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A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage

Author

Listed:
  • Dahu Li

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China)

  • Hongyu Zhou

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yuan Chen

    (China Hubei Emission Exchange, Wuhan 430070, China)

  • Yue Zhou

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China)

  • Yuze Rao

    (State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China)

  • Wei Yao

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm’s effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method’s optimization effect, the DOA algorithm’s optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods.

Suggested Citation

  • Dahu Li & Hongyu Zhou & Yuan Chen & Yue Zhou & Yuze Rao & Wei Yao, 2023. "A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4252-:d:1152949
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    References listed on IDEAS

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    4. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    5. Amit Kumar Bairwa & Sandeep Joshi & Dilbag Singh, 2021. "Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, June.
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