  In this paper, we investigate two decomposition methods for their convergence
rate which are used to solve security constrained economic dispatch (SCED): 1)
Lagrangian Relaxation (LR), and 2) Augmented Lagrangian Relaxation (ALR).
First, the centralized SCED problem is posed for a 6-bus test network and then
it is decomposed into subproblems using both of the methods. In order to model
the tie-line between decomposed areas of the test network, a novel method is
proposed. The advantages and drawbacks of each method are discussed in terms of
accuracy and information privacy. We show that there is a tradeoff between the
information privacy and the convergence rate. It has been found that ALR
converges faster compared to LR, due to the large amount of shared data.
