Determining the risk operation states of power systems in the presence of wind power plants

Author

Razusi, Petre-Cristian ; Eremia, Mircea ; Miranda, V.

Author_Institution

Power Syst. Dept., Univ. Politeh. of Bucharest, Bucharest, Romania

fYear

2013

fDate

16-20 June 2013

Firstpage

1

Lastpage

6

Abstract

The power produced by wind power plants has an extremely random character due to the intermittency of wind. This leads to problems in balancing the power production and demand in the power systems. To overcome this problem, wind power forecast is used. However, as in any prediction tasks, wind power forecasting does not offer perfect results. It is the purpose of this paper to propose a method based on Monte Carlo simulations and artificial intelligence techniques to assess the impact of the deviation of the generated wind power from the predicted values on the power systems when no corrective measures are taken. The method is tested on an IEEE network as well as on a real electric network from the Romanian power system and the results and drawn conclusions are presented here.

Keywords

Monte Carlo methods; artificial intelligence; load forecasting; neural nets; power engineering computing; wind power plants; IEEE network; Monte Carlo simulations; Romania; artificial intelligence; power systems; risk operation states; wind power forecasting; wind power plants; Artificial intelligence; Artificial neural networks; Monte Carlo methods; Neurons; Power demand; Wind power generation; Monte Carlo simulations; artificial neural networks; fuzzy systems; wind power;

fLanguage

English

Publisher

ieee

Conference_Titel

PowerTech (POWERTECH), 2013 IEEE Grenoble

Conference_Location

Grenoble

Type

conf

DOI

10.1109/PTC.2013.6652421

Filename

6652421