Opportunities for quantum computing within net-zero power system optimization

Key conclusions: In this review, we identify significant and wide-ranging opportunities for recent breakthroughs in quantum-accelerated optimization to offer value for the transition to net-zero power systems. These opportunities span a variety of problems across planning and operation, which are key for reliable and affordable decarbonization.

Seminal discoveries highlighted in the review: We review the latest work on quantum computing for combinatorial power system optimization applications, including unit commitment, grid-edge flexibility coordination, and network expansion planning. In addition, we map state-of-the-art theoretical work to applications where quantum computing is underexplored, including convex and machine learning-based optimization.

Implications for research at different scales: Quantum computing creates opportunities for faster, larger-scale, and higher-fidelity optimization. This is relevant for researchers from engineering, economics, and computer science, as well as policymakers, network planners, system operators, and flexibility aggregators.

Potential future directions: To address challenges for industry implementation and scale-up, we propose new research into (1) benchmark problem definitions and performance criteria; (2) domain-specific algorithms and hardware for current noisy intermediate-scale devices; and (3) holistic power industry computing strategies integrating quantum computing with more immediate areas of classical computing innovation.