Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
This work addresses the challenge of optimizing wind farm layouts for renewable energy production, which is incremental as it adapts existing Bayesian optimization methods to a set-based context.
The paper tackled the wind farm layout optimization problem, which involves multiple conflicting objectives, expensive simulations, and set-based designs, by extending Bayesian multi-objective optimization to handle sets using a set-based kernel in Gaussian processes, and demonstrated its potential on a wind energy dataset.
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.