Newton's Lantern is an RL finetuning pipeline that uses iteration count as reward to produce warm starts for AC power flow, outperforming supervised methods by converging on all tested snapshots with lowest mean iterations on IEEE and GOC benchmarks.
CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations
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LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
HH-MPNN achieves under 1% optimality gap on default topologies from 14 to 2000 buses, zero-shot N-1 generalization under 3% gap, and improved size generalization via pre-training on small grids.
GraphOPF applies graph learning with physics-informed self-supervision to solve AC-OPF up to 66 times faster than baselines on large systems including the Korean grid while claiming over 99% feasibility.
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
citing papers explorer
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LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
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Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
HH-MPNN achieves under 1% optimality gap on default topologies from 14 to 2000 buses, zero-shot N-1 generalization under 3% gap, and improved size generalization via pre-training on small grids.
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Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
GraphOPF applies graph learning with physics-informed self-supervision to solve AC-OPF up to 66 times faster than baselines on large systems including the Korean grid while claiming over 99% feasibility.
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Towards Systematic Generalization for Power Grid Optimization Problems
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.