Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects

TL;DR

This paper explores how quantum computing could revolutionize future energy grid management by potentially offering more efficient solutions to complex optimization problems in decentralized energy systems.

Contribution

It provides initial evidence that quantum algorithms may outperform classical methods in solving energy grid optimization problems, highlighting their potential in energy transition.

Findings

Classical optimizers exhibit exponential runtime scaling even for small problems.

Quantum algorithms like QAOA show promise for better runtime scaling.

Quantum computing could enable solutions to challenging energy management problems.

Abstract

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes.