Thesis
Effective planning of end-of-life scenarios for offshore wind farm
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2023
- Thesis identifier
- T16628
- Person Identifier (Local)
- 201980429
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Many offshore wind turbines (OWTs) are approaching the end of their estimated operational life soon. It is challenging to develop a general decommissioning procedure for all OW farms. Therefore, this research aims to comprehend the available end-of-life (EoL) scenario for OWTs to decide on their application procedures and propose an innovative systematic framework for considering the EoL scenario. The first part of the research critically reviewed the various end-of-life strategies for offshore wind farms, available technological options and the influencing factors that can inform such decisions. The study proposed a multi-attribute framework for supporting optimum choices in terms of main constraints, such as the possibility of end-of-life strategies based on unique characteristics and influencing factors. In the selection of techno-economic, the primary procedure parameters influencing the three major end-life strategies, i.e. life extension, repowering, and decommissioning, are discussed, and the benefits and issues related to the influencing variables are also identified. In the next part, an initial comparative assessment between two of these scenarios, repowering and decommissioning, through a purpose-developed technoeconomic analysis model calculates relevant key performance indicators. With numerous OW farms approaching the end of service life, the discussion on planning the most appropriate EoL scenario has become popular. Planning and scheduling those main activities of EoL scenarios depends on forecasting leading environmental indicators such as significant wave height. This research proposes a novel probabilistic methodology based on multivariate and univariate time series forecasting of machine learning (ML) models, including LSTM, BiLSTM, and GRU. In the end, the role of optimum selection of end-of-life scenarios is investigated to achieve the highest profitability of offshore wind farms. Various end-of-life scenarios have been evaluated through a TOPSIS technique as a multi-criteria decision-making procedure to determine an appropriate way according to environmental, financial, safety Criteria, Schedule impact, and Legislation and guidelines.
- Advisor / supervisor
- Brennan, Feargal
- Kolios, Athanasios
- Resource Type
- DOI
Relazioni
Articoli
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PDF of thesis T16628 | 2023-06-20 | Pubblico | Scaricare |