Optimal strategy of the charging station operator participating in day-ahead electricity market: A hierarchical game framework

The charging station operator (CSO) is responsible for the charging scheduling and electricity procurement of many charging stations, participating in the day-ahead electricity market to achieve economic benefits. Due to the oligopoly structure of the market, different bidding curves affect market clearing results. This paper proposes a hierarchical game framework for CSOs participating in the day-ahead market. Specifically, we introduce a power-feasible and energy-feasible region model to characterize the flexible charging load of an electric bus (EB). Then, we establish an aggregation model to aggregate charging operational regions of a large amount of EB energy consumption. Taking into account the uncertainty of arrival time and energy consumption, we adopt the distributionally robust optimization to describe the upper and lower boundaries, which is expressed by the data-driven ambiguity set based on Wasserstein distance. Due to the Stackelberg game between the CSO and the independent system operator (ISO), we formulate its bidding strategy based on schedulable charging load potential as a bilevel optimization model. The collective optimization of bidding curves and scheduling power is formulated as mathematical programming with equilibrium constraints (MPEC), and Karush–Kuhn–Tucker (KKT) conditions are used to accelerate the solution. Finally, a modified 6-bus test system is used to validate effectiveness. In addition, we compare the market clearing results of individual bidding, cooperation bidding, central dispatching mode, and fixed electricity prices. The results show that the cooperation bidding of the charging operator alliance has strong market power, and the average electricity price has been reduced to 0.27 CNY/kWh.