Enhancement of wind energy resources assessment using Multi-Objective Genetic algorithm: A case study at Gabal Al-Zayt wind farm in Egypt

ABSTRACT

Estimation of wind speed distribution is essential for wind energy resources assessment, design of wind farms, and selection of suitable wind turbines. Two-parameter Weibull distribution function is widely used worldwide for wind energy resources assessment. As a case study, 1one-year field measurements at Gabal Al-Zayt wind farm in Egypt are used to estimate the Weibull parameters and to accurately assess the wind energy resource. In this work, seven statistical methods are adopted to estimate the Weibull parameters and their estimation accuracy is compared based on some common estimation errors. However, the improvement in one estimation error does not necessarily improve other types of errors. Consequently, a multi-objective genetic algorithm (MOGA) is adopted to investigate the tradeoffs among the competing estimation errors and to enhance the assessment of wind energy resources. The results show significant improvement in the estimation accuracy of the Weibull parameters using MOGA as compared to conventional statistical estimation methods. On the other hand, the case study at Gabal Al-Zayt wind farm reveals that the selection of wind turbines does not depend only on wind characteristics of the site but also on its environmental characteristics.

KEYWORDS:

• Wind energy assessment
• Gabal Al-Zayt
• Multi-Objective Genetic algorithm
• Weibull distribution
• Statistical estimation

Acknowledgments

The authors would like to acknowledge the collaboration of the New and Renewable Energy Authority (NREA) in Egypt for providing the essential field measurement at Gabal Al-Zayt wind farm to complete and enrich this research effort.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [M. L. Shaltout], upon reasonable request.