Affiliations: Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Correspondence:
[*]
Corresponding author. Mahmud Fotuhi-Firuzabad, Department of Electrical Engineering, Sharif University of Technology, Azadi Ave., Room 621 East, Postal Code: 11365-11155, Tehran, Iran. Tel.: +98 2166165921; [email protected]
Abstract: Operational and planning studies of high-wind penetrated power systems have well come to the light as a major concern of future energy systems. This paper focuses on the procedure of determining required static reserve of the high-wind penetrated power systems which has not been well accompanied by comprehensive analysis and proper modeling tools. To reach this goal, first, a probabilistic algorithm has been proposed to effectively model the variations in output generation of wind turbines. In this algorithm, the fuzzy c-means clustering method (FCM) is exploited as an efficient as well as robust clustering method to find the multi-state model of wind turbines output generation. Based on this probabilistic analytical model, a stochastic framework is developed to investigate the roles of two important factors, i.e. wind power penetration rate and installed capacity of wind farms on the required static reserve of the system. In this regard, different wind power penetration rates have been defined for generation sector of the IEEE-RTS and the adequacy studies of this test system is performed to show that how variations in wind power penetration rate can affect the required static reserve of the system. In addition, a sensitivity analysis based on an exhaustive search algorithm is conducted on the capacity of installed wind farms to examine the effects of this important factor on the reliability level of power systems. This studies not only emphasize on the necessities of employing stochastic approach to determine the require reserve of a high-wind penetrated power system, but also, proves the applicability of proposed analytical approach.
Keywords: Fuzzy clustering, wind farm, probabilistic model of wind turbine, wind penetration, wind farm installed capacity, static reserve
