Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Authors

  • Andrey I. Vlasov Bauman Moscow State Technical University, Russian Federation.
  • Boris V. Artemiev Bauman Moscow State Technical University, Russian Federation.
  • Kirill V. Selivanov Bauman Moscow State Technical University, Russian Federation.
  • Kirill S. Mironov Bauman Moscow State Technical University, Russian Federation.
  • Jasur O. Isroilov Bauman Moscow State Technical University, Russian Federation.

DOI:

https://doi.org/10.32479/ijeep.12912

Abstract

Harmonious integration of renewable energy sources into current energy systems has taken on increasing importance amid the scarcity of carbon resources. Among the key problems is the imbalance in power consumption, power generation, and significant peak overloads. To deal with this issue, an intelligent software and hardware system is needed, which will effectively implement predictive control algorithms for various energy sources. The research examines the fundamental provisions of the concept of predictive control over a variable load hybrid power system on the basis of power output forecast. The analysis performed has allowed developing a method of predictive control over the power system in a small locality based on machine learning algorithms. The method was tested using an electric power complex simulation, which included four energy sources (solar panel, wind turbines, small hydrogenerator, and standard carbon-fueled generator). The proposed predictive control method has proved to be productive. The algorithms have allowed diversifying the reliability of power supply by ensuring the sustainability of the power grid.

Keywords:

Hybrid power system, Renewable energy sources, Power balance, Decision tree methodology, energy efficiency

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Published

2022-05-18

How to Cite

Vlasov, A. I., Artemiev, B. V., Selivanov, K. V., Mironov, K. S., & Isroilov , J. O. (2022). Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast. International Journal of Energy Economics and Policy, 12(3), 1–7. https://doi.org/10.32479/ijeep.12912

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Articles