A two-stage robust optimal capacity configuration method for charging station integrated with photovoltaic and energy storage system considering vehicle-to-grid and uncertainty

This paper proposes a novel capacity configuration method for charging station integrated with photovoltaic and energy storage system, considering vehicle-to-grid technology and the uncertainties of photovoltaic and electric vehicles’ behavior. Firstly, the improved k-means method is used to cluster the electric vehicles participating in vehicle-to-grid, and then the corresponding scheduling model is established by considering the constraints of schedulable time, charging and discharging power, state of charge and so on. Secondly, for uncertain scenarios, using the predicted charging load obtained through Monte Carlo simulation as the basis for robust optimization, a two-stage robust optimal configuration model with the objective of minimizing the comprehensive cost of investment and operation is established, so as to improve the reliability of the solution by only solving for the worst-case scenario and its uncertainty problem. Finally, the column-and-constraint generation algorithm and Karush-Kuhn-Tucker conditions are used to solve the problem. The study results show that the configuration capacity of energy storage system and the composite cost of investment and operation can be effectively reduced when vehicle-to-grid is considered, meanwhile considering uncertainty can improve the ability of the charging station to resist risks.