Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation
•A method for the charging coordination of electric vehicles is proposed.•Optimization algorithms based on metaheuristics are developed.•A 449-node distribution system is used to test the proposed method. This paper proposes three metaheuristic optimization techniques to solve the plug-in electric v...
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| Veröffentlicht in: | Electric power systems research 2017-01, Vol.142, p.351-361 |
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| creator | Arias, Nataly Bañol Franco, John F. Lavorato, Marina Romero, Rubén |
| description | •A method for the charging coordination of electric vehicles is proposed.•Optimization algorithms based on metaheuristics are developed.•A 449-node distribution system is used to test the proposed method.
This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints. |
| doi_str_mv | 10.1016/j.epsr.2016.09.018 |
| format | Article |
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This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints.</description><identifier>ISSN: 0378-7796</identifier><identifier>EISSN: 1873-2046</identifier><identifier>DOI: 10.1016/j.epsr.2016.09.018</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Adaptive search techniques ; Adaptive systems ; Algorithms ; Batteries ; Distributed generation ; Electric power distribution ; Electric vehicle charging ; Electric vehicles ; Electrical distribution system ; Electricity distribution ; Heuristic methods ; Hybrid algorithm ; Impact strength ; Metaheuristic ; Optimization ; Optimization algorithms ; Plug-in electric vehicle charging coordination ; Rechargeable batteries ; Simulation ; Tabu search</subject><ispartof>Electric power systems research, 2017-01, Vol.142, p.351-361</ispartof><rights>2016 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Jan 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-252c416fb4af5a8423b6149966c81150441c4d41a97026e21c7b084f6b1c332a3</citedby><cites>FETCH-LOGICAL-c430t-252c416fb4af5a8423b6149966c81150441c4d41a97026e21c7b084f6b1c332a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.epsr.2016.09.018$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000</link.rule.ids></links><search><creatorcontrib>Arias, Nataly Bañol</creatorcontrib><creatorcontrib>Franco, John F.</creatorcontrib><creatorcontrib>Lavorato, Marina</creatorcontrib><creatorcontrib>Romero, Rubén</creatorcontrib><title>Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation</title><title>Electric power systems research</title><description>•A method for the charging coordination of electric vehicles is proposed.•Optimization algorithms based on metaheuristics are developed.•A 449-node distribution system is used to test the proposed method.
This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints.</description><subject>Adaptive search techniques</subject><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Batteries</subject><subject>Distributed generation</subject><subject>Electric power distribution</subject><subject>Electric vehicle charging</subject><subject>Electric vehicles</subject><subject>Electrical distribution system</subject><subject>Electricity distribution</subject><subject>Heuristic methods</subject><subject>Hybrid algorithm</subject><subject>Impact strength</subject><subject>Metaheuristic</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Plug-in electric vehicle charging coordination</subject><subject>Rechargeable batteries</subject><subject>Simulation</subject><subject>Tabu search</subject><issn>0378-7796</issn><issn>1873-2046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9Uctq3TAQFaGF3qb9ga4EXdsZybJkQzYhpEkhpZtkLWR5bOviWK4kp6S_0p-tblzorqsZOK9hDiGfGJQMmLw4lrjGUPK8l9CWwJozcmCNqgoOQr4hB6hUUyjVynfkfYxHAJCtqg_k9zdMZsItuJicpX5N7sn9Msn5hZp59MGl6SnSwQeaJtxxM1PrfejdsvP8QNd5Gwu3UJzRppCNnnFydkZqJxNGt4w0g33OCK7bXkXxJSbMzj9zwD8EezriguHV-AN5O5g54se_85w8frl5uL4r7r_ffr2-ui-sqCAVvOZWMDl0wgy1aQSvOslE20ppG8ZqEIJZ0QtmWgVcImdWddCIQXbMVhU31Tn5vPuuwf_YMCZ99FtYcqTmUEPdKlWpzOI7ywYfY8BBryH_IrxoBvpUgj7qUwn6VIKGVucSsuhyF2G-_9lh0NE6XCz2LuRP6d67_8n_AEIClIg</recordid><startdate>201701</startdate><enddate>201701</enddate><creator>Arias, Nataly Bañol</creator><creator>Franco, John F.</creator><creator>Lavorato, Marina</creator><creator>Romero, Rubén</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>201701</creationdate><title>Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation</title><author>Arias, Nataly Bañol ; Franco, John F. ; Lavorato, Marina ; Romero, Rubén</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-252c416fb4af5a8423b6149966c81150441c4d41a97026e21c7b084f6b1c332a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive search techniques</topic><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Batteries</topic><topic>Distributed generation</topic><topic>Electric power distribution</topic><topic>Electric vehicle charging</topic><topic>Electric vehicles</topic><topic>Electrical distribution system</topic><topic>Electricity distribution</topic><topic>Heuristic methods</topic><topic>Hybrid algorithm</topic><topic>Impact strength</topic><topic>Metaheuristic</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Plug-in electric vehicle charging coordination</topic><topic>Rechargeable batteries</topic><topic>Simulation</topic><topic>Tabu search</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arias, Nataly Bañol</creatorcontrib><creatorcontrib>Franco, John F.</creatorcontrib><creatorcontrib>Lavorato, Marina</creatorcontrib><creatorcontrib>Romero, Rubén</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Electric power systems research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arias, Nataly Bañol</au><au>Franco, John F.</au><au>Lavorato, Marina</au><au>Romero, Rubén</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation</atitle><jtitle>Electric power systems research</jtitle><date>2017-01</date><risdate>2017</risdate><volume>142</volume><spage>351</spage><epage>361</epage><pages>351-361</pages><issn>0378-7796</issn><eissn>1873-2046</eissn><abstract>•A method for the charging coordination of electric vehicles is proposed.•Optimization algorithms based on metaheuristics are developed.•A 449-node distribution system is used to test the proposed method.
This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.epsr.2016.09.018</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
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| ispartof | Electric power systems research, 2017-01, Vol.142, p.351-361 |
| issn | 0378-7796 1873-2046 |
| language | eng |
| recordid | cdi_proquest_journals_2050597737 |
| source | Elsevier ScienceDirect Journals Complete |
| subjects | Adaptive search techniques Adaptive systems Algorithms Batteries Distributed generation Electric power distribution Electric vehicle charging Electric vehicles Electrical distribution system Electricity distribution Heuristic methods Hybrid algorithm Impact strength Metaheuristic Optimization Optimization algorithms Plug-in electric vehicle charging coordination Rechargeable batteries Simulation Tabu search |
| title | Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation |
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