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

Between cyber-intensive OPF and local droop control, we propose training deep neural networks (DNNs) to set inverter injections. The DNN embeds the feeder model and is trained to minimize a grid-wide objective subject to inverter/network constraints in expectation over uncertainty. Learning is posed as stochastic OPF with primal–dual updates. A master–slave architecture broadcasts a condensed utility signal to local inverter DNNs that combine it with local measurements, enabling operation under bandwidth constraints.