Quantum-Compatible Unit Commitment Modeling through Logarithmic Discretization

The unit commitment (UC) problem is essential to operating the power system in an efficient and reliable manner. However, modern power systems are growing increasingly complex with the growing penetration of distributed energy resources, making classical approaches to the UC problem computationally inefficient. Quantum computing approaches have been investigated for the UC problem due to the potential to outperform classical solutions. In many quantum models, including quadratic unconstrained binary optimization, or QUBO, the problem must be reformulated by discretizing continuous variables into "bins" or "segments" to ensure compatibility with the quantum processing unit (QPU). This would increase the number of variables due to discretization and lead to sub-optimal solutions. In this paper, we propose a new reformulation strategy for converting optimization problems into QPU-compatible binary quadratic models while creating significantly fewer binary variables, resulting in faster computation, increased efficiency, and further scalability. The performance of this approach is examined in terms of computation time and solution optimality to demonstrate its advantages and to illustrate the potential capability to solve the UC problem more efficiently than classical solutions.