Control of the Power Flows of a Stochastic Power System Article Swipe

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Zhen Wang , Kaihua Xi , Aijie Cheng , Hai Xiang Lin , Jan H. van Schuppen ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2311.16282

How to determine the vector of power supplies of a stochastic power system for the next short horizon, such that the probability is less than a prespecified value that any phase-angle difference of a power line of the power network exits from a safe set? The power system is modelled such that the differential equation of each frequency is affected by a Brownian motion process. A safe set can be selected to be any subset of the interval $(-π/2, ~ +π/2)$, which is a sufficient condition for not losing synchronization. That the controlled system has an improved performance is shown by numerical results of three academic examples including a particular eight-node academic network, a twelve-node ring network, and a Manhattan-grid network.

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Electric power system Power (physics) Control theory (sociology) Node (physics) Power control Function (biology) Computer science Transient (computer programming) Stability (learning theory) State (computer science) Control (management) Mathematics Mathematical optimization Engineering Physics Algorithm Machine learning Biology Artificial intelligence Structural engineering Operating system Evolutionary biology Quantum mechanics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2311.16282
PDF: https://arxiv.org/pdf/2311.16282
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Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2311.16282

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Title: Control of the Power Flows of a Stochastic Power System

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Language: en

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Publication year: 2023

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Publication date: 2023-11-27

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Authors: Zhen Wang, Kaihua Xi, Aijie Cheng, Hai Xiang Lin, Jan H. van Schuppen

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Landing page: https://arxiv.org/abs/2311.16282

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PDF URL: https://arxiv.org/pdf/2311.16282

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Concepts: Electric power system, Power (physics), Control theory (sociology), Node (physics), Power control, Function (biology), Computer science, Transient (computer programming), Stability (learning theory), State (computer science), Control (management), Mathematics, Mathematical optimization, Engineering, Physics, Algorithm, Machine learning, Biology, Artificial intelligence, Structural engineering, Operating system, Evolutionary biology, Quantum mechanics

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Cited by: 2

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