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Scenario-based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties
Li HP(李鹤鹏); Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌); Li ZW(李忠文)
Department工业控制网络与系统研究室
Conference Name2014 2nd International Conference on Renewable Energy and Environmental Technology (REET 2014)
Conference DateAugust 19-20, 2014
Conference PlaceDalian, China
Source PublicationApplied Mechanics and Materials
PublisherTrans Tech Publications
Publication PlaceZurich-Durnten, Switzerland
2014
Pages1322-1328
Indexed ByEI
EI Accession number20150300434193
Contribution Rank1
ISBN1662-7482
KeywordMicrogrid Energy Scheduling Optimization Stochastic Programming Scenarios Generation
AbstractThe inherent random and intermittence of the renewable energy resources pose a huge challenge to the Microgrid (MG) energy management systems (EMS). In order to mitigate the effects of uncertainties, we propose a novel two-stage stochastic programming model for the energy scheduling optimization by considering the uncertainties in solar and wind generation, and the plug-in electric vehicles (EV). The random nature of uncertainty is characterized by a scenarios generation approach based on autoregressive moving average (ARMA) model according to probability density function of each random variable. By use of the strategy of scenarios simulation, the stochastic problem is decomposed into the deterministic equivalent problem. The firefly algorithm (FA) is used to solve the equivalent model. The effectiveness and robust of proposed stochastic energy scheduling optimization strategy for MG is valid by comparison with the simulation results of deterministic method.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/15365
Collection工业控制网络与系统研究室
Corresponding AuthorLi HP(李鹤鹏)
Affiliation1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Li HP,Zang CZ,Zeng P,et al. Scenario-based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties[C]. Zurich-Durnten, Switzerland:Trans Tech Publications,2014:1322-1328.
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