Offshore wind energy is now recognized as a promising and cost-effective approach to diversify energy sources and support environmental sustainability. However, despite Algeria’s extensive coastline, offshore wind energy potential has not been fully investigated. The case study undertaken for this paper proposes a multi‑source, screening‑to‑yield workflow that combines open datasets to rank candidate offshore zones and to obtain turbine‑level performance, turbine specificity enters through the rotor equivalent wind speed (REWS) and through the power curve, while site dependence is governed by the three -parameters Weibull, the shear exponent and the air density. Potential sites are first screened using Global Wind Atlas wind maps and then refined using bathymetry constraints; extreme coastal wave height information is used to check operational clearance assumptions. Multi‑year ERA5 reanalysis is then employed to quantify wind climate and direction at different heights and to derive key atmospheric parameters at a common hub height including Weibull statistics, wind direction and shear exponent, and air density. In the turbine‑matching stage, the turbine‑specific REWS is computed to account for differences among four commercially available 3‑MW‑class turbines considering their power curves and technical specifications, and taking into account the coastal wave height data obtained from the Copernicus Marine Service for minimum turbine hub height selection, followed by air‑density correction prior to power‑curve mapping. Net annual energy production (Net AEP) and capacity factor are finally reported after applying an aggregated 15% loss factor. Results identify the most favorable zone which achieves a 2019–2021 mean net annual energy production of 10.397 GWh/yr and a net capacity factor of 39.56%, per turbine, aligning with global offshore wind performance standards. The proposed approach provides a cost-effective starting point for future offshore wind assessments. Further work should incorporate higher‑resolution modeling, wake/array effects, and uncertainty quantification to support project implementation.
Khoudiri et al. (Thu,) studied this question.