Engineering an optimal wind farm using surrogate models
Engineering an optimal wind farm using surrogate models
A framework for optimal design of wind farm layouts using a surrogate-based Dynamic Wake Meandering model is presented. The optimization platform is set-up as a hybrid strategy combining genetic search with the gradient-based algorithm. The design variables are the number of turbines in the layout and their relative position within the bounded area. The objective function is defined as the net present value of the wind farm's profit, thus including the relevant expenditures throughout the farm's lifespan. Results show that an optimal design is reached by maximizing investment and accepting a minor sacrifice of the wind farm performance.
1296-1308
Mahulja, Stjepan
bd6da236-235b-4873-8485-250fb3a60cc0
Larsen, Gunner Chr.
f76293ba-d9c4-4031-a8ec-990f907b39ef
Elham, A.
676043c6-547a-4081-8521-1567885ad41a
1 December 2018
Mahulja, Stjepan
bd6da236-235b-4873-8485-250fb3a60cc0
Larsen, Gunner Chr.
f76293ba-d9c4-4031-a8ec-990f907b39ef
Elham, A.
676043c6-547a-4081-8521-1567885ad41a
Mahulja, Stjepan, Larsen, Gunner Chr. and Elham, A.
(2018)
Engineering an optimal wind farm using surrogate models.
Wind Energy, 21 (12), .
(doi:10.1002/we.2255).
Abstract
A framework for optimal design of wind farm layouts using a surrogate-based Dynamic Wake Meandering model is presented. The optimization platform is set-up as a hybrid strategy combining genetic search with the gradient-based algorithm. The design variables are the number of turbines in the layout and their relative position within the bounded area. The objective function is defined as the net present value of the wind farm's profit, thus including the relevant expenditures throughout the farm's lifespan. Results show that an optimal design is reached by maximizing investment and accepting a minor sacrifice of the wind farm performance.
This record has no associated files available for download.
More information
Accepted/In Press date: 14 June 2018
Published date: 1 December 2018
Identifiers
Local EPrints ID: 470879
URI: http://eprints.soton.ac.uk/id/eprint/470879
ISSN: 1095-4244
PURE UUID: bea6031c-052d-41ac-98d7-1524828e8ef9
Catalogue record
Date deposited: 20 Oct 2022 16:45
Last modified: 16 Mar 2024 21:27
Export record
Altmetrics
Contributors
Author:
Stjepan Mahulja
Author:
Gunner Chr. Larsen
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics