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Optimal planning of intra-city public charging stations
Shandong University, Jinan, China; Harvard University, Cambridge, MA, United States.
Nankai University, Tianjin, China.
Shandong University, Jinan, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Shandong University, Jinan, China; Hubei University of Arts and Science, Xiangyang City, Hubei Province, China.ORCID iD: 0000-0002-6279-4446
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2022 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 238, article id 121948Article in journal (Refereed) Published
Abstract [en]

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2022. Vol. 238, article id 121948
Keywords [en]
Agent-based model, Electric vehicle (EV), Geographic information system (GIS), Optimal planning, Public charging stations, Autonomous agents, Charging (batteries), Electric vehicles, Geographic information systems, Information systems, Information use, Integer programming, Land use, Location, Planning, Profitability, Charging demands, Charging station, Electric vehicle, Geographic information system, Optimal locations, Optimal size, Public charging station, Urban land use, Computational methods
National Category
Transport Systems and Logistics Energy Systems
Identifiers
URN: urn:nbn:se:mdh:diva-55965DOI: 10.1016/j.energy.2021.121948ISI: 000701905000007Scopus ID: 2-s2.0-85114712061OAI: oai:DiVA.org:mdh-55965DiVA, id: diva2:1596841
Available from: 2021-09-23 Created: 2021-09-23 Last updated: 2021-10-14Bibliographically approved

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Li, HailongWallin, Fredrik

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