals • Develop model to project 8760 hourly loads 10 years out for a specified geography • Develop “business as usual” projections that account for economic and demographic changes, changes in sector shares, and other factors • Adjust business as usual projections to account for increasing amounts of PV and EVs, along with AAEE, DR, and TOU pricing 3

California Energy Commission Model Estimation and Data • First version relies on system level hourly data (e. g. CAISO EMS data) to project hourly loads • Later versions for future forecasts would use AMI data to estimate individual models by sector as well as by more granular geography 4

California Energy Commission Hourly Load Model Estimation Estimate ratio of hourly load to annual average load for each hour (24 regressions for each TAC) as a function of weather, day of the week, weekend/holiday, month, using hourly data by TAC for 2006 -2016 Li, d /Ly = f(g(t), dowd, wkhold, monthd, constanti) i=1, 24 d=1, 365, y=1, 11, g(t)=weather (temperatures, dew point and cloud cover) 5

California Energy Commission Implementing Hourly Load Model • Apply estimated ratios to annual forecast consumption load (load served by utilities plus PV energy minus EV load) – Annual load forecast accounts for economic/demographic growth and other changes • Adjust consumption load using EV charging profiles, PV generation profiles, residential TOU hourly impacts, and hourly AAEE 6

California Energy Commission Hourly Profiles • EV charging profiles developed by Idaho National Lab and LBNL – Profiles applied to annual EV forecast – Developed using varying assumptions for TOU coverage (0%, 63%, and 83%) • PV generation profiles from CSI data • Residential TOU in upcoming presentation • AAEE profiles “under construction” 7

California Energy Commission Average Weather Year Required • Simulate 17 years using hourly weather and calendar effects assuming 7 different calendars (17 x 7 = 149 simulations for each hour) • Take highest hourly ratio for each simulation, rank, and select median—this becomes weather-normalized peak ratio – Similarly for 2 nd highest hourly load, etc. , through 8760 hours • Assign ratios to actual day and hour using “average” weather year in terms of CDD and HDD – 2009 for SCE and SDG&E, 2012 for PG&E 8

California Energy Commission Example of Projected Hourly Consumption Loads: PG&E in 2030 9

California Energy Commission Example of Peak Shift: SCE Peak Day in 2030 30, 000 B C 28, 000 A 26, 000 D "Traditional" Peak MW 24, 000 Shifted Peak Consumption Load 22, 000 Consumption+EV+TOU+PV 20, 000 18, 000 13 14 15 16 17 18 19 20 21 22 23 10

California Energy Commission Limitations • Not accounting for climate change at hourly level • Need updated PV profiles • EV shapes based on relatively small sample • System level analysis—sector and more disaggregated geographic level would improve accuracy 11

California Energy Commission Questions/Comments? 12