电动汽车接入微电网在调度运行中存在多种不确定因素,给系统安全稳定造成一定影响。本研究将条件风险价值理论(CVaR)与信息间隙决策理论(IGDT)相结合,建立了含电动汽车微电网多时间尺度调度模型。在日前阶段,以微电网日运行成本最小为优化目标,首先采用蒙特卡洛模拟和吸引子传播聚类算法对电价和电动汽车行为的不确定性进行建模,并利用CVaR量化不确定风险;针对风光出力预测精度较低的问题,采用IGDT应对风光出力的不确定性,在保证优化目标满足期望的前提下最大化风光出力的波动范围。在日内阶段,以日前下发的调度计划为参考,基于模型预测控制对含电动汽车微电网进行日内滚动优化,并引入误差系数进行反馈校正,修正日前调度偏差。最后,通过算例验证了本研究模型的有效性,分析了收益偏差系数和风险偏好系数对优化结果的影响。

There are many uncertain factors in the dispatch and operation of electric vehicles connected to microgrid, which have a certain impact on the safety and stability of the system. In this study, the conditional value-at-risk (CVaR) theory and the information gap decision theory (IGDT) were combined to establish a multi-time scale dispatching model for microgrid with electric vehicles. In the day-ahead stage, with the minimum daily operation cost of the microgrid as the optimization objective, Monte Carlo simulation and attractor propagation clustering algorithm were used to model the uncertainty of electricity price and electric vehicle behavior and CVaR was used to quantify the uncertainty risk. Then, to solve the problem of low prediction accuracy of wind and photovoltaic output, IGDT was used to deal with the uncertainty of wind and photovoltaic output, maximizing the fluctuation range of wind and photovoltaic output while ensuring that the optimization target value was within an acceptable range. In the intra-day stage, with the day-ahead dispatching planning as a reference, the model predictive control was used to optimize the microgrid including electric vehicles in the intra-day rolling, and the error coefficient was introduced for feedback correction to correct the day-ahead dispatching deviation. Finally, the effectiveness of the proposed model was verified by an example, and the effects of the income deviation coefficient and the risk preference coefficient on the optimization results were analyzed.