Wind energy resource assessment based on the use of multiple satellite data for sustainable energy production in Sudan

This study examines Libya's pursuit of sustainable wind energy solutions, using nine sites with mast measurements before the 2011 civil war and six gridded datasets, including CFSR, ERA5, EAR5-Ag, MERRA2, EAR5-Land, and TerraClimate. Employing the Weibull distribution function with ten methods, the empirical method of Justus proved to be optimal for calculating Weibull parameters across datasets. Al Bayda and Darnah exhibit substantial wind power potential (116.80-123.00 W/m²) based on MERRA2 data, making them ideal for large-scale wind turbine deployment. Furthermore, the results showed that wind power density was estimated below 100 W/m² for all selected locations according to CFSR, ERA5, EAR5-Ag, EAR5-Land, and TerraClimate. This study emphasizes the need for new mast measurements to refine dataset selection, which is crucial for accurate assessments and large wind farm planning. Consequently, this study provides key insights into optimizing wind energy utilization in diverse Libyan regions, addressing both the potential and the challenges in sustainable energy development.

Wind Resource Evaluation in Libya: A Comparative Study of Ten Numerical Methods for the Estimation of Weibull Parameters using Multiple Datasets

Kassem,

Camur,

Mosbah

2024

Evaluation and Improvement of the Accuracy of Reanalysis and Analysis Datasets for Wind Resource Assessment in Sudan

Kassem,

Camur,

Abdalla

2024

Wind speed datasets are used to evaluate wind resources and energy production of wind farms. In locations where measured data are not available, reanalysis and analysis datasets can be used as an alternative to assess wind resources. This study evaluated the accuracy of wind speed data collected from reanalysis and analysis datasets against mast-measured data between 1975 and 1985 in Sudan, using monthly statistical analyses. Three bias correction methods, based on Measure-Correlate-Predict (MCP) and Linear Adaptation (LA1 and LA2), were applied to determine the original wind speed. The results indicate that LA1 outperformed MCP and LA2. Furthermore, the Weibull distribution function was employed to analyze the wind speed characteristics. In addition, wind power density was calculated using data from different sources. The findings show that although the wind power potential of the chosen locations is not suitable for large wind turbines, wind power can still be exploited with small wind turbines. Consequently, this study introduces a wind energy roadmap to attract investors in clean energy for sustainable development in Sudan, address energy problems, and meet domestic demands. The study also identifies the most important grid datasets for assessing the country's wind potential, enhancing the accuracy of assessments for investors and policymakers.

Assessment of Wind Energy Potential for achieving Sustainable Development Goal 7 in the Rural Region of Jeje, Nigeria

Kassem,

Camur,

Apreala

2024

The implementation of a decentralized energy system has the potential to improve the life quality of the people who live in remote rural areas with limited or nonexistent power sources. Renewable energy technologies can be very important in the production of power. The main purpose of this study is to assess Jeje, Nigeria's wind energy potential, using a reanalysis and analysis dataset. To this aim, data on wind speed at a height of 10 m were gathered from a variety of sources, including EAR5, EAR5 Ag, EAR5 Land, CFSR, and MERRA-2. The Weibull distribution function, commonly employed to evaluate wind energy potential, was utilized. A maximum wind power density value of 15.75 W/m2 was obtained when the MERRA-2 dataset was implemented. The results indicate that large-scale wind turbines are not a viable alternative in this area. Thus, the performance of six wind turbines, expressed by output power with a cut in speed ranging between 1 and 1.5 m/s, was investigated. The results demonstrated that the AWI-E1000T is the most efficient wind turbine under consideration. In addition, it has been shown that each considered turbine can be installed in this area based on the data acquired from the MERRA-2 and CFSR.