  Unit Commitment (UC) is a fundamental problem in power system operations.
When coupled with generation maintenance, the joint optimization problem poses
significant computational challenges due to coupling constraints linking
maintenance and UC decisions. Obviously, these challenges grow with the size of
the network. With the introduction of sensors for monitoring generator health
and condition-based maintenance(CBM), these challenges have been magnified.
ADMM-based decentralized methods have shown promise in solving large-scale UC
problems, especially in vertically integrated power systems. However, in their
current form, these methods fail to deliver similar computational performance
and scalability when considering the joint UC and CBM problem.
  This paper provides a novel decentralized optimization framework for solving
large-scale, joint UC and CBM problems. Our approach relies on the novel use of
the subgradient method to temporally decouple various subproblems of the
ADMM-based formulation of the joint problem along the maintenance horizon. By
effectively utilizing multithreading, our decentralized subgradient approach
delivers superior computational performance and eliminates the need to move
sensor data thereby alleviating privacy and security concerns. Using
experiments on large scale test cases, we show that our framework can provide a
speedup of upto 50x as compared to various state of the art benchmarks without
compromising on solution quality.
