Coordinating maintenance with spares logistics to minimize levelized cost of wind energy

Wind power emerges as a sustainable energy resource to meet the increasing electricity needs in the next 20-30 years. Power volatility and maintenance costs are the key challenges in harvesting this type of renewable energy. The levelized cost of energy (LCOE) allows the utility and investors to compare the costs of various generation technologies of unequal lifetimes and capacities. In this study we propose a probabilistic-based LCOE model to assess the investment risks by taking into account four major factors: wind speed, system availability, maintenance policy, and spares stock level. Moment methods are applied to estimate the mean and the variance of the energy yield. The goal of the study is to develop a decision aid methodology guiding the wind farmers to minimize the ownership cost by jointly optimizing the maintenance and the spares inventory. We assume the maintenance and repair service is carried by a third party logistics provider. Genetic algorithm is used to search the optimality of the mixed integer non-linear decision model.

[1]  Geert-Jan van Houtum,et al.  Optimization of component reliability in the design phase of capital goods , 2010, Eur. J. Oper. Res..

[2]  Sameer Vittal,et al.  Performance and Reliability Analysis of Wind Turbines Using Monte Carlo Methods Based on System Transport Theory , 2005 .

[3]  Tongdan Jin,et al.  A multi-objective decision making on reliability growth planning for in-service systems , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[4]  M. Hand,et al.  Wind Turbine Design Cost and Scaling Model , 2006 .

[5]  P. Dougherty.  Wind Powering America , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[6]  Luca Podofillini,et al.  Multiobjective spare part allocation by means of genetic algorithms and Monte Carlo simulation , 2005, Reliab. Eng. Syst. Saf..

[7]  Tongdan Jin,et al.  Uncertainty analysis for wind energy production with dynamic power curves , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[8]  Peter Tavner,et al.  Reliability analysis for wind turbines , 2007 .

[9]  R. Billinton,et al.  A simplified wind power generation model for reliability evaluation , 2006, IEEE Transactions on Energy Conversion.

[10]  Torbjörn Thiringer,et al.  Control by variable rotor speed of a fixed-pitch wind turbine operating in a wide speed range , 1993 .

[11]  Tongdan Jin,et al.  Condition based maintenance optimization for wind power generation systems under continuous monitoring , 2011 .

[12]  Semra Tunali,et al.  Joint optimization of spare parts inventory and maintenance policies using genetic algorithms , 2007 .

[13]  Wenbin Wang,et al.  A stochastic model for joint spare parts inventory and planned maintenance optimisation , 2012, Eur. J. Oper. Res..

[15]  P. Wang,et al.  Planning performance based contracts considering reliability and uncertain system usage , 2012, J. Oper. Res. Soc..

[16]  Dragan Banjevic,et al.  Optimization models for critical spare parts inventories—a reliability approach , 2011, J. Oper. Res. Soc..

[17]  J. Lobry,et al.  Impact of the Wind Geographical Correlation Level for Reliability Studies , 2007, IEEE Transactions on Power Systems.

[18]  Alenka Hudoklin,et al.  Joint optimization of block-replacement and periodic-review spare-provisioning policy , 2003, IEEE Trans. Reliab..

[19]  L. Bertling,et al.  Maintenance Management of Wind Power Systems Using Condition Monitoring Systems—Life Cycle Cost Analysis for Two Case Studies , 2007, IEEE Transactions on Energy Conversion.