Novel Resolution of Unit Commitment Problems Through Quantum Surrogate Lagrangian Relaxation

Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address...

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Vydáno v:IEEE transactions on power systems Ročník 38; číslo 3; s. 2460 - 2471
Hlavní autoři: Feng, Fei, Zhang, Peng, Bragin, Mikhail A., Zhou, Yifan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation algorithms
Combinatorial analysis
Complexity
Complexity theory
Computers
Distributed quantum optimization
Optimization
quantum approximate optimization algorithm
Quantum computers
Quantum computing
Quantum entanglement
Qubit
Qubits (quantum computing)
surrogate lagrangian relaxation
Unit commitment
ISSN:0885-8950, 1558-0679
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Shrnutí:Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address the growing complexity is therefore required. Quantum computing offers a promise to overcome the combinatorial complexity through the use of the so-called "qubits." To make the best use of near-term quantum computers to solve UC problems with a much larger number of binary variables than the number of qubits available, this paper devises a novel solution methodology based on a synergistic combination of quantum computing and Surrogate Lagrangian Relaxation (SLR) to solve UC problems. Our new contributions include: 1) A Quantum-SLR (QSLR) algorithm incorporating quantum approximate optimization algorithm (QAOA) into the SLR method, which overcomes the fundamental difficulties of previous LR-based quantum methods such as zigzagging of multipliers and the need to know or estimate the optimal dual value for convergence; 2) A Distributed QSLR framework (D-QSLR) capable of coordinating local quantum/classical computing resources with those within neighborhoods and, in the meantime, protecting data privacy; 3) A Quantized UC model to obtain accurate commitment unit subproblems decision by using a quantum machine; and 4) A time-unit-decomposed quantum UC approach to overcoming the quantum resources' limitations. Promising quantum test results validate the effectiveness of QSLR and the scalability of the UC-oriented D-QSLR algorithm, which demonstrate QSLR's enormous potential in UC optimization.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2022.3181221