Optimization of social welfare and mitigating privacy risks in P2P energy trading: Differential privacy for secure data reporting

With the growing demand for energy and the integration of distributed energy resources (DERs), Peer-to-Peer (P2P) energy trading has emerged as a promising approach that enables participants in the energy market to coordinate and trade energy. However, due to the intermittent generation from DERs and irregular load patterns, energy shortages can occur in the P2P market. To address this issue, an effective demand response-based energy allocation policy is proposed with usage-based billing in the P2P market that differentiates cooperative and non-cooperative buyers based on the supply-to-demand ratio. In general, monitoring buyers' demand and sellers' generation data is performed to formulate contracts, posing risks to participants' privacy. The utility grid requires this data for timely decision-making regarding load forecasting and demand response programs. However, real-time reporting of this data in the P2P market can inadvertently reveal participants' lifestyles, potentially leading to privacy breaches. To mitigate this issue, Differential Privacy (DP) is integrated to preserve participants' pri-vacy. Extensive simulations are performed over the one-year dataset, demonstrating the proposed method's effectiveness that maximizes social welfare, ensures data privacy, and enhances participant satisfaction. The results indicate an average reduction in energy costs, ranging from 9.83% to 27.86% for all buyers, and an average increase in revenues, ranging from 5.42% to 10.69% for all sellers, compared to the previously proposed study and the grid, respectively. This research contributes to the advancement of P2P energy trading by providing an innovative approach that simultaneously addresses available energy, privacy concerns, satisfaction level, and economic benefits for participants.