A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment
Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi-day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these...
| Authors | Muhy Eddin Za'ter, Anna Van Boven, Bri-Mathias Hodge et al. |
|---|---|
| Primary category | eess.SY |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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