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The large penetration of renewable resources has resulted in rapidly changingnet loads, resulting in the characteristic "duck curve". The resulting rampingrequirements of bulk system resources is an operational challenge. To addressthis, we propose a distributed optimization framework within which distributedresources located in the distribution grid are coordinated to provide supportto the bulk system. We model the power flow of the multi-phase unbalanceddistribution grid using a Current Injection (CI) approach, which leveragesMcCormick Envelope based convex relaxation to render a linear model. We thensolve this CI-OPF with an accelerated Proximal Atomic Coordination (PAC) whichemploys Nesterov type acceleration, termed NST-PAC. We evaluate our distributedapproach against a local approach, on a case study of San Francisco,California, using a modified IEEE-34 node network and under a high penetrationof solar PV, flexible loads, and battery units. Our distributed approachreduced the ramping requirements of bulk system generators by up to 23%.

Flattening the Duck Curve: A Case for Distributed Decision Making