Chair: Thomas Brunschwiler
IBM Research
| This session explores state-of-the-art optimization methods for power system operation and planning. Topics include, but are not limited to, (i) benchmarking cases that report on computational bottlenecks and high-impact application scenarios; (ii) generation of high-quality datasets and benchmarks, including reference solutions from classical solvers, for training and evaluating AI-based approaches; (iii) advances in neural solvers, Grid Foundation Models (GridFM), and hybrid AI–classical workflows that improve computational efficiency while maintaining optimality, feasibility, and physical consistency; and (iv) GPU-accelerated classical optimization algorithms that continue to provide the reference for accuracy and reliability. Together, these topics highlight synergies between optimization, accelerated computing, and AI for next-generation power systems. |