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Aiming for the median solution between cyber-intensive optimal power flow(OPF) solutions and subpar local control, this work advocates deciding inverterinjection setpoints using deep neural networks (DNNs). Instead of fitting OPFsolutions in a black-box manner, inverter DNNs are naturally integrated withthe feeder model and trained to minimize a grid-wide objective subject toinverter and network constraints enforced on the average over uncertain gridconditions. Learning occurs in a quasi-stationary fashion and is posed as astochastic OPF, handled via stochastic primal-dual updates acting on grid datascenarios. Although trained as a whole, the proposed DNN is operated in amaster-slave architecture. Its master part is run at the utility to output acondensed control signal broadcast to all inverters. Its slave parts areimplemented by inverters and are driven by the utility signal along with localinverter readings. This novel DNN structure uniquely addresses the small-bigdata conundrum where utilities collect detailed smart meter readings yet on anhourly basis, while in real time inverters should be driven by local inputs andminimal utility coordination to save on communication. Numerical testscorroborate the efficacy of this physics-aware DNN-based inverter solution overan optimal control policy.

Deep Learning for Reactive Power Control of Smart Inverters under Communication Constraints