Data-Driven Stochastic AC-OPF using Gaussian Processes

arXiv:2402.11365v1 Announce Type: new
Abstract: The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The proposed approach aims to address this limitation and demonstrate its empirical efficiency through applications to multiple IEEE test cases. To solve the non-convex and computationally …

abstract academic algorithm arxiv chance cs.lg current data data-driven flow gaussian processes impact machine machine learning math.oc power practical processes solve stat.ml stochastic thesis type