Coordinated optimization for controlling short circuit current and multi-infeed DC interaction
Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimi...
Ausführliche Beschreibung
Autor*in: |
Dong Yang [verfasserIn] Kang Zhao [verfasserIn] Yutian Liu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2014 |
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Übergeordnetes Werk: |
In: Journal of Modern Power Systems and Clean Energy - IEEE, 2016, 2(2014), 4, Seite 274-284 |
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Übergeordnetes Werk: |
volume:2 ; year:2014 ; number:4 ; pages:274-284 |
Links: |
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DOI / URN: |
10.1007/s40565-014-0081-z |
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Katalog-ID: |
DOAJ008188238 |
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520 | |a Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. | ||
650 | 4 | |a Operation and planning | |
650 | 4 | |a Multiple DC infeed | |
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10.1007/s40565-014-0081-z doi (DE-627)DOAJ008188238 (DE-599)DOAJ7493ba3e76a74d6c846c2dcbde8f0ca8 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Dong Yang verfasserin aut Coordinated optimization for controlling short circuit current and multi-infeed DC interaction 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. Operation and planning Multiple DC infeed Short circuit current Sensitivity analysis Multi-objective optimization Production of electric energy or power. Powerplants. Central stations Renewable energy sources Kang Zhao verfasserin aut Yutian Liu verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 2(2014), 4, Seite 274-284 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:2 year:2014 number:4 pages:274-284 https://doi.org/10.1007/s40565-014-0081-z kostenfrei https://doaj.org/article/7493ba3e76a74d6c846c2dcbde8f0ca8 kostenfrei https://ieeexplore.ieee.org/document/9005341/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 274-284 |
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10.1007/s40565-014-0081-z doi (DE-627)DOAJ008188238 (DE-599)DOAJ7493ba3e76a74d6c846c2dcbde8f0ca8 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Dong Yang verfasserin aut Coordinated optimization for controlling short circuit current and multi-infeed DC interaction 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. Operation and planning Multiple DC infeed Short circuit current Sensitivity analysis Multi-objective optimization Production of electric energy or power. Powerplants. Central stations Renewable energy sources Kang Zhao verfasserin aut Yutian Liu verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 2(2014), 4, Seite 274-284 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:2 year:2014 number:4 pages:274-284 https://doi.org/10.1007/s40565-014-0081-z kostenfrei https://doaj.org/article/7493ba3e76a74d6c846c2dcbde8f0ca8 kostenfrei https://ieeexplore.ieee.org/document/9005341/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 274-284 |
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10.1007/s40565-014-0081-z doi (DE-627)DOAJ008188238 (DE-599)DOAJ7493ba3e76a74d6c846c2dcbde8f0ca8 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Dong Yang verfasserin aut Coordinated optimization for controlling short circuit current and multi-infeed DC interaction 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. Operation and planning Multiple DC infeed Short circuit current Sensitivity analysis Multi-objective optimization Production of electric energy or power. Powerplants. Central stations Renewable energy sources Kang Zhao verfasserin aut Yutian Liu verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 2(2014), 4, Seite 274-284 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:2 year:2014 number:4 pages:274-284 https://doi.org/10.1007/s40565-014-0081-z kostenfrei https://doaj.org/article/7493ba3e76a74d6c846c2dcbde8f0ca8 kostenfrei https://ieeexplore.ieee.org/document/9005341/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 274-284 |
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10.1007/s40565-014-0081-z doi (DE-627)DOAJ008188238 (DE-599)DOAJ7493ba3e76a74d6c846c2dcbde8f0ca8 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Dong Yang verfasserin aut Coordinated optimization for controlling short circuit current and multi-infeed DC interaction 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. Operation and planning Multiple DC infeed Short circuit current Sensitivity analysis Multi-objective optimization Production of electric energy or power. Powerplants. Central stations Renewable energy sources Kang Zhao verfasserin aut Yutian Liu verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 2(2014), 4, Seite 274-284 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:2 year:2014 number:4 pages:274-284 https://doi.org/10.1007/s40565-014-0081-z kostenfrei https://doaj.org/article/7493ba3e76a74d6c846c2dcbde8f0ca8 kostenfrei https://ieeexplore.ieee.org/document/9005341/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 274-284 |
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TK1001-1841 TJ807-830 Coordinated optimization for controlling short circuit current and multi-infeed DC interaction Operation and planning Multiple DC infeed Short circuit current Sensitivity analysis Multi-objective optimization |
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Coordinated optimization for controlling short circuit current and multi-infeed DC interaction |
abstract |
Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. |
abstractGer |
Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. |
abstract_unstemmed |
Due to increased penetration of renewable energies, DC links and other emerging technologies, power system operation and planning have to cope with various uncertainties and risks. In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction, a coordinated optimization method is presented in this paper. Firstly, a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix. Secondly, the impact of network structure changes on the multi-infeed DC system is derived. Then the coordinated optimization model is established, which considers the cost and effect of current limiting measures, the tightness of network structure and the voltage support capability of AC system to multiple DCs. Finally, the non-dominated sorting genetic algorithm II combining with the branch selection strategy, is used to find the Pareto optimal schemes. Case studies on a planning power system demonstrated the feasibility and speediness of this method. |
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Coordinated optimization for controlling short circuit current and multi-infeed DC interaction |
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|
score |
7.400275 |