Abstract Uncertainty is fundamental in modern power systems, where renewable generation and fluctuating demand make stochastic optimization indispensable. In stochastic optimization, chance constraints enable the representation of uncertainty in renewable energy sources that affect the dispatch and commitment of bulk generation. This is commonly known as the chance-constrained unit commitment problem (UCP), which rapidly becomes computationally challenging as the number of scenarios grows. Quantum computing has been proposed as a potential route to overcome such scaling barriers. In this work, we evaluate the applicability of quantum annealing platforms to the chance-constrained UCP. Focusing on a scenario approximation, we reformulate the problem as a mixed-integer linear program (MILP) and solve it using D-Wave’s hybrid quantum–classical solver alongside the classical solver Gurobi. The hybrid solver proves competitive under strict runtime limits for large scenario sets (15,000 in our experiments), while Gurobi remains superior on smaller cases. QUBO reformulations have also been tested, but current annealers cannot accommodate stochastic UCPs due to hardware limits, and deterministic cases suffer from embedding overhead. Our study delineates where chance-constrained UCPs can already be addressed with hybrid quantum–classical methods, and where current quantum annealers remain fundamentally limited.
Ribes Marzá et al. (2026) studied this question.