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Design of tidal range energy generation schemes using a genetic algorithm model

Xue, Jingjing, Ahmadian, Reza ORCID:, Jones, Owen ORCID: and Falconer, Roger A. ORCID: 2021. Design of tidal range energy generation schemes using a genetic algorithm model. Applied Energy 286 , 116506. 10.1016/j.apenergy.2021.116506

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One of the key aspects of Tidal Range Schemes globally is identifying the most appropriate site and the optimised design and operation of the scheme, to maximise societal needs and the benefits from electricity generation. Variations in the design parameters of Tidal Range Schemes for electricity generation could therefore lead to a very large number of design and operation scenarios. In this study, a novel Genetic Algorithm model was developed to deliver the complete design of the most optimised Tidal Range Schemes for electricity generation, including the number of turbines, sluicing areas and the maximum amount of electricity that could be generated, through identifying the most optimised operation scheme for a particular site. The Genetic Algorithm model has been used to design a new Tidal Range Scheme proposed for development in the Bristol Channel, UK, with a potential to generate about 7.16 TWh/yr. The design of the scheme was also investigated using a traditional grid search approach for a range of scenarios, together with the model being used to investigate the performance of the complete design of the scheme, evaluated through a comparison of the most optimised design in terms of electricity generation. This comparison has shown that the Genetic Algorithm model was capable of achieving largely the same outcomes and reducing the computational time by approximately 95% to that based on using traditional Grid Search methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Advanced Research Computing @ Cardiff (ARCCA)
Publisher: Elsevier
ISSN: 1872-9118
Date of First Compliant Deposit: 21 January 2021
Date of Acceptance: 12 January 2021
Last Modified: 08 Nov 2023 06:26

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