  With increased reliance on cyber infrastructure, large scale power networks
face new challenges owing to computational scalability. In this paper we focus
on developing an asynchronous decentralized solution framework for the Unit
Commitment(UC) problem for large scale power networks. We exploit the inherent
asynchrony in a region based decomposition arising out of imbalance in regional
subproblems to boost computational efficiency. A two phase algorithm is
proposed that relies on the convex relaxation and privacy preserving valid
inequalities in order to deliver algorithmic improvements. Our algorithm
employs a novel interleaved binary mechanism that locally switches from the
convex subproblem to its binary counterpart based on consistent local
convergent behavior. We develop a high performance computing (HPC) oriented
software framework that uses Message Passing Interface (MPI) to drive our
benchmark studies. Our simulations performed on the IEEE 3012 bus case are
benchmarked against the centralized and a state of the art synchronous
decentralized method. The results demonstrate that the asynchronous method
improves computational efficiency by a significant amount and provides a
competitive solution quality rivaling the benchmark methods.
