AI 中文总结
本文提出结合DQAOA的ADMM框架求解UC问题，将QUBO分配至多QPU求解，在五机组实例上验证了三种求解模式的解一致性与多QPU适配能力。
AI 中文摘要
本文提出了一种启用分布式量子近似优化算法（DQAOA）的三块交替方向乘子法（ADMM）框架，用于机组组合（UC）。松弛的机组组合与调度变量在连续二次规划块中求解，而二进制机组组合块被建模为二次无约束二进制优化（QUBO）问题。DQAOA接口允许通过暴力枚举、整体QAOA或分布式QAOA求解该QUBO，同时保持其余ADMM更新不变。在分布式模式下，逻辑组合量子比特被分配到多个容量受限的量子处理单元（QPU），避免了完整二进制问题需适配单个设备的要求。该框架在包含15个二进制变量的五机组UC实例上进行评估，所有三种求解模式均将ADMM原始残差降至某一容差以下，且恢复了相同的机组组合计划、调度方案和运行成本。结果表明三种求解模式间的解一致性，以及分布式QAOA方法提供的多QPU容量适配能力。
英文摘要
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.
Comments6 pages, 3 figures, 2 tables, 1 algorithm
