- Title
- Probabilistic optimal power flow with correlated wind power uncertainty via Markov Chain Quasi-Monte-Carlo sampling
- Creator
- Sun, Weigao; Zamani, Mohsen; Zhang, Hai-Tao; Li, Yuanzheng
- Relation
- IEEE Transactions on Industrial Informatics Vol. 15, Issue 11, p. 6058-6069
- Publisher Link
- http://dx.doi.org/10.1109/TII.2019.2928054
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2019
- Description
- The irregular and truncated probabilistic characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this article, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such uncertainties, while taking into account the correlations among the wind generation power in multiple wind farms. A truncated multivariate Gaussian mixture model (Trun-MultiGMM) is designed to describe the irregular and multimodal wind power distributions with its typical truncation feature. Then an efficient Markov chain quasi-Monte-Carlo (MCQMC) sampler is developed to deliver wind power samples from the customized Trun-MultiGMM. Numerical simulations are conducted on the publicly available wind generation datasets and multiple benchmark power systems. The results have verified the effectiveness and efficiency of Trun-MultiGMM as well as the proposed POPF framework with MCQMC sampler.
- Subject
- Gaussian model; Markov chain; probabilistic optimal power flow (POPF); quasi-Monte Carlo (QMC); wind uncertainty
- Identifier
- http://hdl.handle.net/1959.13/1424554
- Identifier
- uon:38103
- Identifier
- ISSN:1551-3203
- Language
- eng
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