File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Data-driven planning of plug-in hybrid electric taxi charging stations in urban environments: A case in the central area of Beijing

TitleData-driven planning of plug-in hybrid electric taxi charging stations in urban environments: A case in the central area of Beijing
Authors
Chen, HuimiaoJia, YinghaoHu, ZechunWu, GuangleiShen, Zuo Jun Max
Keywordsspatial and temporal charging demand forecasting
Plug-in hybrid electric taxis
data-driven approach
charging station planning
Issue Date2017
Citation
2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings, 2017 How to Cite?
DOI: http://dx.doi.org/10.1109/ISGTEurope.2017.8260264
Abstract© 2017 IEEE. Plug-in electric vehicles (PEVs) can contribute to the improvement of energy and environmental issues. Among different types of PEVs, plug-in hybrid electric taxis (PHETs) go in advance. In this study, we provide a spatial and temporal PHET charging demand forecasting method based on one-month global positioning system (GPS)-based taxi travel data in Beijing. Then, using the charging demand forecasting results, a mixed integer linear programming (MILP) model is formulated to plan PHET charging stations in the central area of Beijing. The model minimizes both investment and operation costs of all the PHET charging stations and takes into account the service radius of charging stations, charging demand satisfaction and rational occupation rates of chargers. At last, the test of the planning method is carried out numerically through simulations and the analysis is complemented according to the results.
Persistent Identifierhttp://hdl.handle.net/10722/296171
ISI Accession Number ID
WOS:000428016500173

 

DC FieldValueLanguage
dc.contributor.authorChen, Huimiao-
dc.contributor.authorJia, Yinghao-
dc.contributor.authorHu, Zechun-
dc.contributor.authorWu, Guanglei-
dc.contributor.authorShen, Zuo Jun Max-
dc.date.accessioned2021-02-11T04:52:59Z-
dc.date.available2021-02-11T04:52:59Z-
dc.date.issued2017-
dc.identifier.citation2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings, 2017-
dc.identifier.urihttp://hdl.handle.net/10722/296171-
dc.description.abstract© 2017 IEEE. Plug-in electric vehicles (PEVs) can contribute to the improvement of energy and environmental issues. Among different types of PEVs, plug-in hybrid electric taxis (PHETs) go in advance. In this study, we provide a spatial and temporal PHET charging demand forecasting method based on one-month global positioning system (GPS)-based taxi travel data in Beijing. Then, using the charging demand forecasting results, a mixed integer linear programming (MILP) model is formulated to plan PHET charging stations in the central area of Beijing. The model minimizes both investment and operation costs of all the PHET charging stations and takes into account the service radius of charging stations, charging demand satisfaction and rational occupation rates of chargers. At last, the test of the planning method is carried out numerically through simulations and the analysis is complemented according to the results.-
dc.languageeng-
dc.relation.ispartof2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings-
dc.subjectspatial and temporal charging demand forecasting-
dc.subjectPlug-in hybrid electric taxis-
dc.subjectdata-driven approach-
dc.subjectcharging station planning-
dc.titleData-driven planning of plug-in hybrid electric taxi charging stations in urban environments: A case in the central area of Beijing-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISGTEurope.2017.8260264-
dc.identifier.scopuseid_2-s2.0-85046258745-
dc.identifier.isiWOS:000428016500173-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats