  We propose a distributed method to solve a multi-agent optimization problem
with strongly convex cost function and equality coupling constraints. The
method is based on Nesterov's accelerated gradient approach and works over
stochastically time-varying communication networks. We consider the standard
assumptions of Nesterov's method and show that the sequence of the expected
dual values converge toward the optimal value with the rate of
$\mathcal{O}(1/k^2)$. Furthermore, we provide a simulation study of solving an
optimal power flow problem with a well-known benchmark case.
