Combined Optimization for Offshore Wind Turbine Micro Siting

Research output: Contribution to journalJournal article

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%.
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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%.
Original languageEnglish
JournalApplied Energy
Volume189
Pages (from-to)271–282
Number of pages12
ISSN0306-2619
DOI
StatePublished - Mar 2017
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • Mixed integer particle swarm optimization (MIPSO), Wind farm layout optimization, Offshore substation (OS) locating, Cable connection configuration optimization, Levelized production cost (LPC)
ID: 246628769