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A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism

Author

Listed:
  • Ji, Jie
  • Zhou, Mengxiong
  • Guo, Renwei
  • Tang, Jiankang
  • Su, Jiaoyue
  • Huang, Hui
  • Sun, Na
  • Nazir, Muhammad Shahzad
  • Wang, Yaodong

Abstract

This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000.

Suggested Citation

  • Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008911
    DOI: 10.1016/j.renene.2023.118985
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