Distributionally Robust Optimization of Load Recovery Employing Kullback-Leibler Divergence Distance to Handle Wind Power Uncertainty
Under the background of constructing a new power system dominated by new energy sources,it is of great significance to actively utilize new energy in the process of restoration control and fully address its potential risks to op-erational safety to reduce power outage losses.Firstly,in order to characterize the uncertainty of wind power output,we used the KL divergence(Kullback-Leibler)distance as a control condition for screening extreme wind power output scenarios,and constructed a fuzzy set accordingly as a typical wind power output scenario.Under the premise of satis-fying the relevant operational safety constraints,we established a distributed robust optimization model with the optimi-zation objective of maximizing the weighted load recovery amount to formulate a load recovery scheme considering wind power.The mixed-integer second-order cone model obtained by relaxation and dual transformation can be solved by calling commercial solvers.Taking the IEEE 10-machine 39 busbar system of a large-scale wind farm as an example,the simulation results show that compared with the traditional robust optimization method,this method reduces the con-servatism of the optimization results,and helps to speed up the load recovery and reduce the power outage losses.
blackoutload restorationwind power uncertaintyKullback-Leibler divergence distancedistributional-ly robust optimization
万方数据
维普
