Multi-agent cooperative operation method incorporating electric vehicle operator considering user service selection

To more realistically characterize electric vehicle (EV) charging services and promote the marketization of charging services and energy transactions, this paper proposes a multi-agent cooperative operation method incorporating EV operator considering user service selection. Firstly, based on the evolutionary game, an EV user service selection model is constructed considering user heterogeneity. Secondly, an EV operator operation model with a service price fine-tuning technique is built to schedule charging and discharging plans and energy transactions with other forms of operators, taking into account EV users' dynamic service selection. The bidirectional impact between the service price fine-tuning and the users' service selection results is fully considered by iterations. Then, a cooperative operation method incorporating an EV operator, an intelligent building operator, and a distributed generation operator is formulated based on Nash bargaining, forming a composite methodology containing multiple games and multilayer iterations. The interrelationships among EV users, between EV users and the EV operator, and between operators are considered simultaneously. Finally, the scheduling results of multi-agent cooperation are compared and analyzed in an office community scenario. Numerical results show that EV users’ heterogeneous needs are satisfied and the benefits of each operator are improved, which verifies that the proposed method can accommodate both EV users and operators. The convergence and scalability of the methodology are also validated.