3 papers
cs.LG2026
Scaling Laws for Physics-Aware ACOPF Surrogate Learning
Yijiang Li, Emon Dey, Stefano Fenu +3
Learning-based surrogates for AC optimal power flow (ACOPF) promise large speedups over classical solvers, but their operational value depends on physical feasibility as much as pr…
eess.SY2026
Dynamic Operational Reserve Margin Assessment from Risk-Constrained Unit Commitment States
Pratishtha Shukla, Shaked Regev, Evan J. R. Brody +2
We propose Dynamic Reserve Margin (DRM) as a time-varying operational adequacy metric derived from risk-constrained unit commitment (RCUC) states. DRM quantifies reserve adequacy u…
cs.LG2026
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1
Fast and reliable optimal power flow (OPF) approximation is essential for reliable smart-grid operation, yet many learning-based surrogates either flatten the native heterogeneous…