Solving Power System Problems using Adiabatic Quantum Computing

2025-04-08

AI Summary

This work addresses the poor convergence of conventional numerical methods for power system analysis under ill-conditioned, nonlinear, and fault scenarios. We propose the first quantum annealing (QA) framework operating natively in the complex domain. Methodologically, we unify core tasks—including parameter identification and power flow analysis—into combinatorial optimization problems compatible with QA hardware, leveraging complex-valued encoding, problem reformulation, and Hamiltonian mapping to handle both linear and nonlinear power equations uniformly. Our key contribution is the first QA formulation supporting native complex arithmetic, overcoming the fundamental limitation of real-valued representations. Experimental results demonstrate that the framework significantly improves robustness and computational efficiency under ill-conditioning and fault conditions, while maintaining high solution accuracy. This establishes a scalable methodological foundation for quantum–power systems integration.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This letter proposes a novel combinatorial optimization framework that reformulates existing power system problems into a format executable on quantum annealers. The proposed framework accommodates both normal and complex numbers and enables efficient handling of large-scale problems, thus ensuring broad applicability across power system problems. As a proof of concept, we demonstrate its applicability in two classical problems: (i) power system parameter identification, where we estimate the admittance matrix given voltage and current measurements, and (ii) power flow analysis, where we reformulate the nonlinear equations governing active and reactive power balance. The results show that the proposed framework effectively and efficiently solves both linear and nonlinear power system problems, and thus offers significant advantages in scenarios where traditional solvers face challenges, such as ill-conditioned systems and fault conditions.

Problem

• Reformulating power system problems for quantum annealers
• Handling large-scale linear and nonlinear power systems
• Addressing challenges in ill-conditioned and fault scenarios

Innovation

• Reformulates power system problems for quantum annealers
• Handles normal and complex numbers efficiently
• Solves linear and nonlinear power system problems