Uncertainty and global sensitivity analysis of levelized cost of energy in wind power generation

Modern wind turbines tend to large scale and capacity, which amplifies the uncertainty of the cost of wind power generation. This paper proposes an analytical framework for uncertainty in the cost of wind power. To do so, the levelized cost of the energy model is improved by considering inflation and the learning curve. On this basis, results from the quasi-Monte Carlo simulation are used for uncertainty analysis that is performed to qualify the uncertainty degree of the levelized cost of energy. Meanwhile, sensitivity analysis based on variance is conducted to study the impact of the uncertainty factors on the levelized cost of energy. Results reveal that through improving the cost model, the levelized cost of energy is changed from 54.11 $/MWh to 37.03 $/MWh in 2018, which is close to the real value of projects built in 2018. Meanwhile, the deterministic values of the levelized cost of energy are similar to P-50, which means that only 50% credibility is guaranteed by the deterministic design. To achieve a 95% reliability when considering changes caused by uncertainty factors, the margin should be not less than 38% of the determined value. Finally, sensitivity analysis reveals that the interaction effects from scale parameter, shape parameter and air density make the total effect coefficient increased, while the scale parameter is the most influential factor.

Automated summary

This paper presents an analytical framework for uncertainty in the cost of wind power generation, improving cost prediction accuracy by considering inflation and the learning curve. The study finds that the scale parameter has the most significant impact on the levelized cost of energy, and a 38% margin is needed to ensure a 95% reliability for changes caused by uncertainty factors.