Privacy-preserving P2P energy trading for economic optimization in urban microgrids

Peer-to-peer energy trading has emerged as a capable approach to raise economic efficiency and renewable energy usage in urban community microgrids. Hence, large-scale implementation faces challenges associated with operational uncertainty, privacy concerns, and extreme demand conditions. The presented study brings out a two-stage privacy-preserving peer-to-peer framework, which combines autonomous local energy management with a distributed heuristic iterative double auction mechanism. The suggested model enables real-time energy trading while minimizing the exchange of sensitive information. Simulation outcomes for a 94-prosumer urban community microgrid validate the effectiveness of the framework in reducing system costs and limiting carbon emissions under both normal and extreme conditions. Specifically, the framework reduces grid bills and lowers total system costs. Renewable energy self-consumption increases from 56.68% to 86.99%, and peak-to-valley power variation is lowered by 48.94%. Through the uncertainty scenario, the framework maintains resilience with a total cost of 1468.82 CNY and renewable self-consumption at 70.11%. During extreme heatwave scenarios, transmitted grid energy declines by 49.1%, and system costs remain only 0.53% higher than the centralized peer-to-peer approach, which reflects the benefits of privacy preservation. In a carbon emission scenario, total emissions are reduced by 19.4%, from 1209.30 kgCO2e to 622.89 kgCO2e, while grid bills decline by over 71%. These outputs depict that the proposed framework successfully addresses privacy, economic efficiency, and sustainability, offering a promising solution for decentralized energy systems.