Combined Optimization for Offshore Wind Turbine Micro Siting

Peng Hou, Weihao Hu, Mohsen N. Soltani, Cong Chen, Zhe Chen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

37 Citationer (Scopus)

Resumé

In order to minimize the wake loss, wind turbines (WT) should be separated with large intervening spaces. However, this will incur an increase in the capital expenditure on electrical systems and even in the operation and maintenance costs. In order to realize a cost-effective wind farm, an integrated optimization method in which the positions of the WTs and offshore substations (OS) and the cable connection configuration are optimized simultaneously is proposed in this paper. Since the optimization variables are both continuous and discrete, the mixed integer particle swarm optimization (MIPSO) algorithm is adopted to minimize the levelized production cost (LPC) of the wind farm. Simulation results are given for validating the proposed approach and comparison is made with results obtained using other methods. It is found that the proposed method can reduce the levelized production cost (LPC) by 5.00% and increase the energy yields by 3.82% compared with the Norwegian centre for offshore wind energy (NORCOWE) reference wind farm layout. This is better than the traditional method which only achieves a 1.45% LPC reduction although it increases the energy yields by 3.95%.
OriginalsprogEngelsk
TidsskriftApplied Energy
Vol/bind189
Sider (fra-til)271–282
Antal sider12
ISSN0306-2619
DOI
StatusUdgivet - mar. 2017

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Offshore wind turbines
wind turbine
wind farm
production cost
Farms
Costs
energy
Cost reduction
cost
cable
Wind turbines
Particle swarm optimization (PSO)
Wind power
expenditure
Cables
method
simulation

Citer dette

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title = "Combined Optimization for Offshore Wind Turbine Micro Siting",
abstract = "In order to minimize the wake loss, wind turbines (WT) should be separated with large intervening spaces. However, this will incur an increase in the capital expenditure on electrical systems and even in the operation and maintenance costs. In order to realize a cost-effective wind farm, an integrated optimization method in which the positions of the WTs and offshore substations (OS) and the cable connection configuration are optimized simultaneously is proposed in this paper. Since the optimization variables are both continuous and discrete, the mixed integer particle swarm optimization (MIPSO) algorithm is adopted to minimize the levelized production cost (LPC) of the wind farm. Simulation results are given for validating the proposed approach and comparison is made with results obtained using other methods. It is found that the proposed method can reduce the levelized production cost (LPC) by 5.00{\%} and increase the energy yields by 3.82{\%} compared with the Norwegian centre for offshore wind energy (NORCOWE) reference wind farm layout. This is better than the traditional method which only achieves a 1.45{\%} LPC reduction although it increases the energy yields by 3.95{\%}.",
keywords = "Mixed integer particle swarm optimization (MIPSO), Wind farm layout optimization, Offshore substation (OS) locating, Cable connection configuration optimization, Levelized production cost (LPC)",
author = "Peng Hou and Weihao Hu and {N. Soltani}, Mohsen and Cong Chen and Zhe Chen",
year = "2017",
month = "3",
doi = "10.1016/j.apenergy.2016.11.083",
language = "English",
volume = "189",
pages = "271–282",
journal = "Applied Energy",
issn = "0306-2619",
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}

Combined Optimization for Offshore Wind Turbine Micro Siting. / Hou, Peng; Hu, Weihao; N. Soltani, Mohsen; Chen, Cong; Chen, Zhe.

I: Applied Energy, Bind 189, 03.2017, s. 271–282.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Combined Optimization for Offshore Wind Turbine Micro Siting

AU - Hou, Peng

AU - Hu, Weihao

AU - N. Soltani, Mohsen

AU - Chen, Cong

AU - Chen, Zhe

PY - 2017/3

Y1 - 2017/3

N2 - In order to minimize the wake loss, wind turbines (WT) should be separated with large intervening spaces. However, this will incur an increase in the capital expenditure on electrical systems and even in the operation and maintenance costs. In order to realize a cost-effective wind farm, an integrated optimization method in which the positions of the WTs and offshore substations (OS) and the cable connection configuration are optimized simultaneously is proposed in this paper. Since the optimization variables are both continuous and discrete, the mixed integer particle swarm optimization (MIPSO) algorithm is adopted to minimize the levelized production cost (LPC) of the wind farm. Simulation results are given for validating the proposed approach and comparison is made with results obtained using other methods. It is found that the proposed method can reduce the levelized production cost (LPC) by 5.00% and increase the energy yields by 3.82% compared with the Norwegian centre for offshore wind energy (NORCOWE) reference wind farm layout. This is better than the traditional method which only achieves a 1.45% LPC reduction although it increases the energy yields by 3.95%.

AB - In order to minimize the wake loss, wind turbines (WT) should be separated with large intervening spaces. However, this will incur an increase in the capital expenditure on electrical systems and even in the operation and maintenance costs. In order to realize a cost-effective wind farm, an integrated optimization method in which the positions of the WTs and offshore substations (OS) and the cable connection configuration are optimized simultaneously is proposed in this paper. Since the optimization variables are both continuous and discrete, the mixed integer particle swarm optimization (MIPSO) algorithm is adopted to minimize the levelized production cost (LPC) of the wind farm. Simulation results are given for validating the proposed approach and comparison is made with results obtained using other methods. It is found that the proposed method can reduce the levelized production cost (LPC) by 5.00% and increase the energy yields by 3.82% compared with the Norwegian centre for offshore wind energy (NORCOWE) reference wind farm layout. This is better than the traditional method which only achieves a 1.45% LPC reduction although it increases the energy yields by 3.95%.

KW - Mixed integer particle swarm optimization (MIPSO)

KW - Wind farm layout optimization

KW - Offshore substation (OS) locating

KW - Cable connection configuration optimization

KW - Levelized production cost (LPC)

U2 - 10.1016/j.apenergy.2016.11.083

DO - 10.1016/j.apenergy.2016.11.083

M3 - Journal article

VL - 189

SP - 271

EP - 282

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

ER -