Forecasting for day-ahead offshore maintenance scheduling under uncertainty

J. Browell, I. Dinwoodie, D. McMillan

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Offshore wind farm maintenance operations are complex and dangerous, and as such are subject to strict safety constraints. In addition, crew and vessels must be scheduled in advance for both planned and reactive maintenance operations. Meteorological forecasts on many time-scales are used to inform scheduling decisions, but are imperfect. Short-term maintenance scheduling is therefore a problem of decision-making under uncertainty. This paper proposes a probabilistic approach to the short-term scheduling problem based on a cost-loss model for individual maintenance missions, and probabilistic forecasts of appropriate access windows. This approach is found to increase the utilisation of possible access windows compared to using deterministic decision rules. The impact of forecasting on the availability and operational costs of oshore wind is then examined using a Monte Carlo-based cost model. This has quantified the impact on availability and revenue performance under a range of site conditions.
LanguageEnglish
Title of host publicationProceedings of the European Safety and Reliability (ESREL) Conference, 2016
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Pages1-8
Number of pages8
Publication statusAccepted/In press - 13 Jun 2016
EventEuropean Safety and Reliability Conference 2016 - University of Strathclyde, Glasgow, United Kingdom
Duration: 25 Sep 201629 Sep 2016
http://esrel2016.org/ (Link to conference web site)

Conference

ConferenceEuropean Safety and Reliability Conference 2016
Abbreviated titleESREL 2016
CountryUnited Kingdom
CityGlasgow
Period25/09/1629/09/16
Internet address

Fingerprint

Scheduling
Availability
Offshore wind farms
Costs
Decision making
Uncertainty

Keywords

  • forecasting
  • decision support
  • offshore wind
  • maintenance
  • maintenance and availability modeling

Cite this

Browell, J., Dinwoodie, I., & McMillan, D. (Accepted/In press). Forecasting for day-ahead offshore maintenance scheduling under uncertainty. In Proceedings of the European Safety and Reliability (ESREL) Conference, 2016 (pp. 1-8). Glasgow: University of Strathclyde.
Browell, J. ; Dinwoodie, I. ; McMillan, D. / Forecasting for day-ahead offshore maintenance scheduling under uncertainty. Proceedings of the European Safety and Reliability (ESREL) Conference, 2016. Glasgow : University of Strathclyde, 2016. pp. 1-8
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Browell, J, Dinwoodie, I & McMillan, D 2016, Forecasting for day-ahead offshore maintenance scheduling under uncertainty. in Proceedings of the European Safety and Reliability (ESREL) Conference, 2016. University of Strathclyde, Glasgow, pp. 1-8, European Safety and Reliability Conference 2016, Glasgow, United Kingdom, 25/09/16.

Forecasting for day-ahead offshore maintenance scheduling under uncertainty. / Browell, J.; Dinwoodie, I.; McMillan, D.

Proceedings of the European Safety and Reliability (ESREL) Conference, 2016. Glasgow : University of Strathclyde, 2016. p. 1-8.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N2 - Offshore wind farm maintenance operations are complex and dangerous, and as such are subject to strict safety constraints. In addition, crew and vessels must be scheduled in advance for both planned and reactive maintenance operations. Meteorological forecasts on many time-scales are used to inform scheduling decisions, but are imperfect. Short-term maintenance scheduling is therefore a problem of decision-making under uncertainty. This paper proposes a probabilistic approach to the short-term scheduling problem based on a cost-loss model for individual maintenance missions, and probabilistic forecasts of appropriate access windows. This approach is found to increase the utilisation of possible access windows compared to using deterministic decision rules. The impact of forecasting on the availability and operational costs of oshore wind is then examined using a Monte Carlo-based cost model. This has quantified the impact on availability and revenue performance under a range of site conditions.

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Browell J, Dinwoodie I, McMillan D. Forecasting for day-ahead offshore maintenance scheduling under uncertainty. In Proceedings of the European Safety and Reliability (ESREL) Conference, 2016. Glasgow: University of Strathclyde. 2016. p. 1-8