Fast Optimal Power Flow With Guarantees via an Unsupervised Generative Model

Abstract

Climate change concerns are driving the widespread integration of renewable generation sources, storage systems, electric vehicles and diverse consumer loads. Inherent variabilities of these power entities introduce uncertainties that affect the economical operations and integrity of the electrical grid. Thus, optimal power flow (OPF) studies must be conducted at high granularity in order to account for these fluctuations. However, due to the non-convex nature of the OPF problem, solving it in real-time still remains an open challenge. In this paper, a novel data-driven approach that combines generative learning, information theory and domain knowledge is proposed to enable real-time OPF studies. In order to train this model, only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feasible</i> datapoints are necessary - not optimal values. Once trained, the time required to compute the solution is extremely fast and falls within the sub-second range. No information pertaining to the topology or power flow equations is required once the model is trained. Theoretical guarantees on optimality and feasibility of the solutions generated by the proposed ML model are established. Comprehensive practical and comparative studies conducted demonstrate the efficacy of the proposed data-driven approach for solving OPF in real-time.