  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.
