Model Predictive Control for Peer-To-Peer Energy Exchange in Network-Aware Residential Communities

Residential energy communities with distributed photovoltaic generation and battery storage require intelligent coordination to maximize local energy utilization while ensuring grid compliance. This paper proposes a centralized Model Predictive Control (MPC) framework for peer-to-peer (P2P) energy trading implemented in Python, co-simulated with OpenDSS for power flow validation. The method uses 24-hour prediction horizons and convex optimization to prioritize local energy exchange over grid transactions while coordinating battery storage systems. Validation on a modified IEEE 13-node feeder with four residential buildings over 720 hours demonstrates substantial improvements: prosumer self-consumption increases by 32-36%, community electricity costs decrease by 13.6%, and grid imports reduce by 21.4%, while maintaining voltage compliance. These findings highlight 432.5 kWh monthly energy exchange volume, representing 16.6% of community demand, and establish economic viability through differential tariff structures that benefit both prosumers and consumers. Future work will integrate distributed MPC with energy management strategies for community-based development.