Multi-objective Evolutionary Learning for Near Pareto-Optimal Optimization of Solar Deployment

Abstract

Existing residential rooftop photovoltaic (PV) installations in the United States are inequitable, as they are concentrated in high-income neighborhoods, and carbon-inefficient because they are often not located in electric grids dominated by fossil-fuel generators. Prior work, however, shows that prioritizing socioeconomic equity can also significantly increase the carbon efficiency of new installations. In this paper, we formalize the problem of site selection for rooftop PV installations as a multi-objective optimization problem, with metrics including energy generation, carbon offsetting, and demographic equity. We introduce a novel method called Evolutionary Value Assignment (EVA) that uses a neural network trained via evolutionary learning to select ideal sites for deployment. We evaluate our proposed approach in a case study using a dataset of U.S. solar generation and demographic information. Compared to projections of current installation trends, our method improves Carbon Efficiency by 43%, Income Equity by 41%, and Racial Equity by 24%, while increasing Energy Generation Potential by up to 10%. Therefore, our optimized placement can achieve the estimated carbon offset needed for net-zero emissions from electricity generation earlier than current deployment trends.