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Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties
Li ZW(李忠文); Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌); Li HP(李鹤鹏)
Department工业控制网络与系统研究室
Conference NameInternational Conference on Probabilistic Methods Applied to Power Systems
Conference DateOctober 16-20, 2016
Conference PlaceBeijing
Source Publication2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
PublisherIEEE
Publication PlaceNew York
2016
Pages1-6
Indexed ByEI ; CPCI(ISTP)
EI Accession number20171203453752
WOS IDWOS:000392327900032
Contribution Rank1
ISBN978-1-5090-1971-7
KeywordEnergy Management Microgrid Model Predictive Control Stochastic Programming Uncertainty
AbstractMicrogrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19515
Collection工业控制网络与系统研究室
Corresponding AuthorLi ZW(李忠文)
AffiliationLab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Li ZW,Zang CZ,Zeng P,et al. Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties[C]. New York:IEEE,2016:1-6.
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