New approach to prioritize wind farm sites by data envelopment analysis method: A case study. (1st March 2023) - Daoudi, Mohammed; Ait Sidi Mou, Abdelaziz; Idrissi, Ahmed; Ihoume, Ilham; Arbaoui, Nora; Benchrifa, Mohammed
Abstract: The offshore wind potential estimated at 199 GW is analyzed in this study on the Atlantic and Mediterranean coast in Morocco, through a dynamic and static study. The Data Envelopment Analysis method (DEA) is used in this study to list and classify 14 offshore sites according to six criteria in order to prioritize their suitability for a wind farm. The most important criterion is the wind power density calculated by the Weibull distribution function over the period 2016–2020. The study showed that the power density varies considerably between sites, with an annual maximum value of 436.09 W/m 2 recorded in Dakhla and another minimum of 15.02 W/m 2 recorded in Casablanca. The application of the new approach based on the DEA method revealed favorable sites with high potential for offshore energy production, in particular in Dakhla which occupied the first place with a score of 4.77 followed closely by Laâyoune with 3.9 then Tanger with 3.15, while the sites of Kenitra, Rabat and Casablanca recorded low scores with respectively 0.61, 0.58 and 0.55 each. On the other hand, the study of the continental shelf revealed the presence of a topographic variation of the seabed, which favored the establishment of this technology. Highlights: Central role to the development of renewable energies in Morocco's vision for 2030. The dynamic and static results justify the usefulness that offshore wind farms can bring. A topographical analysis of the Moroccan continental shelf was carried out, which showed an important diversity of seabed geology. A new approach based on the DEA method is used to classify 14 offshore wind farm sites on the coasts of Morocco. The results contribute to support and advance the United Nations Sustainable Development Goals, in particular SDG 7, SDG 11 and SDG 13.