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This paper introduces a distributed quantum approximate optimization algorithm (DQAOA) within a three-block alternating direction method of multipliers (ADMM) framework specifically for unit commitment (UC) problems. By formulating the binary commitment block as a quadratic unconstrained binary optimization (QUBO) problem, the authors enable the use of various solving methods, including brute-force enumeration and distributed QAOA, across multiple quantum processing units (QPUs). The evaluation on a five-unit UC instance shows that all solver modes achieve consistent results in terms of commitment schedules and operating costs, highlighting the effectiveness of the distributed approach in accommodating capacity constraints of QPUs.
Achieving consistent unit commitment solutions across multiple quantum processors could revolutionize how we tackle complex optimization problems in energy systems.
This paper presents a distributed quantum approximate optimization algorithm (DQAOA)-enabled three-block alternating direction method of multipliers (ADMM) framework for unit commitment (UC). The relaxed commitment and dispatch variables are solved in a continuous quadratic programming block, while the binary commitment block is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The DQAOA interface allows this QUBO to be solved using brute-force enumeration, monolithic QAOA, or distributed QAOA, while the remaining ADMM updates are kept unchanged. In the distributed mode, the logical commitment qubits are allocated across multiple capacity-constrained quantum processing units (QPU), avoiding the requirement that the complete binary problem fits on a single device. The framework is evaluated on a five-unit UC instance containing 15 binary variables. All three solver modes reduce the ADMM primal residual below a certain tolerance and recover the same commitment schedule, dispatch, and operating cost. The results demonstrate solution consistency across the three solver modes and the multi-QPU capacity accommodation provided by the distributed QAOA method.
