LAPSE:2023.29324
Published Article
LAPSE:2023.29324
Cable Connection Optimization for Heterogeneous Offshore Wind Farms via a Voronoi Diagram Based Adaptive Particle Swarm Optimization with Local Search
April 13, 2023
Abstract
Offshore wind energy, as one of the featured rich renewable energy sources, is getting more and more attention. The cable connection layout has a significant impact on the economic performance of offshore wind farms. To make better use of the wind resources of a given sea area, a new method for optimal construction of offshore wind farms with different types of wind turbines has emerged in recent years. In such a wind farm, the capacities of wind turbines are not identical which brings new challenges for the cable connection layout optimization. In this work, an optimization model named CCLOP is proposed for such wind farms. The model incorporates both the cable capital cost and the cost of power losses associated with the cables in its objective function. To get an optimized result, a Voronoi diagram based adaptive particle swarm optimization with local search is proposed and applied. The simulation results show that the proposed method can help find a solution that is 12.74% outperformed than a benchmark.
Offshore wind energy, as one of the featured rich renewable energy sources, is getting more and more attention. The cable connection layout has a significant impact on the economic performance of offshore wind farms. To make better use of the wind resources of a given sea area, a new method for optimal construction of offshore wind farms with different types of wind turbines has emerged in recent years. In such a wind farm, the capacities of wind turbines are not identical which brings new challenges for the cable connection layout optimization. In this work, an optimization model named CCLOP is proposed for such wind farms. The model incorporates both the cable capital cost and the cost of power losses associated with the cables in its objective function. To get an optimized result, a Voronoi diagram based adaptive particle swarm optimization with local search is proposed and applied. The simulation results show that the proposed method can help find a solution that is 12.74% outperformed than a benchmark.
Record ID
Keywords
adaptive particle swarm optimization, cable connection layout, local search, multiple wind turbine types, offshore wind farm, power losses, Voronoi diagram
Subject
Suggested Citation
Qi Y, Hou P, Liu G, Jin R, Yang Z, Yang G, Dong Z. Cable Connection Optimization for Heterogeneous Offshore Wind Farms via a Voronoi Diagram Based Adaptive Particle Swarm Optimization with Local Search. (2023). LAPSE:2023.29324
Author Affiliations
Qi Y: School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Hou P: SEWPG European Innovation Center, 8000 Aarhus, Denmark [ORCID]
Liu G: School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China [ORCID]
Jin R: Department of Operation, University of Groningen, 9747 Groningen, The Netherlands [ORCID]
Yang Z: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yang G: Center of Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Dong Z: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Hou P: SEWPG European Innovation Center, 8000 Aarhus, Denmark [ORCID]
Liu G: School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China [ORCID]
Jin R: Department of Operation, University of Groningen, 9747 Groningen, The Netherlands [ORCID]
Yang Z: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yang G: Center of Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Dong Z: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Journal Name
Energies
Volume
14
Issue
3
First Page
644
Year
2021
Publication Date
2021-01-27
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14030644, Publication Type: Journal Article
Record Map
Published Article
LAPSE:2023.29324
This Record
External Link
https://doi.org/10.3390/en14030644
Publisher Version
Download
Meta
Record Statistics
Record Views
314
Version History
[v1] (Original Submission)
Apr 13, 2023
Verified by curator on
Apr 13, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
http://psecommunity.org/LAPSE:2023.29324
Record Owner
Auto Uploader for LAPSE
Links to Related Works
(0.08 seconds)
[0.08 s]