计及需求响应的电动汽车充电站多时间尺度随机优化调度

电动汽车充电站源-荷资源优化互补与多时间尺度协调配合,能够降低充电站运营成本,减小源-荷随机性对系统调度策略的影响。提出一种计及需求响应的电动汽车充电站多时间尺度随机优化调度模型。在日前阶段,以日运行成本最小为优化目标,采用条件风险价值(CVaR)度量不确定性风险。同时引入价格型和激励型需求响应优化充电站净负荷曲线,构建了计及运行风险约束的充电站多场景优化调度模型。在此基础上,以日前期望值调度策略为参考,提出基于模型预测控制(MPC)的电动汽车充电站日内滚动优化和反馈校正控制方法,从而降低净负荷预测精度不足对优化决策的影响。最后,以某实际电动汽车充电站为算例进行仿真分析,验证了所提模型的可行性,并分析了需求响应以及不确定性风险偏好对充电站运行的影响。

Multi-time scale stochastic optimal dispatch of electric vehicle charging station considering demand response

Optimal complementarity of source-load resources and coordination of multi-time scales in an Electric Vehicle (EV) charging station can reduce the operational cost and the impact of source-load randomness on system scheduling strategy. A multi-time scale stochastic optimal dispatch model for an EV charging station considering demand response is proposed. In the day-ahead stage, with the minimum daily operating cost as the optimization objective, the conditional value-at-risk (CVaR) is used to measure uncertain risk, and price-based and incentive-based demand response are introduced to optimize the net load. A multi-scenario optimal dispatch model for the charging station considering operational risk constraints is established. On this basis, an intraday rolling optimization and feedback correction control method for EV charging station based on Model Predictive Control (MPC) is proposed. This can reduce the impact of poor net load forecasting accuracy on optimization decision-making. Finally, the feasibility of the proposed model is verified with simulation of an EV charging station, and the impact of demand response and uncertainty risk preference on the operation of the charging station is analyzed. This work is supported by National Key Research and Development Program of China (No. 2018YFB0904500).