Wind energy potential assessment for the site of Inner Mongolia in China

Abstract

An accurate quantification and characterization of the available wind resources is necessary to optimally design a wind farm. To effectively evaluate the wind energy, studying the wind's statistical characteristics is required. The probability distribution of wind speed is a very important piece of information needed in the assessment of wind energy potential since wind power is proportional to the cube of wind speed. Therefore, choosing a probability function having high goodness of fit with the observation data plays a quite significant role in wind energy assessment. In this study, three probability density functions, i.e., two-parameter Weibull, Logistic and Lognormal are employed to wind speed distribution modeling using data measured at a typical site in Inner Mongolia, China, over the latest three year period from 2009 to 2011. The performance of these three functions is compared so as to select the best one. As one of the most favorable distributions, Weibull function is a most applicable approach in describing wind speed's distribution in many cases. However, though performances of three particle swarm optimization algorithms and 18 differential evolution approaches of estimating the shape parameter estimation in the Weibull function are compared, then the one which performs best is selected to determine the optimal shape parameter to obtain the most accurate shape parameter estimation results. The performance of the Weibull function is worse than the Logistic under the measured wind speed data and the chosen error evaluation criteria. Besides, as compared to the Lognormal function, the Logistic function provides a more adequate result in wind speed distribution modeling. Therefore, in this work, the Logistic function is applied to the consequent wind energy assessment through the availability factor, capacity factor, and turbine efficiency of a wind turbine. Assessment results have shown that it is suitable to build a wind farm in this area.