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Impact of battery degradation models on energy management of a grid-connected DC microgrid

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  • Wang, Shuoqi
  • Guo, Dongxu
  • Han, Xuebing
  • Lu, Languang
  • Sun, Kai
  • Li, Weihan
  • Sauer, Dirk Uwe
  • Ouyang, Minggao

Abstract

Battery degradation cost is one of the major concerns when designing energy management strategies of DC microgrids. However, many battery degradation models used in the previous works are over-simplified and the effectiveness of which has not been verified. As a result, this paper presents a comparative study of the impact of battery aging models on energy management of the microgrid. Four popular single factor-based semi-empirical models are investigated while a combined factor-based Combined Arrhenius-Peukert-NREL (CAPN) model is proposed with the best fitting performance compared with the experimental data. The five degradation models are considered as part of the objective function in the particle swarm optimization-based energy management structure of a grid-connect microgrid. The optimized power scheduling and state of charge trajectory of the battery under different single factor-based models exhibit enormous deviations, so as the calculated total costs, which have the maximum error of 63.9% compared with the CAPN model. The application of the studied single factor-based models will also result in 3.5%–12.5% additional actual operating cost under non-optimal conditions. This paper first reveals the significant and unneglectable influence of the simplified degradation models on the microgrid energy management, the abandon of the single factor-based models is also recommended.

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  • Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313359
    DOI: 10.1016/j.energy.2020.118228
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    16. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Li, Weihan & Fan, Yue & Ringbeck, Florian & Jöst, Dominik & Sauer, Dirk Uwe, 2022. "Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression," Applied Energy, Elsevier, vol. 306(PB).
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