Quantum Electromagnetic Transients Program

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 everincreasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

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“…To tackle the challenge, ref. [13] establishes HHLenabled quantum state estimation (QSE) algorithms. The complexity of HHL, i.e.…”

Section: Quantum State Estimationmentioning

confidence: 99%

“…Therefore, ref. [13] proposes a preconditioned-HHL for the QSE implementation. The overall idea is to use a preconditioned iterative optimization to obtain the power system states instead of directly calculating them through the HHL algorithm.…”

Section: Quantum State Estimationmentioning

confidence: 99%

Quantum computing in power systems

Zhou

Tang

Nikmehr

et al. 2022

iEnergy

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“…For instance, with a QDC network, the quantum computing process can be exponentially accelerated, as more qubits can be accumulated from multiple quantum nodes to achieve a stronger computing capability. This will help realize the advantages of quantum power grid analytics such as quantum electromagnetic transient simulations 9 and quantum unit commitment. 10 Additionally, a QDC network provides ultra-secure communication for electric grids, and offers potent tools to upgrade the performance of grids protection, control, and automation.…”

Section: Architecture Of Qdc-empowered Electric Gridsmentioning

confidence: 99%

Quantum internet for resilient electric grids

Jiang

Tang

Qin

et al. 2021

Int Trans Electr Energ Syst

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“…Quantum technology is emerging as a new hope to address challenging computational tasks in power systems, including quantum chemistry simulation for new type batteries [1][2][3], efficient power system analysis by solving linear systems of equations [4][5][6][7][8], forecasting highly chaotic systems [9], scheduling and dispatching power grids [10], unit commitment [11], optimal reconfiguration of distribution grids [12], etc. However, the existing algorithms require substantial quantum resources, limiting their near-term utilization on noisy intermediate-scale quantum (NISQ) devices [13].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems

Hang¹,

Wang²,

Li³

2022

Preprint

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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.

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Quantum Electromagnetic Transients Program

Cited by 26 publications

References 5 publications

Quantum computing in power systems

Quantum computing in power systems

Quantum internet for resilient electric grids

Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems

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