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Optimized power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. The major technological changes associated with net zero, including the rapid adoption of renewables, electrification of transport and heating, and system-wide digitalization, each increase the scope for optimization to create value, but at the cost of greater computational complexity. Although power system optimization problems are now posing challenges for even the largest exa-scale supercomputers, a new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs and is reaching a level of maturity where, for the first time, a practical advantage over classical computing is available for specific applications. In this review, we identify significant and wide-ranging opportunities for quantum computing to offer value for power system optimization. In addition to reviewing the latest work on quantum computing for simulation-based and combinatorial power system optimization applications, we also review state-of-the-art theoretical work on quantum convex optimization and machine learning and map this to power system optimization applications where quantum computing is underexplored. Based on our review, we analyze challenges for industry implementation and scale-up and propose directions for future research.
Morstyn et al. (Mon,) studied this question.