Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911638221750272 |
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| author | Zhu, Jie Xu, Yinliang Sun, Hongbin |
| author_facet | Zhu, Jie Xu, Yinliang Sun, Hongbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees Zhu, Jie Xu, Yinliang Sun, Hongbin Systems and Control 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. |
| title | Real-Time Neural Distributed Energy Resources Dispatch with Feasibility Guarantees |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.00317 |