Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm

The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objec...
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Autor*in:	Zheng Zhao [verfasserIn] Kuan Zheng [verfasserIn] Yong Xing [verfasserIn] Jinpu Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions

Übergeordnetes Werk:	In: Energy Reports - Elsevier, 2016, 9(2023), Seite 529-534
Übergeordnetes Werk:	volume:9 ; year:2023 ; pages:529-534

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.egyr.2023.09.105

Katalog-ID:	DOAJ096685093

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520			\|a The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology.
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653		0	\|a Electrical engineering. Electronics. Nuclear engineering
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700	0		\|a Yong Xing \|e verfasserin \|4 aut
700	0		\|a Jinpu Yu \|e verfasserin \|4 aut
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allfields	10.1016/j.egyr.2023.09.105 doi (DE-627)DOAJ096685093 (DE-599)DOAJbaf2bc3107f54e578225b2a3fd1a35ae DE-627 ger DE-627 rakwb eng TK1-9971 Zheng Zhao verfasserin aut Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology. DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering Kuan Zheng verfasserin aut Yong Xing verfasserin aut Jinpu Yu verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 529-534 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:529-534 https://doi.org/10.1016/j.egyr.2023.09.105 kostenfrei https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723013501 kostenfrei https://doaj.org/toc/2352-4847 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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 9 2023 529-534
spelling	10.1016/j.egyr.2023.09.105 doi (DE-627)DOAJ096685093 (DE-599)DOAJbaf2bc3107f54e578225b2a3fd1a35ae DE-627 ger DE-627 rakwb eng TK1-9971 Zheng Zhao verfasserin aut Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology. DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering Kuan Zheng verfasserin aut Yong Xing verfasserin aut Jinpu Yu verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 529-534 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:529-534 https://doi.org/10.1016/j.egyr.2023.09.105 kostenfrei https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723013501 kostenfrei https://doaj.org/toc/2352-4847 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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 9 2023 529-534
allfields_unstemmed	10.1016/j.egyr.2023.09.105 doi (DE-627)DOAJ096685093 (DE-599)DOAJbaf2bc3107f54e578225b2a3fd1a35ae DE-627 ger DE-627 rakwb eng TK1-9971 Zheng Zhao verfasserin aut Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology. DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering Kuan Zheng verfasserin aut Yong Xing verfasserin aut Jinpu Yu verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 529-534 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:529-534 https://doi.org/10.1016/j.egyr.2023.09.105 kostenfrei https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723013501 kostenfrei https://doaj.org/toc/2352-4847 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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 9 2023 529-534
allfieldsGer	10.1016/j.egyr.2023.09.105 doi (DE-627)DOAJ096685093 (DE-599)DOAJbaf2bc3107f54e578225b2a3fd1a35ae DE-627 ger DE-627 rakwb eng TK1-9971 Zheng Zhao verfasserin aut Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology. DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering Kuan Zheng verfasserin aut Yong Xing verfasserin aut Jinpu Yu verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 529-534 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:529-534 https://doi.org/10.1016/j.egyr.2023.09.105 kostenfrei https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723013501 kostenfrei https://doaj.org/toc/2352-4847 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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 9 2023 529-534
allfieldsSound	10.1016/j.egyr.2023.09.105 doi (DE-627)DOAJ096685093 (DE-599)DOAJbaf2bc3107f54e578225b2a3fd1a35ae DE-627 ger DE-627 rakwb eng TK1-9971 Zheng Zhao verfasserin aut Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology. DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering Kuan Zheng verfasserin aut Yong Xing verfasserin aut Jinpu Yu verfasserin aut In Energy Reports Elsevier, 2016 9(2023), Seite 529-534 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:9 year:2023 pages:529-534 https://doi.org/10.1016/j.egyr.2023.09.105 kostenfrei https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484723013501 kostenfrei https://doaj.org/toc/2352-4847 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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 9 2023 529-534
language	English
source	In Energy Reports 9(2023), Seite 529-534 volume:9 year:2023 pages:529-534
sourceStr	In Energy Reports 9(2023), Seite 529-534 volume:9 year:2023 pages:529-534
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Energy Reports
authorswithroles_txt_mv	Zheng Zhao @@aut@@ Kuan Zheng @@aut@@ Yong Xing @@aut@@ Jinpu Yu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	820689033
id	DOAJ096685093
language_de	englisch
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callnumber-first	T - Technology
author	Zheng Zhao
spellingShingle	Zheng Zhao misc TK1-9971 misc DC distribution network misc DG misc BESS misc Optimal locating and sizing misc Multi-objective optimization misc Pareto optimal solutions misc Electrical engineering. Electronics. Nuclear engineering Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm
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topic_title	TK1-9971 Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm DC distribution network DG BESS Optimal locating and sizing Multi-objective optimization Pareto optimal solutions
topic	misc TK1-9971 misc DC distribution network misc DG misc BESS misc Optimal locating and sizing misc Multi-objective optimization misc Pareto optimal solutions misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc DC distribution network misc DG misc BESS misc Optimal locating and sizing misc Multi-objective optimization misc Pareto optimal solutions misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc DC distribution network misc DG misc BESS misc Optimal locating and sizing misc Multi-objective optimization misc Pareto optimal solutions misc Electrical engineering. Electronics. Nuclear engineering
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title	Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm
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title_full	Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm
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title_sort	optimal planning of distributed generation and energy storage systems in dc distribution networks with application of category-based multi-objective algorithm
callnumber	TK1-9971
title_auth	Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm
abstract	The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology.
abstractGer	The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology.
abstract_unstemmed	The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology.
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title_short	Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm
url	https://doi.org/10.1016/j.egyr.2023.09.105 https://doaj.org/article/baf2bc3107f54e578225b2a3fd1a35ae http://www.sciencedirect.com/science/article/pii/S2352484723013501 https://doaj.org/toc/2352-4847
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up_date	2024-07-03T21:32:48.796Z
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Optimal planning of distributed generation and energy storage systems in DC distribution networks with application of category-based multi-objective algorithm

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