California’s public policy efforts to incentivize the expansion of solar rooftop photovoltaic (PV) systems in the California wholesale electricity market have led to a tripling of PV capacity between 2013 and 2017 (CAISOMM, 2016). However, this unprecedented expansion has contributed to significant network congestion, entailing significant economic costs and affecting greenhouse gas emissions from fossil fuel generation technologies that PV generation is expected to displace. If this unprecedented expansion continues--as it is widely projected to--PV expansion could have profound unexpected impacts on network congestion and greenhouse gas emissions. My research focuses on the first phase of this project, which involves using non-parametric machine learning models to predict PV solar generation for the entire population of installed PV systems in California. This information will be used in the second phase of the project to infer the impact of new PV expansion on congestion costs and displaced emissions from existing fossil fuel generation.
Monday Poster Session
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