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Problem-structure-informed quantum approximate optimization for large-scale unit commitment with limited qubits
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PRE
AI
DOI：10.23919/IEN.2025.0025.png)
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Abstract
En 中文
As power systems expand, solving the unit commitment problem (UCP) becomes increasingly challenging due to the curse of dimensionality, and traditional methods often struggle to balance computational efficiency and solution optimality. To tackle this issue, we propose a problem-structure-informed quantum approximate optimization algorithm (QAOA) framework that fully exploits the quantum advantage under extremely limited quantum resources. Specifically, we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems, which are solvable in parallel by limited number of qubits. This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse. Consequently, our approach can be extended to future power systems that are larger and more complex.
Keywords:
Power systems
Qubit
Topology
Optimal scheduling
Linear programming
Costs
Scalability
Complexity theory
Tuning
Quantum entanglement
Unit commitment problem
quadratic unconstrained binary optimization
quantum approximate optimization algorithm
Journal
I
IF:
5.1
Papers:
15
Citations:
0
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