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Cooperative Game-Based Collaborative Optimal Regulation-Assisted Digital Twins for Wide-Area Distributed Energy

Author

Listed:
  • Pengcheng Ni

    (Anhui Jiyuan Software Co., Ltd., Hefei 230000, China)

  • Zhiyuan Ye

    (Anhui Jiyuan Software Co., Ltd., Hefei 230000, China)

  • Can Cao

    (Anhui Jiyuan Software Co., Ltd., Hefei 230000, China)

  • Zhimin Guo

    (State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China)

  • Jian Zhao

    (State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China)

  • Xing He

    (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

With the wide use of renewable energy sources and the requirement for energy storage technology, the field of power systems is facing the need for further technological innovation. This paper proposes a wide-area distributed energy model based on digital twins. This model was constructed to more fully optimize the coordination of wide-area distributed energy in order to rationally deploy and utilize new energy units. Moreover, the minimization of the power deviation between the dispatch command and the actual power regulation output was also taken into account. In contrast to previous dispatch research, the cooperative game co-optimization algorithm was applied to this model, enabling a distributed approach that can quickly obtain a high-quality power command scheduling scheme. Finally, the simulation and comparison experiments using this algorithm with the wide-area distributed energy (WDE) model showed that it had the advantages of significantly reducing the tracking error, average error, and total error and effectively improving the tracking accuracy. The proposed method can help reduce total power deviations by about 61.1%, 55.7%, 53.1%, and 74.8%.

Suggested Citation

  • Pengcheng Ni & Zhiyuan Ye & Can Cao & Zhimin Guo & Jian Zhao & Xing He, 2023. "Cooperative Game-Based Collaborative Optimal Regulation-Assisted Digital Twins for Wide-Area Distributed Energy," Energies, MDPI, vol. 16(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2598-:d:1092610
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    References listed on IDEAS

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    1. Ivan Smajla & Daria Karasalihović Sedlar & Branko Drljača & Lucija Jukić, 2019. "Fuel Switch to LNG in Heavy Truck Traffic," Energies, MDPI, vol. 12(3), pages 1-19, February.
    2. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    3. Zhang, Xiaoshun & Yu, Tao & Yang, Bo & Li, Li, 2016. "Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid," Energy, Elsevier, vol. 101(C), pages 34-51.
    4. Michael W. Grieves, 2005. "Product lifecycle management: the new paradigm for enterprises," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 2(1/2), pages 71-84.
    5. Tingyi He & Shengnan Li & Shuijun Wu & Chuangzhi Li & Biao Xu, 2021. "Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, March.
    6. Mohammadhafez Bazrafshan & Likhitha Yalamanchili & Nikolaos Gatsis & Juan Gomez, 2019. "Stochastic Planning of Distributed PV Generation," Energies, MDPI, vol. 12(3), pages 1-20, January.
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