A privacy-preserving dynamic pricing framework for P2P energy trading with local differential privacy and grid data fidelity

The proliferation of distributed energy resources (DERs) and IoT-enabled smart meters has enabled peer-to-peer (P2P) energy trading, allowing direct transactions between producers and consumers and reducing intermediary costs. However, prosumer generation from DERs and demand is uncertain, and sharing fine-grained consumption data with intermediaries and the utility grid may expose sensitive personal information if confidentiality is compromised. To address these challenges, this paper presents a privacy-preserving, fair, and efficient P2P energy trading framework within smart grids. The contributions are threefold. First, an incentivized dynamic pricing mechanism with an efficient energy allocation policy is proposed, adapting to real-time supply-to-demand ratio (SDR) and allowing buyers contributing to peak consumption to participate in demand reduction programs with fair incentives. Second, local differential privacy (LDP) is applied using the Laplace mechanism to protect individual energy consumption data. Third, an efficient group noise cancellation strategy mitigates the distortion introduced by perturbation, ensuring individual-level privacy, efficient data aggregation and maintaining grid-level data accuracy. Simulation results for the winter and summer seasons show that the proposed framework achieves an average percentage reduction in buyers' energy costs, ranging from 3.6% to 17.8% in winter and 3.7% to 18.85% in summer. It also enhances the time averaged satisfaction index by up to 43.4%, while providing superior privacy protection and data utility at the utility grid level, as measured by the mean absolute error (MAE) across 1000 Monte Carlo runs, compared with existing schemes.