Reproducible Agent-Based Modelling of Residential PV Adoption in Community Microgrids: Integrating Economic, Infrastructural, and Social Drivers

Household adoption of residential photovoltaic systems in community microgrids is shaped by economic, infrastructural, and social factors. Previous studies have shown that agent-based modelling can help analyse adoption, but it often lacks clear mathematical foundations, systematic validation, and reproducibility. This study presents an open-source agent-based model with two advances: (1) a fuzzy-utility method for household decision-making and (2) combined modelling of financial incentives, grid reliability, infrastructure access, and peer effects as adoption drivers. The model explores adoption under diverse policy and technical scenarios, validates results against Bass diffusion and discrete choice models, and applies a Sobol-based sensitivity analysis to identify key parameters. Results clarify how incentives, barriers, and social influence shape adoption trajectories. By demonstrating cost-sharing dynamics and peer network effects and openly sharing model code and data, this study provides a transparent and reproducible benchmark for future community microgrid research.