Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow

arXiv:2311.18072v2 Announce Type: replace
Abstract: Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds. Indeed, PDL-SCOPF remedies the limitations of supervised counterparts that rely on training instances with their optimal solutions, which becomes impractical for large-scale SCOPF problems. PDL-SCOPF mimics an Augmented Lagrangian Method (ALM) for training primal and …

abstract arxiv cs.lg flow framework grid indeed math.oc near paper power primal role scale security self-supervised learning solutions stability supervised learning systems type