Efficient Gradient-Based Optimization for Joint Layout Design and Control of Wind Turbines

TL;DR

This paper presents an efficient gradient-based optimization framework for joint wind farm layout and control design, significantly reducing convergence times by exploiting problem structure under a continuous wake effect model.

Contribution

It introduces a multi-level reformulation that leverages problem sub-structure to solve large-scale joint layout and control optimization problems more efficiently.

Findings

Order-of-magnitude reduction in convergence times

Effective exploitation of problem sub-structure

Improved optimization efficiency over prior methods

Abstract

A central challenge in the design of energy-efficient wind farms is the presence of wake effects between turbines. When a wind turbine harvests energy from free wind, it produces a turbulent region with reduced energy for downstream turbines. Strategies for increasing the efficiency of wind farms by mitigating wake effects have been the subject of much computational research. To this end, both the positioning of the turbines and their cooperative control must be taken into account. Since they are mutually dependent, increasing attention has been paid to the joint consideration of layout design and control. However, joint modeling approaches lead to large-scale and difficult optimization problems, which lack efficient solution strategies. This paper describes an efficient optimization framework for the joint design and steady-state control of wind turbines under a continuous wake effect model. Through a multi-level reformulation of the joint optimization problem, it shows how problem sub-structure can be efficiently exploited to yield order-of-magnitude reduction in convergence times over naive approaches and prior proposals.