Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

Abstract

The construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic studies are lacking. This paper proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed, the rotor radius of wind turbines and their layout. Firstly, a new, mixed mathematical formulation of the energy cost is presented, considering the Weibull distribution for wind, the characterizing parameters of wind turbines and the distance between two turbines. Secondly, to obtain the minimum energy cost, a composite optimization algorithm was developed, which consists of an iterative method and an improved particle swarm optimization algorithm. The former was used to search the minimal energy costs that relate to the design parameters of a single wind turbine, while the latter was adopted for optimizing the layout of the wind turbines iteratively. Finally, the proposed method was applied to three case studies with variable wind speed and constant wind direction. Results of the case studies show that the reduced energy cost after optimization has a range of 0–0.001 $/kWh, which confirms the effectiveness of the proposed approach. Meanwhile, the layout of the wind turbines after optimization tends to locate the two wind turbines with the biggest spacing in the wind direction, which justifies the utilization of layout optimization. Furthermore, exploring the optimally designed parameters of wind turbines revealed that the wind farms with a high mean wind speed can have a wider range of turbine capacity than those with a low wind speed, which offers more freedom for the designers when constructing offshore wind farms at wind sites with rich wind resources.

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

“…A novel approach using self-organizing maps (SOM) is proposed for solving problems based on turbine diameter and placement [70]. Some studies [71][72][73][74][75] have also established levelized cost of energy (LCOE) models for wind farms using variables like rotor diameter, employing metaheuristic algorithms to find optimal solutions. Regarding wind turbine power and capacity parameters as design variables, a turbine index is innovatively proposed to rank and find the most suitable turbine [76].…”

Special Issue on “Wind Energy Conversion Systems”

“…A power tracking predictive model and the model predictive control (MPC) method is used to optimize the power output of the wind farm. The paper "Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout" [11] proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed and the rotor radius of the wind turbines and the layout. The method could be used for selecting wind turbines and designing the wind farm layout.…”

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

“…Wind energy has a leading role in the current great demand for renewable energy and is one of the sources that has experienced a greater growth in recent decades [1]. In order for this energy to be competitive, it is necessary to reduce its production and maintenance costs [2]. From a control point of view, this can be achieved by operating wind turbines to generate energy captured from the wind more efficiently and to reduce their different structural loads improving their reliability [3].…”