Probabilistic power flow using improved Monte Carlo simulation method with correlated wind sources

Pei Bie; Buhan Zhang; Hang Li; Weisi Deng; Jiasi Wu

doi:10.1117/12.2265154

23 January 2017 Probabilistic power flow using improved Monte Carlo simulation method with correlated wind sources

Pei Bie, Buhan Zhang, Hang Li, Weisi Deng, Jiasi Wu

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 103224A (2017) https://doi.org/10.1117/12.2265154
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

Abstract

Probabilistic Power Flow (PPF) is a very useful tool for power system steady-state analysis. However, the correlation among different random injection power (like wind power) brings great difficulties to calculate PPF. Monte Carlo simulation (MCS) and analytical methods are two commonly used methods to solve PPF. MCS has high accuracy but is very time consuming. Analytical method like cumulants method (CM) has high computing efficiency but the cumulants calculating is not convenient when wind power output does not obey any typical distribution, especially when correlated wind sources are considered. In this paper, an Improved Monte Carlo simulation method (IMCS) is proposed. The joint empirical distribution is applied to model different wind power output. This method combines the advantages of both MCS and analytical method. It not only has high computing efficiency, but also can provide solutions with enough accuracy, which is very suitable for on-line analysis.

Citation Download Citation

Pei Bie, Buhan Zhang, Hang Li, Weisi Deng, and Jiasi Wu "Probabilistic power flow using improved Monte Carlo simulation method with correlated wind sources", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103224A (23 January 2017); https://doi.org/10.1117/12.2265154

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