1. California Energy Commission 2015 IEPR Self-Generation Forecast Sacramento, CA 7/07/2015 Asish Gautam Demand Analysis Office Energy Assessments Division Asish.Gautam@energy.ca.gov / 916-654-3900 1

2. California Energy Commission Data Sources Rebate program data vs interconnection data In prior IEPR demand forecast, staff relied on rebate program data to track DG installations –California Solar Initiative, New Solar Homes Partnership, Emerging Renewables Program, Self-Generation Incentive Program As programs sunset but strong interest in installing DG (mainly PV) – challenge for staff collecting installation data 2

3. California Energy Commission Data Sources 2013 IEPR demand forecast – utilities pointed out discrepancy in PV capacity relative to staff’s estimate of PV stock Generally due to continued interest by utility customers in installing PV without seeking rebates: –Driven by PV cost reduction, financing options, and availability of Federal tax credit 3

4. California Energy Commission Data Sources By 2013 difference in PV capacity addition becomes more pronounced. 4

5. California Energy Commission Data/Model Updates Updated PV production profile based on data provided by CPUC Updated PV peak factors to better reflect impact coincident with utility system peak PV cost data/trend from CPUC Net Metering Public Tool (Draft version) Residential model redone to use actual electric tariffs (IOUs) instead of average sector rate 5

6. California Energy Commission Data/Model Updates Using residential load shape data to model net metering Assuming that current electric rates and net metering compensation structure will stay in place over forecast period –CPUC currently examining revisions to residential retail rates and developing alternative net metering compensation program 6

7. California Energy Commission Data/Model Updates DG incentive program EM&V reports CEC sponsored report on CHP market assessment (ICF CHP Policy Analysis) Revised electric and gas rates Revised housing data and floor space 7

8. California Energy Commission Forecasting Approach Underlying structure similar to payback/cash flow model used by EIA/NREL Payback calculations based on system and maintenance costs, incentives, and fuel rates Estimated payback applied to a Bass Diffusion adoption curve Results for adoption differ by demand scenario since projected fuel rates and number of homes/floor space vary by scenario 8

9. California Energy Commission Statewide Non-PV Self-Gen Energy Impacts All 3 scenarios close to one another and slightly above CED 2013 Mid case 9

10. California Energy Commission Statewide Non-PV Self-Gen Peak Impacts All 3 scenarios close to another and ~100 MW higher than CED 2013 Mid case 10

11. California Energy Commission Statewide PV Self-Gen Energy Impacts All 3 scenarios above CED 2013 Mid Case. Impacts range between 21,000 GWH to 25,000 GWH representing 9% - 11% of electricity Consumption. Slower growth after 2016 due to expiration/step-down of Federal tax credit. 11

12. California Energy Commission Statewide PV Self-Gen Peak Impacts All 3 scenarios above CED 2013 Mid Case. Impacts range between 4,500 MW to 5,400 MW and correspond to installed capacity of 12,000 MW to 14,000 MW 12

13. California Energy Commission Optional Scenario – PV Capacity under IOU Rate Reform PV adoption ~1,200 MW lower with rate reform compared to CED 2015 Residential Mid Case. 13

14. California Energy Commission Next Steps Near Term (Revised Forecast): –Update historical installation data –Other Updates Net metering, Residential Rate Reform, Storage, and Distribution Resource Planning Proceeding Longer Term (2017 IEPR): –Refinement to long term peak demand forecast methodology to integrate impacts from DG, EE, storage, and transportation using a common framework 14