Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

TL;DR

This paper introduces a data-driven method to design local Volt/Var controllers for power distribution networks, ensuring stability and improved efficiency by learning optimal operating points and synthesizing controllers that converge globally.

Contribution

It proposes a novel two-stage framework combining neural network-based surrogate learning with stability-guaranteed local control synthesis for DER Volt/Var regulation.

Findings

Controllers converge globally to optimal operating points

Neural network surrogates effectively model the optimal manifold

Empirical results show improved efficiency over benchmarks

Abstract

This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution networks (DNs). Aiming to improve DN operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DN operating points, in the second stage, we develop local control schemes that steer the DN to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DN operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.