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Coordinated Frequency Control for Electric Vehicles and a Thermal Power Unit via an Improved Recurrent Neural Network

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  • Jianhua Zhang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yongyue Wang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With the advancement of intelligent power generation and consumption technologies, an increasing number of renewable energy sources (RESs), smart loads, and electric vehicles (EVs) are being integrated into smart grids. This paper proposes a coordinated frequency control strategy for hybrid power systems with RESs, smart loads, EVs, and a thermal power unit (TPU), in which EVs and the TPU participate in short-term frequency regulation (FR) jointly. All EVs provide FR auxiliary services as controllable loads; specifically, the EV aggregations operate in charging mode when participating in FR. The proposed coordinated frequency control strategy is implemented by an improved recurrent neural network (IRNN), which combines a recurrent neural network with a functional-link layer. The weights and biases of the IRNN are trained by an improved backpropagation through time (BPTT) algorithm, in which a chaotic competitive swarm optimizer (CCSO) is proposed to optimize the learning rates. Finally, the simulation results verify the superiority of the coordinated frequency control strategy.

Suggested Citation

  • Jianhua Zhang & Yongyue Wang, 2025. "Coordinated Frequency Control for Electric Vehicles and a Thermal Power Unit via an Improved Recurrent Neural Network," Energies, MDPI, vol. 18(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:533-:d:1575873
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    References listed on IDEAS

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