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In this work, the levelised cost of energy (LCOE) from floating solar photovoltaics (PV) is calculated and mapped in the Mediterranean Sea. A breakdown of the factors that influence the LCOE is presented, including site-specific aspects such as distance to shore, water depth and solar resources. To address the uncertainties inherent to any novel technology, a sensitivity analysis is conducted, in which the effects on the LCOE of variations in its factors are quantified. With respect to the numerator of the LCOE (the costs), the results indicate that installation costs, which increase rapidly with distance to shore, play the main role. With respect to the denominator (the energy output), the substantial solar resources of the Mediterranean Sea translate into large annual energy productions overall – a clear advantage for the development of floating solar PV. Going into greater detail, LCOE values are found to be particularly sensitive to: (i) parameters directly linked to the production of energy, e.g., panel capacity and number of panels per structure; (ii) general project and project finance parameters, e.g., number of structures, project lifetime and discount rate; (iii) sources of costs, e.g., structure weight and installation of components. The lowest LCOEs occur in nearshore areas of Libya and Egypt. The Iberian Peninsula presents LCOE values in the range 340–380 €/MWh, and Italy, in the range 380–400 €/MWh. In the Aegean Sea there is high spatial variability, with LCOE values ranging from 320 €/MWh to 500 €/MWh. These results help to identify optimal locations for the deployment of floating PV in the Mediterranean Sea and indicate the key parameters for reducing its cost in future. • Levelised Cost of Energy (LCOE) from floating solar PV is mapped in the Mediterranean Sea. • Sensitivity analysis is conducted to evaluate the influence of multiple factors on LCOE. • Installation costs, which increase with distance to the coast, play the main role. • Solar resources and material costs also influence LCOE values. • Optimal spots for development and opportunities for cost reductions are identified.
Martinez et al. (Sat,) studied this question.