PHM Based Predictive Maintenance Option Model for Offshore Wind Farm O&M Optimization

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Xin Lei Peter A. Sandborn Navid Goudarzi Maira A. Bruck

Abstract

A simulation-based real options analysis (ROA) approach is used to determine the optimum predictive maintenance opportunity for multiple wind turbines with remaining useful life (RUL) predictions in offshore wind farms managed under outcome-based contracts, i.e., power purchase agreements (PPAs). When an RUL is predicted for a subsystem in a single turbine using PHM, a predictive maintenance option is triggered that the decision-maker has the flexibility to decide if and when to exercise before the subsystem or turbine fails. The predictive maintenance value paths are simulated by considering the uncertainties in the RUL predictions and wind speeds (that govern the turbine’s revenue earning potential). By valuating a series of European options expiring on all possible predictive maintenance opportunities, a series of option values can be obtained, and the optimum predictive maintenance opportunity can be selected. The optimum predictive maintenance opportunity can also be determined using a stochastic discounted cash flow (DCF) approach that assumes the predictive maintenance will always be implemented on the selected opportunity. For a wind farm managed via a PPA with multiple turbines indicating RULs concurrently, the predictive maintenance value for each turbine depends on the operational state of the other turbines, the amount of energy delivered and to be delivered by the whole wind farm. A case study is presented in which the stochastic DCF and European ROA approaches are applied to a single turbine and to a wind farm managed via a PPA. The optimum predictive maintenance opportunities obtained from the two approaches are compared and it is demonstrated that the European ROA approach will suggest a more conservative opportunity for predictive maintenance with a higher expected option value than the expected net present value (NPV) from the stochastic DCF approach.

How to Cite

Lei, X. ., A. Sandborn, P., Goudarzi, N., & A. Bruck, M. . (2015). PHM Based Predictive Maintenance Option Model for Offshore Wind Farm O&M Optimization. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2677
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Keywords

prognostics and health managemen, predictive maintenance, maintenance optimization, offshore wind farms, real options analysis, power purchase agreement

References
Anaheim. (2003). Long-term power purchase agreement.City of Anaheim. http://www.anaheim.net/docsagend/questyspub/MG35236/AS35275/AS35278/AI35947/D O35950/DO35950.pdf

Barradale, M. J. (2008). Impact of policy uncertainty on renewable energy investment: wind power and PTC. http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=108 5063

Bonneville. (2007). Klondike III wind project power purchase. Bonneville Power Admin. https://www.bpa. gov/power/pgc/wind/KlondikeROD.pdf

Delmarva. (2008). Renewable wind energy power purchase agreement. Delmarva Power & Light Company. http:// www.delmarva.com/uploadedFiles/wwwdelmarvacom/ AESPPA.pdf

Fried, L., Sawyer, S., Shukla, S., & Qiao, L. (2014a). Global wind report annual market update 2013. Brussels: GWEC.

Fried, L., Shukla, S., Sawyer, S., & Teske, S. (2014b). Global wind energy outlook 2014. Brussels: GWEC.

Gloucester. (2011). Power purchase agreement. City of Gloucester. http://gloucestema.gov/DocumentCenter/ Home/View/1125

Haddad, G., Sandborn, P. A., & Pecht, M. G. (2014). Using maintenance options to maximize the benefits of prognostics for wind farms. Wind Energy, 17(5), 775- 791. doi:10.1002/we.1610

IRENA Secretariat. (2012). Renewable energy technologies: cost analysis series. Abu Dhabi: IRENA.

Karyotakis, A. (2011). On the optimization of operation and maintenance strategies for offshore wind farms. Doctoral dissertation. Department of Mechanical Engineering.
University College London, London. http://discovery.ucl.ac.uk/1302066/1/1302066.pdf

Kodukula, P., & Papudesu, C. (2006). Project Valuation Using Real Options. Fort Lauderdale: J. Ross Publishing, Inc.

Kovács, A., Erdős, G., Viharos, Z. J., & Monostori, L. (2011). A system for the detailed scheduling of wind farm maintenance. CIRP Annals-Manufacturing Technology, 60(1), 497-501.
doi:10.1016/j.cirp.2011.03.049

Lei, X., Sandborn, P. A., Bakhshi, R., & Kashani-Pour, A. (2015). Development of a Maintenance Option Model to Optimize Offshore Wind Farm O&M. EWEA OFFSHORE 2015, Mar 10-12, Copenhagen.

National Data Buoy Center. (2013). Station 44009 (LLNR 168) - DELAWARE BAY 26 NM Southeast of Cape May, NJ. http://www.ndbc.noaa.gov/station_history.php? station=44009

Nilsson, J., & Bertling, L. (2007). Maintenance management of wind power systems using condition monitoring systems—life cycle cost analysis for two case studies. Energy
Conversion, IEEE Transactions on, 22(1), 223- 229. doi:10.1109/TEC.2006.889623

PacifiCorp. (2008). Power purchase agreement. http://www.pacificorp.com/content/dam/pacificorp/doc/ Energy_Sources/Customer_Generation/Company_Qual ified_Facility_Program.pdf

Sandborn, P. A., & Wilkinson, C. (2007). A maintenance planning and business case development model for the application of prognostics and health management (PHM) to electronic systems. Microelectronics Reliability, 47(12), 1889-1901. doi:10.1016/j.microrel. 2007.02.016

Sonoma. (2014). Feed-in tariff power purchase agreement. Sonoma Clean Power. http://sonoma cleanpower.org/ wpcontent/uploads/2014/09/SCP-FIT-PPA-Approved- 2014-07.pdf
Stoel Rives Wind Team. (2014). The law of wind: a guide to business and legal issues, 7th ed., Portland: Stoel Rives LLP.

Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui- Hasnaoui, F., Tameghe, T. A., & Ekemb, G. (2014). Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 7(4), 2595-2630. doi:10.3390/en7042595

Vestas. (2013). 3 MW Platform. http://pdf.directindustry. com/pdf/vestas/3-mwplatform/20680-398713.html

World Bank. (2002). Namibia IPP and investment market framework technical assistance. World Bank. http:// siteresources.worldbank.org/INTINFANDLAW/Resour
ces/namibiamediumscaleppawind.pdf

Xcel. (2013). Wind energy purchase agreement. Xcel Energy. http://www.xcelenergy.com/stateselector? stateSelected=true &goto=/
Section
Technical Papers