Psychological modeling for community energy systems

This paper introduces a novel framework for community energy system (CES) optimization that integrates Social Cognitive Theory (SCT) with Distributionally Robust Optimization (DRO) to address both behavioral and technical challenges. The rapid integration of renewable energy resources and the proliferation of peer-to-peer (P2P) trading platforms necessitate solutions that balance economic efficiency, environmental sustainability, and user engagement under uncertainty. While existing studies focus predominantly on technical optimization, they often neglect the significant influence of behavioral dynamics on community energy systems. This research bridges the gap by explicitly incorporating peer influence, observational learning, and engagement incentives into a robust optimization framework. The proposed methodology models user behavior through SCT, enabling dynamic adjustments to trading patterns and energy-sharing decisions. A DRO model is employed to handle uncertainties in renewable energy generation and demand, ensuring system reliability and resilience. To enhance computational efficiency, a primal–dual algorithm is developed, offering faster convergence compared to traditional DRO methods. The framework is validated through a comprehensive case study on a 50-household microgrid equipped with solar, wind, and storage systems, alongside a P2P trading platform. Results demonstrate the framework's ability to reduce carbon emissions by up to 30%, improve renewable energy utilization to over 90%, and increase trading participation by 25%, compared to baseline scenarios. This study makes four key contributions: (1) introducing SCT-based behavioral modeling to enhance user engagement in CES operations, (2) leveraging DRO to address uncertainties in renewable energy and demand profiles, (3) proposing a primal–dual algorithm for scalable and efficient optimization, and (4) presenting a unified framework that balances economic, environmental, and social objectives. The findings highlight the transformative potential of integrating behavioral and technical approaches for sustainable and resilient community energy management.