LF Energy Summit Europe 2026

Last year, we introduced a basic Grid Foundation Model (GridFM) for power flow. This year we released GENCO – the GEometric Neural Corrective Optimizer, a neural solver for steady-state transmission-grid analysis. It unifies three grid-analysis tasks that are typically handled by separate pipelines: power flow (PF), optimal power flow (OPF), and state estimation (SE). GENCO represents the grid as a heterogeneous graph and iteratively refines solutions using task-specific physical decoders, box constraints, and explicit power-balance residuals. GENCO achieves state-of-the-art performance against specialized model baselines and classical solvers, with 5× and 200× speedup for AC-PF and AC-OPF respectively, as well as improved robustness in SE under sparse observability conditions. Beyond the model, we released gridfm-datakit for realistic grid data generation, gridfm-graphkit for low-code training and evaluation, and benchmark datasets spanning PF and OPF instances. By unifying model architecture, data generation, training, inference, and benchmarking, this end-to-end framework shortens innovation cycles and reduces integration efforts for the LFE community.