干旱区生态环境知识资源中心(Arid): Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

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DOI	10.1016/j.enconman.2020.113608
	Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach
	Zhang, Guozhou; Hu, Weihao; Cao, Di; Liu, Wen; Huang, Rui; Huang, Qi; Chen, Zhe; Blaabjerg, Frede
通讯作者	Hu, WH (corresponding author), Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Sichuan Prov Key Lab Power Syst Wide Area Measure, Chengdu, Peoples R China.
来源期刊	ENERGY CONVERSION AND MANAGEMENT
ISSN	0196-8904
EISSN	1879-2227
出版年	2021
卷号	227
英文摘要	Significant dependence on fossil fuels and freshwater shortage are common problems in remote and arid regions. In this context, the operation of a wind-solar-diesel-battery-reverse osmosis hybrid energy system has become a suitable option to solve this problem. However, owing to the uncertainties of renewable energy availability and load demand, it is a challenge for operators to develop an energy management scheme for such a system. This study aims to determine a real-time dynamic energy management strategy considering the uncertainties of the system. To this end, the energy management of a hybrid energy system is presented as an optimal control objective, and multi-targets are considered along with constraints. The information entropy theory is introduced to calculate the weight factor for the trade-off between different targets. Then, a deep reinforcement learning algorithm is adopted to solve this problem and obtain the optimal control policy. Finally, the proposed method is applied to a typical hybrid energy system, and numerous data are applied to train an agent to obtain the optimal energy management policy. Simulation results demonstrate that a well-trained agent can provide a better control policy and reduce costs by up to 14.17% in comparison with other methods.
英文关键词	Hybrid energy system Energy management Information entropy theory Cost reduction Deep reinforcement learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000603342500006
WOS关键词	WATER DESALINATION SYSTEM ; PERFORMANCE EVALUATION ; SUPPLY-SYSTEM ; POWER-SYSTEM ; OPTIMIZATION ; DESIGN ; FEASIBILITY ; DEMAND ; PLANT ; PSO
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/347841
作者单位	[Zhang, Guozhou; Hu, Weihao; Cao, Di; Huang, Qi] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Sichuan Prov Key Lab Power Syst Wide Area Measure, Chengdu, Peoples R China; [Liu, Wen] Univ Utrecht, Copernicus Inst Sustainable Dev, Princetonlaan 8a, NL-3584 CB Utrecht, Netherlands; [Huang, Rui] Chongqing Inst Higher Learning Ctr Forens Sci Eng, Southwest Polit Sci & Law, Chongqing, Peoples R China; [Chen, Zhe; Blaabjerg, Frede] Aalborg Univ, Dept Energy Technol, Pontoppidanstraede 111, Aalborg, Denmark
推荐引用方式 GB/T 7714	Zhang, Guozhou,Hu, Weihao,Cao, Di,et al. Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach[J],2021,227.
APA	Zhang, Guozhou.,Hu, Weihao.,Cao, Di.,Liu, Wen.,Huang, Rui.,...&Blaabjerg, Frede.(2021).Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach.ENERGY CONVERSION AND MANAGEMENT,227.
MLA	Zhang, Guozhou,et al."Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach".ENERGY CONVERSION AND MANAGEMENT 227(2021).