Research On Loss Reduction Technology Of Power Distribution District Based On Reactive Power Optimization

In the context of the transformation and development of the energy system to adapt to the "carbon peak" and "carbon neutrality",after the large-scale access of distributed new energy and active loads to the distribution station area,it can provide rich technical means for improving the operation efficiency of the distribution network and reducing energy loss of the distribution network.Considering the reactive power optimization and loss reduction technology of distribution station area with scheduling potential of various available resources such as new energy and active loads,it is of great significance to reduce network loss and improve voltage level.In order to deeply explore the energy-saving and consumptionreducing potential of reactive power optimization technology considering active resources,this thesis studies the loss reduction technology of distribution station area based on reactive power optimization,and its main contents are as follows:Firstly,this thesis elaborates on the development status of loss reduction technology and reactive power optimization algorithm in distribution station area in detail.On this basis,the paper analyzes the power consumption characteristics of distribution station area users’ loads,establishes a model of multi-type loads of distribution station area users considering the adjustable characteristics of active loads,and analyzes the characteristics of various controllable devices in distribution station area,establishes a mathematical model of active and reactive output of various controllable devices in distribution station area including grouped switching capacitors,static reactive power compensators,energy storage devices and photovoltaic power generation devices.Then,several methods for distribution station area power flow calculation are summarized to provide theoretical basis for subsequent reactive power optimization and loss reduction in distribution station area.Secondly,considering the economy and safety of the operation of the distribution substation area,a multi-objective reactive power optimization and loss reduction mathematical model for the distribution substation area is established,and a loss reduction strategy based on an improved grey wolf optimization algorithm is proposed.The control variables are the switching quantity of capacitor banks in groups,the input quantity of static reactive power compensators,and the reactive power output of new energy grid-connected inverters.The optimization problem is a multi-objective mixed integer nonlinear programming problem with the minimum active power loss of the distribution substation area,the minimum reactive device adjustment cost,and the minimum node voltage deviation and photovoltaic grid-connected point voltage deviation as objectives.This thesis introduces cuckoo search strategy and variable weight strategy into grey wolf algorithm to improve the convergence speed and accuracy of the algorithm.The improved grey wolf intelligent optimization algorithm proposed in this thesis is used to solve the established optimization model.The correctness and effectiveness of the proposed multi-objective reactive power optimization and loss reduction mathematical model and solution method are verified by using a 17-node distribution substation area.Finally,the loss reduction strategy of the distribution station area considering the coordinated effect of active load is studied.The optimization space of different types of active loads and energy storage devices in the operation process is fully explored,including transferable loads,interruptible loads and reducible loads,and the improvement effect of active loads and energy storage devices on optimization is analyzed.By improving the grey wolf optimization algorithm to solve the proposed model,the optimal active and reactive power scheduling results of the system are obtained.The system power loss level and voltage deviation level under different scenarios are compared and analyzed by using 17-node distribution station area examples to verify the effectiveness of the proposed model and solution method.