This paper presents a distributed approach to optimal power flow (OPF) in an electrical network, suitable for\napplication in a future smart grid scenario where access to resource and control is decentralized. The non-convex OPF\nproblem is solved by an augmented Lagrangian method, similar to the widely known ADMM algorithm, with the key\ndistinction that penalty parameters are constantly increased. A (weak) assumption on local solver reliability is required\nto always ensure convergence. A certificate of convergence to a local optimum is available in the case of bounded\npenalty parameters. For moderate sized networks (up to 300 nodes, and even in the presence of a severe partition of\nthe network), the approach guarantees a performance very close to the optimum, with an appreciably fast\nconvergence speed. The generality of the approach makes it applicable to any (convex or non-convex) distributed\noptimization problem in networked form. In the comparison with the literature, mostly focused on convex SDP\napproximations, the chosen approach guarantees adherence to the reference problem, and it also requires a smaller\nlocal computational complexity effort.
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