A multi objective optimization framework for offshore wind farm design in deep water seas

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.