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Finding the optimal location for public charging stations - A GIS-based MILP approach
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Academy of Chinese Energy Strategy, China University of Petroleum-Beijing, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-4589-7045
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-8191-4901
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2019 (English)In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 6582-6588Conference paper, Published paper (Refereed)
Abstract [en]

Electric Vehicles (EVs) have achieved a significant development because of the continuous technology revolution and policy supports in recent years, which leads to a larger demand of charging stations. Strategies about how to find the optimal location for charging facilities are urgently needed in order to further assist the development of EVs. This paper focus on the return of investments on EV charging stations and proposes a Mixed Integer Linear Programming (MILP) model based on Geographic Information System (GIS) to identify the optimal location of charging stations in cities. Traffic flow data and land-use classifications are used as important inputs, and six important constraints are included in the MILP model with the objective function of maximizing the total profits of new charging stations. The effectiveness of the proposed method is then demonstrated by implementing a case study in Västerås, Sweden.

Place, publisher, year, edition, pages
Elsevier Ltd , 2019. Vol. 158, p. 6582-6588
Keywords [en]
EV, GIS, MILP, Optimal location, Public charging startions, Charging (batteries), Economics, Integer programming, Investments, Land use, Location, Electric Vehicles (EVs), Landuse classifications, Mixed integer linear programming model, Optimal locations, Return of investments, Technology revolution, Geographic information systems
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-43192DOI: 10.1016/j.egypro.2019.01.071ISI: 000471031706145Scopus ID: 2-s2.0-85063879695OAI: oai:DiVA.org:mdh-43192DiVA, id: diva2:1306903
Conference
10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-07-11Bibliographically approved

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Li, HailongWallin, FredrikAvelin, Anders

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