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A reliability assessment approach for electric power systems considering wind power uncertainty
Yang, Xiyun1,2; Yang, Yuwei1; Liu, YQ(刘玉琦)3; Deng, Ziqi1
Department工业控制网络与系统研究室
Corresponding AuthorYang, Yuwei(18511791255@163.com)
Source PublicationIEEE Access
ISSN2169-3536
2020
Volume8Pages:12467-12478
Indexed BySCI ; EI
EI Accession number20200608122024
WOS IDWOS:000525409100052
Contribution Rank3
Funding OrganizationNational Natural Science Foundation of China under Grant 51677067 ; Fundamental Research Funds for the Central Universities under Grant 2018MS27
KeywordBayesian estimation interval prediction reliability index sequential Monte Carlo method
Abstract

The intermittence and uncertainty of wind power pose challenges to large-scale wind power grid integration. The study of wind power uncertainty is becoming increasingly important for power system planning and operation. This paper proposes a wind power probabilistic interval prediction model, and a novel reliability assessment approach is presented for electrical power systems. First, the unknown parameters estimation of the autoregressive integrated moving average (ARIMA) prediction model is based on the Markov chain Monte Carlo (MCMC)-based Bayesian estimation method to improve the quality of statistical inference. Then, a quantum genetic algorithm is used to segment the power to determine the best output for each power segment weight and calculate the probabilistic prediction interval of wind power. Finally, reliability assessment by the sequential Monte Carlo simulation is presented combining with the probabilistic prediction interval of wind power on IEEE-RTS79 reliability test system. The simulation results that proposed variation range of reliability assessment indices consider the uncertain scenario of wind power and has guiding significance for power generation scheduling. Compared with genetic algorithm and particle swarm optimization algorithm, it is proved that the proposed prediction interval model has better prediction interval coverage probability index and interval average bandwidth index. 

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordSPEED ; PREDICTION ; MODEL
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Natural Science Foundation of China[51677067] ; Fundamental Research Funds for the Central Universities[2018MS27]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26227
Collection工业控制网络与系统研究室
Corresponding AuthorYang, Yuwei
Affiliation1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2.Key Lab. of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Beijing 102206, China;
3.Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Yang, Xiyun,Yang, Yuwei,Liu, YQ,et al. A reliability assessment approach for electric power systems considering wind power uncertainty[J]. IEEE Access,2020,8:12467-12478.
APA Yang, Xiyun,Yang, Yuwei,Liu, YQ,&Deng, Ziqi.(2020).A reliability assessment approach for electric power systems considering wind power uncertainty.IEEE Access,8,12467-12478.
MLA Yang, Xiyun,et al."A reliability assessment approach for electric power systems considering wind power uncertainty".IEEE Access 8(2020):12467-12478.
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