A Novel Data-Driven Intelligent Computing Method for the Secure Control of a Benchmark Microgrid System

Shunjiang Wang1, Yan Zhao2, He Jiang2, Rong Chen1 and Lina Cao1

  1. State Grid Liaoning Electric Power Company Limited
    110006 Shenyang, China
    wangshunjiang@163.com, chenrong516@126.com, 13700007416@126.com
  2. School of Renewable Energy, Shenyang Institute of Engineering
    110136 Shenyang, China
    zhaoyan@sie.edu.cn, jianghescholar@163.com

Abstract

Microgrid is a small-scale cyber-physical system, and it generally suffers from various uncertainties. In this paper, we investigate the secure control problem of a benchmark microgrid with system uncertainties by using data-driven edge computing technology. First, the state-space function of the benchmark microgrid system is formulated, and parameter uncertainties are taken into consideration. Sec-ond, a novel data-driven intelligent computing method is derived from the model-based reinforcement learning algorithm, which only requires system data instead of system models. By utilizing this computing method, the optimal control policy can be obtained in the model-free environment. Third, the Lyapunov stability theory is employed to prove that the uncertain microgrid can be asymptotically stabilized under the optimal control policy. Finally, simulation results demonstrate the control performance can be improved by tuning the parameters in the performance index function.

Key words

edge computing, microgrid system, secure control, reinforcement learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS190912023W

Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, S., Zhao, Y., Jiang, H., Chen, R., Cao, L.: A Novel Data-Driven Intelligent Computing Method for the Secure Control of a Benchmark Microgrid System. Computer Science and Information Systems, Vol. 17, No. 3, 835–848. (2020), https://doi.org/10.2298/CSIS190912023W