中文摘要：

根据超短期风电功率预测信息提出了基于可变递进步长及期望输出的储能系统(ESS)优化控制策略。通过设定荷电状态(SOC)上下行限值范围,构建递进控制区间的SOC临界限值循环充放电模式,提升ESS利用率;同时严格限定递进控制区间的充放电状态转换次数,以保证ESS的性能状态。基于该思路,以单次充放电区间作为递进控制步长构建ESS充放电策略,并以递进区间步长最大和整体期望输出波动率最小为目标函数建立多目标优化模型,在临界限值循环充放电模式下协调控制风电功率输出与本区间期望输出的关系,由此利于ESS在SOC限值内的充分利用,同时大幅降低其充放电状态转换次数,并可有效减少期望输出的波动程度,提升其可调度性。利用带精英策略的快速非支配排序遗传算法求解模型的Pareto最优解,通过风电场实际运行数据验证了所提方法的有效性。

英文摘要：

By considering the very short-term wind power forecasting information,an optimal control strategy for energy storage system (ESS) based on the variable progressive steps and expected outputs is proposed.The strategy can improve the ESS utilization by setting the range of state of charge(SOC)limits and building the cyclical charge and discharge mode within SOC critical limits based on progressive control intervals,while guaranteeing the ESS operation state by restricting the times of charge-discharge state transition in each progressive control interval.To build the ESS charge and discharge strategy,the single charge and discharge interval is studied as the control progressive step.Moreover,a multi-obj ective optimization model is developed to increase the time of the progressive step and reduce the rate of fluctuation of the expected output,while coordinately controlling the wind power output and the expected output in the cyclical charge and discharge mode within the critical limits.Full use is made of ESS and the times of state transitions are reduced,as well as the rate of fluctuation of the expected output,which is conducive to improving the capability of dispatch.Finally,the Pareto optimal solution is obtained by the fast and elitist non-dominant sorting genetic algorithm.The effectiveness of the proposed method is verified through actual wind farm operating data.