Economic energy scheduling through chaotic gorilla troops optimizer
Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation S...
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| Autor*in: |
Bhadoria, Ashutosh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of energy and environmental engineering - Berlin : SpringerOpen, 2010, 14(2022), 4 vom: 12. Dez., Seite 803-827 |
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| Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:4 ; day:12 ; month:12 ; pages:803-827 |
| Links: |
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| DOI / URN: |
10.1007/s40095-022-00550-0 |
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| Katalog-ID: |
SPR053228650 |
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| 520 | |a Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. | ||
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| 650 | 4 | |a Chaotic search |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Marwaha, Sanjay |4 aut | |
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10.1007/s40095-022-00550-0 doi (DE-627)SPR053228650 (SPR)s40095-022-00550-0-e DE-627 ger DE-627 rakwb eng Bhadoria, Ashutosh verfasserin aut Economic energy scheduling through chaotic gorilla troops optimizer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. Artificial gorilla troops optimizer (dpeaa)DE-He213 Metaheuristic algorithm (dpeaa)DE-He213 Generation Scheduling Problem (dpeaa)DE-He213 Chaotic search (dpeaa)DE-He213 Hybrid optimizer (dpeaa)DE-He213 Marwaha, Sanjay aut Enthalten in International journal of energy and environmental engineering Berlin : SpringerOpen, 2010 14(2022), 4 vom: 12. Dez., Seite 803-827 (DE-627)737702699 (DE-600)2705695-8 2251-6832 nnns volume:14 year:2022 number:4 day:12 month:12 pages:803-827 https://dx.doi.org/10.1007/s40095-022-00550-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 4 12 12 803-827 |
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10.1007/s40095-022-00550-0 doi (DE-627)SPR053228650 (SPR)s40095-022-00550-0-e DE-627 ger DE-627 rakwb eng Bhadoria, Ashutosh verfasserin aut Economic energy scheduling through chaotic gorilla troops optimizer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. Artificial gorilla troops optimizer (dpeaa)DE-He213 Metaheuristic algorithm (dpeaa)DE-He213 Generation Scheduling Problem (dpeaa)DE-He213 Chaotic search (dpeaa)DE-He213 Hybrid optimizer (dpeaa)DE-He213 Marwaha, Sanjay aut Enthalten in International journal of energy and environmental engineering Berlin : SpringerOpen, 2010 14(2022), 4 vom: 12. Dez., Seite 803-827 (DE-627)737702699 (DE-600)2705695-8 2251-6832 nnns volume:14 year:2022 number:4 day:12 month:12 pages:803-827 https://dx.doi.org/10.1007/s40095-022-00550-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 4 12 12 803-827 |
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10.1007/s40095-022-00550-0 doi (DE-627)SPR053228650 (SPR)s40095-022-00550-0-e DE-627 ger DE-627 rakwb eng Bhadoria, Ashutosh verfasserin aut Economic energy scheduling through chaotic gorilla troops optimizer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. Artificial gorilla troops optimizer (dpeaa)DE-He213 Metaheuristic algorithm (dpeaa)DE-He213 Generation Scheduling Problem (dpeaa)DE-He213 Chaotic search (dpeaa)DE-He213 Hybrid optimizer (dpeaa)DE-He213 Marwaha, Sanjay aut Enthalten in International journal of energy and environmental engineering Berlin : SpringerOpen, 2010 14(2022), 4 vom: 12. Dez., Seite 803-827 (DE-627)737702699 (DE-600)2705695-8 2251-6832 nnns volume:14 year:2022 number:4 day:12 month:12 pages:803-827 https://dx.doi.org/10.1007/s40095-022-00550-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 4 12 12 803-827 |
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10.1007/s40095-022-00550-0 doi (DE-627)SPR053228650 (SPR)s40095-022-00550-0-e DE-627 ger DE-627 rakwb eng Bhadoria, Ashutosh verfasserin aut Economic energy scheduling through chaotic gorilla troops optimizer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. Artificial gorilla troops optimizer (dpeaa)DE-He213 Metaheuristic algorithm (dpeaa)DE-He213 Generation Scheduling Problem (dpeaa)DE-He213 Chaotic search (dpeaa)DE-He213 Hybrid optimizer (dpeaa)DE-He213 Marwaha, Sanjay aut Enthalten in International journal of energy and environmental engineering Berlin : SpringerOpen, 2010 14(2022), 4 vom: 12. Dez., Seite 803-827 (DE-627)737702699 (DE-600)2705695-8 2251-6832 nnns volume:14 year:2022 number:4 day:12 month:12 pages:803-827 https://dx.doi.org/10.1007/s40095-022-00550-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 4 12 12 803-827 |
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10.1007/s40095-022-00550-0 doi (DE-627)SPR053228650 (SPR)s40095-022-00550-0-e DE-627 ger DE-627 rakwb eng Bhadoria, Ashutosh verfasserin aut Economic energy scheduling through chaotic gorilla troops optimizer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. Artificial gorilla troops optimizer (dpeaa)DE-He213 Metaheuristic algorithm (dpeaa)DE-He213 Generation Scheduling Problem (dpeaa)DE-He213 Chaotic search (dpeaa)DE-He213 Hybrid optimizer (dpeaa)DE-He213 Marwaha, Sanjay aut Enthalten in International journal of energy and environmental engineering Berlin : SpringerOpen, 2010 14(2022), 4 vom: 12. Dez., Seite 803-827 (DE-627)737702699 (DE-600)2705695-8 2251-6832 nnns volume:14 year:2022 number:4 day:12 month:12 pages:803-827 https://dx.doi.org/10.1007/s40095-022-00550-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 4 12 12 803-827 |
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economic energy scheduling through chaotic gorilla troops optimizer |
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Economic energy scheduling through chaotic gorilla troops optimizer |
| abstract |
Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness. © The Author(s), under exclusive licence to Islamic Azad University 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Economic energy scheduling through chaotic gorilla troops optimizer |
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Marwaha, Sanjay |
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