Cooperative fault management for resilient integration of renewable energy

Wan, Wenfeng; Zhang, Peng; Bragin, Mikhail A.; Luh, Peter B.

doi:10.1016/j.epsr.2022.108147

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Experiments show that QTSA achieves an accuracy over 98% even for largescale systems such as a 300-bus power grid and remains satisfactory noise-resilience, which indicates its potential for the NISQ appli-cations. Some research has also demonstrated that QML can be potentially more expressible for complicated data relationships, e. g., achieving a comparable accuracy against classical machine learning while saving more parameters for the neural network [124] .…”

Section: Power System Stability Assessment Via Quantum Machine Learningmentioning

confidence: 99%

Quantum computing in power systems

Zhou

Tang

Nikmehr

et al. 2022

iEnergy

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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 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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Section: Power System Stability Assessment Via Quantum Machine Learningmentioning

confidence: 99%

Quantum computing in power systems

Zhou

Tang

Nikmehr

et al. 2022

iEnergy

Self Cite

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“…Distributed fault management is implemented by assigning each subproblem to a different CPU core. Those cores are within one CPU [15] . In this way, the computation sequence of subproblems is totally decided by each core's computation capability.…”

mentioning

confidence: 99%

Safety-assured, real-time neural active fault management for resilient microgrids integration

Wan

Zhang

Bragin

et al. 2022

iEnergy

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Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid' s network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.

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Cooperative fault management for resilient integration of renewable energy

Cited by 2 publications

References 19 publications

Quantum computing in power systems

Quantum computing in power systems

Safety-assured, real-time neural active fault management for resilient microgrids integration

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