# A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters

## TL;DR

This paper presents a hybrid evolutionary algorithm framework that optimizes the placement and power-take-off settings of wave energy converters to maximize energy output, demonstrating improved performance over existing methods.

## Contribution

It introduces a novel hybrid heuristic search approach combining local and direct search methods for wave farm optimization, addressing hydrodynamic complexity constraints.

## Key findings

- Hybrid approach outperforms previous techniques by up to 3%
- Effective in two real wave scenarios (Sydney and Perth)
- Optimizes both WEC placement and PTO settings efficiently

## Abstract

Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's). We consider state-of-the-art fully submerged three-tether converters deployed in arrays. The goal of this work is to use heuristic search to optimise the power output of arrays in a size-constrained environment by configuring WEC locations and the power-take-off (PTO) settings for each WEC. Modelling the complex hydrodynamic interactions in wave farms is expensive, which constrains search to only a few thousand model evaluations. We explore a variety of heuristic approaches including cooperative and hybrid methods. The effectiveness of these approaches is assessed in two real wave scenarios (Sydney and Perth) with farms of two different scales. We find that a combination of symmetric local search with Nelder-Mead Simplex direct search combined with a back-tracking optimization strategy is able to outperform previously defined search techniques by up to 3\%.