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A Novel Allocation Strategy Based on the Model Predictive Control of Primary Frequency Regulation Power for Multiple Distributed Energy Storage Aggregators

Author

Listed:
  • Tian Mao

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Shan He

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

  • Yingcong Guan

    (College of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Mingbo Liu

    (College of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Wenmeng Zhao

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Tao Wang

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Wenjun Tang

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

Abstract

As the amount of distributed energy storage (DES) in a power system continues to increase, it will not be long before there are multiple DES aggregators participating in frequency regulation, and the realization of their coordinated control is a critical topic of current research. This study focused on the primary frequency regulation (PFR) power allocation strategy among multiple DES aggregators participating in PFR. This study first calculated the PFR demand according to a system frequency response model of the power system with DESs. Next, a PFR power allocation model of DES aggregators was developed based on model predictive control. The objective of this model was to minimize the overall frequency regulation cost while satisfying all of the constraints of DESs. Finally, the distributed interior point method was used to solve the model rapidly. The correctness and effectiveness of the proposed model and algorithm were verified on two unified transmission and distribution systems with DES aggregators used to supply the PFR service. The results revealed that the proposed model could effectively allocate PFR power to the various types of energy storage, with the additional benefits of slowing down the shift in the state of charge for energy storage units and ensuring the continuity of energy storage participation in frequency regulation.

Suggested Citation

  • Tian Mao & Shan He & Yingcong Guan & Mingbo Liu & Wenmeng Zhao & Tao Wang & Wenjun Tang, 2023. "A Novel Allocation Strategy Based on the Model Predictive Control of Primary Frequency Regulation Power for Multiple Distributed Energy Storage Aggregators," Energies, MDPI, vol. 16(17), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6140-:d:1223491
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    References listed on IDEAS

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    1. Xiuli Wang & Xudong Li & Weidong Ni & Fushuan Wen, 2023. "A Model Predictive Control Based Optimal Task Allocation among Multiple Energy Storage Systems for Secondary Frequency Regulation Service Provision," Energies, MDPI, vol. 16(3), pages 1-16, January.
    2. Bin Zhou & Xiaodong Shen & Caimei Pan & Yuanbao Bai & Tian Wu, 2022. "Optimal Reactive Power Dispatch under Transmission and Distribution Coordination Based on an Accelerated Augmented Lagrangian Algorithm," Energies, MDPI, vol. 15(11), pages 1-22, May.
    3. Fernández-Guillamón, Ana & Gómez-Lázaro, Emilio & Muljadi, Eduard & Molina-García, Ángel, 2019. "Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Mejia-Ruiz, Gabriel E. & Paternina, Mario R. Arrieta & Segundo Sevilla, Felix Rafael & Korba, Petr, 2022. "Fast hierarchical coordinated controller for distributed battery energy storage systems to mitigate voltage and frequency deviations," Applied Energy, Elsevier, vol. 323(C).
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