A decision support system for strategic maintenance planning in offshore wind farms

Published Date
December 2016, Vol.99:784–799, doi:10.1016/j.renene.2016.07.037

Author 

• Xiaodong Li ^a,,
• Djamila Ouelhadj ^a,,
• Xiang Song ^a,,
• Dylan Jones ^a
• Graham Wall ^a
• Kerry E. Howell ^b
• Paul Igwe ^b
• Simon Martin ^c
• Dongping Song ^d
• Emmanuel Pertin ^e

• aCentre for Operational Research and Logistics (CORL), Department of Mathematics, University of Portsmouth, UK
• bPlymouth Business School, University of Plymouth, UK
• cComputational Heuristics Operational Research Decision Support Group, University of Stirling, UK
• dManagement School, University of Liverpool, UK
• eInstitut Superieur D'etudes Logistiques (ISEL), Le Havre University, France

Received 30 October 2015. Revised 12 July 2016. Accepted 16 July 2016. Available online 31 July 2016.

Highlights

• •
  A Decision Support System is designed for multiple offshore wind stakeholders.
• •
  A deterministic model is intended for users with access to accurate failure rate.
• •
  A stochastic model is intended for users who have less certainty about failure.
• •
  Maintenance resources are identified to meet the requirement of workload.

Abstract

This paper presents a Decision Support System (DSS) for maintenance cost optimisation at an Offshore Wind Farm (OWF). The DSS is designed for use by multiple stakeholders in the OWF sector with the overall goal of informing maintenance strategy and hence reducing overall lifecycle maintenance costs at the OWF. Two optimisation models underpin the DSS. The first is a deterministic model that is intended for use by stakeholders with access to accurate failure rate data. The second is a stochastic model that is intended for use by stakeholders who have less certainty about failure rates. Solutions of both models are presented using a UK OWF that is in construction as an example. Conclusions as to the value of failure rate data are drawn by comparing the results of the two models. Sensitivity analysis is undertaken with respect to the turbine failure rate frequency and number of turbines at the site, with near linear trends observed for both factors. Finally, overall conclusions are drawn in the context of maintenance planning in the OWF sector.

Keywords

Offshore wind

Renewable energy

Operations and maintenance (O&M)

Decision support

Stochastic optimisation

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• ∗ 
  Corresponding author.
• ∗∗ 
  Corresponding author.
• ∗∗∗ 
  Corresponding author.

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