Reactive Power Coordination Optimization For Multi-objective Active Distribution Network

Active power distribution network contains distributed power,energy storage unit,electric vehicle,controllable load and other diversified power supply and active load.It has the advantages of flexible operation mode and high user participation.Planning and operation management considering active load response is an important guarantee for realizing the advantages of active distribution network.The inherent randomness and intermittency of the distributed power supply and the uncertain factors of the multi scene operation of the active distribution network lead to the problems of the node voltage limit and the complex operation management of the active distribution network,and the reactive power coordination optimization of the active distribution network is different from the reactive power optimization of the traditional distribution network.Based on the complex and varied operation scenarios of active distribution network,the Multi-Objective Reactive Power Coordinated Optimization of active distribution network is studied and discussed in this paper.Models of reactive power coordination and optimization related components in active distribution network are established.The model of reactive power compensation devices,such as renewable distributed power(wind distributed power and PV distributed power),active load model,energy storage unit model,capacitor and static reactive compensator,is used to establish reactive power coordination optimization flow analysis and model simulation of active distribution network.Set the foundation.Then,models are established,which contain renewable distributed power distributed wind power and photovoltaic(pv),transferable load,load reducing,the energy storage unit model,capacitor and static reactive power compensator model of reactive power compensation device to active power reactive power coordination optimization in this paper,to pave the way for the optimization of active distribution network.A multi-objective reactive power allocation optimization scheme for active distribution network is put forward.Based on the actual running rules of the components in the active distribution network,the operation scene of the active distribution network is analyzed and studied in detail.The installation position of the reactive power compensation device is determined by using the first order network loss / reactive sensitivity method,the integrated electrical distance and the adaptation of the scene,and the distributed power supply and energy storage are taken into account.The operation constraints of unit and active load are selected.Particle swarm optimization algorithm is applied to determine the installation capacity of reactive power compensation device for Multi-Objective reactive power optimization.A multi-objective reactive power optimization scheme for active distribution network isput forward.The multi-objective function is set up with minimum reactive power compensation capacity,minimum active network loss and minimum operating node voltage deviation,and the reactive power of the active distribution network is calculated by the renewable distributed power supply.The particle swarm optimization algorithm is used to solve the reactive power compensation device and the renewable distributed electricity in the actual operation of the active distribution network.The reactive power compensation capacity of the source ensures that the operation voltage of the active distribution network is not limited.Simulation results show that the proposed method is effective.DIgSILENT simulation platform is used to build an improved IEEE 33 node distribution network model,set up the different operating parameters of active load,energy storage unit and distributed power,and obtain the operation scene of the actual operation of the analog active distribution network.The particle swarm optimization algorithm is implemented by MATLAB programming language,and the effectiveness of reactive power optimization is verified by optimizing the calculation results before and after.