Stochastic unit commitment considering Markov process of wind power forecast

Nhung Nguyen-Hong, Yosuke Nakanishi

研究成果: Conference contribution

3 引用 (Scopus)

抜粋

Unit commitment (UC) is a major problem in power system operation which determines the operation schedule of the generating units by minimizing system operation cost. Because of the uncertainty of wind power, the UC problem needs to solve as a multi-period stochastic optimization. In this stochastic problem, scenarios tree is generated and may be too large to be solved when time horizon is longer. This paper presents an approach based on Maximum Entropy principle to generate and reduce scenarios by transforming a stochastic process to a finite-state Markov chain process and finding transition probability matrix. This approach is applied to transform a wind power process modeled by ARMA(1,1) model with Stochastic Volatility. A simple stochastic unit commitment is solved in this article. Because of power system security, reserve constraints also considered.

元の言語English
ホスト出版物のタイトル2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017
出版者Institute of Electrical and Electronics Engineers Inc.
ページ348-353
ページ数6
2017-January
ISBN(電子版)9781538620953
DOI
出版物ステータスPublished - 2017 12 12
イベント6th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2017 - San Diego, United States
継続期間: 2017 11 52017 11 8

Other

Other6th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2017
United States
San Diego
期間17/11/517/11/8

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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  • これを引用

    Nguyen-Hong, N., & Nakanishi, Y. (2017). Stochastic unit commitment considering Markov process of wind power forecast. : 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017 (巻 2017-January, pp. 348-353). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DISTRA.2017.8191084