Data-driven maintenance and operations scheduling in power systems under decision-dependent uncertainty

Generator maintenance scheduling plays a pivotal role in ensuring uncompromised operations of power systems. There exists a tight coupling between the condition of the generators and corresponding operational schedules, significantly affecting the reliability of the system. In this study, we effectively model and solve an integrated condition-based maintenance and operations scheduling problem for a fleet of generators with an explicit consideration of decision-dependent generator conditions. We propose a sensor-driven degradation framework with remaining lifetime estimation procedures under time-varying load levels. We present estimation methods by adapting our model to the underlying signal variability. Then, we develop a stochastic optimization model that considers the effect of the operational decisions on the generators’ degradation levels along with the uncertainty of the unexpected failures. As the resulting problem includes nonlinearities, we adopt piecewise linearization along with other linearization techniques and propose formulation enhancements to obtain a stochastic mixed-integer linear programming formulation. We develop a decision-dependent simulation framework for assessing the performance of a given solution. Finally, we present computational experiments demonstrating significant cost savings and reductions in failures in addition to highlighting computational benefits of the proposed approach.