Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach

With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e...
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Gespeichert in:

Autor*in:	Su, Shuai [verfasserIn] Wang, Xuekai [verfasserIn] Tang, Tao [verfasserIn] Wang, Guang [verfasserIn] Cao, Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning

Übergeordnetes Werk:	Enthalten in: Control engineering practice - Amsterdam [u.a.] : Elsevier Science, 1993, 116
Übergeordnetes Werk:	volume:116

DOI / URN:	10.1016/j.conengprac.2021.104901

Katalog-ID:	ELV006664431

Internformat


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245	1	0	\|a Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
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520			\|a With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized.
650		4	\|a Energy saving
650		4	\|a Cooperative control
650		4	\|a Regenerative energy
650		4	\|a Multi-agent reinforcement learning
700	1		\|a Wang, Xuekai \|e verfasserin \|4 aut
700	1		\|a Tang, Tao \|e verfasserin \|4 aut
700	1		\|a Wang, Guang \|e verfasserin \|4 aut
700	1		\|a Cao, Yuan \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Control engineering practice \|d Amsterdam [u.a.] : Elsevier Science, 1993 \|g 116 \|h Online-Ressource \|w (DE-627)306716119 \|w (DE-600)1501351-0 \|w (DE-576)259271012 \|x 1873-6939 \|7 nnns
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936	b	k	\|a 50.23 \|j Regelungstechnik \|j Steuerungstechnik
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Indexfelder

author_variant	s s ss x w xw t t tt g w gw y c yc
matchkey_str	article:18736939:2021----::nryfiinoeainyoprtvcnrlmntanautaete
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publishDate	2021
allfields	10.1016/j.conengprac.2021.104901 doi (DE-627)ELV006664431 (ELSEVIER)S0967-0661(21)00178-7 DE-627 ger DE-627 rda eng 620 DE-600 50.23 bkl Su, Shuai verfasserin aut Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized. Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning Wang, Xuekai verfasserin aut Tang, Tao verfasserin aut Wang, Guang verfasserin aut Cao, Yuan verfasserin aut Enthalten in Control engineering practice Amsterdam [u.a.] : Elsevier Science, 1993 116 Online-Ressource (DE-627)306716119 (DE-600)1501351-0 (DE-576)259271012 1873-6939 nnns volume:116 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.23 Regelungstechnik Steuerungstechnik AR 116
spelling	10.1016/j.conengprac.2021.104901 doi (DE-627)ELV006664431 (ELSEVIER)S0967-0661(21)00178-7 DE-627 ger DE-627 rda eng 620 DE-600 50.23 bkl Su, Shuai verfasserin aut Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized. Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning Wang, Xuekai verfasserin aut Tang, Tao verfasserin aut Wang, Guang verfasserin aut Cao, Yuan verfasserin aut Enthalten in Control engineering practice Amsterdam [u.a.] : Elsevier Science, 1993 116 Online-Ressource (DE-627)306716119 (DE-600)1501351-0 (DE-576)259271012 1873-6939 nnns volume:116 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.23 Regelungstechnik Steuerungstechnik AR 116
allfields_unstemmed	10.1016/j.conengprac.2021.104901 doi (DE-627)ELV006664431 (ELSEVIER)S0967-0661(21)00178-7 DE-627 ger DE-627 rda eng 620 DE-600 50.23 bkl Su, Shuai verfasserin aut Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized. Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning Wang, Xuekai verfasserin aut Tang, Tao verfasserin aut Wang, Guang verfasserin aut Cao, Yuan verfasserin aut Enthalten in Control engineering practice Amsterdam [u.a.] : Elsevier Science, 1993 116 Online-Ressource (DE-627)306716119 (DE-600)1501351-0 (DE-576)259271012 1873-6939 nnns volume:116 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.23 Regelungstechnik Steuerungstechnik AR 116
allfieldsGer	10.1016/j.conengprac.2021.104901 doi (DE-627)ELV006664431 (ELSEVIER)S0967-0661(21)00178-7 DE-627 ger DE-627 rda eng 620 DE-600 50.23 bkl Su, Shuai verfasserin aut Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized. Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning Wang, Xuekai verfasserin aut Tang, Tao verfasserin aut Wang, Guang verfasserin aut Cao, Yuan verfasserin aut Enthalten in Control engineering practice Amsterdam [u.a.] : Elsevier Science, 1993 116 Online-Ressource (DE-627)306716119 (DE-600)1501351-0 (DE-576)259271012 1873-6939 nnns volume:116 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.23 Regelungstechnik Steuerungstechnik AR 116
allfieldsSound	10.1016/j.conengprac.2021.104901 doi (DE-627)ELV006664431 (ELSEVIER)S0967-0661(21)00178-7 DE-627 ger DE-627 rda eng 620 DE-600 50.23 bkl Su, Shuai verfasserin aut Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized. Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning Wang, Xuekai verfasserin aut Tang, Tao verfasserin aut Wang, Guang verfasserin aut Cao, Yuan verfasserin aut Enthalten in Control engineering practice Amsterdam [u.a.] : Elsevier Science, 1993 116 Online-Ressource (DE-627)306716119 (DE-600)1501351-0 (DE-576)259271012 1873-6939 nnns volume:116 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.23 Regelungstechnik Steuerungstechnik AR 116
language	English
source	Enthalten in Control engineering practice 116 volume:116
sourceStr	Enthalten in Control engineering practice 116 volume:116
format_phy_str_mv	Article
bklname	Regelungstechnik Steuerungstechnik
institution	findex.gbv.de
topic_facet	Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning
dewey-raw	620
isfreeaccess_bool	false
container_title	Control engineering practice
authorswithroles_txt_mv	Su, Shuai @@aut@@ Wang, Xuekai @@aut@@ Tang, Tao @@aut@@ Wang, Guang @@aut@@ Cao, Yuan @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	306716119
dewey-sort	3620
id	ELV006664431
language_de	englisch
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author	Su, Shuai
spellingShingle	Su, Shuai ddc 620 bkl 50.23 misc Energy saving misc Cooperative control misc Regenerative energy misc Multi-agent reinforcement learning Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
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topic_title	620 DE-600 50.23 bkl Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach Energy saving Cooperative control Regenerative energy Multi-agent reinforcement learning
topic	ddc 620 bkl 50.23 misc Energy saving misc Cooperative control misc Regenerative energy misc Multi-agent reinforcement learning
topic_unstemmed	ddc 620 bkl 50.23 misc Energy saving misc Cooperative control misc Regenerative energy misc Multi-agent reinforcement learning
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title	Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
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title_full	Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
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title_sort	energy-efficient operation by cooperative control among trains: a multi-agent reinforcement learning approach
title_auth	Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
abstract	With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized.
abstractGer	With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized.
abstract_unstemmed	With the ever-increasing operating mileage of urban rail transit systems, a large amount of energy is consumed for train operation, and the energy-saving for train operation has become an attractive topic in the research community in recent years. In order to minimize the net energy consumption (i.e., the difference between the traction energy and the reused regenerative energy), an energy-efficient train operation method using the cooperative control approach is proposed in this paper. Firstly, a set of mathematical models for the cooperative control are formulated with considering the transmission mechanism of regenerative energy. Then, a multi-agent reinforcement learning algorithm is designed to obtain the cooperative driving strategy for trains. In this algorithm, the global Q-function is decomposed into several local Q-functions by using value function factorization method. A proposed update method is applied to update the local Q-functions according to the transmission mechanism of regenerative energy. Finally, the case studies built upon a metro line is performed to illustrate the effectiveness of the proposed method. According to the simulation results, the net energy consumption is reduced by more than 11.1% compared to the method in which the driving strategy of each train is independently optimized.
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title_short	Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach
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author2	Wang, Xuekai Tang, Tao Wang, Guang Cao, Yuan
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doi_str	10.1016/j.conengprac.2021.104901
up_date	2024-07-06T22:08:50.465Z
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Energy-efficient operation by cooperative control among trains: A multi-agent reinforcement learning approach

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