Coupled wind turbine design and layout optimization with nonhomogeneous wind turbines

Andrew P J Stanley,Andrew Ning

doi:10.5194/wes-4-99-2019

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.

Highlights

Mitigating wake interactions among wind turbines is one of the most difficult challenges in wind farm design
To successfully optimize the many variables that come from coupling layout and turbine design, we used exact analytic gradients as opposed to one of the gradient-free optimization methods commonly used in wind farm design
Like wind farm design spaces, are often well suited for gradient-free algorithms, gradient-based optimization methods can be useful in some cases, such as when using many turbines or when considering more design variables than just turbine layout

Summary

Introduction

Mitigating wake interactions among wind turbines is one of the most difficult challenges in wind farm design. Upstream turbines remove energy from the wind, decreasing the energy available to and increasing the loading on downstream turbines. These wake losses often reduce the power production by 10 %–20 % when compared to unwaked conditions (Barthelmie et al, 2007, 2009; Briggs, 2013). To successfully optimize the many variables that come from coupling layout and turbine design, we used exact analytic gradients as opposed to one of the gradient-free optimization methods commonly used in wind farm design. Like wind farm design spaces, are often well suited for gradient-free algorithms, gradient-based optimization methods can be useful in some cases, such as when using many turbines or when considering more design variables than just turbine layout. When considering several design variables or wind farms with many turbines, gradient-based optimization with

Objectives

Methods

Results

Conclusion