Trustworthiness Layer for Foundation Models in Power Systems: Application for N-k Contingency Assessment

Abstract: This work introduces for the first time, to our knowledge, a trustworthiness layer for foundation models in power systems. Using stratified conformal prediction, we devise adaptive, statistically valid confidence bounds for each output of a foundation model. For regression, this allows users to obtain an uncertainty estimate for each output; for screening, it supports conservative decisions that minimize false negatives. We demonstrate our method by enhancing GridFM, the first open-source Foundation Model for power systems, with statistically valid prediction intervals instead of heuristic error margins. We apply it for N-k contingency assessment, a combinatorial NP-Hard problem. We show that trustworthy GridFM can offer richer and more accurate information than DC Power Flow, having 2x-3x higher precision, while running up to 18x faster than AC Power Flow for systems up to 118 buses. Moving a step further, we also examine the ability of trustworthy GridFM to generalize to unseen high-order contingencies: through a rigorous analysis, we assess how a model trained on N-1 or N-2 outages extrapolates to unseen contingencies up to N-5.