A Hybrid Game-Based Coordinated Optimization Method for Distribution Networks and Multiple Microgrids Considering P2P Energy Trading

Abstract

To address the complex multi-agent trading interactions in the coordinated operation of a distribution network and multiple microgrids, which may prevent microgrids from achieving optimal trading decisions under bounded rationality and consequently reduce the economic efficiency of system operation, this paper proposes a hybrid game-based coordinated optimization method considering peer-to-peer energy trading. First, a Stackelberg game is established between the distribution network and the multiple microgrids, with the distribution network acting as the leader and the microgrids as followers. Second, an evolutionary game is introduced to characterize the evolution of microgrid trading strategies under bounded rationality, and a strategy evolution model based on minimum expected operating cost is developed. On this basis, a nodal pricing mechanism based on optimal power flow is formulated by considering the effects of dynamic power flow on the operating states of different microgrid connection nodes. The upper- and lower-level optimization models are solved using the BKA-CMA-ES algorithm and the CPLEX solver, respectively. Case studies on the IEEE 33-bus distribution system demonstrate that the proposed method can effectively guide microgrids in adjusting their electricity purchasing and selling behaviors, reducing the total system operating cost by 31.71% compared with the conventional Stackelberg game method and thereby improving the economic performance of coordinated operation between the distribution network and multiple microgrids.