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Home / Journals / ENERGY / Online First / doi:10.32604/ee.2026.086358
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End-to-End Constraint Injection Neural Optimization for Flexible Load Scheduling in Low-Voltage Distribution Networks

Lixiao Wang1, Jiaqi Li2,3,*, Haifeng Li2, Hongfeng Li1, Yuhan Wen3, Runhuan Lin1, Huansheng Zhou4
1 School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou, China
2 College of Electric Power Engineering, South China University of Technology, Guangzhou, China
3 College of Electrical Engineering, Guangzhou City University of Technology, Guangzhou, China
4 China Southern Power Grid Energy Development Research Institute, Guangzhou, China
* Corresponding Author: Jiaqi Li. Email: email
(This article belongs to the Special Issue: Active System Support, Resilience, and Electricity Markets of Large-Scale Renewable Energy Systems)

Energy Engineering https://doi.org/10.32604/ee.2026.086358

Received 28 May 2026; Accepted 21 August 2026; Published online 31 August 2026

Abstract

Economic scheduling of flexible loads in low-voltage distribution networks requires a balance between the accuracy of nonlinear load modeling and the efficiency of scheduling optimization. To address this challenge, this paper proposes a constraint-injected neural warm-start optimization framework termed End-to-End Constraint Injection Neural Optimization (ECINO). The framework comprises four stages: scenario data generation and simulation, end-to-end neural network model design, constraint-injection self-supervised learning, and scheduling optimization and constraint verification. Compared with the no-optimization benchmark, cold-start algorithm, relaxation-based warm-start algorithm, and supervised learning warm-start algorithm, ECINO achieves the lowest operating cost in the fixed-seed base case. Across the base scenario and five controlled perturbation scenarios, ECINO achieves a mean cost-reduction rate of 42.90% relative to the no-optimization benchmark in the corresponding scenario, and constraint verification detects no constraint violations. The results indicate that ECINO balances economic performance, solution efficiency, and constraint handling, while exhibiting favorable cross-scenario generalization to variations in photovoltaic output, load level, outdoor temperature, and peak-period electricity prices.

Keywords

Low-voltage distribution network; flexible load scheduling; neural warm-start optimization; constraint-aware optimization; self-supervised learning

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