Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees
It addresses the critical need for fast, guaranteed-feasible scheduling in power systems with high renewable penetration, offering a practical solution for real-time operations.
This work proposes a solver-free neural dispatch framework that enforces feasibility of nonconvex power flow constraints in real-time distributed energy resources scheduling, achieving feasibility restoration in ~10^{-3} seconds with near-optimal performance.
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.