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Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method
Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, 5005, SA, Australia.
Mälardalens universitet, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
Delmar Systems, Perth, 6000, WA, Australia.
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2024 (engelsk)Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 362, artikkel-id 122955Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.

sted, utgiver, år, opplag, sider
Elsevier Ltd , 2024. Vol. 362, artikkel-id 122955
Emneord [en]
Genetic algorithms, Hybrid wave-wind energy systems, Multi-objective optimisation algorithm, Offshore wind turbine, Sustainable energy, Swarm-intelligence algorithms, Wave energy converters, Australia, Offshore oil well production, Offshore wind turbines, Power quality, Power takeoffs, Wave energy conversion, Wind power, Hybrid wave-wind energy system, Hybrid waves, Multi-objective optimization algorithm, Multi-objectives optimization, Optimization algorithms, Swarm intelligence algorithms, Wave wind, Wind energy systems, acceleration, damping, wave energy, wind turbine, Multiobjective optimization
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Identifikatorer
URN: urn:nbn:se:mdh:diva-66338DOI: 10.1016/j.apenergy.2024.122955ISI: 001219088400001Scopus ID: 2-s2.0-85187778014OAI: oai:DiVA.org:mdh-66338DiVA, id: diva2:1848190
Merknad

Article; Export Date: 02 April 2024; Cited By: 0; Correspondence Address: D. Astiaso Garcia; Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy; email: davide.astiasogarcia@uniroma1.it; CODEN: APEND

Tilgjengelig fra: 2024-04-02 Laget: 2024-04-02 Sist oppdatert: 2024-05-29bibliografisk kontrollert

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