LF Energy Summit Europe 2026

Modern AI for power flow and OPF is on the rise, but it will still require large amounts of trustworthy training data and strong conventional solvers for validation (“solver‑certified AI”). An important application are adjacent‑state studies needed for N‑1 screening and decision planning. This session argues that classic algorithms will not vanish, but must be redesigned for batch computation: solving many related grid states faster than repeated Newton-style runs. We reshape ideas from perturbation theory into a load-flow solver pattern that reuses structure across scenarios and maps well to parallelizable hardware. We introduce the key mathematics behind the approaches including the holomorphic embedding load flow method (HELM) and related methods. We then show how to turn them into an engineering workflow that can outperform repeated Newton-Raphson solves in high-throughput studies. We close with an open roadmap: an industrial‑grade implementation under development at 50Hertz/Elia Group and a clear invitation to collaborate on open interfaces, benchmarks, and integration with LF Energy tooling.