为了解决风电和电动汽车大量接入主动配电网所引发的随机优化调度问题,利用基于无迹变换的随机潮流计算方法处理风电出力的波动性、电动汽车充电的随机性以及电网负荷的随机波动.进而建立了以电动汽车充电功率和分布式电源出力为优化变量,以配电网运行费用最小、有功网损最小和负荷方差最小为优化目标的主动配电网随机优化模型.同时,采用多目标粒子群算法对模型进行求解,并以改进的IEEE 33节点测试系统为例对该模型进行仿真.仿真结果表明:考虑不确定性和电动汽车有序充电的优化调度模型,可以有效地减少配电网运行的成本、降低网损和缩小峰谷差,验证了所提模型的正确性和有效性.%In order to address the stochastic optimization scheduling problems brought by the high penetration of wind power and electric vehicles connected into active distribution network, this paper adopts a stochastic power flow method based on unscented transform to deal with uncertainties of wind power output, the charging pattern of electric vehicles, and the load of power grid. On this basis, this paper proposes a novel multi-objective stochastic optimization model of active distribution networks that aims to minimize the operational costs, the power losses, and load variance though controlling the charging power of electric vehicles and the output power of distributed generation. In addition, the multi-objective particle swarm algorithm is used to solve the proposed model. The proposed model is examined on transformed IEEE 33-bus test system. The simulation results show that optimal dispatching models that consider uncertainty and coordinated charging of electric vehicles can effectively cut down the operation cost of distribution networks, reduce the power losses, and decrease the difference between peak and valley, thereby verifying the validity and effectiveness of the proposed model.
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