Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 11; pp. 6058 - 6069
• Main Authors: Sun, Weigao, Zamani, Mohsen, Zhang, Hai-Tao, Li, Yuanzheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; Correlation; Gaussian model; Markov analysis; Markov chain; Markov chains; Monte Carlo simulation; Power flow; Power systems; Probabilistic logic; Probabilistic models; probabilistic optimal power flow (POPF); Probability theory; quasi-Monte Carlo (QMC); System effectiveness; Uncertainty; Wind farms; Wind power; Wind power generation; Wind speed; wind uncertainty
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2928054

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.