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Scalable Distributed Non-Convex ADMM-based Active Distribution System Service Restoration

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Document pages: 9 pages

Abstract: Distributed restoration can harness distributed energy resources (DER) toenhance the resilience of active distribution networks. However, the largenumber of decision variables, especially the binary decision variables ofreconfiguration, bring challenges on developing effective distributeddistribution service restoration (DDSR) strategies. This paper proposes ascalable distributed optimization method based on the alternating directionmethod of multipliers (ADMM) for non-convex mixed-integer optimization problemsand applies to develop the DDSR framework. The non-convex ADMM method consistsof relax-drive-polish phases, 1) relaxing binary variables and applying theconvex ADMM as a warm start; 2) driving the solutions toward Boolean valuesthrough a proximal operator; 3) fixing the obtained binary variables to polishcontinuous variables for a high-quality solution. Then, an autonomousclustering strategy together with consensus ADMM is developed to realize thedistributed cluster-based framework of restoration. The nonconvex ADMM-basedDDSR can determine DER scheduling and switch status for reconfiguration andload pickup in a distributed manner, energizing the out-of-service area fromlocal faults or total blackouts in large-scale distribution networks. Theeffectiveness and scalability of the proposed DDSR framework are demonstratedthrough testing on the IEEE 123-node and IEEE 8500-node test feeders.

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