Optimizing Peer-to-Peer Energy Trading with MILP-Based Transaction Reduction

Peer-to-peer (P2P) energy trading in photovoltaic (PV) communities presents a promising alternative to centralized models like Feed-in Tariffs and net metering by enabling local energy exchanges among prosumers and consumers. However, ensuring secure and fair energy transactions with minimal computational and energy overhead remains a challenge, particularly when leveraging blockchainbased platforms. In this study, we investigate the planning and optimization of P2P PV communities through two scenarios: one with a centralized PV producer and multiple consumers, and another with a mix of prosumers and consumers. Using simulations based on real metering data and bootstrapped load profiles, we assess system performance in terms of satisfaction rate, PV valorization, transaction volume, and fairness. Results show that MILP-based matching reduces the number of transactions by up to 31.3% while maintaining high participation and energy performance. Scenario 1 achieves up to 60.1% satisfaction and 66.3% valorization, while Scenario 2 reaches 47.3% satisfaction and 46.7% valorization. The proposed system provides a viable pathway to balance energy efficiency, fairness, and computational sustainability in local PV communities.