  Distributed optimization has attracted lots of attention in the operation of
power systems in recent years, where a large area is decomposed into smaller
control regions each solving a local optimization problem with periodic
information exchange with neighboring regions. However, most distributed
optimization methods are iterative and require synchronization of all regions
at each iteration, which is hard to achieve without a centralized coordinator
and might lead to under-utilization of computation resources due to the
heterogeneity of the regions. To address such limitations of synchronous
schemes, this paper investigates the applicability of asynchronous distributed
optimization methods to power system optimization. Particularly, we focus on
solving the AC Optimal Power Flow problem and propose an algorithmic framework
based on the Alternating Direction Method of Multipliers (ADMM) method that
allows the regions to perform local updates with information received from a
subset of but not all neighbors. Through experimental studies, we demonstrate
that the convergence performance of the proposed asynchronous scheme is
dependent on the communication delay of passing messages among the regions.
Under mild communication delays, the proposed scheme can achieve comparable or
even faster convergence compared with its synchronous counterpart, which can be
used as a good alternative to centralized or synchronous distributed
optimization approaches.
