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Article . 2017 . Peer-reviewed
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Offshore wind farm repowering optimization

Authors: Peng Hou; Peter Enevoldsen; Weihao Hu; Cong Chen; Zhe Chen;

Offshore wind farm repowering optimization

Abstract

Decommissioning is usually the last stage of the offshore wind farm life cycle. Due to the challenges of the decommissioning process, such as the impact on the marine environment, severe weather conditions, vessel limitations and lack of operational experience, the decommissioning strategy should be planned to avoid complications, which ultimately can cause radical changes to the levelized cost of energy (LCoE) and the wind farm owner's business case. Instead of dismantling, repowering may be a sustainable alternative solution to extend the lifetime of a wind farm. In this paper, the research is focused on optimization of offshore wind farm repowering, which is one option for the wind farm owner at end of life for the offshore wind farm. The LCoE is used as the evaluation index to identify whether it is economical to invest in such a way. In an optimized repowering strategy, different types of wind turbines are selected to replace the original wind turbines to reconstruct the wind farm, which is demonstrated to be better than the refurbishment approach which replaces the old wind turbines with the same type. The simulations performed in this research reveal that the reconstructed wind farm, which consists of multiple types of wind turbine, has a smaller LCoE (10.43%) than the refurbishment approach, which shows the superiority of the proposed method. This research contributes an optimization tool to the wind industry, which consequently drives down the cost of energy produced by offshore wind turbines.

Country
Denmark
Keywords

Repowering, Levelised cost of energy (LCoE), Offshore wind power

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    71
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
71
Top 1%
Top 10%
Top 10%