Optimization Of Loss Reduction Measures For Distribution Network With Distributed Generation Based On Load Forecasting And Monte Carlo Simulation

Line loss rate is an important index which usually has been used to evaluate the management level of distribution network.It is one of the most important task to reduce the loss of distribution network.With the rapid development and extensive utilization of renewable energy,more and more distributed generations(DGs)has been connecting to the distribution network,which changes the power flow distribution and affects the line loss.In order to overcome the blindness of loss reduction measures and achieve the purpose of optimal allocation of resources,this thesis studies the optimization of loss reduction measures for distribution network with DG.This thesis reviews the current research status of loss reduction measures for distribution network.Most of the research results do not consider the comprehensive benefits of measures,and the formulation of measures is relatively extensive.Therefore,this thesis puts forward the optimization ideas of loss reduction measures considering the timeliness of measures,loss reduction benefits,load growth and change,DG output fluctuation,peak valley price.Taking into account the timeliness of Measures refers to the impact of load growth and load change curve on loss reduction measures within 10 years after the implementation of measures.The improved residual grey model is used to forecast the load.Based on Markov and Fuzzy Analytic Hierarchy,the residual error of load forecasting data is determined so that it can improve the accuracy of forecasting.The sequential Monte Carlo method is used to simulate the change of future load and the fluctuation of DG output as the basic data for future power flow calculation of distribution network,so as to take into account the influence of these two on the accuracy of line loss calculation,so as to further improve the accuracy of line loss calculation based on forward backward substitution method.Based on the profit of loss reduction under the factor of peak valley price and the expenses of distribution network structure transformation,conductor replacement,distribution transformer replacement and capacitor bank installation,the optimization model of loss reduction measures for maximizing comprehensive benefits is established and solved by using improved genetic algorithm.The improvement of genetic algorithm coding method takes into account the conductor size,distribution transformer model,compensation capacity and other factors;crossover operation realizes the change of distribution network structure;mutation operation realizes the transformation of different wire specifications and distribution transformer types,as well as the change of compensation capacity.Finally,through the case study,the optimal loss reduction measures of distribution network with DG are obtained and analyzed.In this thesis,based on load forecasting and Monte Carlo simulation,taking into account the peak valley price,conductor replacement,distribution transformer replacement and capacitor bank installation,the optimal loss reduction measures can achieve the highest benefit of distribution network loss reduction and the optimal allocation of loss reduction resources,which overcomes the blindness and extensive of measures formulation.This loss reduction idea and method provides a useful reference for the energy saving and loss reduction transformation of distribution network.