Research On The Collaborative Optimal Allocation Of Distributed Power Supply And Energy Storage In Distribution Network Bssed On Bi-level Programming Model

As the increasing demand for electricity and the popularization of energy conservation and environmental awareness,Distributed Generation(DG)has attracted much attention from the international community.Due to the randomness and volatility of DG output,it has brought many adverse effects to the power grid.Taking the active control of DG,active distribution network(ADN)improves the system security,excessive power loss and other issues,which are caused by the DG supply.Energy storage system(ESS)has the functions of improving voltage,peak-shaving and valley filling,and suppressing output power fluctuations,and it is an important part of active distribution network for active control.Therefore,the research on collaborative optimization of DGs and ESSs in active distribution network has important practical significance.The main research of the paper is as follows:First,output models of wind and solar power generation are established based on the analysis of the output characteristics.The probability density distribution of wind and solar power output is discretized with the sequence calculation theory,and then the time-series wind and solar power output expected value sequence is obtained,which lays the foundation for dealing with the uncertainty of wind and solar power output.At the same time,the influence mechanism of the energy storage system connected to the distribution network on the distribution network voltage and network loss is analyzed,which lays the foundation for the establishment of the objective function in the later programming model.Secondly,a bi-level programming model is adopted to plan the distributed power supply.In the first stage of the DG supply programming,a method of applying active network loss sensitivity variance is proposed to determine the location and initial capacity of the DG supply to be installed.In the second stage,the DG is optimized using the improved particle swarm optimization algorithm with the minimum investment cost of distributed power,minimum active network loss and minimum voltage deviation as the objectives.The results of the simulation in the IEEE33 node test system verifies the effectiveness of the proposed model.