基于态势感知的智能配电网精细化日前优化调度

Fine Tuned Optimization Scheduling of Intelligent Distribution Network Based on Situational Awareness

由于可再生能源的广泛接入,智能配电网面临电压波动挑战,直接导致了电压裕度增加。因此,设计了基于态势感知的智能配电网精细化日前优化调度方法。该方法构建了一个由数据捕获层、数据分析层、未来预测层构成的3层态势感知架构。基于电网数据,设定约束与目标函数,建立日前调度模型。模型通过识别储能系统荷电状态(State of Charge,SoC)极值点,估算虚拟电量消耗,并线性化资本回收系数,实现精细化调度。实验显示,该方法将总电压裕度降至54.26 V,远低于同类研究,有效降低了电压裕度,提高了配电网运行效率。

Due to the widespread access of renewable energy,intelligent distribution networks face voltage fluctuation challenges,which directly lead to an increase in voltage margin.Therefore,a refined day ahead optimization scheduling method for intelligent distribution networks based on situational awareness was designed.This method constructs a three-layer situational awareness architecture consisting of a data capture layer,a data analysis layer,and a future prediction layer.Based on the grid data,set constraints and objective functions,and establish a day ahead scheduling model.The model identifies the extreme points of the State of Charge(SoC)of the energy storage system,estimates virtual electricity consumption,and linearizes the capital recovery coefficient to achieve refined scheduling.The experiment shows that this method reduces the total voltage margin to 54.26 V,which is much lower than similar studies,effectively reducing the voltage margin and improving the operational efficiency of the distribution network.