A Probabilistic Load Flow Method Based on Markov Chain Wind Power Prediction

Abstract:

With the wind power installed capacity increased, it brings great challenges to the stable operation of the power grid. In order to take consideration of the wind power impact, this paper proposes a wind power variability model applied to the probabilistic load flow calculation using the method of combined cumulants and improved Von-Mises Expansion. This wind power variability model combines the concept of Markov chain process and scenario tree theory, taking in to account the relationships of the outputs of wind turbines at adjacent moment by considering the coupling interaction at different time intervals. Computer simulations verify the effectiveness and applicability of the proposed method through IEEE bus-39 New England case study.

• Periodical: Advanced Materials Research (Volumes 953-954)
• Pages: 561-564
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.953-954.561
• Online since: June 2014
• Authors: Wei Si Deng, Bu Han Zhang, Hong Fa Ding, Ke Wang, Dan Zeng, Kai Min Zhang, Jian Shao, Teng Yu Ge, Bing Jie Jin
• Keywords: Cumulant Method, Markov Chain, Probabilistic Load Flow, Von-Mises Expansion, Wind Power Prediction

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