Evaluation of wind capacity credit using discrete convolution considering the mechanical failure of wind turbines

In view of the increasing role of wind power generation, there is an evolving body of reliability methods that are concerned with improved modeling of wind generation and related phenomena. An important consideration in the planning of wind generation projects is the capacity value of the farm at the proposed location. The modeling considerations in this process should take into account not only the variable nature of wind and the mechanical failure of turbines, but also the correlation between the individual turbines on the farm. This paper introduces an analytical method to calculate the capacity credit of wind farms including the mechanical failure of wind turbines. The proposed method is based on the discrete convolution technique and takes into account the stochastic nature of wind power as well as the forced outage rates (FOR) of wind turbines. The discrete convolution method has been used in this work to build a generation model in the form of a capacity outage probability table (COPT). A comparison of wind power capacity credit with and without considering the mechanical failures of wind turbines is shown to demonstrate the impact of turbine failure. Also, the capacity credit is calculated based on two reliability indices which are Loss of Load Expectation, LOLE, and Loss of Energy Expectation, LOEE. The proposed method is applied on the IEEE RTS-79 and the hourly wind speed data were taken from Abee Agdm Alberta, Canada. The results show the importance of inclusion of FOR of wind turbines on estimating wind power capacity credit. The results are validated using Monte Carlo simulation.