1. READ AND DELETE For best results with this template, use PowerPoint 2003 Presentation to the Demand Side Analysis Working Group (DAWG), convened by the California Energy Commission Melanie McCutchan (mym6@pge.com) Distributed Generation Policy & Strategy September 14, 2015 Recommendations for Retail PV Adoption Forecasting

2. 2 Presentation Overview Recent trends in retail PV adoption Importance of retail PV forecast for electric procurement and system planning Recommendations for updating CEC’s retail PV forecasting approach

3. Overview of retail PV in PG&E’s service area 3 Significant growth in retail PV adoption in last several years, primarily driven by the residential sector that has seen YOY growth rates in annual additions of ~70% since 2013 As of the end of Aug 2015, PG&E has 1,700 MWs of retail PV capacity interconnected (95% through NEM tariff, 5% non-export)

4. 4 Most analysts have under-predicted PV adoption Source: Meister Consulting Group. “Renewable Energy Revolution” (2015) http://www.mc-group.com/wp-content/uploads/2015/03/MCG-Renewable-Energy-Revolution-Additional-Projections.pdf Forecasts are from the US Energy Information Administration (USEIA) and the International Energy Agency (IEA). "I always avoid prophesying beforehand because it is much better to prophesy after the event has already taken place. “ --Winston Churchill

5. 5 Forecast Vintages: PG&E2015 IEPR Form 3.3 Submittal April 13, 2015 CECCalifornia Energy Demand Final Forecast Mid-Case Final Baseline Demand Forecast Form 1.2 for 2012 IEPR Update, 2014 IEPR Update, draft 2015 CED for IEPR BNEFBloomberg New Energy Finance (BNEF), Jan 2015. H1 2015 North American PV Outlook “wide-open throttle” GTM GTM Research, 2015. U.S. Solar Market Insight Report, 2014 Year in Review Projections for retail solar adoption have increased in recent years

6. 6 Significant retail PV growth creates opportunities and challenges that need to be incorporated into electric procurement and system planning ~1,400 MWs Notes: Figures are from PG&E’ Distribution Resources Plan Capacity numbers represent end of year for PG&E’s service area ~6,500 MWs

7. 7 Recommendations for CEC’s PV forecasting approach PG&E recognizes the CEC’s improvements to its PV forecasting approach implemented for the draft 2015 IEPR submittal, including: Incorporating PV interconnection data rather than CSI data Using a more rigorous bill savings analysis Updating the PV production profile PG&E recommends that the CEC incorporate the following improvements: 1.Reassess PV cost projections 2.Revise consumer cost-effectiveness response curves by leveraging research on solar adoption behavior from US DOE’s Solar Energy Evolution and Diffusion Studies (SEEDS) and other sources http://energy.gov/eere/sunshot/solar-energy-evolution-and-diffusion- studies

8. Rec. 1 – Reassess PV cost projections 8 Relying on self-reported CSI data may overestimate retail PV prices Bottom-up cost modeling suggests that solar providers have been pegging PV prices at a set percentage below bill savings This “value-based pricing” is not sustainable over the long term, which will put downward pressure on retail PV prices Source of Figure: Feldman et. al. National Renewable Energy Laboratory (NREL ) Presentation for SunShot Initiative, NREL/PR-6A20-62558 http://www.nrel.gov/docs/fy14osti/62558.pdf Slide 24http://www.nrel.gov/docs/fy14osti/62558.pdf

9. 9 Rec. 2 - Leverage recent research to update consumer cost effectiveness response curves for retail PV “By framing the proposition for adopting solar as a series of monthly savings—as opposed to a large upfront payment, greater portions of the general population could be enticed than if projects’ returns were expressed in terms of the payback time.” Current estimates of how changes in PV cost-effectiveness impact adoption used in CEC modeling based on payback appear to underestimate adoption Using payback as the cost-effectiveness metric may not be appropriate. Sigrin and Drury* (2013) found that most customers use monthly bill savings and not payback to evaluate a PV investment and state: Source of Figures: Figure on Left - NREL, 2009. Solar Deployment System (SolarDS) Model: Documentation, pg 19 Figure on Right – Sigrin and Drury, 2013. *https://www.aaai.org/ocs/index.php/FSS/FSS14/paper/viewFile/9222/9123*https://www.aaai.org/ocs/index.php/FSS/FSS14/paper/viewFile/9222/9123

10. READ AND DELETE For best results with this template, use PowerPoint 2003 10 Thank you for your attention. In conclusion: Retail PV growth is likely to be significant This growth should be estimated as accurately as possible for electric procurement and system planning purposes Revisions to the CEC’s PV forecasting approach ‒ especially in the areas of cost projections and modeled consumer response to cost effectiveness ‒ could better capture current information