Investigation of the stochastic behaviour of surface wind speed using K-moments on global scale for energy management

Currently, more and more countries make a shift toward renewable energy sources to reduce the environmental impact from fossil fuel use. Wind energy has a significant position in this hierarchy, as one of the most efficient to convert to electric energy, covering the society’s needs. Typically, the characterization of the probability distribution of wind speed is based on classical and L-moments for moment orders 2 to 4, beyond which the estimation from samples is problematic. The aim of this work is to investigate the stochastic behaviour of surface wind speed and develop a model of simulation of the latter. In this framework, we also investigate and try to comprehend the occurrence of extremes, which become important for the engineering design of the wind turbine structures. Hourly datasets of wind speed from thousand stations throughout the world are used to perform various analyses based on knowable (K-)moments and comparison to classical and L-moments. The results of the K-moments’ application are used as input to a Monte-Carlo analysis, to an accurate simulate wind speed distribution tails.