Analysis of Life Extension Performance Metrics for Optimal Management of Offshore Wind Assets1

The main objective of this study is to develop an optimal life extension management strategy for ageing offshore wind farms. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. To this end, the key metrics to...

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Bibliographic Details
Published in:Journal of Offshore Mechanics and Arctic Engineering
Main Authors: Yeter, Baran, Garbatov, Yordan, Guedes Soares, C.
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://vbn.aau.dk/da/publications/56f5b482-30fc-45cc-9aca-e7e75f2d3ed4
https://doi.org/10.1115/1.4054708
http://www.scopus.com/inward/record.url?scp=85132975287&partnerID=8YFLogxK
Description
Summary:The main objective of this study is to develop an optimal life extension management strategy for ageing offshore wind farms. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. To this end, the key metrics to evaluate the life extension performance of an offshore wind farm are investigated. The mean value and the standard deviation of each performance metric are calculated using a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by the multi-asset portfolio optimization. In this regard, the Markowitz modern portfolio theory is applied to estimate a risk-adjusted return parameter, the Sharpe ratio of the overall portfolio of offshore wind assets. Later on, the key performance metrics are compared to identify the most suitable metrics at different stages of life extension, and a further discussion is given for different offshore wind farm sizes. Moreover, the optimal management strategy, which maximizes the Sharpe ratio of the overall offshore wind farm, is analyzed using one of the key performance metrics under optimistic, moderate, and pessimistic scenarios. Finally, the optimal allocation (portfolio) of offshore wind assets attained based on the mean-variance optimization is presented for the different stages of the life extension of the offshore wind farms accounting for the uncertainty propagation during the life extension.