Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees

TL;DR

This paper introduces a neural dispatch framework for real-time energy resource management that guarantees feasibility without external solvers, enabling fast and reliable renewable energy scheduling.

Contribution

It develops a solver-free neural dispatch method with rigorous feasibility guarantees using convex approximation and robust optimization, advancing real-time renewable energy management.

Findings

Restores feasibility within approximately 10^{-3} seconds.

Maintains near-optimal performance in energy dispatch.

Operates without external solvers, ensuring efficiency.

Abstract

The growing penetration of renewable energy necessitates high-frequency real-time scheduling. While neural network-based surrogates enable computationally efficient scheduling, strictly enforcing nonconvex power flow constraints without external solvers remains a fundamental challenge. To bridge this gap, this letter proposes a solver-free neural dispatch framework with rigorous feasibility guarantees. A convex inner approximation of the DistFlow model is first derived via the convex envelope theorem. Building upon this approximation, a robust optimization-based affine policy is formulated to yield a theoretically certified interior-point mapping rule, which is then embedded within a bisection-based projection scheme to efficiently recover feasibility for infeasible NN outputs without any external solver. Experimental results demonstrate that the proposed method restores feasibility on the order of $10^{-3}$ s while maintaining near-optimal performance.