Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method

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 conve...

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Published in:	Applied energy Vol. 362; p. 122955
Main Authors:	Neshat, Mehdi, Sergiienko, Nataliia Y., Nezhad, Meysam Majidi, da Silva, Leandro S.P., Amini, Erfan, Marsooli, Reza, Astiaso Garcia, Davide, Mirjalili, Seyedali
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.05.2024
Subjects:	acceleration algorithms Australia coasts damping decline energy Genetic algorithms Hybrid wave-wind energy system Hybrid wave-wind energy systems Hybrid waves hybrids income longevity Multi-objective optimisation algorithm Multi-objective optimization algorithm Multi-objectives optimization Multiobjective optimization Offshore oil well production Offshore wind turbine Offshore wind turbines Optimization algorithms power generation Power quality Power takeoffs spring Sustainable energy Swarm intelligence algorithms swarms system optimization water power wave energy Wave energy conversion Wave energy converters Wave wind wind Wind energy systems Wind power wind turbine wind turbines Australia Swarm-intelligence algorithms Offshore wind turbine Hybrid wave-wind energy systems Sustainable energy Wave energy converters Multi-objective optimisation algorithm Genetic algorithms
ISSN:	0306-2619, 1872-9118, 1872-9118
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Abstract	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. •A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform.•A new combination of chaotic adaptive exploration and dynamic archive is introduced.•Effective and practical discretisation method improved performance of hybrid platform.•The performance of the proposed optimiser is validated on four real sea studies.
AbstractList	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. 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. •A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform.•A new combination of chaotic adaptive exploration and dynamic archive is introduced.•Effective and practical discretisation method improved performance of hybrid platform.•The performance of the proposed optimiser is validated on four real sea studies.
ArticleNumber	122955
Author	Marsooli, Reza Nezhad, Meysam Majidi Sergiienko, Nataliia Y. Neshat, Mehdi Amini, Erfan da Silva, Leandro S.P. Astiaso Garcia, Davide Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Mehdi orcidid: 0000-0002-9537-9513 surname: Neshat fullname: Neshat, Mehdi email: mehdi.neshat@torrens.edu.au, mehdi.neshat@uts.edu.au organization: Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, QLD, 4006, Australia – sequence: 2 givenname: Nataliia Y. orcidid: 0000-0002-3418-398X surname: Sergiienko fullname: Sergiienko, Nataliia Y. email: nataliia.sergiienko@adelaide.edu.au organization: School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, SA, 5005, Australia – sequence: 3 givenname: Meysam Majidi surname: Nezhad fullname: Nezhad, Meysam Majidi email: meysam.majidi.nezhad@uniroma1.it, meysam.majidi.nezhad@mdu.se organization: Department of Sustainable Energy Systems, Mälardalen University, Västerås, SE 72123, Sweden – sequence: 4 givenname: Leandro S.P. surname: da Silva fullname: da Silva, Leandro S.P. email: ldasilva@delmarsystems.com organization: Delmar Systems, Perth, WA, 6000, Australia – sequence: 5 givenname: Erfan orcidid: 0000-0001-9002-3203 surname: Amini fullname: Amini, Erfan email: eamini@stevens.edu organization: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States – sequence: 6 givenname: Reza surname: Marsooli fullname: Marsooli, Reza email: rmarsool@stevens.edu organization: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States – sequence: 7 givenname: Davide surname: Astiaso Garcia fullname: Astiaso Garcia, Davide email: davide.astiasogarcia@uniroma1.it organization: Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy – sequence: 8 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.Mirjalili@torrens.edu.au organization: Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, QLD, 4006, Australia
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Keywords	Swarm-intelligence algorithms Offshore wind turbine Hybrid wave-wind energy systems Sustainable energy Wave energy converters Multi-objective optimisation algorithm Genetic algorithms
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Snippet	Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or...
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SubjectTerms	acceleration algorithms Australia coasts damping decline energy Genetic algorithms Hybrid wave-wind energy system Hybrid wave-wind energy systems Hybrid waves hybrids income longevity Multi-objective optimisation algorithm Multi-objective optimization algorithm Multi-objectives optimization Multiobjective optimization Offshore oil well production Offshore wind turbine Offshore wind turbines Optimization algorithms power generation Power quality Power takeoffs spring Sustainable energy Swarm intelligence algorithms swarms system optimization water power wave energy Wave energy conversion Wave energy converters Wave wind wind Wind energy systems Wind power wind turbine wind turbines
Title	Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method
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