Real-time energy management strategy for flexible traction power supply system

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in...
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Gespeichert in:

Autor*in:	Zhang, Shanshan [verfasserIn] Yang, Shaobing [verfasserIn] He, Tingting [verfasserIn] Liu, Qiujiang [verfasserIn] Wu, Mingli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system

Übergeordnetes Werk:	Enthalten in: International journal of electrical power & energy systems - Amsterdam [u.a.] : Elsevier Science, 1979, 156
Übergeordnetes Werk:	volume:156

DOI / URN:	10.1016/j.ijepes.2023.109768

Katalog-ID:	ELV06677036X

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520			\|a Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.
650		4	\|a Energy management strategy
650		4	\|a Hybrid energy storage system
650		4	\|a Lyapunov optimization technique
650		4	\|a Stochastic optimization
650		4	\|a Traction power supply system
700	1		\|a Yang, Shaobing \|e verfasserin \|4 aut
700	1		\|a He, Tingting \|e verfasserin \|4 aut
700	1		\|a Liu, Qiujiang \|e verfasserin \|4 aut
700	1		\|a Wu, Mingli \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of electrical power & energy systems \|d Amsterdam [u.a.] : Elsevier Science, 1979 \|g 156 \|h Online-Ressource \|w (DE-627)320411907 \|w (DE-600)2001425-9 \|w (DE-576)259271101 \|x 0142-0615 \|7 nnns
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936	b	k	\|a 53.30 \|j Elektrische Energietechnik: Allgemeines \|q VZ
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allfields	10.1016/j.ijepes.2023.109768 doi (DE-627)ELV06677036X (ELSEVIER)S0142-0615(23)00825-6 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Zhang, Shanshan verfasserin (orcid)0000-0003-2270-9865 aut Real-time energy management strategy for flexible traction power supply system 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system Yang, Shaobing verfasserin aut He, Tingting verfasserin aut Liu, Qiujiang verfasserin aut Wu, Mingli verfasserin aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 156 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:156 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_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_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 156
spelling	10.1016/j.ijepes.2023.109768 doi (DE-627)ELV06677036X (ELSEVIER)S0142-0615(23)00825-6 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Zhang, Shanshan verfasserin (orcid)0000-0003-2270-9865 aut Real-time energy management strategy for flexible traction power supply system 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system Yang, Shaobing verfasserin aut He, Tingting verfasserin aut Liu, Qiujiang verfasserin aut Wu, Mingli verfasserin aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 156 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:156 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_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_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 156
allfields_unstemmed	10.1016/j.ijepes.2023.109768 doi (DE-627)ELV06677036X (ELSEVIER)S0142-0615(23)00825-6 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Zhang, Shanshan verfasserin (orcid)0000-0003-2270-9865 aut Real-time energy management strategy for flexible traction power supply system 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system Yang, Shaobing verfasserin aut He, Tingting verfasserin aut Liu, Qiujiang verfasserin aut Wu, Mingli verfasserin aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 156 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:156 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_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_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 156
allfieldsGer	10.1016/j.ijepes.2023.109768 doi (DE-627)ELV06677036X (ELSEVIER)S0142-0615(23)00825-6 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Zhang, Shanshan verfasserin (orcid)0000-0003-2270-9865 aut Real-time energy management strategy for flexible traction power supply system 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system Yang, Shaobing verfasserin aut He, Tingting verfasserin aut Liu, Qiujiang verfasserin aut Wu, Mingli verfasserin aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 156 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:156 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_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_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 156
allfieldsSound	10.1016/j.ijepes.2023.109768 doi (DE-627)ELV06677036X (ELSEVIER)S0142-0615(23)00825-6 DE-627 ger DE-627 rda eng 620 VZ 53.30 bkl Zhang, Shanshan verfasserin (orcid)0000-0003-2270-9865 aut Real-time energy management strategy for flexible traction power supply system 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system Yang, Shaobing verfasserin aut He, Tingting verfasserin aut Liu, Qiujiang verfasserin aut Wu, Mingli verfasserin aut Enthalten in International journal of electrical power & energy systems Amsterdam [u.a.] : Elsevier Science, 1979 156 Online-Ressource (DE-627)320411907 (DE-600)2001425-9 (DE-576)259271101 0142-0615 nnns volume:156 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_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_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 53.30 Elektrische Energietechnik: Allgemeines VZ AR 156
language	English
source	Enthalten in International journal of electrical power & energy systems 156 volume:156
sourceStr	Enthalten in International journal of electrical power & energy systems 156 volume:156
format_phy_str_mv	Article
bklname	Elektrische Energietechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system
dewey-raw	620
isfreeaccess_bool	false
container_title	International journal of electrical power & energy systems
authorswithroles_txt_mv	Zhang, Shanshan @@aut@@ Yang, Shaobing @@aut@@ He, Tingting @@aut@@ Liu, Qiujiang @@aut@@ Wu, Mingli @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	320411907
dewey-sort	3620
id	ELV06677036X
language_de	englisch
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author	Zhang, Shanshan
spellingShingle	Zhang, Shanshan ddc 620 bkl 53.30 misc Energy management strategy misc Hybrid energy storage system misc Lyapunov optimization technique misc Stochastic optimization misc Traction power supply system Real-time energy management strategy for flexible traction power supply system
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topic_title	620 VZ 53.30 bkl Real-time energy management strategy for flexible traction power supply system Energy management strategy Hybrid energy storage system Lyapunov optimization technique Stochastic optimization Traction power supply system
topic	ddc 620 bkl 53.30 misc Energy management strategy misc Hybrid energy storage system misc Lyapunov optimization technique misc Stochastic optimization misc Traction power supply system
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title	Real-time energy management strategy for flexible traction power supply system
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title_full	Real-time energy management strategy for flexible traction power supply system
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title_sort	real-time energy management strategy for flexible traction power supply system
title_auth	Real-time energy management strategy for flexible traction power supply system
abstract	Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.
abstractGer	Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.
abstract_unstemmed	Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.
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title_short	Real-time energy management strategy for flexible traction power supply system
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author2	Yang, Shaobing He, Tingting Liu, Qiujiang Wu, Mingli
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up_date	2024-07-06T18:56:05.121Z
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Real-time energy management strategy for flexible traction power supply system

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