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IEEE International Conference on Quantum Computing and Engineering (QCE)

Quantum Computing-Enabled Contingency Analysis for Power Systems

Yuqi Jiang, Zhiding Liang, Yan Li, Qiang Guan, Ganesh Kumar Venayagamoorthy

Published Aug 30, 2025• 0 citations
Publisher

Abstract

The rapid development of quantum computing has provided a ground-breaking tool for solving problems that are insurmountable on classical computers. In the power system domain, quantum computing is still in the nascent stage, with limited but ongoing studies. The Quantum Approximation Optimization Algorithm (QAOA) has been applied in solving several NPhard problems and can be further studied to address challenging problems in power systems. Key applications include contingency analysis, optimal power flow, and unit commitment, among others. With the significant integration of renewable energy, the intermittent nature of renewable energy introduces substantial volatility to the system. An overhead contingency analysis is required to evaluate the possible system outage scenarios, which will identify critical contingencies to maintain system stability. However, the contingency analysis is a prototypical NP-Hard problem, requiring substantial classical computation resources. In this work, QAOA is developed for the$N-k$contingency analysis on line contingency as well as the general contingency scenarios, with the aim of minimizing the shedding of the power load while ensuring the power flow constraints. A tailored objective function is developed to encode the$N-k$contingency condition as well as the power flow loss constraints into a problem Hamiltonian for QAOA computation. The developed method is verified on IEEE 9- and 14-bus systems compared with classical methods as the baseline. In addition, a QAOA parameter study including the number of QAOA parameters, the number of repetitions of the quantum circuits execution, and the number of iteration steps of the classical optimization algorithm, is conducted to offer insights for the parameter tuning under different problem sizes. This work is anticipated to establish a quantum framework for solving critical and complex power system problems in the Noisy intermediate-scale quantum (NISQ) era.

DOI
10.1109/qce65121.2025.00211

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Unprecedented extreme climate events cause devastating infrastructure outages within power systems. Comprehensive outage identification is essential for the identification of critical components to ensure the uninterrupted power supply in a secure manner to withstand extreme weather events. Accurate outage identification, however, requires simulations of a large number of outage scenarios necessitating highly scalable computations thus challenging classical computing paradigms. Quantum computing provides a promising resolution by exploiting exponential scalability achieved through superposition and entanglement of voltage states. This paper devises a quantum contingency analysis (QCA) method to identify outage scenarios on Noisy Intermediate-Scale Quantum (NISQ) devices. Advanced quantum circuits incorporating Pauli-twirling, dynamic decoupling, and matrix-free measurement are designed to mitigate hardware-induced errors. A preconditioned hybrid method is devised to alleviate the computation burden of parameter optimization of quantum gates. Case studies identify line and generation outages via QCA in typical power systems. Our research underscores that quantum computing exhibits exponential scalability in identifying power grid outages and critical components.
Quantum mechanics (QM) can be understood as a set of rules that forms the basis for developing all quantum theories. One of these theories is quantum computation (QC), i.e., computation based on QM logic. It is believed that QC provides paths to the problem solution that may not be possible for classical computers. Therefore, it has received attention to solve complex computational problems in different areas. Most of the research efforts, however, have concentrated on problems in theoretical physics and computer science, leaving little attention to solve practical problems in industrial applications. This is particularly true in power system applications where QC is mostly unknown. This paper mainly aims to attract the attention of power system researchers/engineers to QC as a potential solution to address emerging computational challenges of power systems. To this end, the historical development of QC and its fundamental concepts are first described. Then, recent contributions to solving computationally-demanding power system problems such as AC and DC power flow (PF), contingency analysis, state estimation, electromagnetic transients simulation (EMT), fault diagnosis, unit commitment (UC), and facility location–allocation (FLA) problems are discussed. Unfortunately, power system researchers have not yet been able to convincingly demonstrate a quantum advantage in solving large-scale power system problems mainly because we are in the noisy intermediate-scale quantum (NISQ) era, where quantum devices are noisy and have limited quantum resources. However, it may be demonstrated in the future with technological advances and increased research efforts in the area.
Scenario-based stochastic optimization is a prevalent method for the economic dispatch of renewables-rich power systems. To reduce the time and space complexities for generating massive scenarios and solving pertinent mixed-integer stochastic optimization efficiently, quantum computing can provide critical assistance. To this end, we first propose the unified quantum amplitude estimation (UQAE) to generate massive scenarios with quadratic acceleration by a few qubits, after which the dimension of state variables embedded in the two-stage stochastic optimization also decreases exponentially. Then, by utilizing quantum-classical Benders decomposition, we accelerate the mixed integer master problem in the first stage on quantum processing units, while convex subproblems are solved on central processing units. Especially considering limited qubits in the noisy intermediate-scale quantum era, we propose to reduce the scale of master problem by scenario clustering and cut selection using the customized quantum approximation optimization algorithm (QAOA). Mathematically, we not only prove the decreases of complexities by UQAE-QAOA but also give the feasible ranges of penalty factors for transforming constrained problems into Ising models. Finally, we perform numerical experiments on modified IEEE systems to test the efficiency and accuracy of UQAE-QAOA, as well as the scalability of the designed quantum scheme.
Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems, especially for Distributed Energy Resources (DERs) dominant cyber-physical systems that have been widely developed to promote energy sustainability. The systems’ maximum power or data sections are essential for monitoring, operation, and control, while high computational effort is required. Quantum Approximate Optimization Algorithm (QAOA) provides a promising means to search for these sections by leveraging quantum resources. However, its performance highly relies on the critical parameters, especially for weighted graphs. We present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density, and verify the strategy with 39, 774 instances. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots the practical application of quantum technique to power systems in noisy intermediate-scale quantum devices, heralding its next-generation computation in the quantum era.
In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.