23 January 2017 Probabilistic power flow using improved Monte Carlo simulation method with correlated wind sources
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pei Bie, Pei Bie, Buhan Zhang, Buhan Zhang, Hang Li, Hang Li, Weisi Deng, Weisi Deng, Jiasi Wu, 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); doi: 10.1117/12.2265154; https://doi.org/10.1117/12.2265154
PROCEEDINGS
6 PAGES


SHARE
RELATED CONTENT

Target modelling for SAR image simulation
Proceedings of SPIE (October 20 2014)
Spatial stochastic models for seabed object detection
Proceedings of SPIE (July 21 1997)
Target identification with Bayesian networks
Proceedings of SPIE (April 02 2000)
Statistical tolerancing for optics
Proceedings of SPIE (August 18 1996)

Back to Top