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Forecasting residential solar photovoltaic deployment in California [electronic resource].

Published
Washington, D.C. : United States. Energy Information Administration, 2016.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
pages 251-265 : digital, PDF file
Additional Creators
National Renewable Energy Laboratory (U.S.), United States. Energy Information Administration, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Residential distributed photovoltaic (PV) deployment in the United States has experienced robust growth, and policy changes impacting the value of solar are likely to occur at the federal and state levels. To establish a credible baseline and evaluate impacts of potential new policies, this analysis employs multiple methods to forecast residential PV deployment in California, including a time-series forecasting model, a threshold heterogeneity diffusion model, a Bass diffusion model, and National Renewable Energy Laboratory's dSolar model. As a baseline, the residential PV market in California is modeled to peak in the early 2020s, with a peak annual installation of 1.5-2 GW across models. We then use the baseline results from the dSolar model and the threshold model to gauge the impact of the recent federal investment tax credit (ITC) extension, the newly approved California net energy metering (NEM) policy, and a hypothetical value-of-solar (VOS) compensation scheme. We find that the recent ITC extension may increase annual PV installations by 12%-18% (roughly 500 MW, MW) for the California residential sector in 2019-2020. The new NEM policy only has a negligible effect in California due to the relatively small new charges (< 100 MW in 2019-2020). Moreover, impacts of the VOS compensation scheme (0.12 cents per kilowatt-hour) are larger, reducing annual PV adoption by 32% (or 900-1300 MW) in 2019-2020.
Report Numbers
E 1.99:nrel/ja--6a20-67441
nrel/ja--6a20-67441
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Note
Published through SciTech Connect.
12/06/2016.
"nrel/ja--6a20-67441"
Technological Forecasting and Social Change 117 C ISSN 0040-1625 AM
Changgui Dong; Benjamin Sigrin; Gregory Brinkman.
Funding Information
AC36-08GO28308
View MARC record | catkey: 23763635