Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Bias Correction

TL;DR

This study introduces a novel non-Gaussian bias correction method for assessing future wind power reliability under climate change, applied to Saudi Arabia's wind energy prospects, with promising profit increase estimates.

Contribution

It develops a new trans-Gaussian transformation and cluster-wise Kullback-Leibler divergence minimization method for improved wind simulation under climate change.

Findings

Projected increase in daily profits by $272,000 in Saudi Arabia.

Enhanced wind prediction accuracy with the new bias correction method.

Identification of optimal locations for future wind farms.

Abstract

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience, and hence profitability of future wind farms to a changing climate, given that current wind turbines have lifespans of up to thirty years. In this work, we develop a new non-Gaussian method data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a cluster-wise minimization of the Kullback-Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation, and power curves from a collection of popular wind turbines. We find an overall increase in the daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.