Neural Two-Stage Stochastic Volt-VAR Optimization for Three-Phase Unbalanced Distribution Systems with Network Reconfiguration

TL;DR

This paper introduces a neural two-stage stochastic Volt-VAR optimization method for unbalanced distribution systems, significantly speeding up computations while maintaining solution quality under uncertainty.

Contribution

It proposes a neural network-based acceleration strategy for large-scale stochastic VVO problems, integrating it into a mixed-integer linear programming framework.

Findings

Achieves over 50 times speedup compared to traditional methods.

Maintains an optimality gap below 0.30%.

Demonstrates scalability on a 123-bus system.

Abstract

The increasing integration of intermittent distributed energy resources (DERs) has introduced significant variability in distribution networks, posing challenges to voltage regulation and reactive power management. This paper presents a novel neural two-stage stochastic Volt-VAR optimization (2S-VVO) method for three-phase unbalanced distribution systems considering network reconfiguration under uncertainty. To address the computational intractability associated with solving large-scale scenario-based 2S-VVO problems, a learning-based acceleration strategy is introduced, wherein the second-stage recourse model is approximated by a neural network. This neural approximation is embedded into the optimization model as a mixed-integer linear program (MILP), enabling effective enforcement of operational constraints related to the first-stage decisions. Numerical simulations on a 123-bus unbalanced distribution system demonstrate that the proposed approach achieves over 50 times speedup compared to conventional solvers and decomposition methods, while maintaining a typical optimality gap below 0.30%. These results underscore the method's efficacy and scalability in addressing large-scale stochastic VVO problems under practical operating conditions.