Developing a dynamic optimization model for electric bus charging infrastructure

15020 10 80231 3 221631 document 80231 1-s2.0-S2352146517309729-main.pdf application/pdf 8b455892854276d4d090e193a6695307 MD5 1022631 2018-01-08 07:43:04 https://pure.iiasa.ac.at/id/eprint/15020/1/1-s2.0-S2352146517309729-main.pdf 15020 1 1 application/pdf text en public cc_by_nc_nd

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published 80233 1 221647 document 80233 indexcodes.txt text/x-asm cdef740c5e5d580edb2fdce725c123c1 MD5 8847 2018-01-08 07:53:00 https://pure.iiasa.ac.at/id/eprint/15020/2/indexcodes.txt 15020 2 2 text/x-asm other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/80231 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/80231 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/80231 archive 353 disk0/00/01/50/20 2018-01-08 07:45:32 2021-08-27 17:29:45 2018-01-08 07:45:32 article show Xylia M. Leduc S. 8228 Patrizio P. 8844 Silveira S. Kraxner F. 7904 Developing a dynamic optimization model for electric bus charging infrastructure pub prog_esm Electric bus; charging infrastructure; optimization; Mixed Integer Linear Programming; public transport; Sweden Urban regions account for 64% of global primary energy use and 70% of carbon emissions. For that reason, options to decarbonize urban environments are receiving increasing attention. In this context, public transport shall play a key role in decarbonizing urban road transport. One efficient way to achieve that is shifting towards clean fuels and modern electric buses, an option that is already under implementation in several cities around the world. In this paper, the basis for developing a dynamic optimization model for establishing charging infrastructure for electric buses is presented, using Stockholm, Sweden, as a case study. The model places constraints depending on the bus stop type (end or middle stop) which affects the time available for charging at each particular location. It also identifies the optimal technology type for the buses: conductive or inductive. In addition, the electric buses compete with buses run on biogas or biodiesel. In this paper, we present the results of a cost minimization scenario with constraints placed on the available charging time and power, differentiated between end stops and major public transport hubs. The mean charging time is 7.33 minutes, with a standard deviation of 4.78 minutes for all bus stops. The inner city bus routes require less charging time, which ranges on average at around 3 minutes. The installation of chargers at the locations proposed in the model would require scheduling adjustments and careful planning for the density of charging occasions. 2017 published Elsevier 10.1016/j.trpro.2017.12.075 175 2853 165 public Transportation Research Procedia 27 776-783 TRUE 2352-1465 FALSE no info:eu-repo/semantics/article