Coupled wind turbine design and layout optimization with nonhomogeneous wind turbines

Abstract

Abstract. In this study, wind farms were optimized to show the benefit of coupling complete turbine design and layout optimization as well as including two different turbine designs in a fixed 1-to-1 ratio in a single wind farm. For our purposes, the variables in each turbine optimization include hub height, rotor diameter, rated power, tower diameter, tower shell thickness, and implicit blade chord-and-twist distributions. A 32-turbine wind farm and a 60-turbine wind farm were both considered, as well as a variety of turbine spacings and wind shear exponents. Structural constraints as well as turbine costs were considered in the optimization. Results indicate that coupled turbine design and layout optimization is superior to sequentially optimizing turbine design, then turbine layout. Coupled optimization results in an additional 2 %–5 % reduction in the cost of energy compared to optimizing sequentially for wind farms with turbine spacings of 8.5–11 rotor diameters. Smaller wind farms benefit even more from coupled optimization. Furthermore, wind farms with closely spaced wind turbines can greatly benefit from nonuniform turbine design throughout the farm. Some of these wind farms with heterogeneous turbine design have an additional 10 % cost-of-energy reduction compared to wind farms with identical turbines throughout the farm.

Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

“…design that captures both energy production and costs (Chen and MacDonald, 2014;Fleming et al, 2016;Stanley and Ning, 2019a). We calculated COE as a combination of costs divided by the AEP:…”

Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

“…Within an optimization problem, increasing the degrees of freedom of the decision space can lead to the increase of the problem complexity (and therefore its computational cost), but also reach more efficient solutions (e.g., [6][7][8]). In the case of WFLO, the degrees of freedom can be raised through e.g., increasing the resolution of the search space, or removing constraints, such as that on the turbine hub height [9][10][11], the rotor diameter [12], or the wind farm boundaries. To this last respect, the possibility to optimize the wind farm shape has been traditionally neglected in the wind energy industry [13].…”

“…Tong et al [19] assessed a range of optimization-fixed rectangular wind farm aspect ratios and orientations (in separate runs), and highlight the sensitivity of the wind farm shape (in terms of the rectangle aspect ratio) to the overall performance compared to other design aspects. Stanley and Ning [12] carried out gradient-based WFLO on different design variables including the expansion of the area size, while keeping the same area shape unaltered. Finally, Wu et al [13] proposed the single-objective optimization on the annual energy production, providing flexibility to the area of the wind farm, by allowing the optimization of the best performing parallelogram and orientation for the wind farm area shape.…”

Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

“…One of the previously mentioned optimization methods is then used to determine at which of the predefined locations a turbine should be placed. In more recent years, some studies also showed success optimizing wind power plant layouts with gradient-based methods (Thomas and Ning, 2018;Stanley and Ning, 2019a;Baker et al, 2019). This type of optimization requires a continuous design space and computationally or analytically provided gradients that increase the complexity of the problem formulation.…”