Multi-objective optimal decision for orderly power utilization based on improved ε-constraint method in active distribution networks
With the increasing demand for electricity load in China, orderly power utilization are important measures to alleviate electricity shortages during peak periods. This article establishes a multi-objective optimization model for orderly power utilization in active distribution networks is establishe...
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description | With the increasing demand for electricity load in China, orderly power utilization are important measures to alleviate electricity shortages during peak periods. This article establishes a multi-objective optimization model for orderly power utilization in active distribution networks is established, with the optimization objectives of minimizing the total operation cost, minimizing the cost for users, and minimizing the load fluctuation of the system. This model contains a large number of integer variables and nonlinear constraints, which is difficult to solve. To reduce computation time, convex relaxation techniques are adopted to transform the original model into a mixed-integer second-order cone programming (MISOCP) model, which has lower computational complexity. Furthermore, The improved ε-constraint method is proposed to solve the model, which can directly and quickly find the compromise optimal solution of the multi-objective problem. By using simplex search algorithm, the proposed method dose not need to traverse all grid points, which can significantly reduce computation time. Finally, case study on the the IEEE-33 bus distribution network demonstrate the effectiveness of the proposed method. |
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This article establishes a multi-objective optimization model for orderly power utilization in active distribution networks is established, with the optimization objectives of minimizing the total operation cost, minimizing the cost for users, and minimizing the load fluctuation of the system. This model contains a large number of integer variables and nonlinear constraints, which is difficult to solve. To reduce computation time, convex relaxation techniques are adopted to transform the original model into a mixed-integer second-order cone programming (MISOCP) model, which has lower computational complexity. Furthermore, The improved ε-constraint method is proposed to solve the model, which can directly and quickly find the compromise optimal solution of the multi-objective problem. By using simplex search algorithm, the proposed method dose not need to traverse all grid points, which can significantly reduce computation time. Finally, case study on the the IEEE-33 bus distribution network demonstrate the effectiveness of the proposed method.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0309437</identifier><identifier>PMID: 39446755</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Alternative energy ; China ; Computation ; Constraints ; Consumption ; Decision making ; Electric power demand ; Electric power distribution ; Electric Power Supplies ; Electrical loads ; Electricity ; Energy storage ; Engineering and Technology ; Fines & penalties ; Load distribution ; Load fluctuation ; Methods ; Mixed integer ; Models, Theoretical ; Multiple objective analysis ; Operating costs ; Optimization models ; Optimization techniques ; Pareto optimum ; Peak periods ; Physical Sciences ; Power supply ; Renewable resources ; Search algorithms ; Utilization ; Wind farms</subject><ispartof>PloS one, 2024-10, Vol.19 (10), p.e0309437</ispartof><rights>Copyright: © 2024 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>2024 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Wen et al 2024 Wen et al</rights><rights>2024 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c340t-720a6a0890788b37040f6f7afc8227c54a712e8b499c18a0b9b9048b378f168d3</cites><orcidid>0009-0009-3690-3993</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500852/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500852/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39446755$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Crisostomi, Emanuele</contributor><creatorcontrib>Wen, Xin</creatorcontrib><creatorcontrib>Li, Hui</creatorcontrib><creatorcontrib>Wu, Xiaoqiang</creatorcontrib><creatorcontrib>Li, Yiwei</creatorcontrib><creatorcontrib>Siliang, Liu</creatorcontrib><creatorcontrib>Huang, Guohua</creatorcontrib><title>Multi-objective optimal decision for orderly power utilization based on improved ε-constraint method in active distribution networks</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>With the increasing demand for electricity load in China, orderly power utilization are important measures to alleviate electricity shortages during peak periods. This article establishes a multi-objective optimization model for orderly power utilization in active distribution networks is established, with the optimization objectives of minimizing the total operation cost, minimizing the cost for users, and minimizing the load fluctuation of the system. This model contains a large number of integer variables and nonlinear constraints, which is difficult to solve. To reduce computation time, convex relaxation techniques are adopted to transform the original model into a mixed-integer second-order cone programming (MISOCP) model, which has lower computational complexity. Furthermore, The improved ε-constraint method is proposed to solve the model, which can directly and quickly find the compromise optimal solution of the multi-objective problem. By using simplex search algorithm, the proposed method dose not need to traverse all grid points, which can significantly reduce computation time. Finally, case study on the the IEEE-33 bus distribution network demonstrate the effectiveness of the proposed method.</description><subject>Algorithms</subject><subject>Alternative energy</subject><subject>China</subject><subject>Computation</subject><subject>Constraints</subject><subject>Consumption</subject><subject>Decision making</subject><subject>Electric power demand</subject><subject>Electric power distribution</subject><subject>Electric Power Supplies</subject><subject>Electrical loads</subject><subject>Electricity</subject><subject>Energy storage</subject><subject>Engineering and Technology</subject><subject>Fines & penalties</subject><subject>Load distribution</subject><subject>Load fluctuation</subject><subject>Methods</subject><subject>Mixed integer</subject><subject>Models, Theoretical</subject><subject>Multiple objective analysis</subject><subject>Operating costs</subject><subject>Optimization models</subject><subject>Optimization techniques</subject><subject>Pareto optimum</subject><subject>Peak periods</subject><subject>Physical Sciences</subject><subject>Power supply</subject><subject>Renewable resources</subject><subject>Search algorithms</subject><subject>Utilization</subject><subject>Wind 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One</addtitle><date>2024-10-24</date><risdate>2024</risdate><volume>19</volume><issue>10</issue><spage>e0309437</spage><pages>e0309437-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>With the increasing demand for electricity load in China, orderly power utilization are important measures to alleviate electricity shortages during peak periods. This article establishes a multi-objective optimization model for orderly power utilization in active distribution networks is established, with the optimization objectives of minimizing the total operation cost, minimizing the cost for users, and minimizing the load fluctuation of the system. This model contains a large number of integer variables and nonlinear constraints, which is difficult to solve. To reduce computation time, convex relaxation techniques are adopted to transform the original model into a mixed-integer second-order cone programming (MISOCP) model, which has lower computational complexity. Furthermore, The improved ε-constraint method is proposed to solve the model, which can directly and quickly find the compromise optimal solution of the multi-objective problem. By using simplex search algorithm, the proposed method dose not need to traverse all grid points, which can significantly reduce computation time. Finally, case study on the the IEEE-33 bus distribution network demonstrate the effectiveness of the proposed method.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39446755</pmid><doi>10.1371/journal.pone.0309437</doi><orcidid>https://orcid.org/0009-0009-3690-3993</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Alternative energy China Computation Constraints Consumption Decision making Electric power demand Electric power distribution Electric Power Supplies Electrical loads Electricity Energy storage Engineering and Technology Fines & penalties Load distribution Load fluctuation Methods Mixed integer Models, Theoretical Multiple objective analysis Operating costs Optimization models Optimization techniques Pareto optimum Peak periods Physical Sciences Power supply Renewable resources Search algorithms Utilization Wind farms |
title | Multi-objective optimal decision for orderly power utilization based on improved ε-constraint method in active distribution networks |
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