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.