Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
This work addresses the challenge of renewable integration for grid operators by enabling scalable inverter control with reliability assurances, though it is incremental in combining existing DNN and OPF methods.
The paper tackles the problem of coordinating smart inverters in distribution grids under uncertainty by integrating deep neural network (DNN)-based policies into optimal power flow (OPF) formulations, achieving feasibility guarantees through chance constraints and handling partial or noisy grid data.
Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inverter schedules, guaranteeing feasibility is largely elusive. Rather than training DNNs to imitate already computed optimal power flow (OPF) solutions, this work integrates DNN-based inverter policies into the OPF. The proposed DNNs are trained through two OPF alternatives that confine voltage deviations on the average and as a convex restriction of chance constraints. The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions. This is important when OPF has to be solved for an unobservable feeder. DNN weights are trained via back-propagation and upon differentiating the AC power flow equations assuming the network model is known. Otherwise, a gradient-free variant is put forth. The latter is relevant when inverters are controlled by an aggregator having access only to a power flow solver or a digital twin of the feeder. Numerical tests compare the DNN-based inverter control schemes with the optimal inverter setpoints in terms of optimality and feasibility.