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Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage

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  • Gao, Mingfei
  • Han, Zhonghe
  • Zhang, Ce
  • Li, Peng
  • Wu, Di
  • Li, Peng

Abstract

This paper proposes a configuration method for a multi-element hybrid energy storage system (MHESS) to address renewable energy fluctuations and user demand in regional integrated energy systems (RIES). To reduce the investment cost of energy storage applications in RIES, a multi-timescale capacity configuration model is formulated, containing a day-ahead power planning model to optimize the power output of energy supply equipment on the hour-level scale and a day-in power operation model that considers the power response characteristics of MHESS to smooth out renewable energy fluctuations on the minute-level scale. In addition, an active energy storage operation strategy is proposed to minimize the configuration investment of MHESS in the day-ahead planning stage. The empirical mode decomposition algorithm is employed to decompose the target power of MHESS to derive the optimal capacity configuration and power output of each energy storage unit. The simulation results indicate that the proposed method is successful in reducing the investment cost when compared to both a passive and a simple Li-ion battery method. Specifically, the proposed method reduced the investment cost by 8.8% and 183.64%, respectively. This work provides a valuable reference for RIES optimization and further exploration of the potential for MHESS development.

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  • Gao, Mingfei & Han, Zhonghe & Zhang, Ce & Li, Peng & Wu, Di & Li, Peng, 2023. "Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010666
    DOI: 10.1016/j.energy.2023.127672
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    1. Ghandehariun, Samane & Ghandehariun, Amir M. & Ziabari, Nima Bahrami, 2023. "Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning," Energy, Elsevier, vol. 282(C).

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