Quantum Computing for Energy Systems: A Literature Review and Strategic Assessment
report
This report reviews quantum computing applications in energy systems, focusing on optimization, planning, operation, and decision-making problems. The motivation is the increasing computational complexity of modern power systems, driven by high renewable penetration, stochastic operation, sector coupling, and multi-timescale decision-making, factors that stress classical methods on large-scale mixed-integer, stochastic, and network-constrained problems. The review concentrates on approaches relevant under current Noisy Intermediate-Scale Quantum (NISQ) constraints, like quantum annealing, variational algorithms including the Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantum–classical workflows. Core attention is given to mapping energy-system problems into quantum-compatible formulations, e.g., Quadratic Unconstrained Binary Optimization (QUBO) and Ising models, and to the associated challenges of discretization, constraint handling, embedding, and scalability. Quantum computing shows the strongest alignment with combinatorial discrete problems, including decomposed unit commitment, network reconfiguration, contingency screening, and selected planning decisions. Problems involving continuous variables, nonlinear physics, or dense constraints, such as full Alternating Current Optimal Power Flow (AC-OPF) or integrated planning-operation models, remain poorly suited to current quantum hardware and formulations.
TNO Identifier
1030456
Collation
53 p.
Place of publication
Petten