Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management
	Boaro, Matteo 1; Fuselli, Danilo 1; De Angelis, Francesco 1; Liu, Derong 2; Wei, Qinglai2 ; Piazza, Francesco 1
发表期刊	COGNITIVE COMPUTATION
	2013-06-01
卷号	5 期号:2 页码:264-277
文章类型	Article
摘要	The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and power imported from the grid, an intelligent and adaptive system which manages the battery is able to satisfy the load demand and minimize the entire energy cost related to the scenario under study. In this paper, an adaptive dynamic programming-based algorithm is presented to face dynamic situations, in which some conditions of the environment or habits of customer may vary with time, especially using renewable energy. Based on the idea of smart grid, we propose an intelligent management scheme for renewable resources combined with battery implemented with a faster and simpler scheme of dynamic programming, by considering only one critic network and some optimization policies in order to satisfy the load demand. Since this kind of problem is suitable to avoid the training of an action network, the training loop among the two neural networks is deleted and the training process is greatly simplified. Computer simulations confirm the effectiveness of this self-learning design in a typical residential scenario.
关键词	Adaptive Dynamic Programming Approximate Dynamic Programming Neural Networks Energy Scheduling Battery Management
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
关键词[WOS]	PARTICLE SWARM OPTIMIZATION ; TIME NONLINEAR-SYSTEMS ; STORAGE SYSTEM ; CRITIC DESIGNS ; WIND ; SMART
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000318648900012
引用统计	被引频次：64[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3828
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
作者单位	1.Univ Politecn Marche, Dipartimento Ingn Informaz, I-60131 Ancona, Italy 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Boaro, Matteo,Fuselli, Danilo,De Angelis, Francesco,et al. Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management[J]. COGNITIVE COMPUTATION,2013,5(2):264-277.
APA	Boaro, Matteo,Fuselli, Danilo,De Angelis, Francesco,Liu, Derong,Wei, Qinglai,&Piazza, Francesco.(2013).Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management.COGNITIVE COMPUTATION,5(2),264-277.
MLA	Boaro, Matteo,et al."Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management".COGNITIVE COMPUTATION 5.2(2013):264-277.