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IEEE Power Engineering Society General Meeting

A Novel Integrated Quantum Computing Approach for Unit Commitment

Bing Yan, Junpeng Zhan, Obumneme Umeonwuka, Abraham Ashvil, Tongxin Zheng, Feng Zhao, Dane A. Schiro, Izudin Lelic, Lefu J. Maqelepo, Ioannis Giannikopoulos

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/pesgm58988.2026.11693686

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The penetration of distributed energy resources (DERs) within the microgrids (MGs) increases the complexity of Unit Commitment (UC) problem. In this paper, the idea of quantum computing (QC) is introduced in power system distributed optimization to solve the combinatorially complex UC problem. To take advantage of QC, the quantum-amenable model of UC problem is innovatively developed. Then, by using the hybrid nature of quantum-inspired alternate direction method of multipliers (ADMM) and by exploiting the superposition and entanglement of quantum bits (qubits), the classical ADMM coordinates the subproblems to obtain feasible solutions. Additionally, this paper uses a decomposition and coordination-supported framework for quantum optimization, which allows to solve problems larger that currently available quantum computers are capable of solving. The quantum distributed UC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing,

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At present, the construction of the electricity spot market is becoming increasingly sophisticated. Security constrained unit commitment (SCUC) can effectively reduce power procurement costs. However, as the system scale expands, the computational complexity of solving the SCUC problem increases significantly. This paper focuses on transforming the SCUC into a quadratic unconstrained binary optimization (QUBO) model suitable for quantum computers, with the aim of leveraging quantum computing technologies to accelerate the solution of the unit commitment problem. Based on the Benders decomposition framework, the SCUC model is decomposed to generate a set of unit commitment samples together with the corresponding optimal values of their Benders subproblems. To establish the functional relationship between unit commitment and the optimal cost (i.e., the SCUC value function) in a manner compatible with quantum computing technologies, a high-dimensional quadratic function is employed to fit the obtained unit commitment samples and their corresponding optimal cost values, based on the value function theory of mixed-integer programming. The fitted high-dimensional quadratic function is then solved using both commercial solvers and quantum computing platforms. The results indicate that the QUBO can provide high-quality suboptimal solutions to the original SCUC problem.

IET conference proceedings.•Jul 1, 2026•0 citations
The penetration of distributed energy resources (DERs) within the microgrids (MGs) increases the complexity of Unit Commitment (UC) problem. In this paper, the idea of quantum computing (QC) is introduced in power system distributed optimization to solve the combinatorially complex UC problem. To take advantage of QC, the quantum-amenable model of UC problem is innovatively developed. Then, by using the hybrid nature of quantum-inspired alternate direction method of multipliers (ADMM) and by exploiting the superposition and entanglement of quantum bits (qubits), the classical ADMM coordinates the subproblems to obtain feasible solutions. Additionally, this paper uses a decomposition and coordination-supported framework for quantum optimization, which allows to solve problems larger that currently available quantum computers are capable of solving. The quantum distributed UC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing,
At present, the construction of the electricity spot market is becoming increasingly sophisticated. Security constrained unit commitment (SCUC) can effectively reduce power procurement costs. However, as the system scale expands, the computational complexity of solving the SCUC problem increases significantly. This paper focuses on transforming the SCUC into a quadratic unconstrained binary optimization (QUBO) model suitable for quantum computers, with the aim of leveraging quantum computing technologies to accelerate the solution of the unit commitment problem. Based on the Benders decomposition framework, the SCUC model is decomposed to generate a set of unit commitment samples together with the corresponding optimal values of their Benders subproblems. To establish the functional relationship between unit commitment and the optimal cost (i.e., the SCUC value function) in a manner compatible with quantum computing technologies, a high-dimensional quadratic function is employed to fit the obtained unit commitment samples and their corresponding optimal cost values, based on the value function theory of mixed-integer programming. The fitted high-dimensional quadratic function is then solved using both commercial solvers and quantum computing platforms. The results indicate that the QUBO can provide high-quality suboptimal solutions to the original SCUC problem.