a two-stage optimization model for p2p market design considering role of retailer and demand response programs

Objective: with the increasing penetration of distributed energy resources (ders), peer-to-peer (p2p) energy trading has emerged as a promising mechanism to enhance renewable energy utilization and market efficiency. this study aims to design a p2p electricity market for grid-connected microgrids that coordinates local trading with retail and wholesale markets while accounting for geographical distance and demand response programs.methods: a two-stage optimization framework is proposed. in the first stage, a mixed-integer linear programming (milp) model determines the optimal neighborhood set of prosumers by maximizing renewable energy consumption and minimizing the geographical distance between trading peers. in the second stage, a mixed-integer nonlinear programming (minlp) model is developed to optimize energy exchanges, battery storage, and pricing decisions, with the objectives of maximizing retailer profit and minimizing prosumer costs. the model incorporates time-based and incentive-based demand response programs and is validated using real residential data from iran.results: the numerical results show that limiting p2p transactions to geographically closer peers improve local renewable energy utilization. sensitivity analysis on time-based dr programs indicates that the optimal pricing mechanism applies real-time pricing (rtp) to both the retail and p2p markets.conclusion: the proposed two-stage p2p optimization framework enhances renewable energy utilization by prioritizing local trading and rtp-based pricing. results indicate that applying real-time pricing in both retail and p2p markets increases renewable energy share and economic efficiency, while providing actionable insights for sustainable microgrid and p2p market design.