Capacity optimization configuration of live gas storage system in independent power systems

As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more...
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Autor*in:	Hongjun Fu [verfasserIn] Jinggang Wang [verfasserIn] Yang Cui [verfasserIn] Yabin Si [verfasserIn] Dawei Xia [verfasserIn] Xiaojiu Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration

Übergeordnetes Werk:	In: International Journal of Thermofluids - Elsevier, 2020, 21(2024), Seite 100526-
Übergeordnetes Werk:	volume:21 ; year:2024 ; pages:100526-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ijft.2023.100526

Katalog-ID:	DOAJ101622309

Internformat


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520			\|a As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability.
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allfields	10.1016/j.ijft.2023.100526 doi (DE-627)DOAJ101622309 (DE-599)DOAJd4ef07d2aed745f191223dce1d17c06b DE-627 ger DE-627 rakwb eng QC251-338.5 Hongjun Fu verfasserin aut Capacity optimization configuration of live gas storage system in independent power systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability. Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat Jinggang Wang verfasserin aut Yang Cui verfasserin aut Yabin Si verfasserin aut Dawei Xia verfasserin aut Xiaojiu Ma verfasserin aut In International Journal of Thermofluids Elsevier, 2020 21(2024), Seite 100526- (DE-627)1760627569 26662027 nnns volume:21 year:2024 pages:100526- https://doi.org/10.1016/j.ijft.2023.100526 kostenfrei https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b kostenfrei http://www.sciencedirect.com/science/article/pii/S2666202723002410 kostenfrei https://doaj.org/toc/2666-2027 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 100526-
spelling	10.1016/j.ijft.2023.100526 doi (DE-627)DOAJ101622309 (DE-599)DOAJd4ef07d2aed745f191223dce1d17c06b DE-627 ger DE-627 rakwb eng QC251-338.5 Hongjun Fu verfasserin aut Capacity optimization configuration of live gas storage system in independent power systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability. Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat Jinggang Wang verfasserin aut Yang Cui verfasserin aut Yabin Si verfasserin aut Dawei Xia verfasserin aut Xiaojiu Ma verfasserin aut In International Journal of Thermofluids Elsevier, 2020 21(2024), Seite 100526- (DE-627)1760627569 26662027 nnns volume:21 year:2024 pages:100526- https://doi.org/10.1016/j.ijft.2023.100526 kostenfrei https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b kostenfrei http://www.sciencedirect.com/science/article/pii/S2666202723002410 kostenfrei https://doaj.org/toc/2666-2027 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 100526-
allfields_unstemmed	10.1016/j.ijft.2023.100526 doi (DE-627)DOAJ101622309 (DE-599)DOAJd4ef07d2aed745f191223dce1d17c06b DE-627 ger DE-627 rakwb eng QC251-338.5 Hongjun Fu verfasserin aut Capacity optimization configuration of live gas storage system in independent power systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability. Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat Jinggang Wang verfasserin aut Yang Cui verfasserin aut Yabin Si verfasserin aut Dawei Xia verfasserin aut Xiaojiu Ma verfasserin aut In International Journal of Thermofluids Elsevier, 2020 21(2024), Seite 100526- (DE-627)1760627569 26662027 nnns volume:21 year:2024 pages:100526- https://doi.org/10.1016/j.ijft.2023.100526 kostenfrei https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b kostenfrei http://www.sciencedirect.com/science/article/pii/S2666202723002410 kostenfrei https://doaj.org/toc/2666-2027 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 100526-
allfieldsGer	10.1016/j.ijft.2023.100526 doi (DE-627)DOAJ101622309 (DE-599)DOAJd4ef07d2aed745f191223dce1d17c06b DE-627 ger DE-627 rakwb eng QC251-338.5 Hongjun Fu verfasserin aut Capacity optimization configuration of live gas storage system in independent power systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability. Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat Jinggang Wang verfasserin aut Yang Cui verfasserin aut Yabin Si verfasserin aut Dawei Xia verfasserin aut Xiaojiu Ma verfasserin aut In International Journal of Thermofluids Elsevier, 2020 21(2024), Seite 100526- (DE-627)1760627569 26662027 nnns volume:21 year:2024 pages:100526- https://doi.org/10.1016/j.ijft.2023.100526 kostenfrei https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b kostenfrei http://www.sciencedirect.com/science/article/pii/S2666202723002410 kostenfrei https://doaj.org/toc/2666-2027 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 100526-
allfieldsSound	10.1016/j.ijft.2023.100526 doi (DE-627)DOAJ101622309 (DE-599)DOAJd4ef07d2aed745f191223dce1d17c06b DE-627 ger DE-627 rakwb eng QC251-338.5 Hongjun Fu verfasserin aut Capacity optimization configuration of live gas storage system in independent power systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability. Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat Jinggang Wang verfasserin aut Yang Cui verfasserin aut Yabin Si verfasserin aut Dawei Xia verfasserin aut Xiaojiu Ma verfasserin aut In International Journal of Thermofluids Elsevier, 2020 21(2024), Seite 100526- (DE-627)1760627569 26662027 nnns volume:21 year:2024 pages:100526- https://doi.org/10.1016/j.ijft.2023.100526 kostenfrei https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b kostenfrei http://www.sciencedirect.com/science/article/pii/S2666202723002410 kostenfrei https://doaj.org/toc/2666-2027 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 100526-
language	English
source	In International Journal of Thermofluids 21(2024), Seite 100526- volume:21 year:2024 pages:100526-
sourceStr	In International Journal of Thermofluids 21(2024), Seite 100526- volume:21 year:2024 pages:100526-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration Heat
isfreeaccess_bool	true
container_title	International Journal of Thermofluids
authorswithroles_txt_mv	Hongjun Fu @@aut@@ Jinggang Wang @@aut@@ Yang Cui @@aut@@ Yabin Si @@aut@@ Dawei Xia @@aut@@ Xiaojiu Ma @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	1760627569
id	DOAJ101622309
language_de	englisch
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callnumber-first	Q - Science
author	Hongjun Fu
spellingShingle	Hongjun Fu misc QC251-338.5 misc Independent power system misc Electrified GSS misc Capacity-optimized configuration misc Genetic algorithm misc Optimal configuration misc Heat Capacity optimization configuration of live gas storage system in independent power systems
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topic_title	QC251-338.5 Capacity optimization configuration of live gas storage system in independent power systems Independent power system Electrified GSS Capacity-optimized configuration Genetic algorithm Optimal configuration
topic	misc QC251-338.5 misc Independent power system misc Electrified GSS misc Capacity-optimized configuration misc Genetic algorithm misc Optimal configuration misc Heat
topic_unstemmed	misc QC251-338.5 misc Independent power system misc Electrified GSS misc Capacity-optimized configuration misc Genetic algorithm misc Optimal configuration misc Heat
topic_browse	misc QC251-338.5 misc Independent power system misc Electrified GSS misc Capacity-optimized configuration misc Genetic algorithm misc Optimal configuration misc Heat
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title	Capacity optimization configuration of live gas storage system in independent power systems
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title_full	Capacity optimization configuration of live gas storage system in independent power systems
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title_sort	capacity optimization configuration of live gas storage system in independent power systems
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title_auth	Capacity optimization configuration of live gas storage system in independent power systems
abstract	As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability.
abstractGer	As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability.
abstract_unstemmed	As the energy demand and the continuous improvement of environmental performance continuous grow, the use of Independent Power Systems (IPS) is becoming increasingly common. Energy storage facilities not only achieve reliable power supply through IPS, but also face the problem of how to achieve more efficient and energy-saving. Therefore, this paper chose to establish a charged gas storage system (GSS for short here) of an independent power system to establish the mathematical model of the GSS through the Linear programming model, including the energy balance equation of the GSS and the load balance equation of the power system. It then used genetic algorithm (GA) to optimize the capacity of the GSS and obtained the optimal capacity configuration plan. In the simulation experiment analysis of a live GSS in view of GA for capacity optimization configuration, the live GSS proposed in this paper outperformed traditional GSS and battery GSS in terms of performance, power load, energy conversion, and capacity configuration. In terms of power load, the six indexes of regulation capacity, response speed, stability, discharge efficiency, power density and energy storage capacity are compared. Among them, the system capacity designed in this paper is 90, 83, 97, 83 and 90 % respectively, which are much higher than the other two. Among them, the six dimensional power load capacity was outstanding, and the energy conversion efficiency was also around 80 %. The capacity configuration was distributed between 85 and 94. This article proposed a design scheme for an electrified GSS based on GA for capacity optimization configuration, which can better meet the needs of independent power systems and improve their reliability and stability.
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title_short	Capacity optimization configuration of live gas storage system in independent power systems
url	https://doi.org/10.1016/j.ijft.2023.100526 https://doaj.org/article/d4ef07d2aed745f191223dce1d17c06b http://www.sciencedirect.com/science/article/pii/S2666202723002410 https://doaj.org/toc/2666-2027
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up_date	2024-07-03T21:37:35.739Z
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Capacity optimization configuration of live gas storage system in independent power systems

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