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Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation

Author

Listed:
  • Yang Wang

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yifan Wang

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zhenghui Zhao

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zhiquan Zhou

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zhihao Hou

    (Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S102TN, UK)

Abstract

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.

Suggested Citation

  • Yang Wang & Yifan Wang & Zhenghui Zhao & Zhiquan Zhou & Zhihao Hou, 2023. "Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation," Energies, MDPI, vol. 16(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3577-:d:1128605
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    References listed on IDEAS

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    1. Shuhui Ren & Xun Dou & Zhen Wang & Jun Wang & Xiangyan Wang, 2020. "Medium- and Long-Term Integrated Demand Response of Integrated Energy System Based on System Dynamics," Energies, MDPI, vol. 13(3), pages 1-24, February.
    2. Wang, Yang & Lai, Kexing & Chen, Fengyun & Li, Zhengming & Hu, Chunhua, 2019. "Shadow price based co-ordination methods of microgrids and battery swapping stations," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Zhenghui Zhao & Joseph Mutale, 2019. "Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm," Energies, MDPI, vol. 12(11), pages 1-20, May.
    4. Sun, Peng & Teng, Yun & Chen, Zhe, 2021. "Robust coordinated optimization for multi-energy systems based on multiple thermal inertia numerical simulation and uncertainty analysis," Applied Energy, Elsevier, vol. 296(C).
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    Cited by:

    1. Jiyu Song & Xinhang Zhou & Zhiquan Zhou & Yang Wang & Yifan Wang & Xutao Wang, 2023. "Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration," Energies, MDPI, vol. 16(16), pages 1-19, August.

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