Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms
Abstract
Abstract Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependencymore »
- Authors:
-
- MaREI Centre, Environmental Research Institute University College Cork Cork Ireland, School of Engineering and Architecture‐Electrical and Electronic Engineering University College Cork Cork Ireland, Offshore Renewable Energy Catapult Glasgow UK
- School of Engineering and Architecture‐Electrical and Electronic Engineering University College Cork Cork Ireland
- Offshore Renewable Energy Catapult Glasgow UK
- National Renewable Energy Laboratory (NREL) Golden Colorado USA
- MaREI Centre, Environmental Research Institute University College Cork Cork Ireland
- Gavin and Doherty Geosolutions Dublin Ireland
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; European Union's Horizon 2020
- OSTI Identifier:
- 2221876
- Alternate Identifier(s):
- OSTI ID: 2222833; OSTI ID: 2229073
- Report Number(s):
- NREL/JA-5000-86662
Journal ID: ISSN 1095-4244
- Grant/Contract Number:
- AC36-08GO28308; 860737
- Resource Type:
- Published Article
- Journal Name:
- Wind Energy
- Additional Journal Information:
- Journal Name: Wind Energy Journal Volume: 27 Journal Issue: 2; Journal ID: ISSN 1095-4244
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 17 WIND ENERGY; cost estimation; floating offshore wind farm; operations and maintenance; robotics; WOMBAT
Citation Formats
Khalid, Omer, Hao, Guangbo, MacDonald, Hamish, Cooperman, Aubryn, Devoy McAuliffe, Fiona, and Desmond, Cian. Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms. United Kingdom: N. p., 2023.
Web. doi:10.1002/we.2881.
Khalid, Omer, Hao, Guangbo, MacDonald, Hamish, Cooperman, Aubryn, Devoy McAuliffe, Fiona, & Desmond, Cian. Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms. United Kingdom. https://doi.org/10.1002/we.2881
Khalid, Omer, Hao, Guangbo, MacDonald, Hamish, Cooperman, Aubryn, Devoy McAuliffe, Fiona, and Desmond, Cian. Thu .
"Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms". United Kingdom. https://doi.org/10.1002/we.2881.
@article{osti_2221876,
title = {Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms},
author = {Khalid, Omer and Hao, Guangbo and MacDonald, Hamish and Cooperman, Aubryn and Devoy McAuliffe, Fiona and Desmond, Cian},
abstractNote = {Abstract Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependency on the efficacy of the robotic system and the associated parameterization e.g., cost elements and the inspection rates. Conversely, the increased MRP day rate results in a higher annualized OPEX. Residual risk is calculated to assess the net benefit of incorporating the MRP. Furthermore, sensitivity analysis is conducted to find the key parameters influencing the OPEX and the inspection time variation. A key output of this work is a robust and realistic framework which can be used for the cost‐benefit assessment of future MRP systems for specific FOWF activities.},
doi = {10.1002/we.2881},
journal = {Wind Energy},
number = 2,
volume = 27,
place = {United Kingdom},
year = {Thu Nov 23 00:00:00 EST 2023},
month = {Thu Nov 23 00:00:00 EST 2023}
}
https://doi.org/10.1002/we.2881
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