Monte Carlo methods to include the effect of asymmetrical uncertainty sources in wind farm yield assessment

Abstract

This article presents a method for incorporating the effect on expected annual energy production of a wind farm caused by asymmetric uncertainty distributions of the applied losses and the nonlinear response in turbine production. The necessity for such a correction is best illustrated by considering the effect of uncertainty in the oncoming wind speed distribution on the production of a wind turbine. Due to the shape of the power curve, variations in wind speed will result in a skewed response in annual energy production. For a site where the mean wind speed is higher than 50% of the rated wind speed of the turbine (in practice all sites with sufficiently high wind speed to motivate the establishment of a wind farm), a reduction in mean wind will cause a larger reduction in annual energy production than a corresponding increase in mean wind would increase the annual energy production. Consequently, the expected annual energy production response when considering the uncertainty of the wind will be lower than the expected annual energy production based on the most probable incoming wind. This difference is due to a statistical bias in the industry standard methods to calculate expected annual energy production of a wind farm, as implemented in tools in common use in the industry. A method based on a general Monte Carlo approach is proposed to calculate and correct for this bias. A sensitivity study shows that the bias due to wind speed uncertainty and nonlinear turbine response will be on the order of 0.5%-1.5% of expected annual energy production. Furthermore, the effect on expected annual energy production due to asymmetrical distributions of site specific losses, for example, loss of production due to ice, can constitute additional losses of several percent.

Evaluation of Wind Resources and the Effect of Market Price Components on Wind-Farm Income: A Case Study of Ørland in Norway

“…Fazelpour et al [12] used the Windographer software [13] to perform wind resource analysis but they did not have access to the real wind data from LIDAR. Gleim et al [14] dealt with uncertainties associated with the calculation of annual energy production using the MCS approach. They found that the wind speed uncertainty may cause error between 0.5% and 1.5% in estimation of annual energy production.…”