Genetic based optimisation of the design parameters for an array-on-device orbital motion wave energy converter. (15th December 2020)

• Record Type: Journal Article
• Authors: Faraggiana, E.; Chapman, J.C.; Williams, A.J.; Masters, I.
• Is Part Of: Ocean engineering. Volume 218(2020)
• Publication Date: 2020-12-15
• Subjects: Renewable energy -- Wave energy -- Optimisation -- LCOE; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.108251
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Optimisation of Wave Energy Converters (WECs) is a very important topic to obtain competitive devices in the energy market. Wave energy is a renewable resource that could contribute significantly to a future sustainable world. Research is on-going to reduce costs and increase the amount of energy captured. This work aims to optimise a WaveSub device made up of multiple floats in a line by investigating the influence of 6 different design parameters such as the number of floats. Here we show that a multi-float configuration of 6 floats is more competitive in terms of Levelised Cost Of Energy (LCOE) compared to a single float configuration with a LCOE reduction of around 21%. We demonstrate that multi-float configurations of this device reduce the LCOE especially because of the reduction of grid connection, installation, control and mooring costs. From the power capture perspective, optimized multi-float configurations still have similar capacity factors to the single float configuration. This research gives important indications for further development of the WECs from an optimisation perspective. These promising results show that more complex, optimized, multi-float configurations could be investigated in future. Highlights: Design improvement of a wave energy converter for both performance and cost using an optimisation algorithm. Linear potential flow model improved by inclusion of drag terms. Genetic algorithm optimisation of the WaveSub device based on a Levelised Cost of Energy. Large number of individual designs evaluated using parallel computing. A Taguchi method and a Kriging surrogate model sensitivity analysis.