An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates...

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Autores: Syama, S., Ramprabhakar, J., Anand, Ruchika, Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/427214
Acceso en línea:https://hdl.handle.net/2117/427214
https://dx.doi.org/10.1038/s41598-024-75743-0
Access Level:acceso abierto
Palabra clave:Unit commitment
Combined economic emission dispatch
Crow search algorithm
Grey Wolf optimization
Enhanced lambda iteration
Emissions
Economic dispatch
Forecasting
Àrees temàtiques de la UPC::Enginyeria elèctrica
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repository_id_str
spelling An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systemsSyama, S.Ramprabhakar, J.Anand, RuchikaGuerrero Zapata, Josep Maria|||0000-0001-5236-4592Unit commitmentCombined economic emission dispatchCrow search algorithmGrey Wolf optimizationEnhanced lambda iterationEmissionsEconomic dispatchForecastingÀrees temàtiques de la UPC::Enginyeria elèctricaThe current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-¿ ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.Peer ReviewedSpringer20242024-12-0120252025-03-27journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/427214https://dx.doi.org/10.1038/s41598-024-75743-0reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4272142026-05-27T15:37:01Z
dc.title.none.fl_str_mv An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
title An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
spellingShingle An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
Syama, S.
Unit commitment
Combined economic emission dispatch
Crow search algorithm
Grey Wolf optimization
Enhanced lambda iteration
Emissions
Economic dispatch
Forecasting
Àrees temàtiques de la UPC::Enginyeria elèctrica
title_short An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
title_full An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
title_fullStr An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
title_full_unstemmed An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
title_sort An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
dc.creator.none.fl_str_mv Syama, S.
Ramprabhakar, J.
Anand, Ruchika
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author Syama, S.
author_facet Syama, S.
Ramprabhakar, J.
Anand, Ruchika
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author_role author
author2 Ramprabhakar, J.
Anand, Ruchika
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author2_role author
author
author
dc.subject.none.fl_str_mv Unit commitment
Combined economic emission dispatch
Crow search algorithm
Grey Wolf optimization
Enhanced lambda iteration
Emissions
Economic dispatch
Forecasting
Àrees temàtiques de la UPC::Enginyeria elèctrica
topic Unit commitment
Combined economic emission dispatch
Crow search algorithm
Grey Wolf optimization
Enhanced lambda iteration
Emissions
Economic dispatch
Forecasting
Àrees temàtiques de la UPC::Enginyeria elèctrica
description The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-¿ ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-01
2025
2025-03-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/427214
https://dx.doi.org/10.1038/s41598-024-75743-0
url https://hdl.handle.net/2117/427214
https://dx.doi.org/10.1038/s41598-024-75743-0
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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