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An adaptive virtual inertia control strategy for distributed battery energy storage system in microgrids

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  • Xing, Wei
  • Wang, Hewu
  • Lu, Languang
  • Han, Xuebing
  • Sun, Kai
  • Ouyang, Minggao

Abstract

Recently with the large-scale access of renewable energy into power system through power electronics, distributed energy systems attract more attention. However, low inertia decreases the voltage or frequency stability and anti-disturbance capability of systems, causing power quality is vulnerable to the intermittency and uncertainty in photovoltaics or wind plants. Therefore, the virtual inertia control (VIC) is proposed to maintain system stability. This paper proposes a virtual adaptive inertia control (VAIC) strategy. The states of energy storage battery packs (ESBPs) are estimated online by the dual extended Kalman filter. Then the virtual inertia and droop parameters are designed through the fuzzy logic and virtual battery algorithms based on battery states and bus voltage fluctuations, aiming at distributing inertia and power in the dynamic and steady periods respectively. The stability is analyzed using the small signal model, and its feasibility is verified on the Matlab/Simulink platform. In microgrids with multiple ESBPs, the VAIC distributes power and inertia among ESBPs considering their different capabilities of power supplying and the system requirement for inertia. It can suppress the voltage fluctuations, improve the system stability, and achieve the decentralized and coordinated control during the whole running process of microgrid.

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  • Xing, Wei & Wang, Hewu & Lu, Languang & Han, Xuebing & Sun, Kai & Ouyang, Minggao, 2021. "An adaptive virtual inertia control strategy for distributed battery energy storage system in microgrids," Energy, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:energy:v:233:y:2021:i:c:s0360544221014031
    DOI: 10.1016/j.energy.2021.121155
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    2. Miranda, Rodolfo Farías & Salgado-Herrera, Nadia Maria & Rodríguez-Hernández, Osvaldo & Rodríguez-Rodríguez, Juan Ramon & Robles, Miguel & Ruiz-Robles, Dante & Venegas-Rebollar, Vicente, 2022. "Distributed generation in low-voltage DC systems by wind energy in the Baja California Peninsula, Mexico," Energy, Elsevier, vol. 242(C).
    3. Feng Wang & Lizheng Sun & Zhang Wen & Fang Zhuo, 2022. "Overview of Inertia Enhancement Methods in DC System," Energies, MDPI, vol. 15(18), pages 1-25, September.
    4. Jin, Yuhui & Wu, Xiao & Shen, Jiong, 2022. "Power-heat coordinated control of multiple energy system for off-grid energy supply using multi-timescale distributed predictive control," Energy, Elsevier, vol. 254(PB).
    5. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
    6. Li, Yalun & Gao, Xinlei & Feng, Xuning & Ren, Dongsheng & Li, Yan & Hou, Junxian & Wu, Yu & Du, Jiuyu & Lu, Languang & Ouyang, Minggao, 2022. "Battery eruption triggered by plated lithium on an anode during thermal runaway after fast charging," Energy, Elsevier, vol. 239(PB).

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