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Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids
Wei, Qinglai1,2; Liu, Derong3; Lewis, Frank L.4; Liu, Yu5; Zhang, Jie1,6
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
2017-05-01
Volume64Issue:5Pages:4110-4120
SubtypeArticle
AbstractIn this paper, a novel mixed iterative adaptive dynamic programming (ADP) algorithm is developed to solve the optimal battery energy management and control problem in smart residential microgrid systems. Based on the data of the load and electricity rate, two iterations are constructed, which are P-iteration and V-iteration, respectively. The V-iteration is implemented based on value iteration, which aims to obtain the iterative control law sequence in each period. The P-iteration is implemented based on policy iteration, which updates the iterative value function according to the iterative control law sequence. Properties of the developed mixed iterative ADP algorithm are analyzed. It is shown that the iterative value function is monotonically nonincreasing and converges to the solution of the Bellman equation. In each iteration, it is proven that the performance index function is finite under the iterative control law sequence. Finally, numerical results and comparisons are given to illustrate the performance of the developed algorithm.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Mixed Iteration Optimal Control Policy Iteration Smart Grid Value Iteration
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIE.2017.2650872
WOS KeywordTIME NONLINEAR-SYSTEMS ; OPTIMAL TRACKING CONTROL ; NEURAL-NETWORK ; LEARNING ALGORITHM ; FEEDBACK-CONTROL ; STORAGE SYSTEM ; CONTROL SCHEME ; REINFORCEMENT ; MANAGEMENT ; DESIGN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61233001 ; Open Research Project from SKLMCCS(20150104) ; 61273140 ; 61374105 ; 61379099 ; 61304079 ; 61673054 ; 61533017 ; 71402178 ; 61533019 ; 71232006 ; U1501251)
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000399674000065
Citation statistics
Cited Times:39[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13637
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
4.Univ Texas Arlington, UTA Res Inst, Arlington, TX 76118 USA
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
6.Qingdao Acad Intelligent Ind, Qingdao 266000, Shandong, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wei, Qinglai,Liu, Derong,Lewis, Frank L.,et al. Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2017,64(5):4110-4120.
APA Wei, Qinglai,Liu, Derong,Lewis, Frank L.,Liu, Yu,&Zhang, Jie.(2017).Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,64(5),4110-4120.
MLA Wei, Qinglai,et al."Mixed Iterative Adaptive Dynamic Programming for Optimal Battery Energy Control in Smart Residential Microgrids".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 64.5(2017):4110-4120.
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