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Decentralized Smart Grid Stability Modeling with Machine Learning

Author

Listed:
  • Borna Franović

    (HEP Group, Distribution System Operator Ltd., Viktora Cara Emina 2, 51000 Rijeka, Croatia)

  • Sandi Baressi Šegota

    (Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Nikola Anđelić

    (Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Zlatan Car

    (Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

Abstract

Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction. In this paper, the authors utilize four algorithms—a multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector machines (SVMs), and genetic programming (GP). A public dataset containing 30,000 points was used, with inputs consisting of τ —the time needed for a grid participant to adjust consumption/generation, p —generated power, and γ —the price elasticity coefficient for four grid elements; and outputs consisting of s t a b —the eigenvalue of stability and s t a b f , the categorical stability of the system. The system was modeled using the aforementioned methods as a regression model (targeting s t a b ) and a classification model (targeting s t a b f ). Modeling was performed with and without the τ values due to their low correlation. The best results were achieved with the XGB algorithm for classification, with and without the τ values as inputs—indicating them as being unnecessary.

Suggested Citation

  • Borna Franović & Sandi Baressi Šegota & Nikola Anđelić & Zlatan Car, 2023. "Decentralized Smart Grid Stability Modeling with Machine Learning," Energies, MDPI, vol. 16(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7562-:d:1279528
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    References listed on IDEAS

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