Demand Response-based Multi-Layer Peer-to-Peer Energy Trading Strategy for Renewable-powered Microgrids with Electric Vehicles

The integration of prosumers in power systems can be beneficial considering the advantages of on-site electrical power supplies in contributing to peak shaving and postponing the investment costs to build new capacity in electrical power systems. This paper presents a two-stage day-ahead peer-to-peer pricing and power exchange among local market participants, including the upstream grid, consumers, prosumers, and electric vehicles (EVs). In the first stage, initial pricing is determined by the mid-market rate pricing method, considering the declared demand of each participant and forecasting the solar production of prosumers based on the demand response program. The random behavior of electric vehicles is modeled in the second stage via scenario generation and final pricing, and then, the electrical power exchanged between participants is determined considering the stochastic mechanism of EVs’ charging and discharging. The proposed two-objective problem is formulated as a single objective by the epsilon constraint method. The proposed mixed integer nonlinear programming (MINLP) is solved in GAMS using the DICOPT solver. The operating cost of the system using the proposed method is reduced by 21.66%, and the power loss cost is reduced by 19.99% compared to the base scenario.