Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing
This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepte...
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| Veröffentlicht in: | IEEE transactions on power systems 2006-05, Vol.21 (2), p.955-964 |
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| container_title | IEEE transactions on power systems |
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| creator | Saber, A.Y. Senjyu, T. Miyagi, T. Urasaki, N. Funabashi, T. |
| description | This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms. |
| doi_str_mv | 10.1109/TPWRS.2006.873017 |
| format | Article |
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In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. 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(IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-7e6b3c24559b175878be347209819e1c4061cd74016808e08b2bd3a4f85f1f733</citedby><cites>FETCH-LOGICAL-c421t-7e6b3c24559b175878be347209819e1c4061cd74016808e08b2bd3a4f85f1f733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1626403$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1626403$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Saber, A.Y.</creatorcontrib><creatorcontrib>Senjyu, T.</creatorcontrib><creatorcontrib>Miyagi, T.</creatorcontrib><creatorcontrib>Urasaki, N.</creatorcontrib><creatorcontrib>Funabashi, T.</creatorcontrib><title>Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms.</description><subject>Algorithms</subject><subject>Best heat rate</subject><subject>Cost engineering</subject><subject>Costs</subject><subject>Fuzzy</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Heuristic</subject><subject>Large-scale systems</subject><subject>linguistic fuzzy control</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Minimization methods</subject><subject>Operations research</subject><subject>Power generation economics</subject><subject>Power system modeling</subject><subject>Power system simulation</subject><subject>Scheduling</subject><subject>sign vector</subject><subject>Simulated annealing</subject><subject>simulated annealing (SA)</subject><subject>Stochastic processes</subject><subject>Stochasticity</subject><subject>Studies</subject><subject>unit commitment (UC)</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90U1LxDAQBuAgCq4fP0C8FA966jppmmR6FHFVEBRd8RjS7FQr_dAmOez-eruuIHjwMnOYZwaGl7EjDlPOoTifP7w8Pk0zADVFLYDrLTbhUmIKShfbbAKIMsVCwi7b8_4dRjgOJuxhFlerZRK7OiSub9s6tNSFxLs3WsSm7l6T6NfVlr5vYqBmmfjQuzfrQ-0SX7exsYEWie06smt_wHYq23g6_On77Hl2Nb-8Se_ur28vL-5Sl2c8pJpUKVyWS1mUXEvUWJLIdQYF8oK4y0Fxt9A5cIWABFhm5ULYvEJZ8UoLsc_ONnc_hv4zkg-mrb2jprEd9dEbLFQGQoEe5em_MkPgBSo1wpM_8L2PQzd-YVBJFBpyPiK-QW7ovR-oMh9D3dphaTiYdRTmOwqzjsJsohh3jjc7NRH9epWpHIT4AuPbhQ8</recordid><startdate>20060501</startdate><enddate>20060501</enddate><creator>Saber, A.Y.</creator><creator>Senjyu, T.</creator><creator>Miyagi, T.</creator><creator>Urasaki, N.</creator><creator>Funabashi, T.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>20060501</creationdate><title>Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing</title><author>Saber, A.Y. ; Senjyu, T. ; Miyagi, T. ; Urasaki, N. ; Funabashi, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-7e6b3c24559b175878be347209819e1c4061cd74016808e08b2bd3a4f85f1f733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Best heat rate</topic><topic>Cost engineering</topic><topic>Costs</topic><topic>Fuzzy</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Heuristic</topic><topic>Large-scale systems</topic><topic>linguistic fuzzy control</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Minimization methods</topic><topic>Operations research</topic><topic>Power generation economics</topic><topic>Power system modeling</topic><topic>Power system simulation</topic><topic>Scheduling</topic><topic>sign vector</topic><topic>Simulated annealing</topic><topic>simulated annealing (SA)</topic><topic>Stochastic processes</topic><topic>Stochasticity</topic><topic>Studies</topic><topic>unit commitment (UC)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saber, A.Y.</creatorcontrib><creatorcontrib>Senjyu, T.</creatorcontrib><creatorcontrib>Miyagi, T.</creatorcontrib><creatorcontrib>Urasaki, N.</creatorcontrib><creatorcontrib>Funabashi, T.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Saber, A.Y.</au><au>Senjyu, T.</au><au>Miyagi, T.</au><au>Urasaki, N.</au><au>Funabashi, T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2006-05-01</date><risdate>2006</risdate><volume>21</volume><issue>2</issue><spage>955</spage><epage>964</epage><pages>955-964</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2006.873017</doi><tpages>10</tpages></addata></record> |
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| subjects | Algorithms Best heat rate Cost engineering Costs Fuzzy Fuzzy control Fuzzy logic Fuzzy set theory Heuristic Large-scale systems linguistic fuzzy control Mathematical analysis Mathematical models Minimization methods Operations research Power generation economics Power system modeling Power system simulation Scheduling sign vector Simulated annealing simulated annealing (SA) Stochastic processes Stochasticity Studies unit commitment (UC) |
| title | Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing |
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