Agent-based modeling of residential photovoltaic adoption and diffusion: Implications for energy policy design

Abstract

Residential Photovoltaics (RPV) systems, a type of building-integrated photovoltaics for residential buildings, play a significant role in energy transformation as residential buildings account for approximately 38 % of total electricity consumption. Countries worldwide employ different energy policies to promote RPV adoption, such as Feed-in Tariff (FIT), Net Billing (NetB), and Net Metering (NetM). However, existing research often neglects the impact of these policies on consumers' post-installation energy behaviors, such as the "rebound effect" on energy usage, which may counteract the policy's original design. Additionally, there currently is a lack of a systematic approach for choosing suitable energy policies under different scenarios and determining optimal policy parameters in RPV policy analysis. This study implements a simulation-optimization approach based on survey data to address the aforementioned issues by innovatively incorporating energy usage behaviors into the analysis. Using Singapore as the Base Case, different scenarios are synthesized to simulate RPV adoption environments in other representative districts. This study identifies valuable policy modification patterns that can assist policymakers in designing optimal energy policies. Furthermore, the methodology can be applied globally to other renewable energy technologies.