Multi‐microgrid bi‐layer economic scheduling strategy considering evolutionary‐stackelberg hybrid game of electric vehicles

Abstract

Abstract Aiming at the problem that the mobility characteristics of electric vehicles (EVs) lead to the complexity of optimal scheduling among multiple decision‐making subjects, the authors propose a multi‐microgrid bi‐layer economic scheduling strategy considering evolutionary‐stackelberg hybrid game of EVs. Firstly, in order to accurately analyse the influence of interaction among EVs, an electric vehicle aggregator (EVA) selection strategy for EV users based on evolutionary game among microgrids and a reconciliation strategy of EVA service fee are established. Secondly, a two‐layer economic scheduling strategy for microgrids is proposed based on the Stackelberg game. The microgrid operator, as a leader, sets the internal price of microgrid based on the supply‐demand balance; aggregators, as followers, adjust their electricity consumption and EVA choices based on the internal price and the evolutionary game model. Then, a multi‐microgrid electricity‐sharing trading strategy is constructed using the supply‐demand ratio to encourage sub‐microgrids to participate in internal transactions. Finally, the case shows that the proposed strategy can optimise the distribution of EVs among microgrids. Combining the across‐time‐and‐space energy transmission potential of EVs and the flexible complementary capability of multi‐microgrid, it can improve the operating economy of each microgrid.

Keywords

• demand side management
• Design of Microgrid, Nanogrid, and Peer to Peer Energy Trading
• electric vehicle charging
• Electric Vehicle Energy Management
• Energy demand, storage, and EVs
• Energy Demand Management