DNN-based Policies for Stochastic AC OPF

TL;DR

This paper introduces a deep neural network (DNN) policy for stochastic optimal power flow that learns dispatch decisions in real-time, effectively managing uncertainties in power grid operations and ensuring safety constraints.

Contribution

It proposes a DNN-based control policy trained via stochastic primal-dual updates, eliminating the need for pre-generated labels and explicitly handling feasibility constraints.

Findings

DNN policies outperform affine policies in safety and cost.

The approach produces near-optimal solutions in test cases.

Explicit constraint handling improves reliability.

Abstract

A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF formulations consider simple control policies such as affine policies that are mathematically simple and resemble many policies used in current practice. Motivated by the efficacy of machine learning (ML) algorithms and the potential benefits of general control policies for cost and constraint enforcement, we put forth a deep neural network (DNN)-based policy that predicts the generator dispatch decisions in real time in response to uncertainty. The weights of the DNN are learnt using stochastic primal-dual updates that solve the SOPF without the need for prior generation of training labels and can explicitly account for the feasibility constraints in the SOPF. The advantages of the DNN policy over simpler policies and their efficacy in enforcing safety limits and producing near optimal solutions are demonstrated in the context of a chance constrained formulation on a number of test cases.