Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation

TL;DR

This paper extends Bayesian multi-objective optimisation to set-based problems, specifically for wind farm layout optimisation, addressing multiple conflicting objectives and expensive simulations, and demonstrating its effectiveness on real data.

Contribution

It introduces a set-based kernel in Gaussian processes to adapt Bayesian optimisation for set-based design problems like wind farm layouts.

Findings

Set-based Bayesian optimisation effectively handles wind farm layout problems.

The approach captures correlations between different wind farm configurations.

Results show promising potential for real-world applications.

Abstract

Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian multi-objective optimisation. However, the traditional Bayesian optimisation cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of Bayesian multi-objective optimisation to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based Bayesian multi-objective optimisation.