Offshore wind farm layout optimization via metaheuristic algorithms using a three-dimensional analytical wake model

Resumo

Due to the increasing demand for renewable energies, the wind energy industry has been in continuous

progress through studies that aim to contribute to its technological advancement. This work proposes the minimiza-
tion of the cost of energy (COE) of an offshore wind farm, given by the ratio between the cost of the farm and its

annual energy production (AEP). For this purpose, the layout (continuous variables) and the different commercial
types of each wind turbine (discrete variables) were considered as decision variables. Furthermore, econometric

models were used to estimate the costs due to wind farm rated power, length of interconnection electricity ca-
bles between wind turbines, and different water depths to account for the supporting structures as a function of

the installation layout for each wind turbine. To account for the mutual interference of the flow among the wind
turbines and its impact on the AEP, and for comparison purposes, this study was conducted under two analytical
wake models in the optimization process, namely, Gaussian wake model and the well-known Jensen wake model.
The performance of the wind farm, subjected to different wind conditions, was then evaluated as a function of the
probability of occurrence of wind direction and speed throughout the year to account for the AEP. To solve this
optimization problem, the metaheuristics (Genetic Algorithm and Differential Evolution) were employed, resulting
in a comparative study of their performance.