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.
@article{arxiv.2605.00317,
title = {Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees},
author = {Jie Zhu and Yinliang Xu and Hongbin Sun},
journal= {arXiv preprint arXiv:2605.00317},
year = {2026}
}