Application of genetic-based neural networks to thermal unit commitment
A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed sche...
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| Veröffentlicht in: | IEEE transactions on power systems 1997-05, Vol.12 (2), p.654-660 |
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| container_title | IEEE transactions on power systems |
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| creator | HUANG, S.-J HUANG, C.-L |
| description | A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach. |
| doi_str_mv | 10.1109/59.589634 |
| format | Article |
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A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/59.589634</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Computer networks ; Costs ; Dynamic programming ; Dynamic scheduling ; Economic data ; Electric energy ; Electric generators ; Electric load management ; Energy ; Energy economics ; Exact sciences and technology ; General, economic and professional studies ; Genetic algorithms ; Integer linear programming ; Methodology. 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A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach.</description><subject>Applied sciences</subject><subject>Computer networks</subject><subject>Costs</subject><subject>Dynamic programming</subject><subject>Dynamic scheduling</subject><subject>Economic data</subject><subject>Electric energy</subject><subject>Electric generators</subject><subject>Electric load management</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Exact sciences and technology</subject><subject>General, economic and professional studies</subject><subject>Genetic algorithms</subject><subject>Integer linear programming</subject><subject>Methodology. Modelling</subject><subject>Neural networks</subject><subject>Power system dynamics</subject><subject>Power system stability</subject><subject>Processor scheduling</subject><subject>Scheduling</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNqFkDFPwzAQhS0EEqUwsDJlQEgMKT7Hce2xqqAgVWKBOXKcCxiSuNiOEP8eQ6uulU53p3vfveERcgl0BkDVXalmpVSi4EdkAmUpcyrm6phMqJRlLlVJT8lZCB-UUpGECVktNpvOGh2tGzLXZm84YLQmr3XAJhtw9LpLI347_xmy6LL4jr5Pt3GwMTOu723scYjn5KTVXcCL3ZyS14f7l-Vjvn5ePS0X69wUQsa8lmyukWtMi5IUKQWtQdeMK1NzxKbgXLQAUmkjsQEKAkCLAhAKwVlTTMnN1nfj3deIIVa9DQa7Tg_oxlAxWTLFZXEYnDMpUjsIgqBUMQkJvN2CxrsQPLbVxtte-58KaPUXflWm-g8_sdc7Ux2M7lqvB2PD_oEJLkQhEna1xSwi7tWdxy8ihos9</recordid><startdate>19970501</startdate><enddate>19970501</enddate><creator>HUANG, S.-J</creator><creator>HUANG, C.-L</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>7SU</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>L7M</scope></search><sort><creationdate>19970501</creationdate><title>Application of genetic-based neural networks to thermal unit commitment</title><author>HUANG, S.-J ; HUANG, C.-L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-b827ae4aeb82980e001aa1ab249cb4eed3446f1189ac8ed101611a631e13642d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Applied sciences</topic><topic>Computer networks</topic><topic>Costs</topic><topic>Dynamic programming</topic><topic>Dynamic scheduling</topic><topic>Economic data</topic><topic>Electric energy</topic><topic>Electric generators</topic><topic>Electric load management</topic><topic>Energy</topic><topic>Energy economics</topic><topic>Exact sciences and technology</topic><topic>General, economic and professional studies</topic><topic>Genetic algorithms</topic><topic>Integer linear programming</topic><topic>Methodology. Modelling</topic><topic>Neural networks</topic><topic>Power system dynamics</topic><topic>Power system stability</topic><topic>Processor scheduling</topic><topic>Scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>HUANG, S.-J</creatorcontrib><creatorcontrib>HUANG, C.-L</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HUANG, S.-J</au><au>HUANG, C.-L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of genetic-based neural networks to thermal unit commitment</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>1997-05-01</date><risdate>1997</risdate><volume>12</volume><issue>2</issue><spage>654</spage><epage>660</epage><pages>654-660</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/59.589634</doi><tpages>7</tpages></addata></record> |
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| ispartof | IEEE transactions on power systems, 1997-05, Vol.12 (2), p.654-660 |
| issn | 0885-8950 1558-0679 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Computer networks Costs Dynamic programming Dynamic scheduling Economic data Electric energy Electric generators Electric load management Energy Energy economics Exact sciences and technology General, economic and professional studies Genetic algorithms Integer linear programming Methodology. Modelling Neural networks Power system dynamics Power system stability Processor scheduling Scheduling |
| title | Application of genetic-based neural networks to thermal unit commitment |
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