Location and turbine parameter selection for offshore wind power maximization

Abstract

Offshore wind turbines can capture more wind than onshore because of their larger structure and location. This higher yield even fails to reduce the high installation and maintenance cost of an offshore wind farm (OWF). Appropriate turbine parameters and installation site selection may maximize the power generation which is a way to trade off these costs. Knowing the wind thrust force, air density, and power coefficient beforehand can help select an appropriate site for turbine location. Once the site is selected, the optimal value of turbine variables such as height and radius can contribute to higher power yield. In this paper, a MINLP (Mixed Integer Non-Linear Programing) model is formulated with these important variables and the optimal values of these variables are determined to maximize the annual power production ([Formula: see text]) from offshore wind farm. The estimated power production, [Formula: see text], is calculated using two methods, mathematical programing method and simulation method. Computational result indicates that mathematical programing method is time consuming but more accurate whereas the accuracy of simulation method is proportional to the number of iterations. Although the result of a simulation can be improved to some extent, it cannot be as accurate as mathematical modeling for this study. These study results have great impact on the managerial decision and long range strategic and technical planning for maximizing power generation from an offshore wind farm.

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

“…The Weibull density distribution is utilized to analyze the productivity of different turbine types, aiming to select the most efficient one [77]. Additionally, mixed integer nonlinear programming models for wind farm LCOE have been developed, taking into account turbine hub height, rotor radius, and layout, with the genetic algorithm applied to determine the best parameters and turbine types [78]. As outlined in Table 5, the selection of the wind turbine based on multiple criteria essentially involves a multicriteria decision-making (MCDM) problem.…”