基于条件风险价值的电动汽车充电站规划

针对电动汽车充电负荷的假日性以及负荷预测的不确定性,基于条件风险价值构建了电动汽车充电站规划模型。基于出行链,结合Dijkstra最短路径算法,利用蒙特卡洛随机模拟得到快慢充负荷的时空分布。以充电站建设数量最少为目标,满足所有充电需求点为约束,建立选址模型。考虑用户等待时长,以充电站建设成本最小为目标,建立定容模型。为解决定容模型中充电负荷假日性以及负荷预测随机性,利用鲁棒优化将定容模型转化为机会约束规划,引入条件风险价值工具进行求解。以某区域充电站规划为仿真算例,算例结果验证了该方法能够增强充电站规划模型的鲁棒性,具有可行性。

Research on Charging Station Planning of EV Based on CVaR

Aiming at the holiday of electric vehicle charging load and the uncertainty of load prediction,the article constructs an electric vehicle charging station planning based on the conditional value-at-risk. Firstly, based on the travel chain, combined with the Dijkstra shortest path algorithm, the Monte Carlo random simulation is used to obtain the spatiotemporal distribution of fast and slow loading. The location model is established by taking the minimum number of charging stations as the target and satisfying all the charging demand points. On this basis, the user"s waiting time is considered, the constant volume model is established with the goal of minimizing the construction cost of the charging station. In order to solve the charging load holiday and load prediction randomness in the capacity model, the constant volume model is transformed into the opportunity constrained programming by robust optimization, and the conditional risk value tool is introduced to solve. Finally, a regional charging station is used as a simulation example to discuss the sensitivity analysis of the model parameters. The numerical results show that the proposed method can enhance the robustness of the charging station planning model and is feasible.