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Maintenance planning plays a key role in power system operations underuncertainty by helping system operators ensure a reliable and secure powergrid. This paper studies a short-term condition-based integrated maintenanceplanning with operations scheduling problem while considering the unexpectedfailure possibilities of generators as well as transmission lines. We formulatethis problem as a two-stage stochastic mixed-integer program with failurescenarios sampled from the sensor-driven remaining lifetime distributions ofthe individual system elements whereas a joint chance-constraint consisting ofPoisson Binomial random variables is introduced to account for failure risks.Because of its intractability, we develop a cutting-plane method to obtain anexact reformulation of the joint chance-constraint by proposing a separationsubroutine and deriving stronger cuts as part of this procedure. To solvelarge-scale instances, we derive a second-order cone programming based safeapproximation of this constraint. Furthermore, we propose a decomposition-basedalgorithm implemented in parallel fashion for solving the resulting stochasticprogram, by exploiting the features of the integer L-shaped method and thespecial structure of the maintenance and operations scheduling problem toderive stronger optimality cuts. We further present preprocessing steps overtransmission line flow constraints to identify redundancies. To illustrate thecomputational performance and efficiency of our algorithm compared to moreconventional maintenance approaches, we design a computational study focusingon a weekly plan with daily maintenance and hourly operational decisionsinvolving detailed unit commitment subproblems. Our computational results onvarious IEEE instances demonstrate the computational efficiency of the proposedapproach with reliable and cost-effective maintenance and operationalschedules.

A Joint Chance-Constrained Stochastic Programming Approach for the  Integrated Predictive Maintenance and Operations Scheduling Problem in Power  Systems