A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

Abstract

The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered.