A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation

• Published in: Energies
• Main Authors: Birgir Hrafnkelsson, Gudmundur V. Oddsson, Runar Unnthorsson
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2016
• Subjects: wind speed; wind energy; Monte Carlo (MC) simulation; modified Weibull simulation; annual energy production (AEP); method; Technology; T; Iceland
• Online Access: https://doi.org/10.3390/en9040286; https://doaj.org/article/f7f49337d6794f7aac4bc4466d01be0f

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP.