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Research On Spatial Distribution Prediction Of Urban Electric Vehicle Charging Demand And Charging Station Layout

Posted on:2019-06-11Degree:MasterType:Thesis
Country:ChinaCandidate:Y F ZhouFull Text:PDF
GTID:2382330575450385Subject:Engineering
Abstract/Summary:PDF Full Text Request
With the increasingly serious global stone energy crisis and the global environmental pollution,the emergence of electric vehicles has provided a new way to solve those problems.But now,electric vehicles are limited by battery technology,which makes their cruising range too small and charging time too long,thus it impossible to popularize the replacement of traditional fuel vehicles.Therefore,it is urgent to establish a complete network of supporting charging facilities.And because the construction of charging facilities has the characteristics of high initial input cost,wide service targets and far-reaching effects,scientific and systematic layout planning is indispensable before construction,and the prediction of the spatial distribution of future charging demand is the core basis of planning.There are few literatures in this area.Based on the above background,this paper proposes a two-stage prediction model for the spatial distribution of charging demand in urban space,and builds a charging station layout planning model based on multi-objective programming theory in order to provide new ideas for urban charging demand distribution prediction and provide a theoretical reference for urban charging station layout planning.The main contents of this paper are as follows:(1)Spatial distribution prediction of urban electric vehicle charging demand.In order to accurately predict the spatial distribution of charging demand,this paper proposes a two-stage forecasting method:The first stage is the spatial distribution forecast of urban electric vehicle charging demand on the urban road network.With the improvement of the traditional four-stage transportation method and the combination of motor vehicle travel generation with urban land planning and utilization patterns,the traffic distribution algorithm is designed based on the UE network equilibrium model.The second stage is the conversion prediction of the space distribution of the charging demand in the road network to the spatial distribution of the charging demand in the planning area.To quantify the spatial distribution of charging demand in the planning area,this paper proposes the concept of charging block and performs conversion calculation,which lays a foundation for the subsequent charging station layout planning.(2)Construction and solution of urban electric vehicle charging station layout model.In order to obtain the optimal layout scheme of the city charging station,this paper takes the annual average charging station construction operation cost and the cost of the user to obtain the charging service minimized as the planning goal,and the main constraint is make sure the layout of the charging station can achieve full coverage of the charging requirements in the area.Finally,a multi-objective programming model for urban electric vehicle charging station layout was constructed,and the model solving algorithm was designed based on immune optimization algorithm.(3)Example analysis.Aiming at the layout problem of the charging station in Hangzhou in 2020,the six main urban districts of Hangzhou as the planning object were analyzed and studied,and the spatial distribution of charging demand in the planning area was obtained.Based on this,the multi-objective planning layout model was carried out,and then sensitivity analysis was also carried out,the optimal layout scheme under different conditions is obtained,and the influence of different parameter changes on the layout scheme is analyzed.It is verified that the prediction method proposed in this paper can better predict the spatial distribution of charging demand in urban areas,which has strong feasibility and effectiveness,and provides new ideas and theoretical references for the subsequent urban charging facilities layout planning.
Keywords/Search Tags:charging station, charging demand, network location planning, multi-objective programming, immune optimization algorithm
PDF Full Text Request
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