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Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty
Li ZW(李忠文); Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌)
Department工业控制网络与系统研究室
Source PublicationENERGIES
ISSN1996-1073
2016
Volume9Issue:7Pages:1-16
Indexed BySCI ; EI
EI Accession number20173304051960
WOS IDWOS:000381497300022
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (NSFC) [61100159, 61233007] ; National High Technology Research and Development Program of China (863 Program) [2011AA040103] ; Foundation of Chinese Academy of Sciences [KGCX2-EW-104] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA06021100] ; Cross-disciplinary Collaborative Teams Program for Science, Technology and Innovation, of the Chinese Academy of Sciences Network ; system technologies for security monitoring and information interaction in smart grid, and energy management system for micro-smart grid
KeywordEnergy Management Microgrid Recording Horizon Control Stochastic Programming Uncertainty
AbstractMicrogrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectEnergy & Fuels
WOS KeywordMODEL-PREDICTIVE CONTROL ; ELECTRIC-POWER SYSTEMS ; ROBUST OPTIMIZATION ; OPTIMAL OPERATION ; STORAGE ; DEMAND ; GENERATION ; FRAMEWORK ; DISPATCH
WOS Research AreaEnergy & Fuels
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/19203
Collection工业控制网络与系统研究室
Corresponding AuthorYu HB(于海斌)
Affiliation1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Li ZW,Zang CZ,Zeng P,et al. Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty[J]. ENERGIES,2016,9(7):1-16.
APA Li ZW,Zang CZ,Zeng P,&Yu HB.(2016).Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty.ENERGIES,9(7),1-16.
MLA Li ZW,et al."Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty".ENERGIES 9.7(2016):1-16.
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