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Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control

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  • Xiaoxuan Guo

    (Electric Power Research Institute Guangxi Power Grid Company, Nanning 520023, China)

  • Yasai Wang

    (Electric Power Research Institute Guangxi Power Grid Company, Nanning 520023, China)

  • Min Guo

    (Electric Power Research Institute Guangxi Power Grid Company, Nanning 520023, China)

  • Leping Sun

    (Electric Power Research Institute Guangxi Power Grid Company, Nanning 520023, China)

  • Xiaojun Shen

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

Abstract

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements.

Suggested Citation

  • Xiaoxuan Guo & Yasai Wang & Min Guo & Leping Sun & Xiaojun Shen, 2024. "Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control," Energies, MDPI, vol. 17(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5626-:d:1518072
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    References listed on IDEAS

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    1. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    2. Arul Rajagopalan & Karthik Nagarajan & Oscar Danilo Montoya & Seshathiri Dhanasekaran & Inayathullah Abdul Kareem & Angalaeswari Sendraya Perumal & Natrayan Lakshmaiya & Prabhu Paramasivam, 2022. "Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer," Energies, MDPI, vol. 15(23), pages 1-24, November.
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