Hooman Khaloie - Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Amir Abdollahi - Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Masoud Rashidinejad - Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

This paper develops a new possibilistic-scenario model for a wind power plant to determine its optimal self-scheduling (SS) in the presence of high-impact low-probability events uncertainty. Nowadays, in the context of the power system, examining the effects of extreme weather events in the category of high-impact low-probability (HILP) events has become one of the most important issues for researchers all around the world. There are so many reports of HILP events which acknowledge that these incidents can directly affect the power plants and cause them to fail. Generally, the self-scheduling of generating units in the pre-extreme weather conditions would be different from normal conditions. In such manners, this paper tries to address the self-scheduling problem of a wind power plant in pre-extreme weather conditions. For this purpose, there are numerous uncertainty sources in the SS problem that could affect the final results which include electricity prices, wind power production and contingency-based lack of production in the face of HILP events. In this regard, this paper proposes an efficient hybrid probabilistic-possibilistic assessment tool for dealing with these uncertainties. Additionally, CVaR evaluation was used as the intrinsic risk management tool of both probabilistic and possibilistic parameters in the SS problem.

Electricity market, High-impact low-probability (HILP) events, risk management, Self-Scheduling, Wind power plant