Locating And Sizing Of Wind-solar-storage Based On Two-stage Coordinated Optimization

Intermittent distributed generation integrated into power grid will not only bring some challenges to power grid planning,but also may aggravate the fluctuation of load and voltage.Many achievements have been made on locating and sizing of distributed generation and energy storage system at home and abroad,but most of the existing literatures don’t consider the early location problem of distributed generation and there are still few studies on their joint planning.Therefore,this paper does the following work on the joint coordinated planning of wind,solar and storage:An integrated evaluation model for location planning of distributed generation based on game combination weighting method is proposed.Firstly,the network loss measurement index and two types of voltage stability index are selected as the basis of location selection;Then,using the game combination weighting method to integrate the weights obtained by the improved analytic hierarchy process method and the improved CRITIC method that can surmount the lacks of the unitary weighting method;Finally,the nodes to be installed of distributed generation are obtained by using the TOPSIS method.Simulation results show that the weight obtained by the game combination weighting method is more reasonable and the location model proposed in this paper is feasible.Considering the uncertainty of source and load as well as the economy and the wave suppression of energy storage system,a model for locating and sizing of wind,solar and storage based on two-stage coordinated optimization is established.In the first stage,the distributed generation is planned with the objective function of minimizing annual comprehensive cost.In the second stage,the energy storage is configured with the objective function of annual investment,operation and maintenance cost,the voltage fluctuations of nade and load fluctuation.Probabilistic scenario analysis method is used to slove the uncertainty of source and load,and more reasonable sample data is obtained by adding error disturbance to the measured data.In addition,the K-S complex clustering algorithm is proposed to reduce scenarios.Simulation results show that the proposed complex clustering algorithm can greatly reduce the calculation time.In order to meet the solution requirment of the planning model,an improved butterfly optimization algorithm and an improved multi-objective whale optimization algorithm are proposed.Multiple strategies include population initialization with Halton sequence,adaptive switching probability,elite reverse learning and premature judgment combined with chaotic search are used to improve the butterfly algorithm’s shortcomings of slow convergence speed and pre mature.The multi-objective whale optimization algorithm is obtained by introducing the maximin fitness function,external archive update and maintenance strategy based on dual performance and the roulette optimal selection strategy.What’s more,the inherent drawbacks of the algorithm are improved by nonlinearizing convergence factor and introducing fuzzy and mutation strategies.The simulation of the standard test function verifies the effectiveness of the two improved algorithms.Finally,the simulation of the IEEE 33 system shows that considering the uncertainty of source and load can reduce the planning cost,and the planning scheme of energy storage system can better balance the economy and wave suppression.In addition,the improved butterfly optimization algorithm proposed in this paper has fast astringent process and optimal search value,and the improved multi-objective whale optimization algorithm has better distribution and diversity.