An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining

Based on the peer-to-peer (P2P) trading framework of interconnected microgrids, this paper proposes an optimal scheduling strategy for interconnected microgrids considering the uncertainty of wind power, to minimize the operation cost of the individual microgrid and obtain profits through active energy trading with other microgrids. The proposed optimization model considers the grid structure of microgrids and uses the robust optimization (RO) method to express uncertainty. The incentive mechanism based on Nash bargaining is used to encourage the individual microgrid to trade energy actively and realize fair benefit sharing. In order to protect the privacy of the individual microgrid, the alternating direction method of multipliers (ADMM) is used to achieve the decentralized solution of the proposed model. Simulation analysis based on four interconnected microgrids shows that each microgrid can ultimately make profits in the trading, verifying the effectiveness and fairness of the proposed method.

Automated summary

This paper proposes an optimal scheduling strategy for interconnected microgrids, aiming to minimize operation costs and generate profits through peer-to-peer energy trading. The use of robust optimization and Nash bargaining mechanism ensures fair benefit sharing, while the alternating direction method of multipliers protects the privacy of individual microgrids. Simulation results demonstrate the effectiveness and fairness of the proposed method.