TY - GEN
T1 - Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer
AU - Pillai, Ajit C.
AU - Chick, John
AU - Johanning, Lars
AU - Khorasanchi, Mahdi
AU - Barbouchi, Sami
N1 - Publisher Copyright:
© Copyright 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84996602584&partnerID=8YFLogxK
U2 - 10.1115/OMAE2016-54145
DO - 10.1115/OMAE2016-54145
M3 - Conference proceedings published in a book
AN - SCOPUS:84996602584
T3 - Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
BT - Ocean Space Utilization; Ocean Renewable Energy
PB - The American Society of Mechanical Engineers(ASME)
T2 - ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2016
Y2 - 19 June 2016 through 24 June 2016
ER -