Data-Driven Robust Day-Ahead Optimal Dispatch of Distribution Network Considering the Electric Vehicle

With the rapid development of electric vehicles (EVs), the number of EVs has surged. Connecting EVs to distribution network to participate in dispatch has become an effective way to reduce the negative impact of EVs on the grid. For this reason, aiming at the uncertainty of the renewable energy considering EVs’ dual characteristics of the source and load, a data-driven two-stage robust optimization model of distribution network is built to find the economically optimal solution. The model uses norm constraints of the uncertainty probability distribution confidence set and flexibly adjusts the conservatism of the model through confidence level. The objective function of minimum cost is established, and the model is transformed into a mixed integer linear programming model and the model is iterated to obtain the optimal solution through the column-and-constraint generation algorithm. The simulation results showed that EVs can effectively reduce the distribution network daily operation cost. The operation cost in the randomly charging mode is higher than that in the orderly charging mode. In addition, the confidence level can flexibly adjust the conservatism of the model, and with the increase of confidence level, the robustness of the model is enhanced.