Smart Grid Optimization Through Asynchronous, Distributed Primal Dual Iterations

The diversity of components in the smart grid and issues, such as scalability, stability, and privacy, have led to the desire for more distributed control paradigms. In this paper, we address the problem of optimizing smart grid operation with separable global costs and separable but nonconvex constraints, while considering important aspects of network operation, such as power flow and nodal voltage constraints. A localized primal dual method is applied through the use of an augmented Lagrange function, which is used to overcome the issues of nonconvexity in the presence of nonlinear equality constraints. The nonseparability of the augmented Lagrange penalty function is addressed through the use of local and neighborhood communication leading to a completely distributed solution of the global problem.