@Article{ee.2026.086358,
AUTHOR = {Lixiao Wang, Jiaqi Li, Haifeng Li, Hongfeng Li, Yuhan Wen, Runhuan Lin, Huansheng Zhou},
TITLE = {End-to-End Constraint Injection Neural Optimization for Flexible Load Scheduling in Low-Voltage Distribution Networks},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/28131},
ISSN = {1546-0118},
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.},
DOI = {10.32604/ee.2026.086358}
}