Network Expansion Planning Using Improved Controlled NSGA-II
SUMMARY This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that...
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| Veröffentlicht in: | Electrical engineering in Japan 2015-12, Vol.193 (4), p.38-48 |
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| container_title | Electrical engineering in Japan |
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| creator | Okabe, Masanori Shahrin, Mohd Aoki, Hidenori |
| description | SUMMARY
This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that are difficult to solve due to their complexity. This research applies a genetic algorithm, which is a meta‐heuristics method. The present study proposes a new method of multiobjective optimization: NSGA‐II, SPEA2, and Controlled NSGA‐II are assumed to be the best methods now. The proposed method introduces the concept of a linkage identification genetic algorithm, enabling more efficient searching than methods hitherto known. In the past, most research on network expansion planning did not include the load curve. This research demonstrates that the investigation must include the load curve. It also proposes a new method of search including the load curve. |
| doi_str_mv | 10.1002/eej.22766 |
| format | Article |
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This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that are difficult to solve due to their complexity. This research applies a genetic algorithm, which is a meta‐heuristics method. The present study proposes a new method of multiobjective optimization: NSGA‐II, SPEA2, and Controlled NSGA‐II are assumed to be the best methods now. The proposed method introduces the concept of a linkage identification genetic algorithm, enabling more efficient searching than methods hitherto known. In the past, most research on network expansion planning did not include the load curve. This research demonstrates that the investigation must include the load curve. It also proposes a new method of search including the load curve.</description><identifier>ISSN: 0424-7760</identifier><identifier>EISSN: 1520-6416</identifier><identifier>DOI: 10.1002/eej.22766</identifier><language>eng</language><publisher>Blackwell Publishing Ltd</publisher><subject>Combinatorial analysis ; Cost engineering ; daily load curve ; distribution network expansion planning ; Electrical engineering ; Genetic algorithms ; linkage identification genetic algorithm ; Linkages ; multiobjective optimization ; Networks ; Optimization ; Searching</subject><ispartof>Electrical engineering in Japan, 2015-12, Vol.193 (4), p.38-48</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3666-31d0f46cbaa678b9aad973b2ad03d20784e3c6ea56fe6fbed9c6ee9fc69fa6483</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Feej.22766$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Feej.22766$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Okabe, Masanori</creatorcontrib><creatorcontrib>Shahrin, Mohd</creatorcontrib><creatorcontrib>Aoki, Hidenori</creatorcontrib><title>Network Expansion Planning Using Improved Controlled NSGA-II</title><title>Electrical engineering in Japan</title><addtitle>Electr Eng Jpn</addtitle><description>SUMMARY
This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that are difficult to solve due to their complexity. This research applies a genetic algorithm, which is a meta‐heuristics method. The present study proposes a new method of multiobjective optimization: NSGA‐II, SPEA2, and Controlled NSGA‐II are assumed to be the best methods now. The proposed method introduces the concept of a linkage identification genetic algorithm, enabling more efficient searching than methods hitherto known. In the past, most research on network expansion planning did not include the load curve. This research demonstrates that the investigation must include the load curve. It also proposes a new method of search including the load curve.</description><subject>Combinatorial analysis</subject><subject>Cost engineering</subject><subject>daily load curve</subject><subject>distribution network expansion planning</subject><subject>Electrical engineering</subject><subject>Genetic algorithms</subject><subject>linkage identification genetic algorithm</subject><subject>Linkages</subject><subject>multiobjective optimization</subject><subject>Networks</subject><subject>Optimization</subject><subject>Searching</subject><issn>0424-7760</issn><issn>1520-6416</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp1kE9PwkAQxTdGExE9-A161ENht9vOtokXQhBrSDVBorfNtp2aQmlxtwh8exer3rzMn-T3Jm8eIdeMDhil3hBxOfA8AXBCeizwqAs-g1PSo77nu0IAPScXxiwppYKJsEfuEmx3jV45k_1G1aZsaue5UnVd1u_OwhxrvN7o5hNzZ9zUrW6qyo7JfDpy4_iSnBWqMnj10_tkcT95GT-4s6dpPB7N3IwDgMtZTgsfslQpEGEaKZVHgqeeyinPPSpCH3kGqAIoEIoU88huGBUZRIUCP-R9ctPdtU4-tmhauS5NhpU1is3WSPsJMAGRH1j0tkMz3RijsZAbXa6VPkhG5TEhaROS3wlZdtixu7LCw_-gnEwefxVupyhNi_s_hdIrCYKLQL4mU8kFTZJ5MJdv_Au3YnaH</recordid><startdate>201512</startdate><enddate>201512</enddate><creator>Okabe, Masanori</creator><creator>Shahrin, Mohd</creator><creator>Aoki, Hidenori</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope></search><sort><creationdate>201512</creationdate><title>Network Expansion Planning Using Improved Controlled NSGA-II</title><author>Okabe, Masanori ; Shahrin, Mohd ; Aoki, Hidenori</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3666-31d0f46cbaa678b9aad973b2ad03d20784e3c6ea56fe6fbed9c6ee9fc69fa6483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Combinatorial analysis</topic><topic>Cost engineering</topic><topic>daily load curve</topic><topic>distribution network expansion planning</topic><topic>Electrical engineering</topic><topic>Genetic algorithms</topic><topic>linkage identification genetic algorithm</topic><topic>Linkages</topic><topic>multiobjective optimization</topic><topic>Networks</topic><topic>Optimization</topic><topic>Searching</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Okabe, Masanori</creatorcontrib><creatorcontrib>Shahrin, Mohd</creatorcontrib><creatorcontrib>Aoki, Hidenori</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Electrical engineering in Japan</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Okabe, Masanori</au><au>Shahrin, Mohd</au><au>Aoki, Hidenori</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network Expansion Planning Using Improved Controlled NSGA-II</atitle><jtitle>Electrical engineering in Japan</jtitle><addtitle>Electr Eng Jpn</addtitle><date>2015-12</date><risdate>2015</risdate><volume>193</volume><issue>4</issue><spage>38</spage><epage>48</epage><pages>38-48</pages><issn>0424-7760</issn><eissn>1520-6416</eissn><abstract>SUMMARY
This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that are difficult to solve due to their complexity. This research applies a genetic algorithm, which is a meta‐heuristics method. The present study proposes a new method of multiobjective optimization: NSGA‐II, SPEA2, and Controlled NSGA‐II are assumed to be the best methods now. The proposed method introduces the concept of a linkage identification genetic algorithm, enabling more efficient searching than methods hitherto known. In the past, most research on network expansion planning did not include the load curve. This research demonstrates that the investigation must include the load curve. It also proposes a new method of search including the load curve.</abstract><pub>Blackwell Publishing Ltd</pub><doi>10.1002/eej.22766</doi><tpages>11</tpages></addata></record> |
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| ispartof | Electrical engineering in Japan, 2015-12, Vol.193 (4), p.38-48 |
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| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Combinatorial analysis Cost engineering daily load curve distribution network expansion planning Electrical engineering Genetic algorithms linkage identification genetic algorithm Linkages multiobjective optimization Networks Optimization Searching |
| title | Network Expansion Planning Using Improved Controlled NSGA-II |
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