A parallel genetic algorithm for generation expansion planning
This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals....
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| Veröffentlicht in: | IEEE Transactions on Power Systems 1996-05, Vol.11 (2), p.955-961 |
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| container_end_page | 961 |
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| container_title | IEEE Transactions on Power Systems |
| container_volume | 11 |
| creator | Fukuyama, Y. Hsaio-Dong Chiang |
| description | This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem. |
| doi_str_mv | 10.1109/59.496180 |
| format | Article |
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The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/59.496180</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>ALGORITHMS ; Applied sciences ; CAPACITY ; Dynamic programming ; Electrical engineering. Electrical power engineering ; Electrical power engineering ; Exact sciences and technology ; EXPANSION ; Genetic algorithms ; Operation. Load control. 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The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.</description><subject>ALGORITHMS</subject><subject>Applied sciences</subject><subject>CAPACITY</subject><subject>Dynamic programming</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical power engineering</subject><subject>Exact sciences and technology</subject><subject>EXPANSION</subject><subject>Genetic algorithms</subject><subject>Operation. Load control. Reliability</subject><subject>PARALLEL PROCESSING</subject><subject>PLANNING</subject><subject>Power networks and lines</subject><subject>POWER SYSTEMS</subject><subject>POWER TRANSMISSION AND DISTRIBUTION</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNo9kMtLAzEQh4MoWB8Hr55WEMHD1slzk4tQxBcUvOg5hGy2jaTJmmxB_3tXt_Q0w_y--RgGoQsMc4xB3XE1Z0pgCQdohjmXNYhGHaIZSMlrqTgco5NSPgFAjMEM3S-q3mQTggvVykU3eFuZsErZD-tN1aX8P81m8ClW7rs3sfx1fTAx-rg6Q0edCcWd7-op-nh6fH94qZdvz68Pi2VtKSVDjQ1wJYhgDW6VVYYKR5QlXDYGKKetpAIE4R1pnQSmGDMgFGklY9C1HBp6iq4mbyqD18X6wdm1TTE6O-jRSxo8MjcT0-f0tXVl0BtfrAvjqS5tiyaSA24UG8HbCbQ5lZJdp_vsNyb_aAz674uaKz19cWSvd1JTrAldNtH6sl-goKARfMQuJ8w75_bpzvELGeN28g</recordid><startdate>19960501</startdate><enddate>19960501</enddate><creator>Fukuyama, Y.</creator><creator>Hsaio-Dong Chiang</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>OTOTI</scope></search><sort><creationdate>19960501</creationdate><title>A parallel genetic algorithm for generation expansion planning</title><author>Fukuyama, Y. ; Hsaio-Dong Chiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c332t-1a059626471d9c9a36e29c2587a0353d8360625f2de804944a0692d8440fd5073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>ALGORITHMS</topic><topic>Applied sciences</topic><topic>CAPACITY</topic><topic>Dynamic programming</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical power engineering</topic><topic>Exact sciences and technology</topic><topic>EXPANSION</topic><topic>Genetic algorithms</topic><topic>Operation. Load control. Reliability</topic><topic>PARALLEL PROCESSING</topic><topic>PLANNING</topic><topic>Power networks and lines</topic><topic>POWER SYSTEMS</topic><topic>POWER TRANSMISSION AND DISTRIBUTION</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fukuyama, Y.</creatorcontrib><creatorcontrib>Hsaio-Dong Chiang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>OSTI.GOV</collection><jtitle>IEEE Transactions on Power Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fukuyama, Y.</au><au>Hsaio-Dong Chiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A parallel genetic algorithm for generation expansion planning</atitle><jtitle>IEEE Transactions on Power Systems</jtitle><stitle>TPWRS</stitle><date>1996-05-01</date><risdate>1996</risdate><volume>11</volume><issue>2</issue><spage>955</spage><epage>961</epage><pages>955-961</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/59.496180</doi><tpages>7</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | ALGORITHMS Applied sciences CAPACITY Dynamic programming Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology EXPANSION Genetic algorithms Operation. Load control. Reliability PARALLEL PROCESSING PLANNING Power networks and lines POWER SYSTEMS POWER TRANSMISSION AND DISTRIBUTION |
| title | A parallel genetic algorithm for generation expansion planning |
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