Investigation into offshore wind farm repowering optimization in Hong Kong

Haiying Sun, Xiaoxia Gao, Hongxing Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

With the growing prosperity of the offshore wind energy market and the approaching end of the lifetime of the first-generation offshore wind farms, the high decommissioning cost has attracted increasing attention all over the world. To decrease this cost, the spotential repowering strategies are applied into the wind farm optimization and investigated in this study. In the repowering optimization strategy, the wind turbine foundations are not dismantled immediately after their service lifetime, and the lifecycle of them is extended to two generations' service time. The costs of removing the first foundations and installing the second foundations are saved. With the layout optimization method, the wind loss caused by wake effect can be decreased, which improve the energy output of the wind farm in the whole lifetime. Levelised cost of energy (LCoE) is set as the criterion to evaluate the wind farm layout. Both aligned and optimized layouts are analyzed in this study. A case study in Sha Chau Island seawater area in Hong Kong is then discussed. The results reveal that Hong Kong has many advantages to exploit offshore wind power and the repowering optimization layout is practical for cost-saving. According to this study, the LCoE of an offshore wind power farm in Sha Chau Island seawater area could be decreased to 0.9130 HK$/kWh.

Original languageEnglish
Pages (from-to)302-311
Number of pages10
JournalInternational Journal of Low-Carbon Technologies
Volume14
Issue number2
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Levelised cost of energy
  • Multi-population genetic algorithm
  • Offshore wind farm layout optimization
  • Repowering strategy
  • Wake effect

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Environmental Science(all)

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