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Energy management strategy of hybrid energy storage based on Pareto optimality

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  • Wang, Huaqing
  • Xie, Zhuoshi
  • Pu, Lei
  • Ren, Zhongrui
  • Zhang, Yaoyu
  • Tan, Zhongfu

Abstract

With the effectiveness of carbon emission reduction and the trend of clean energy utilization, installed photovoltaic (PV) capacity is increasing rapidly. The multi-energy coupling system (MECS), including hybrid energy storage, can effectively reduce the volatility of PV output and reduce its reliance on the grid. An MECS model is constructed, with objectives of carbon emission reduction, economics, and reliability. The NSGA-Ⅱ algorithm is used and the Pareto frontier of optimal solutions is obtained. The calculation example proved the validity of the model. The energy storage ecosystem composed of battery (BAT), hydrogen storage (HYS), and heat storage (HS), can effectively reduce the BAT capacity configuration. The integrated heat system can increase the energy efficiency by approximately 29%. The system revenue of the reliability optimal scheme (ROS) is 3,305 thousand yuan less than the multi-criteria optimal scheme (MOS); however, its reliability is better than that of MOS, as illustrated by the standard deviation reduction of grid interaction by 4.78%.The energy management strategy has been proven to be feasible because the system can find better reliability and economy from PV, energy storage planning, arbitrage between purchase and sale price differences, and electricity-hydrogen-heat conversion processes.

Suggested Citation

  • Wang, Huaqing & Xie, Zhuoshi & Pu, Lei & Ren, Zhongrui & Zhang, Yaoyu & Tan, Zhongfu, 2022. "Energy management strategy of hybrid energy storage based on Pareto optimality," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013526
    DOI: 10.1016/j.apenergy.2022.120095
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    2. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong, 2023. "Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity," Energy, Elsevier, vol. 277(C).
    3. Tu, Yunbo & Meng, Xinzhu & Alzahrani, Abdullah Khames & Zhang, Tonghua, 2023. "Multi-objective optimization and nonlinear dynamics for sub-healthy COVID-19 epidemic model subject to self-diffusion and cross-diffusion," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    4. Fuwu Yan & Jinhai Wang & Changqing Du & Min Hua, 2022. "Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function," Energies, MDPI, vol. 16(1), pages 1-17, December.

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