  Maintenance planning plays a key role in power system operations under
uncertainty by helping system operators ensure a reliable and secure power
grid. This paper studies a short-term condition-based integrated maintenance
planning with operations scheduling problem while considering the unexpected
failure possibilities of generators as well as transmission lines. We formulate
this problem as a two-stage stochastic mixed-integer program with failure
scenarios sampled from the sensor-driven remaining lifetime distributions of
the individual system elements whereas a joint chance-constraint consisting of
Poisson Binomial random variables is introduced to account for failure risks.
Because of its intractability, we develop a cutting-plane method to obtain an
exact reformulation of the joint chance-constraint by proposing a separation
subroutine and deriving stronger cuts as part of this procedure. To solve
large-scale instances, we derive a second-order cone programming based safe
approximation of this constraint. Furthermore, we propose a decomposition-based
algorithm implemented in parallel fashion for solving the resulting stochastic
program, by exploiting the features of the integer L-shaped method and the
special structure of the maintenance and operations scheduling problem to
derive stronger optimality cuts. We further present preprocessing steps over
transmission line flow constraints to identify redundancies. To illustrate the
computational performance and efficiency of our algorithm compared to more
conventional maintenance approaches, we design a computational study focusing
on a weekly plan with daily maintenance and hourly operational decisions
involving detailed unit commitment subproblems. Our computational results on
various IEEE instances demonstrate the computational efficiency of the proposed
approach with reliable and cost-effective maintenance and operational
schedules.
