Optimizing energy output and layout costs for large wind farms using particle swarm optimization

Kalyan Veeramachaneni; Markus Wagner; Una May O'Reilly; Frank Neumann

doi:10.1109/CEC.2012.6253002

Optimizing energy output and layout costs for large wind farms using particle swarm optimization

Kalyan Veeramachaneni, Markus Wagner, Una May O'Reilly, Frank Neumann

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

39 Citations (Scopus)

Abstract

The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.

Original language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)	9781467315098
DOIs	https://doi.org/10.1109/CEC.2012.6253002
Publication status	Published - 2012
Externally published	Yes
Event	IEEE Congress on Evolutionary Computation 2012 - Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 https://ieeexplore.ieee.org/xpl/conhome/6241678/proceeding (Proceedings)

Publication series

Name	2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference	IEEE Congress on Evolutionary Computation 2012
Abbreviated title	IEEE CEC 2012
Country/Territory	Australia
City	Brisbane
Period	10/06/12 → 15/06/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6241678/proceeding (Proceedings)

Keywords

Particle Swarm Optimization
renewable energy
repair strategies

UN SDGs

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

Access to Document

10.1109/CEC.2012.6253002

Cite this

Veeramachaneni, K., Wagner, M., O'Reilly, U. M., & Neumann, F. (2012). Optimizing energy output and layout costs for large wind farms using particle swarm optimization. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012 Article 6253002 (2012 IEEE Congress on Evolutionary Computation, CEC 2012). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CEC.2012.6253002

@inproceedings{effbc0f0c28b4bad856d090f4dd9ee61,

title = "Optimizing energy output and layout costs for large wind farms using particle swarm optimization",

abstract = "The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.",

keywords = "Particle Swarm Optimization, renewable energy, repair strategies",

author = "Kalyan Veeramachaneni and Markus Wagner and O'Reilly, \{Una May\} and Frank Neumann",

year = "2012",

doi = "10.1109/CEC.2012.6253002",

language = "English",

isbn = "9781467315098",

series = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

booktitle = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",

address = "United States of America",

note = "IEEE Congress on Evolutionary Computation 2012, IEEE CEC 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

url = "https://ieeexplore.ieee.org/xpl/conhome/6241678/proceeding",

}

Veeramachaneni, K, Wagner, M, O'Reilly, UM & Neumann, F 2012, Optimizing energy output and layout costs for large wind farms using particle swarm optimization. in 2012 IEEE Congress on Evolutionary Computation, CEC 2012., 6253002, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, IEEE, Institute of Electrical and Electronics Engineers, IEEE Congress on Evolutionary Computation 2012, Brisbane, Queensland, Australia, 10/06/12. https://doi.org/10.1109/CEC.2012.6253002

Optimizing energy output and layout costs for large wind farms using particle swarm optimization. / Veeramachaneni, Kalyan; Wagner, Markus; O'Reilly, Una May et al.
2012 IEEE Congress on Evolutionary Computation, CEC 2012. IEEE, Institute of Electrical and Electronics Engineers, 2012. 6253002 (2012 IEEE Congress on Evolutionary Computation, CEC 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Optimizing energy output and layout costs for large wind farms using particle swarm optimization

AU - Veeramachaneni, Kalyan

AU - Wagner, Markus

AU - O'Reilly, Una May

AU - Neumann, Frank

PY - 2012

Y1 - 2012

N2 - The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.

AB - The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.

KW - Particle Swarm Optimization

KW - renewable energy

KW - repair strategies

UR - https://www.scopus.com/pages/publications/84866850819

U2 - 10.1109/CEC.2012.6253002

DO - 10.1109/CEC.2012.6253002

M3 - Conference Paper

AN - SCOPUS:84866850819

SN - 9781467315098

T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - IEEE Congress on Evolutionary Computation 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

Veeramachaneni K, Wagner M, O'Reilly UM, Neumann F. Optimizing energy output and layout costs for large wind farms using particle swarm optimization. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012. IEEE, Institute of Electrical and Electronics Engineers. 2012. 6253002. (2012 IEEE Congress on Evolutionary Computation, CEC 2012). doi: 10.1109/CEC.2012.6253002

Optimizing energy output and layout costs for large wind farms using particle swarm optimization

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Cite this