Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario
Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or o...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2016-03, Vol.17 (3), p.659-669 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Qi Kang JiaBao Wang MengChu Zhou Ammari, Ahmed Chiheb |
| description | Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging. |
| doi_str_mv | 10.1109/TITS.2015.2487323 |
| format | Article |
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The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2015.2487323</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Batteries ; Battery swap ; centralized charging ; Charging ; Dynamics ; Electric batteries ; Electric power systems ; Electric utilities ; electric vehicle ; Electric vehicles ; genetic algorithm ; Genetic algorithms ; Heuristic algorithms ; particle swarm optimization ; Power demand ; Stations ; Strategy ; Vehicles</subject><ispartof>IEEE transactions on intelligent transportation systems, 2016-03, Vol.17 (3), p.659-669</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-be5c9738cc978bbc5203edf538fe852869b14eec32a15b92b49bb5483f97e2013</citedby><cites>FETCH-LOGICAL-c440t-be5c9738cc978bbc5203edf538fe852869b14eec32a15b92b49bb5483f97e2013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7330009$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7330009$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Qi Kang</creatorcontrib><creatorcontrib>JiaBao Wang</creatorcontrib><creatorcontrib>MengChu Zhou</creatorcontrib><creatorcontrib>Ammari, Ahmed Chiheb</creatorcontrib><title>Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Batteries</subject><subject>Battery swap</subject><subject>centralized charging</subject><subject>Charging</subject><subject>Dynamics</subject><subject>Electric batteries</subject><subject>Electric power systems</subject><subject>Electric utilities</subject><subject>electric vehicle</subject><subject>Electric vehicles</subject><subject>genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Heuristic algorithms</subject><subject>particle swarm optimization</subject><subject>Power demand</subject><subject>Stations</subject><subject>Strategy</subject><subject>Vehicles</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkT1PwzAQhiMEEqXwAxCLJRaWFH_EiTOWqkAlJIa2rJbjXBpXaVJsR6j8ehxaMbDcnV4996F7o-iW4AkhOH9cLVbLCcWET2giMkbZWTQinIsYY5KeDzVN4hxzfBldObcNasIJGUXtDFpvVWO-oUSzWtmNaTdoGSQPmwNSbYmWuoaybwZ92mw6a3y9Q1Vn0bwB7a3R6ANqoxtwaN2WYJFCT8p7sAe0_FL7_e9ADa2ypruOLirVOLg55XG0fp6vZq_x2_vLYjZ9i3WSYB8XwHWeMaFDFEWhOcUMyoozUYHgVKR5QRIAzagivMhpkeRFwRPBqjyD8AU2jh6Oc_e2--zBebkzTkPTqBa63kkiSIp5WJEG9P4fuu1624brJMlERjOCmQgUOVLads5ZqOTemp2yB0mwHByQgwNycECeHAg9d8ceAwB_fMYYxjhnP8Tkgk8</recordid><startdate>20160301</startdate><enddate>20160301</enddate><creator>Qi Kang</creator><creator>JiaBao Wang</creator><creator>MengChu Zhou</creator><creator>Ammari, Ahmed Chiheb</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20160301</creationdate><title>Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario</title><author>Qi Kang ; JiaBao Wang ; MengChu Zhou ; Ammari, Ahmed Chiheb</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-be5c9738cc978bbc5203edf538fe852869b14eec32a15b92b49bb5483f97e2013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Batteries</topic><topic>Battery swap</topic><topic>centralized charging</topic><topic>Charging</topic><topic>Dynamics</topic><topic>Electric batteries</topic><topic>Electric power systems</topic><topic>Electric utilities</topic><topic>electric vehicle</topic><topic>Electric vehicles</topic><topic>genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Heuristic algorithms</topic><topic>particle swarm optimization</topic><topic>Power demand</topic><topic>Stations</topic><topic>Strategy</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qi Kang</creatorcontrib><creatorcontrib>JiaBao Wang</creatorcontrib><creatorcontrib>MengChu Zhou</creatorcontrib><creatorcontrib>Ammari, Ahmed Chiheb</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Qi Kang</au><au>JiaBao Wang</au><au>MengChu Zhou</au><au>Ammari, Ahmed Chiheb</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2016-03-01</date><risdate>2016</risdate><volume>17</volume><issue>3</issue><spage>659</spage><epage>669</epage><pages>659-669</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2015.2487323</doi><tpages>11</tpages></addata></record> |
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| ispartof | IEEE transactions on intelligent transportation systems, 2016-03, Vol.17 (3), p.659-669 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Batteries Battery swap centralized charging Charging Dynamics Electric batteries Electric power systems Electric utilities electric vehicle Electric vehicles genetic algorithm Genetic algorithms Heuristic algorithms particle swarm optimization Power demand Stations Strategy Vehicles |
| title | Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario |
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