Formalizing, Normalizing, and Splitting the Energy Network Re-Dispatch for Quantum Annealing

Adiabatic quantum computation (AQC) is a well-established method to approximate the ground state of a quantum system. Actual AQC devices, known as quantum annealers, have certain limitations regarding the choice of target Hamiltonian. Specifically, the target system must arise from a quadratic unconstrained binary optimization (QUBO) problem. As the name suggests, QUBOs represent unconstrained problems, and the problem must fit within the dimensionality limits of the hardware solver. However, various approaches exist to decompose large QUBOs and encode constraints by penalizing infeasible solutions. Choosing the right penalization and decomposition techniques is problem-specific and cumbersome due to various degrees of freedom. In this work, we investigate these issues in the context of energy network re-dispatch problems. Such problems are paramount for sustainable and cost-effective energy systems and play a crucial role in the transition towards renewable energy sources. Our QUBO instances are derived from open data of the German energy network and our results are compared to baselines from an open-source energy network simulation, thereby fostering reproducibility. Our novel insights regarding the realization of inequality constraints, spatio-temporal state consistency, and problem decomposition highlight the potential of AQC for optimizing complex energy dispatch problems. This provides valuable insights for energy market stakeholders and researchers aiming to improve grid management and reduce carbon emissions.