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Forecasting residential solar photovoltaic deployment in California

Changgui Dong, Benjamin Sigrin and Gregory Brinkman

Technological Forecasting and Social Change, 2017, vol. 117, issue C, 251-265

Abstract: 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 1.5–2GW 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 500MW, 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 (<100MW in 2019–2020). Furthermore, impacts of the VOS compensation scheme ($0.12 per kilowatt-hour) are larger, reducing annual PV adoption by 32% (or 900–1300MW) in 2019–2020.

Keywords: Solar PV; Forecasting; Diffusion models; Policy impact (search for similar items in EconPapers)
Date: 2017
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