| description abstract | Floating offshore wind energy will play a key role in the clean energy transition. The number of large-scale wind farm projects is growing in regions like Northern Europe, the East Coast of the U.S., and the Mediterranean Sea. Offshore wind farms face fewer layout constraints, as they can be situated in vast, open sea areas. Turbines are often arranged in simple layouts, such as grid patterns, but this can cause significant annual energy production (AEP) losses due to wake–rotor interaction. Increasing spacing can mitigate this effect, but it may not always be feasible due to marine space limitations or higher costs for cabling and maintenance. This paper introduces a multi-objective wind farm optimization framework using a non sorted genetic algorithm (NSGA II) to minimize costs and maximize AEP. The method is applied to two case studies in the Mediterranean Sea, assuming 15 MW wind turbines. Case A is located off the coast of Civitavecchia, and case B in the Gulf of Squillace. AEP evaluation is performed with the open-source library FLOw Redirection and Induction in Steady-State (floris), while optimization is done using pymoo. In case A, the layout characterized by the lowest levelized cost of energy (LCOE) features 16 turbines, achieving an AEP of 709 GWh, an LCOE of 112.06 €/MWh, and wake losses of 2.6%. Meanwhile, in case B, the layout with the lowest LCOE consists of 19 turbines, achieving an AEP of 1140 GWh, an LCOE of 80.82 €/MWh, and wake losses of 4%. | |
