{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-1327"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-1327","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Optimal scheduling of thermal generating units in electric power systems","abstract":"The Unit Commitment Problem (UCP) in electric power system problem that consists of finding the startup and shutdown schedule of generating units over a period of time (e.g., 24 hrs) so that the operating cost is minimized; The UCP is often characterized by its prohibitive computational time and memory space requirement. The thesis investigates some computational aspects of the problem in an effort to improve the CPU time as well as the quality of the solution. Two algorithms that show significant improvement over existing methods are presented: One is based on the dynamic programming approach and designed for implementation on high performance computing machines with vector and parallel processing capabilities. The other is based on genetic algorithm techniques and designed for implementation on regular engineering workstations or fast personal computers; Finally, the effect of transmission losses on the quality of the optimal scheduling and the computational time are investigated. Simulation results on 26- and 44-unit power systems are presented to illustrate the effectiveness of the proposed algorithms.","abstract_html":"The Unit Commitment Problem (UCP) in electric power system problem that consists of finding the startup and shutdown schedule of generating units over a period of time (e.g., 24 hrs) so that the operating cost is minimized; The UCP is often characterized by its prohibitive computational time and memory space requirement. The thesis investigates some computational aspects of the problem in an effort to improve the CPU time as well as the quality of the solution. Two algorithms that show significant improvement over existing methods are presented: One is based on the dynamic programming approach and designed for implementation on high performance computing machines with vector and parallel processing capabilities. The other is based on genetic algorithm techniques and designed for implementation on regular engineering workstations or fast personal computers; Finally, the effect of transmission losses on the quality of the optimal scheduling and the computational time are investigated. Simulation results on 26- and 44-unit power systems are presented to illustrate the effectiveness of the proposed algorithms.","abstract_has_math":false,"creators":["Misra, Narsimha"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1993,"date_issued":"1993-01-01T08:00:00Z","date_published":"1993-01-01T08:00:00Z","updated_at":"2026-07-24T05:24:22Z","subjects":[],"languages":["English"],"rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://oasis.library.unlv.edu/rtds/328"],"render_values":[{"text":"https://oasis.library.unlv.edu/rtds/328","href":"https://oasis.library.unlv.edu/rtds/328","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25669/lovr-g2in","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Misra, Narsimha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["University of Nevada, Las Vegas"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["IN COPYRIGHT. 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Two algorithms that show significant improvement over existing methods are presented: One is based on the dynamic programming approach and designed for implementation on high performance computing machines with vector and parallel processing capabilities. The other is based on genetic algorithm techniques and designed for implementation on regular engineering workstations or fast personal computers; Finally, the effect of transmission losses on the quality of the optimal scheduling and the computational time are investigated. Simulation results on 26- and 44-unit power systems are presented to illustrate the effectiveness of the proposed algorithms."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Optimal scheduling of thermal generating units in electric power systems"]}]}],"canonical_facts":{"dc:creator":["Misra, Narsimha"],"dc:description.abstract":["The Unit Commitment Problem (UCP) in electric power system problem that consists of finding the startup and shutdown schedule of generating units over a period of time (e.g., 24 hrs) so that the operating cost is minimized; The UCP is often characterized by its prohibitive computational time and memory space requirement. The thesis investigates some computational aspects of the problem in an effort to improve the CPU time as well as the quality of the solution. Two algorithms that show significant improvement over existing methods are presented: One is based on the dynamic programming approach and designed for implementation on high performance computing machines with vector and parallel processing capabilities. The other is based on genetic algorithm techniques and designed for implementation on regular engineering workstations or fast personal computers; Finally, the effect of transmission losses on the quality of the optimal scheduling and the computational time are investigated. Simulation results on 26- and 44-unit power systems are presented to illustrate the effectiveness of the proposed algorithms."],"dc:format":["pdf"],"dc:identifier":["10.25669/lovr-g2in","https://oasis.library.unlv.edu/rtds/328","https://oasis.library.unlv.edu/context/rtds/article/1327/viewcontent/uc.pdf"],"dc:language":["English"],"dc:publisher":["University of Nevada, Las Vegas"],"dc:rights":["IN COPYRIGHT. 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