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 |
Source Publication | IEEE Access
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ISSN | 2169-3536 |
2020 | |
Volume | 8Pages:12467-12478 |
Indexed By | SCI ; EI |
EI Accession number | 20200608122024 |
WOS ID | WOS:000525409100052 |
Contribution Rank | 3 |
Funding Organization | National Natural Science Foundation of China under Grant 51677067 ; Fundamental Research Funds for the Central Universities under Grant 2018MS27 |
Keyword | Bayesian 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 Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS Keyword | SPEED ; PREDICTION ; MODEL |
WOS Research Area | Computer Science ; Engineering ; Telecommunications |
Funding Project | National Natural Science Foundation of China[51677067] ; Fundamental Research Funds for the Central Universities[2018MS27] |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/26227 |
Collection | 工业控制网络与系统研究室 |
Corresponding Author | Yang, Yuwei |
Affiliation | 1.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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A reliability assess(2867KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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