within net-zero power system optimization Thomas Morstyn1,* and Xiangyue Wang2 SUMMARY Reviews of early work on quantum computing for power systems discuss optimiza-tion alongside analytics, simulation, and communication applications.15–19 How-ever, for optimization, these reviews have primarily focused on initial work applying
Flow method. The developed method is tested for two small power systems and compared to classical simulations. Index Terms—power grids, power system security, quantum computing, quantum power flow, risk assessment I. INTRODUCTION The increasing share of renewable energy sources (RES) in power systems creates a challenge to ensure system security
In recent years, the use of quantum quantum algorithms for power system applications have gained traction due to both increasing complexity and computational demand of the applications as well as advancements in quantum technology. However the research in this direction is still in nascent stages. This paper serves as a primer for the reader by
Electric power system decarbonization is a core component of the global transition to net zero, requiring major infrastructure investments in renewable generation, grid energy storage, and transmission infrastructure, along with the rapid adoption of electric transport and heating. 1 At the same time, power systems are being digitalized, with information and
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This paper attempts to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: an innovative quantum model designed for reliability assessment; a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; and an efficient quantum amplitude estimation (QAE)
Optimized power system planning and operation are increasingly critical due to the net-zero transition. Following recent breakthroughs, quantum computing is reaching a level of maturity where, for the first time, it
Quantum solutions for power system applications to address this challenge [1], [2], [3]. To this end, this paper aims to serve as a primer for bridging the knowledge gap between power system engineers and quantum specialists by adequately highlighting the need for research in this direction followed by providing comprehensive outlook on
Quantum mechanics (QM) can be understood as a set of rules that forms the basis for developing all quantum theories. 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
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Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today''s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy
This electrical power system is widely used to test ideas and concepts by researchers [56], [57]. The IEEE 30-bus system consists of 30 buses, 41 transmission lines, five generators and synchronous condensers, and transformers. Data for the IEEE 30-bus test system is available in the IEEE power systems test case archive [58]. These data are
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To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with
Quantum Machine Learning for Power System Stability Assessment Yifan Zhou1 and Peng Zhang1,2,* 1Department of Electrical and Computer Engineering, Stony Brook University, NY 11794-2350, USA 2Interdisciplinary Science Department, Brookhaven National Laboratory, Upton, NY 11973-5000, USA *p.zhang@stonybrook ABSTRACT Transient stability
Quantum Power Flow Fei Feng, Student Member, IEEE, Yifan Zhou, Member, IEEE and Peng Zhang, Senior Member, IEEE Abstract—This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploit-ing quantum computing. Our contributions are three-
Quantum Computing and Power System Dynamics. Power system dynamics, typically modeled by many differential-algebraic equations (DAEs), present a significant computational challenge due to the sheer number of generators, loads, and transmission lines that form the network. The complexity of these calculations grows exponentially with the size of
As for the research on the application of quantum algorithms in power systems, Ahn et al. claim that quantum computing is a game-changing technology that affects modern distributed energy resource (DER) systems and propose potential quantum attack defense strategies such as post-quantum cryptography (PQC) and quantum key distribution (QKD
Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today''s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy
Transient stability assessment (TSA) is a cornerstone for resilient operations of today''s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power
With its head-spinning size and connections, the power system is so complex that even supercomputers struggle to efficiently solve certain optimization problems. But quantum computers might fare better, and now researchers can explore that prospect thanks to a software interface between quantum computers and grid equipment.
Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from
This paper introduces the fundamentals of quantum computing, briefly summarizes the existing quantum computing applications in literature, and explains the need of exploring quantum computing potentials for power systems. A quantum neural network application in power system contingency analysis is presented as an example to show the potential
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In recent years, especially with the proliferation of power converter interfaced renewable energy resources in power systems, which results in a so-called low-inertia power
(CVaR). Furthermore, others have implemented quantum algorithms for power flow appli-cations utilizing the HHL algorithm [5], which could yield exponential speed-up. On the other hand, as regards to operational security of the power system, quantum com-puting could enable N-1 contingency assessments on large-scale power systems considering
The new quantum power standard will benefit from the use of a programmable Josephson voltage standard (PJVS), which plays a crucial role in the redefinition of the volt unit in the new quantum SI. Multiplexing schemes for quantum power systems. Proc. Conf. on Precision Electrom. Meas., Wellington, New Zealand (2022), pp. 1-2. Google Scholar [9]
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This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow
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