Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer

Ajit C. Pillai; John Chick; Lars Johanning; Mahdi Khorasanchi; Sami Barbouchi

doi:10.1115/OMAE2016-54145

Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer

Ajit C. Pillai^*
, John Chick
, Lars Johanning
, Mahdi Khorasanchi
, Sami Barbouchi

^*Corresponding author for this work

School of Engineering, Computing and Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

Abstract

This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm's layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing wind farm layouts with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.

Original language	English
Title of host publication	Ocean Space Utilization; Ocean Renewable Energy
Publisher	The American Society of Mechanical Engineers(ASME)
ISBN (Electronic)	9780791849972
DOIs	https://doi.org/10.1115/OMAE2016-54145
Publication status	Published - 2016
Event	ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016 - Busan, Korea, Republic of Duration: 19 Jun 2016 → 24 Jun 2016

Publication series

Name	Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
Volume	6

Conference

Conference	ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016
Country/Territory	Korea, Republic of
City	Busan
Period	19/06/16 → 24/06/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

ASJC Scopus subject areas

Ocean Engineering
Energy Engineering and Power Technology
Mechanical Engineering

Access to Document

10.1115/OMAE2016-54145

Cite this

Pillai, A. C., Chick, J., Johanning, L., Khorasanchi, M., & Barbouchi, S. (2016). Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. In Ocean Space Utilization; Ocean Renewable Energy (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; Vol. 6). The American Society of Mechanical Engineers(ASME). https://doi.org/10.1115/OMAE2016-54145

Pillai, Ajit C. ; Chick, John ; Johanning, Lars et al. / Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. Ocean Space Utilization; Ocean Renewable Energy. The American Society of Mechanical Engineers(ASME), 2016. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE).

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title = "Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer",

abstract = "This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm's layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing wind farm layouts with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.",

author = "Pillai, \{Ajit C.\} and John Chick and Lars Johanning and Mahdi Khorasanchi and Sami Barbouchi",

note = "Publisher Copyright: {\textcopyright} Copyright 2016 by ASME.; ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016 ; Conference date: 19-06-2016 Through 24-06-2016",

year = "2016",

doi = "10.1115/OMAE2016-54145",

language = "English",

series = "Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE",

publisher = "The American Society of Mechanical Engineers(ASME)",

booktitle = "Ocean Space Utilization; Ocean Renewable Energy",

address = "United States",

}

Pillai, AC, Chick, J, Johanning, L, Khorasanchi, M & Barbouchi, S 2016, Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. in Ocean Space Utilization; Ocean Renewable Energy. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, vol. 6, The American Society of Mechanical Engineers(ASME), ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016, Busan, Korea, Republic of, 19/06/16. https://doi.org/10.1115/OMAE2016-54145

Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. / Pillai, Ajit C.; Chick, John; Johanning, Lars et al.
Ocean Space Utilization; Ocean Renewable Energy. The American Society of Mechanical Engineers(ASME), 2016. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; Vol. 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review

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AU - Khorasanchi, Mahdi

AU - Barbouchi, Sami

PY - 2016

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AB - This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm's layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing wind farm layouts with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.

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Pillai AC, Chick J, Johanning L, Khorasanchi M, Barbouchi S. Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. In Ocean Space Utilization; Ocean Renewable Energy. The American Society of Mechanical Engineers(ASME). 2016. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE). doi: 10.1115/OMAE2016-54145

Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Access to Document

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