Controlling Smart Inverters Using Proxies: A Chance-Constrained DNN-based Approach

Coordinating inverters at scale under uncertainty is essential for integrating renewables in distribution grids. When only proxies of grid conditions are available, we integrate DNN-based inverter policies directly into OPF and train them via two formulations that confine voltage deviations: an average-violation approach and a convex restriction of chance constraints. The trained DNNs can be driven by partial/noisy/proxy descriptors, enabling operation on unobservable feeders.