uncertainty quantification of the levelized cost of energy for a 20 mw research wind turbine model

Abstract

Wind energy shows a great promise to be a major supplier of clean energy. However, to stay financially competitive with other sources of energy, its cost needs to be further reduced. Unlike the traditional energy resources that have good certainty in predicting the amount and cost of generated electricity, wind energy shows large uncertainties due to several uncertain input parameters. These uncertainties increase the financial investment risks, and thereby introduce higher interest rates that adds up to the cost. This study investigates the influence of the meteorological input parameters on the levelized cost of energy for a 20 MW research wind turbine. These uncertain input parameters are the wind shear, air density, average wind speed and Weibull shape parameter. Aeroservoelastic simulations are performed to evaluate the levelized cost of energy. C-type Gram Charlier series function is used to assess the probability density of levelized cost of energy. For computational efficiency, dimensional reduction technique is employed to compute the raw moments. The results of this study show that the average wind speed and air density have a high influence on the levelized cost of energy in terms of both the magnitude and occurrence of the probability density function. While the deterministic levelized cost of energy for the 20 MW wind turbine design is computed to be 3.45 USCents/kWHr, the probabilistic results of this research only consider 15.9% probability of occurrence for such a value, with a cumulative chance of 3.3% for the levelized cost of energy to be less than 3.45 USCents/kWHr due to uncertain input parameters.