Algorithm evaluation study demonstrates up to 10.6-fold computational speedups in large-scale power grids, indicating enhanced scalability for distributed grid management without central coordination.
In light of the soaring uptake of distributed energy resources, distributed methods are attracting increased focus. This paper proposes an adaptive scheme to improve the convergence of the alternating direction of multipliers method (ADMM) on the component-based dual decomposition of the AC optimal power flow (OPF). The method incorporates a local curvature approximation scheme, with underlying parameter update steps inspired by the local residual balancing scheme, to automatically tune the penalty parameters locally, without central oversight. Substantial speed-ups over the vanilla ADMM, as high as 10.6 times, are demonstrated on real-world test systems with more than 13 600 buses, as well as on other difficult test cases. Furthermore, scalability is assessed by showing that, unlike in the centralized computation, the distributed implementation of the proposed adaptive method is independent of system size.
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Mhanna et al. (2018) studied this question.
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