EWEA OFFSHORE 2015, March 10-12, 2015, Copenhagen, Denmark

Development of a Maintenance Option Model to Optimize Offshore Wind Farm O&M

Xin Lei, Peter Sandborn, Roozbeh Bakhshi and Amir Kashani-Pour

CALCE, Department of Mechanical Engineering
University of Maryland, College Park, MD 20742

Abstract:

The prediction and optimization of maintenance activities provides a significant opportunity for offshore wind farms operation and maintenance (O&M) cost reduction. This paper introduces the concept of predictive maintenance options applied to offshore wind farms managed via power purchase agreements (PPAs). For a single turbine, a predictive maintenance option is created by the incorporation of Health Monitoring (HM) or Prognostics and Health Management (PHM) into subsystems such that a remaining useful life (RUL) is predicted as the subsystem’s health degrades. The option is exercised when predictive maintenance is performed (based on the RUL) before the subsystem or turbine failures. The concept has been extended to offshore wind farms managed under a PPA with multiple turbines indicating RULs. The time-history paths of cost avoidance and cumulative revenue are simulated with the inclusion of uncertainties in wind and the forecasted RULs. Using a simulation-based real options analysis (ROA) that analyses a series of “European” options and all possible predictive maintenance opportunities, the optimum maintenance opportunity that maximizes the value of the predictive maintenance option can be determined. The cumulative revenue and cost avoidance for a single turbine depends on the operational state of the other turbines and the amount of energy that the farm is required to deliver. The optimum predictive maintenance opportunity for the turbines in a farm subjecting to a PPA is different from subjecting to an “as-delivered” contract, and also different from the optimum opportunities for the individual turbines managed in isolation.

Complete article is available from to the CALCE Consortium Members.



[Home Page] [Articles Page]
Copyright © 2015 by CALCE and the University of Maryland, All Rights Reserved