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

TL;DR

This paper presents a novel approach using deep neural networks integrated into optimal power flow to control smart inverters under uncertainty, ensuring feasibility even with proxy or noisy grid data.

Contribution

It introduces a DNN-based inverter control method that incorporates chance constraints and can operate with partial or noisy grid information, enhancing scalability and feasibility.

Findings

DNN-based control achieves near-optimal inverter schedules.

The approach maintains voltage constraints under uncertainty.

Gradient-free training methods work when network models are unknown.

Abstract

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.