PNNL

Abstract

With the global trend of pursuing clean energy and decarbonization, power systems have been evolving in a fast pace that we have never seen in the history of electrification. Power system researchers have been leveraging classical high-performance computing (HPC) techniques to increase the computational speed for better decision support. However, there are intractable problems that cannot be handled by classical HPC. Quantum computing (QC) as an emerging technology has been well recognized and holds promises to significantly revolutionize computing for complex power system applications. This paper introduces the fundamentals of quantum computing, briefly summarizes the existing quantum computing applications in literature, and explains the need of exploring quantum computing potentials for power systems. A quantum neural network application in power system contingency analysis is presented as an example to show the potential of quantum computing. More importantly, this paper emphasizes the need for co-existence of classical HPC and quantum computing and recommends three quantum computing research directions in power systems: quantum-inspired algorithm for large-scale optimization, variational quantum algorithms (VQA) translation, and power system data qubitization.