Cuong D. Dao
Modelling the Effects of Reliability and Maintenance on Levelised Cost of Wind Energy
Dao, Cuong D.; Kazemtabrizi, Behzad; Crabtree, Christopher J.
Authors
Dr Behzad Kazemtabrizi behzad.kazemtabrizi@durham.ac.uk
Associate Professor
Professor Christopher Crabtree c.j.crabtree@durham.ac.uk
Professor
Abstract
Levelised cost of energy is an important measure to evaluate the success of a wind energy project. It includes all the capital and operational expenditures of a wind turbine over its lifetime based on the expected power generated. In the literature, wind turbine reliability is largely neglected in levelised cost of energy estimation. This paper presents a model to evaluate levelised cost of energy while considering the reliability and maintenance of wind turbine subassemblies. The key concept behind this model is that the failure rate of a wind turbine subassembly depends on the preventive maintenance spending. The proposed model makes it possible to relate reliability data, such as failure rate and downtime of wind turbine subassemblies, to the operation and maintenance expenditure, as well as the annual energy production. The model is analysed using a sample set of recently published reliability data and it is observed that both the operation and maintenance expenditure and levelised cost of energy are convex functions of the subassembly’s failure rate and the preventive maintenance spending. This study can help wind turbine manufacturers and operators identify the level of reliability improvement and maintenance investment required to minimise the levelised cost of energy.
Citation
Dao, C. D., Kazemtabrizi, B., & Crabtree, C. J. (2019). Modelling the Effects of Reliability and Maintenance on Levelised Cost of Wind Energy. . https://doi.org/10.1115/gt2019-90015
Conference Name | ASME Turbo Expo 2019 |
---|---|
Conference Location | Phoenix, AZ, USA |
Acceptance Date | Feb 11, 2019 |
Online Publication Date | Nov 5, 2019 |
Publication Date | 2019 |
Deposit Date | Mar 21, 2019 |
DOI | https://doi.org/10.1115/gt2019-90015 |
You might also like
Comparison of ARIMA, FARIMA, and LSTM methods for day-ahead forecasting for scenario generation for wind power systems
(2023)
Presentation / Conference
Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values
(2023)
Presentation / Conference
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search