A Survey on Applications of Quantum Computing for Unit Commitment

TL;DR

This survey reviews how quantum computing techniques are being applied to solve the complex unit commitment problem in power systems, highlighting progress, challenges, and future prospects.

Contribution

It categorizes existing quantum approaches to UC, discusses modeling strategies and hardware, and identifies research gaps and future directions.

Findings

Quantum annealing and variational algorithms are actively explored for UC.

Current quantum methods face scalability and hardware limitations.

Quantum-inspired methods offer promising approximations for UC.

Abstract

Unit Commitment (UC) is a core optimization problem in power system operation and electricity market scheduling. It determines the optimal on/off status and dispatch of generating units while satisfying system, operational, and market constraints. Traditionally, UC has been solved using mixed-integer programming, dynamic programming, or metaheuristic methods, all of which face scalability challenges as systems grow in size and uncertainty. Recent advances in quantum computing, spanning quantum annealing, variational algorithms, and hybrid quantum classical optimization, have opened new opportunities to accelerate UC solution processes by exploiting quantum parallelism and entanglement. This paper presents a comprehensive survey of existing research on the applications of quantum computing for solving the UC problem. The reviewed works are categorized based on the employed quantum paradigms, including annealing-based, variational hybrid, quantum machine learning, and quantum-inspired methods. Key modeling strategies, hardware implementations, and computational trade-offs are discussed, highlighting the current progress, limitations, and potential future directions for large-scale quantum-enabled UC.