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Research On Multistage Planning Of Rural Substations Considering Distributed Photovoltaics And Boosting Sequence

Posted on:2022-09-14Degree:MasterType:Thesis
Country:ChinaCandidate:Y Y XiaoFull Text:PDF
GTID:2492306569979679Subject:Electrical engineering
Abstract/Summary:
With the proposal of the rural revitalization strategy and the effective implementation of related policies,the rural economy has been fully developed,the electricity load in rural areas has increased significantly,and the penetration rate of distributed photovoltaic in some areas has also continued to increase.During the planning period,the substation boosting transformation lacks assistance Decision-making technical support.Therefore,how to adapt to the uneven spatial distribution of rural loads and the large range of changes,while taking into account the role of distributed photovoltaic support,and proposing a substation planning method with transition strategies is the key to the current rural distribution network planning.The main work and research results of the boost planning research in this paper are summarized as follows:First,this paper proposes a multi-stage planning method for substations considering distributed photovoltaic access.The proposed method first evaluates the confidence capacity of distributed photovoltaics based on reliability criteria,and uses a directional improved weighted V-graph algorithm to divide the power supply range of substations,which effectively takes the supporting role of distributed photovoltaics on the power supply capacity of the grid into account,and guarantees The load ratio of each substation is balanced;then,the whole life cycle cost theory is adopted to convert the investment cost,residual value cost,as well as operation and maintenance cost during the planning period into equivalent annual values,so that the construction plans at different stages can be compared on the same basis;Finally,a multi-stage planning model for substations with distributed photovoltaics is constructed,and the genetic algorithm is used to solve this problem.The construction timing and power supply range of the substation in the planning period are given based on the substation construction plan of the target year.On this basis,this paper proposes a multi-stage planning method for substations that considers boosting factors.For multi-stage substation planning,although the timing of construction plays a decisive role,the transition strategy in the planning period also has an important impact.In order to optimize the construction mode in the planning period,the proposed method firstly provides a life-cycle cost measurement method considering the boosting of substation voltage level,which effectively takes the remaining depreciation cost recovery income generated by the early retirement of the boosting transformation into account;then proposes to consider the multi-stage planning model for substations based on boosting factors;finally,a coding strategy that considers different construction modes such as new construction,expansion or boosting of substations during the planning period is proposed,and the genetic algorithm is used to solve the problem,which realizes the multi-stage substation considering the transition strategy in the planning period.Then,a multi-stage programming solution method for substations based on improved GWO is proposed.In order to improve the efficiency of solving multi-stage planning problems in power systems,GWO is introduced and improved.Through comparison with genetic algorithm,improved genetic algorithm and gray wolf algorithm,the searching ability and efficiency of the improved GWO are verified.Finally,an example of the multi-stage step-up plan for a substation with distributed photovoltaics in an county-level region in central China was carried out and compared with the previous planning plan of this area,to verify the effectiveness of the model and the practicality of the algorithm proposed in this paper.
Keywords/Search Tags:Dynamic Boost Planning of Substation, Distributed Generation, Life Cycle Cost, Weighted Voronoi Diagram, Improved Gray Wolf Optimizer
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