Volume 9, Issue 3 (10-2019)                   JEM 2019, 9(3): 72-85 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Fattahi H, Abdi H, Khosravi F, Karimi S. Applying Point Estimation and Monte Carlo Simulation Methods in Solving Probabilistic Optimal Power Flow Considering Renewable Energy Uncertainties. JEM. 2019; 9 (3) :72-85
URL: http://energy.kashanu.ac.ir/article-1-1015-en.html
Razi University
The increasing penetration of renewable energy results in the change of the traditional power system planning and operation tools. As the generated power by the renewable energy resources are probabilistically changed, the certain power system analysis toll cannot be applied in this case. The probabilistic optimal power flow is one of the most useful tools regarding the power system analysis in the presence of uncertainties. In this paper, Monte Carlo simulation and point estimation methods are used to solve the POPF in the presence of wind and solar sources uncertainties. These methods are simulated on the PEGASE 89–bus European system. The most important novelty of this paper is arising from the comparison of detailed studies of point estimation methods with the Monte Carlo simulation method. As the obtained results confirm, the point estimation methods lead to an increase in the computing time efficiency in comparison to the Monte Carlo simulation method. Also, an increase in the number of sampling points in PEMs will result in an increase in the accuracy of the obtained results, while the computing time is still lower than the Monte Carlo simulation method.
Type of Study: Research | Subject: Electrical Engineering

Add your comments about this article : Your username or Email:

© 2020 All Rights Reserved | Energy Engineering & Management

Designed & Developed by : Yektaweb