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在高风电渗透率电网的负荷恢复过程中,适当时机接入一定比例风电有利于提高负荷恢复效率,但同时会增加发生二次停电的风险.考虑负荷恢复过程风电参与的不确定性,建立同时考虑负荷恢复收益与风险的恢复策略价值量优化模型.利用条件风险价值(conditional value at risk,CVaR)方法对负荷恢复过程中的不确定性因素进行定量计算和风险衡量,从而将具有不确定性风电参与的模型转化为风电出力满足一定置信水平的确定性多目标混合整数非线性规划(multi-objective mixed integer non-linear programming,MMINP)模型.为实现模型简洁、快速求解,通过潮流线性化近似(linear programming of AC,LPAC)以及分层序列法(lexicographic optimization method,LOM)将MMINP转化为双层单目标线性规划(bi-level single-objective linear programming,Bi-SLP)问题,在实际应用中可以通过时步递进的方式反复调用所提方法实现负荷恢复优化.仿真结果表明,所构建的方法能够根据恢复需求通过多阶段决策实现风电在负荷恢复过程中的有计划接入,提高恢复效率.“,”Load restoration will benefit from integrating proper percentage of wind power in the suitable time for power system with high penetration of wind power.However,the risk of a second outage would be higher.Considering the uncertainty of wind power in load restoration stage,a reward-oriented optimization model was constructed,which contained the benefit and risk of strategies.Primarily,conditional value at risk (CVaR) method was addressed for the quantification of uncertainty and risk of restoration strategies.In this way,the load restoration model with wind power uncertainty can be transformed to the multi-objective mixed integer non-linear programming (MMINP) model in which wind power was available in a certain confidence level.Furthermore,a linear method was proposed,by which can simplify the MMIPM to bi-level single-objective linear programming (Bi-SLP) with the use of the linear programming of AC power flow (LPAC) and the lexicographic optimization method (LOM).Ultimately,the optimization of load restoration can be completed by the repeated use of the proposed method in several progressive steps.The simulation results demonstrate that the proposed method is effective for scheduling wind power integration during load restoration by multi-stage decision-making and improve recovery efficiency according to restoration demands.