Collector system layout optimization framework for large-scale offshore wind farms

Yingying Chen; Zhao Yang Dong; Ke Meng; Feng Ji Luo; Zhao Xu; Kit Po Wong

doi:10.1109/TSTE.2016.2549602

Collector system layout optimization framework for large-scale offshore wind farms

Yingying Chen, Zhao Yang Dong, Ke Meng, Feng Ji Luo, Zhao Xu, Kit Po Wong

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

58 Citations (Scopus)

Abstract

A new collector system layout optimization (CSLO) framework for large-scale offshore wind farms (OWFs) is proposed in this paper. First, a self-adaptive allocation method is proposed to determine the location of substations and to allocate each turbine to its geographically closest substation. And then, in order to minimize the annualized investment cost, maintenance cost, and levelized cable losses cost, while satisfying a group of operational constraints, the proposed CSLO model is formulated as a mixed-integer nonlinear programming problem, which is solved by the Benders decomposition algorithm. Multiple substations and cable types are also considered in the model. The validity and effectiveness of the proposed methods are demonstrated in two OWF cases consisting of 30 and 100 units.

Original language	English
Article number	7454777
Pages (from-to)	1398-1407
Number of pages	10
Journal	IEEE Transactions on Sustainable Energy
Volume	7
Issue number	4
DOIs	https://doi.org/10.1109/TSTE.2016.2549602
Publication status	Published - 1 Oct 2016

Keywords

Benders decomposition
collector system layout
fuzzy C-means clustering
offshore wind farm planning

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment

UN SDGs

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

More information

10.1109/TSTE.2016.2549602

Cite this

@article{1c97bfd6aac74107b13ce6c8d6d689d3,

title = "Collector system layout optimization framework for large-scale offshore wind farms",

abstract = "A new collector system layout optimization (CSLO) framework for large-scale offshore wind farms (OWFs) is proposed in this paper. First, a self-adaptive allocation method is proposed to determine the location of substations and to allocate each turbine to its geographically closest substation. And then, in order to minimize the annualized investment cost, maintenance cost, and levelized cable losses cost, while satisfying a group of operational constraints, the proposed CSLO model is formulated as a mixed-integer nonlinear programming problem, which is solved by the Benders decomposition algorithm. Multiple substations and cable types are also considered in the model. The validity and effectiveness of the proposed methods are demonstrated in two OWF cases consisting of 30 and 100 units.",

keywords = "Benders decomposition, collector system layout, fuzzy C-means clustering, offshore wind farm planning",

author = "Yingying Chen and Dong, {Zhao Yang} and Ke Meng and Luo, {Feng Ji} and Zhao Xu and Wong, {Kit Po}",

year = "2016",

month = oct,

day = "1",

doi = "10.1109/TSTE.2016.2549602",

language = "English",

volume = "7",

pages = "1398--1407",

journal = "IEEE Transactions on Sustainable Energy",

issn = "1949-3029",

publisher = "IEEE",

number = "4",

}

TY - JOUR

T1 - Collector system layout optimization framework for large-scale offshore wind farms

AU - Chen, Yingying

AU - Dong, Zhao Yang

AU - Meng, Ke

AU - Luo, Feng Ji

AU - Xu, Zhao

AU - Wong, Kit Po

PY - 2016/10/1

Y1 - 2016/10/1

N2 - A new collector system layout optimization (CSLO) framework for large-scale offshore wind farms (OWFs) is proposed in this paper. First, a self-adaptive allocation method is proposed to determine the location of substations and to allocate each turbine to its geographically closest substation. And then, in order to minimize the annualized investment cost, maintenance cost, and levelized cable losses cost, while satisfying a group of operational constraints, the proposed CSLO model is formulated as a mixed-integer nonlinear programming problem, which is solved by the Benders decomposition algorithm. Multiple substations and cable types are also considered in the model. The validity and effectiveness of the proposed methods are demonstrated in two OWF cases consisting of 30 and 100 units.

AB - A new collector system layout optimization (CSLO) framework for large-scale offshore wind farms (OWFs) is proposed in this paper. First, a self-adaptive allocation method is proposed to determine the location of substations and to allocate each turbine to its geographically closest substation. And then, in order to minimize the annualized investment cost, maintenance cost, and levelized cable losses cost, while satisfying a group of operational constraints, the proposed CSLO model is formulated as a mixed-integer nonlinear programming problem, which is solved by the Benders decomposition algorithm. Multiple substations and cable types are also considered in the model. The validity and effectiveness of the proposed methods are demonstrated in two OWF cases consisting of 30 and 100 units.

KW - Benders decomposition

KW - collector system layout

KW - fuzzy C-means clustering

KW - offshore wind farm planning

UR - http://www.scopus.com/inward/record.url?scp=84988851072&partnerID=8YFLogxK

U2 - 10.1109/TSTE.2016.2549602

DO - 10.1109/TSTE.2016.2549602

M3 - Journal article

SN - 1949-3029

VL - 7

SP - 1398

EP - 1407

JO - IEEE Transactions on Sustainable Energy

JF - IEEE Transactions on Sustainable Energy

IS - 4

M1 - 7454777

ER -

Collector system layout optimization framework for large-scale offshore wind farms

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

More information

Other files and links

Fingerprint

Cite this