The total life cycle (also referred to as support time) of a
wind farm can be 20-30 years. Costs occurring in this period are called operation and maintenance (O&M) costs and are the second largest cost contributor (behind the capital costs) to the total life-cycle cost of a wind farm. For example, in offshore installations, O&M costs represent between 24 to 31% of the total life-cycle costs. Accurately forecasting the O&M costs and optimizing the O&M activities to minimize the O&M costs are important issues that must be addressed to reduce risk and maximize the opportunity for success of wind farms (both offshore and on shore).
The research projects summarized in this section all involve the modeling of the life-cycle costs of wind turbines and wind farms that include system health management technologies and power purchase agreements (PPAs).
Contact for these projects:
Peter Sandborn
Department of Mechanical Engineering
CALCE – Center for Advanced Life Cycle Engineering
University of Maryland
(301) 405-3167
sandborn@umd.edu
PI: Peter Sandborn, University of Maryland, College Park
Student: Xin Lei (PhD student)
Summary: The prediction and optimization of maintenance activities provides a significant opportunity for offshore wind farms operation and maintenance (O&M) cost reduction and/or revenue earning maximization. This work developed the concept of predictive maintenance real options applied to offshore wind farms managed via power purchase agreements (PPAs), to answer the question faced by the maintenance decision-makers: “on which day should the predictive maintenance be scheduled”. For a single wind turbine, a predictive maintenance real option is created by the incorporation of Health Monitoring (HM) into subsystems such that a remaining useful life (RUL) is predicted as the subsystems’ health degrades. The option is exercised when predictive maintenance is performed (based on the RUL) before the subsystem or turbine system fails.
The predictive maintenance real option concept has been extended to offshore wind farms managed under PPAs with multiple turbines indicating RULs concurrently. The time-history paths of the avoided corrective maintenance cost and the cumulative predictive maintenance revenue loss are simulated with the inclusion of uncertainties in the forecasted RULs and the future wind. Using a simulation-based real options analysis (ROA) that valulates a series of “European” options, each of which expires on a possible future predictive maintenance date, the optimum predictive maintenance date that maximizes the value of the predictive maintenance real option can be determined. The avoided corrective maintenance cost and the cumulative predictive maintenance revenue loss for each turbine with an RUL depends on the operational state of all the other turbines in the farm, and the amount of energy that the farm is required to deliver (contractually obligated in the PPA). The optimum predictive maintenance date for the turbines with RULs in a farm subject to a PPA is different from an “as-delivered” contract, and also different from the optimum maintenance dates when these turbines are managed in isolation.
Value and Interfaces: Most modern turbines are equipped with Health Monitoring (HM) equipment. However, creating value from this HM technology remains an open issue. By optimizing the predictive maintenance date based on the HM information at each maintenance decision point, the modeling developed in this project is expected to benefit the Maryland wind energy developers by increasing their life-cycle revenue earnings compared with current mainstream preventive maintenance strategies.
Follow-on Work: This work was funded by the Maryland Offshore Wind Energy Center (MOWEC) MOWER grant, which concluded in 2015. Continuing work that is generalizing the maintenance options to non-revenue-earning systems managed via outcome-based contacts is funded by a Naval Postgraduate School Grant that started in January 2016.
Publications:
X. Lei and P. A. Sandborn, “Maintenance Scheduling Based on Remaining Useful Life Predictions for Wind Farms Managed Using Power Purchase Agreements,” to be published in Renewable Energy, 2017.
X. Lei and P. A. Sandborn, “PHM-Based Wind Turbine Maintenance Optimization Using Real Options,” International Journal of Prognostics and Health Management, Vol. 7, No. 1, 2016.
P. Sandborn, N. Goudarzi and X. Lei, “Incorporation of Outcome-Based Contract Requirements in a Real Options Approach for Maintenance Planning,” Proceedings 12th Annual Acquisition Research Symposium, Monterey, CA, May 2016.
X. Lei, P. Sandborn, and N. Goudarzi, “PHM Based Predictive Maintenance Option Model for Offshore Wind Farm O&M Optimization,” Proceedings of the Annual Conference of the PHM Society, San Diego, CA, October 2015.
X. Lei and P. Sandborn, “Offshore Wind Farm O&M Optimization Using Real Options Analysis,” Proceedings of AWEA, Baltimore, MD, September 2015.
N. Goudarzi, X. Lei, A. St. Pe, S. Rabenhorst, R. Delgado, and P. Sandborn, “Wind Turbine Maintenance Optimizations: The Impact of an Accurate Vertical Wind Profile Estimation,” Proceedings of AWEA, Baltimore, MD, September 2015.
X. Lei, P. Sandborn, R. Bakhshi, A. Kashani-Pour, and N. Goudarzi, “PHM Based Predictive Maintenance Optimization for Offshore Wind Farms,” Proceedings of the IEEE Prognostics and Health Management, Austin, TX, June 2015.
N. Goudarzi, X. Lei, and P. Sandborn, “Maintenance Optimization of a Wind Turbine: The Impact of an Accurate Wind Speed Measurement,” Proceedings of the ASME Power Conference, San Diego, CA, June 2015.X. Lei, P. Sandborn, R. Bakhshi, and A. Kashani-Pour, “Development of a Maintenance Option Model to Optimize Offshore Wind Farm O&M,” Proceedings of EWEA Offshore, Copenhagen, Denmark, March 2015.
P. Sandborn, G. Haddad, X. Lei, and A. Kashani-Pour, “Development of a Maintenance Option Model to Optimize Offshore Wind Farm Sustainment,” Proceedings of SciTech, National Harbor, MD, January 2014.
PI: Peter Sandborn, University of Maryland, College Park
Student: Roozbeh Bakhshi (PhD student)
Summary: Accurate life-cycle costing is a key enabler for the financial feasibility of wind farms, especially offshore installations. Research has shown that performance and maintenance is not optimized and that significant opportunities exist for improvement. In this work we have developed a stochastic model for life-cycle cost analysis and associated return on investment (ROI) analysis that can be used to assess offshore wind farm operation and management (O&M) alternatives and technologies.
To develop and demonstrate the ROI analysis we are focusing on the use of LIDAR to improve the efficiency of wind turbines. One of the technical challenges that affects the performance of wind turbines is the accurate measurement of wind flow direction. Traditional methods that are commonly used measure the direction with an inherent level of inaccuracy. These inaccuracies not only reduce the power production but also result in extra loads on critical components in wind turbines. These extra loads accelerate the degradation of components and result in failures sooner than expected. This means extra expensive maintenance events. Light detection and ranging (LIDAR) systems for wind turbines have been successful in measuring accurate wind direction. However, LIDAR devices are expensive and an accurate cost model is required to understand the cost and benefits of using LIDAR systems in wind farms. In this work we have developed a cost model for detailed performance measurement and maintenance simulation to perform a return on investment (ROI) analysis for wind farms, and we are using it to optimize the use of, and build business cases for, the application of LIDAR on wind turbines.
Value and Interfaces: We are working with Avent Technologies in France who is a manufacturer of LIDAR devices. Through our collaborations and exchange of knowledge and data, and by using our model we have developed solutions that maximize the ROI for LIDAR applications in wind farms. Our solution, increases the power production of a wind farm, while concurrently decreasing the maintenance cost.
Follow-on Work: This work was funded by MOWER1409, which ended in 2017. The work to be completed includes reliability analysis on gearbox and blades to investigate the effects of extra loading due to yaw errors on their degradation.
Publications:
R. Bakhshi and P. Sandborn, “Analysis of Wind Turbine Capacity Factor Improvement by Correcting Yaw Error Using LIDAR,” submitted to ASME 2017 International Mechanical Engineering Congress and Exposition (IMECE2017).
R. Bakhshi and P. Sandborn, “A Return on Investment Model for Implementation of New Technologies on Wind Turbines,” to be published in IEEE Transactions on Sustainable Energy, 2017.
R. Bakhshi and P. Sandborn, “Return on Investment Modeling for Implementation of LIDAR Systems on Wind Turbines for Yaw Error Correction,” Proceedings of the European Wind Energy Association, Hamburg, Germany, Sept. 2016.
R. Bakhshi and P. Sandborn, “The Effect of Yaw Error on the Reliability of Wind Turbine Blades,” Proceedings of the ASME 2016 Power & Sustainability Conference, Charlotte, NC, June 2016.
R. Bakhshi and P. Sandborn, “Return on Investment (ROI) Modeling of Offshore Wind Farm O&M to Support Strategic Technology Insertion,” AWEA, Baltimore, MD, September 2015.
R. Bakhshi, P. Sandborn, X. Lei, and A. Kashani-Pour, “Return on Investment Modeling to Support Cost Avoidance Business Cases for Wind Farm O&M,” Proceedings of EWEA Offshore, Copenhagen, Denmark, March 2015.
PI: Peter Sandborn, University of Maryland, College Park
Students: Maira Bruck (MS student), Navid Goudarzi (UNC, Charlotte)
Summary: The cost of energy is an increasingly important issue in the world as renewable energy resources are growing in demand. Energy contracts are designed to keep the price of energy as low as possible while controlling the risk for both parties (i.e., the Buyer and the Seller). Price and risk are often balanced using complex Power Purchase Agreements (PPAs). Since wind is not a constant supply source, to keep risk low, wind PPAs contain clauses that require the purchase and sale of energy to fall within reasonable limits. However, the existence of those limits also creates pressure on prices causing increases in the Levelized Cost of Energy (LCOE). Depending on the variation in capacity factor, the power generator (the Seller) may find that the limitations on power purchasing required by the utility (the Buyer) are unfavorable, which will result in higher costs of energy than predicted.
Existing cost models do not take into account the energy purchase limitations or the variations in energy production (capacity factor) when calculating an LCOE. A new cost model has been developed to evaluate the price of energy during PPA contract design. The new model can be used by the Seller to negotiate delivery penalties within a PPA. This model has been tested on a controlled wind farm and using real data from European offshore wind farms. The results show that LCOE depends on the limitations on energy purchase within PPAs and on the wind farm performance characteristics.
Value and Interfaces: The new LCOE model used during the design of a PPA contract can also be used to find an appropriate value for the Offshore Renewable Energy credits (ORECs). ORECs are designed to facilitate the sale of offshore wind energy within Maryland and ensure the economic success of the offshore wind farm. Similar to PPAs, ORECs have a power purchase limit of 2.5% of Maryland’s annual energy consumption. An appropriate value for the ORECs, based on the LCOE developed from the required annual energy purchase limit, is crucial for the success of the wind farm and offshore wind energy in Maryland. The proposed new LCOE model could lead to determining an optimum price for developing the OREC.
Follow-on Work: This work was funded by MOWER1409, which ended in summer 2017. This work will continue under NSF funding and evaluate the Maryland OREC price including limitations on offshore energy purchases prescribed by the OREC. The objective of this analysis is to assess the impact of the OREC limitations on the OREC price, i.e., determine how accurate $131.93/MWh is once the penalties/limitations for over and under delivery of energy imposed by the OREC are included in the LCOE calculation?
Publications:
M. Bruck, N. Goudarzi, and P. Sandborn, “A Levelized Cost of Energy (LCOE) Model for Wind Farms with Power Purchase Agreements (PPAs),” submitted to Renewable Energy.
M. Bruck, N. Goudarzi, and P. Sandborn, “A Modified Levelized Cost of Energy (LCOE) Mode to Provide Purchase Price Schedules to Power Purchase Agreements,” Proceedings of the European Wind Energy Association, Hamburg, Germany, Sept. 2016.
M. Bruck, N. Goudarzi, and P. Sandborn, “A Levelized Cost of Energy (LCOE) Model for Wind Farms that Includes Power Purchase Agreement (PPA) Energy Delivery Limits,” Proceedings of the ASME 2016 Power Conference, Charlotte, NC, June 2016.
PI: Peter Sandborn, University of Maryland, College Park
Students: Navid Goudarzi (post-doc), Amir Kashani-Pour (PhD student)
Summary: Complex mission, infrastructure and safety-critical systems are shifting away from traditional contract mechanisms of fixed-price followed by the purchase of support. Newer performance-based contracts are growing in popularity for governmental and non-governmental acquisitions of critical systems, such as for energy, defense, transportation and healthcare. These contracts allow the customer to buy the system performance rather than to purchase the product itself, or to buy availability of service rather than to pay for maintenance. Performance contracts are not warranties, lease agreements, or maintenance contracts, which are all “break-fix” guarantees; rather, these contracts are highly quantified "satisfaction guaranteed" contracts where "satisfaction" is a combination of outcomes received from the product, usually articulated as a time (e.g., operational availability), usage measure (e.g., miles), or energy-based availability (e.g., expected energy production). Performance-based contracts (also called outcome-based contracts) can take different forms where the particular performance that the contract specifies distinguishes the contract mechanism, e.g., performance-based logistics (PBL), public-private partnerships (PPPs), and power purchase agreements (PPAs). Unfortunately, the contract design itself is almost always performed separate from the engineering design process and provided as a requirement to the engineering design process. This research approaches contract design as a system design problem where the process of designing contractual terms and requirements that address performance metrics, the payment model, and performance assessment, represents a multidisciplinary design process that can be integrated into the broader engineering design process.
Value and Interfaces: The potential value of this research to the State of Maryland is in optimizing the Public Private Partnerships (PPPs), as well as policy making and capital intensive acquisition, which include long-term sustainment (e.g., the purple line, railroad, highways, bus, cloud-servers and IT structure). The question of how and how much should be paid for improvements or sustainment to keep a win-win contract between the contractor and the public can be answered. For example, the offshore wind PPAs could be a case study for this research.
Follow-on Work: This work is currently funded grants from the National Science Foundation and the Naval Postgraduate School.
Publications:
A. Kashani-Pour, N. Goudarzi, X. Lei, P. Sandborn, “Product-Service Systems (PSS) Under Availability-Based Contracts: Maintenance Optimization and Concurrent System and Contract Design,” Through-life Engineering Services: The Role of Service and Warranty Knowledge Within the Product Creation and Delivery System, ed. L. Redding, R. Roy, and A. Shaw, Springer International, 2016.
P. Sandborn, A. Kashani-Pour, and N. Goudarzi, “Outcome-Based Contracts - Optimizing Maintenance Contract Design for Product-Service Systems,” Proceedings of 5th International Through-Life Engineering Services Conference (Keynote), Cranfield University, November 2016.
A. R. Kashani Pour and P. Sandborn, “Analytics of Methods for Designing Availability-Based Sustainment Contracts,” Journal of Cost Analysis and Parametrics, Vol. 9, pp. 69-91, 2016.
P. Sandborn, Q. Cui, A. Kashani-Pour, and X. Zhu, “A New ‘Availability-Payment’ Model for Pricing Performance Based Logistics Contracts,” Proc. 11th Annual Acq. Res. Symp., Monterey, CA, May 2014.
Amir Krishani-Pour for the Donald J. Bowersox Doctoral Symposium at the Council of Supply Chain Management Professionals 2012 Supply Chain Management Educators’ Conference, Sept. 2012.