A method for optimizing the location of wind farms

M.K. McWilliam, G.C. van Kooten, C. Crawford

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)

Abstract

The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuration that produces electricity at minimum cost. Several economic and regulatory scenarios were used to demonstrate the importance of each factor in siting optimally siting wind farms. We demonstrate how gradient based optimization could be applied to discover optimal wind farm location and size. Although the use of gradient based optimization makes the model sensitive to local minima, numerical smoothing is used to reduce this sensitivity.
Original languageEnglish
Pages (from-to)287-299
JournalRenewable Energy
Volume48
Issue numberDec 2012
DOIs
Publication statusPublished - 2012

Fingerprint

Farms
Electricity
Electric lines
Economics
Costs

Keywords

  • decision-support-system
  • placement
  • turbines

Cite this

McWilliam, M. K., van Kooten, G. C., & Crawford, C. (2012). A method for optimizing the location of wind farms. Renewable Energy, 48(Dec 2012), 287-299. https://doi.org/10.1016/j.renene.2012.05.006
McWilliam, M.K. ; van Kooten, G.C. ; Crawford, C. / A method for optimizing the location of wind farms. In: Renewable Energy. 2012 ; Vol. 48, No. Dec 2012. pp. 287-299.
@article{e0db277208ad4ff4834c94ede81343fb,
title = "A method for optimizing the location of wind farms",
abstract = "The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuration that produces electricity at minimum cost. Several economic and regulatory scenarios were used to demonstrate the importance of each factor in siting optimally siting wind farms. We demonstrate how gradient based optimization could be applied to discover optimal wind farm location and size. Although the use of gradient based optimization makes the model sensitive to local minima, numerical smoothing is used to reduce this sensitivity.",
keywords = "decision-support-system, placement, turbines",
author = "M.K. McWilliam and {van Kooten}, G.C. and C. Crawford",
year = "2012",
doi = "10.1016/j.renene.2012.05.006",
language = "English",
volume = "48",
pages = "287--299",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier",
number = "Dec 2012",

}

McWilliam, MK, van Kooten, GC & Crawford, C 2012, 'A method for optimizing the location of wind farms', Renewable Energy, vol. 48, no. Dec 2012, pp. 287-299. https://doi.org/10.1016/j.renene.2012.05.006

A method for optimizing the location of wind farms. / McWilliam, M.K.; van Kooten, G.C.; Crawford, C.

In: Renewable Energy, Vol. 48, No. Dec 2012, 2012, p. 287-299.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A method for optimizing the location of wind farms

AU - McWilliam, M.K.

AU - van Kooten, G.C.

AU - Crawford, C.

PY - 2012

Y1 - 2012

N2 - The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuration that produces electricity at minimum cost. Several economic and regulatory scenarios were used to demonstrate the importance of each factor in siting optimally siting wind farms. We demonstrate how gradient based optimization could be applied to discover optimal wind farm location and size. Although the use of gradient based optimization makes the model sensitive to local minima, numerical smoothing is used to reduce this sensitivity.

AB - The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuration that produces electricity at minimum cost. Several economic and regulatory scenarios were used to demonstrate the importance of each factor in siting optimally siting wind farms. We demonstrate how gradient based optimization could be applied to discover optimal wind farm location and size. Although the use of gradient based optimization makes the model sensitive to local minima, numerical smoothing is used to reduce this sensitivity.

KW - decision-support-system

KW - placement

KW - turbines

U2 - 10.1016/j.renene.2012.05.006

DO - 10.1016/j.renene.2012.05.006

M3 - Article

VL - 48

SP - 287

EP - 299

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

IS - Dec 2012

ER -