Wind Resource Assessment for Potential Wind Turbine Operations in the City of Yanbu, Saudi Arabia

Makbul A. M. Ramli and Houssem R. E. H. Bouchekara ()
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Makbul A. M. Ramli: Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Houssem R. E. H. Bouchekara: Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia

Energies, 2025, vol. 18, issue 8, 1-29

Abstract: Energy generated from wind (in the form of wind farms (WFs)) is expected to help alleviate rising energy demand in Saudi Arabia, driven by industrial development and population growth. However, before implementing wind farms, conducting a comprehensive wind resource assessment (WRA) study is of paramount importance. This paper presents the analysis of the wind resource potential of a site in Yanbu city, which is located on the western coastal area of Saudi Arabia, using a comprehensive study. The hourly data on wind speed and direction over a one-year period was used in the presented analysis. The plant capacity factor (CF) and annual energy production (AEP) are evaluated for more than 100 commercial wind turbines (WTs). The highest AEP was achieved by the ‘Enercon E126/7.5 MW’ turbine, generating 14.49 GWh, with a corresponding CF of 21.82%. In contrast, the lowest AEP was observed for the ‘Northern Power d’ turbine, producing only 0.13 GWh, with a CF of 14.89%. The highest CF was recorded for the ‘Leitwind LTW104/2.0 MW’ turbine at 40.67%, corresponding to an AEP of 7.12 GWh. The results obtained are very valuable for designers in selecting the appropriate WT to obtain the predicted AEP and CF with the appropriate turbine class. Furthermore, this study applied the K-means clustering algorithm to classify WTs into three distinct categories. Building on this classification, synthetic datasets representing tailored WT configurations were generated—a novel methodology that enables the simulation of site-specific designs not yet available in existing market offerings. These datasets equip wind farm developers with the ability to define WT specifications for manufacturers, guided by two key criteria: the site’s wind resource profile and the target performance metrics of the WT.

Keywords: annual energy production; capacity factor; wind farm; wind power density; wind resource assessment; wind rose (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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