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A novel characteristic-based degradation model of Li-ion batteries for maximum financial benefits of energy storage system during peak demand reductions

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  • Lam, Dylon Hao Cheng
  • Lim, Yun Seng
  • Wong, Jianhui
  • Allahham, Adib
  • Patsios, Charalampos

Abstract

Lithium-ion (Li-ion) batteries are increasingly common in the energy storage system (ESS) to perform grid services such as peak demand reductions for financial and environmental benefits to customers, utility companies and governments. However, to realize the maximum financial benefits of ESS, it is necessary to develop a degradation model that can predict the battery capacity losses and lifespan due to intermittent charging current, discharging current, depth of discharge (DOD) and temperature with time. Therefore, a characteristic-based degradation model is proposed to predict the degradation and lifespan of the batteries based on the characteristics of charging current, discharging current, DOD and temperature with time. This model is verified experimentally based on the second-life batteries set up on the university campus. This model is then integrated with the idling degradation model to predict the service lifespan, optimum capacity and maximum financial benefit of ESS with either first or second-life batteries under different operating conditions. With the DOD of 70 % and 80–20 % as the desired range of state of health (SOH), the optimum capacity of 34kWh ESS can deliver the highest net saving of 21,141 MYR (4727 USD) to the university over 17.3 years. On the other hand, the optimum capacity of 52 kWh ESS can achieve the maximum net saving of 78,924 MYR (17,647 USD) to a factory over 5.2 years under the DOD of 100 % with 100–80 % as the desired range of SOH.

Suggested Citation

  • Lam, Dylon Hao Cheng & Lim, Yun Seng & Wong, Jianhui & Allahham, Adib & Patsios, Charalampos, 2023. "A novel characteristic-based degradation model of Li-ion batteries for maximum financial benefits of energy storage system during peak demand reductions," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005706
    DOI: 10.1016/j.apenergy.2023.121206
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    References listed on IDEAS

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    Cited by:

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    2. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).

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