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Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm. / Butterwick, Thomas; Kheiri, Ahmed; Lulli, Guglielmo et al.
In: Renewable Energy, Vol. 208, 31.05.2023, p. 1-16.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Butterwick, T, Kheiri, A, Lulli, G, Gromicho, J & Kreeft, J 2023, 'Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm', Renewable Energy, vol. 208, pp. 1-16. https://doi.org/10.1016/j.renene.2023.03.075

APA

Butterwick, T., Kheiri, A., Lulli, G., Gromicho, J., & Kreeft, J. (2023). Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm. Renewable Energy, 208, 1-16. https://doi.org/10.1016/j.renene.2023.03.075

Vancouver

Butterwick T, Kheiri A, Lulli G, Gromicho J, Kreeft J. Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm. Renewable Energy. 2023 May 31;208:1-16. Epub 2023 Mar 21. doi: 10.1016/j.renene.2023.03.075

Author

Butterwick, Thomas ; Kheiri, Ahmed ; Lulli, Guglielmo et al. / Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm. In: Renewable Energy. 2023 ; Vol. 208. pp. 1-16.

Bibtex

@article{5f8608eabab04d41ba4428bb42532f78,
title = "Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm",
abstract = "Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.",
keywords = "Windfarm, Optimisation, Metaheuristics, Hyper-heuristic",
author = "Thomas Butterwick and Ahmed Kheiri and Guglielmo Lulli and Joaquim Gromicho and Jasper Kreeft",
year = "2023",
month = may,
day = "31",
doi = "10.1016/j.renene.2023.03.075",
language = "English",
volume = "208",
pages = "1--16",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm

AU - Butterwick, Thomas

AU - Kheiri, Ahmed

AU - Lulli, Guglielmo

AU - Gromicho, Joaquim

AU - Kreeft, Jasper

PY - 2023/5/31

Y1 - 2023/5/31

N2 - Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.

AB - Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.

KW - Windfarm

KW - Optimisation

KW - Metaheuristics

KW - Hyper-heuristic

U2 - 10.1016/j.renene.2023.03.075

DO - 10.1016/j.renene.2023.03.075

M3 - Journal article

VL - 208

SP - 1

EP - 16

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -