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System Optimization Scheduling Considering the Full Process of Electrolytic Aluminum Production and the Integration of Thermal Power and Energy Storage

Author

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  • Yulong Yang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Han Yan

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Jiaqi Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Weiyang Liu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Zhongwen Yan

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China)

Abstract

To address the curtailment phenomenon caused by the high penetration of renewable energy in the system, an optimization scheduling strategy is proposed, considering the full process of electrolytic aluminum production and the integration of thermal power and energy storage. Firstly, to explore the differentiated response capabilities of various devices such as high-energy-consuming electrolytic aluminum units, thermal power units, and energy storage devices to effectively address uncertain variables in the power system, a Variational Mode Decomposition method is introduced to construct differentiated response methods for its low-frequency, medium-frequency, and high-frequency components. Secondly, based on the real production regulation characteristics of the high-energy-consuming electrolytic aluminum load, and considering various influencing factors such as current, temperature, and output, a scheduling model involving electrolytic aluminum load is established. Then, the power generation characteristics in other processes of electrolytic aluminum production are fully exploited to achieve energy storage conversion, replacing the energy storage batteries that respond to high-frequency components. Finally, by combining the deep peak-shaving model of thermal power units, an optimization scheduling model is established for the joint operation of the full electrolytic aluminum production load and thermal-power-storage systems, with the goal of minimizing system operating costs. The case study results show that the proposed model can significantly enhance the system’s renewable energy absorption capacity, reduce energy storage installations, and enhance the economic efficiency of the system’s peak-shaving operation.

Suggested Citation

  • Yulong Yang & Han Yan & Jiaqi Wang & Weiyang Liu & Zhongwen Yan, 2025. "System Optimization Scheduling Considering the Full Process of Electrolytic Aluminum Production and the Integration of Thermal Power and Energy Storage," Energies, MDPI, vol. 18(3), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:598-:d:1578453
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    References listed on IDEAS

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    1. Zhang, Zhikun & Zhu, Zongyuan & Shen, Boxiong & Liu, Lina, 2019. "Insights into biochar and hydrochar production and applications: A review," Energy, Elsevier, vol. 171(C), pages 581-598.
    2. He, Zhenglei & Liu, Chang & Wang, Yutao & Wang, Xu & Man, Yi, 2023. "Optimal operation of wind-solar-thermal collaborative power system considering carbon trading and energy storage," Applied Energy, Elsevier, vol. 352(C).
    3. Yue, Xiaoyu & Liao, Siyang & Xu, Jian & Ke, Deping & Wang, Huiji & Yang, Jiaquan & He, Xuehao, 2024. "Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving," Applied Energy, Elsevier, vol. 373(C).
    4. Fu, Yiwei & Lu, Zongxiang & Hu, Wei & Wu, Shuang & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Research on joint optimal dispatching method for hybrid power system considering system security," Applied Energy, Elsevier, vol. 238(C), pages 147-163.
    5. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
    6. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    7. Liao, Siyang & Xu, Jian & Sun, Yuanzhang & Bao, Yi, 2018. "Local utilization of wind electricity in isolated power systems by employing coordinated control scheme of industrial energy-intensive load," Applied Energy, Elsevier, vol. 217(C), pages 14-24.
    8. Zhe Han & Zehua Li & Wenbo Wang & Wei Liu & Qiang Ma & Sidong Sun & Haiyang Liu & Qiang Zhang & Yue Cao, 2024. "Multi-Time Optimization Scheduling Strategy for Integrated Energy Systems Considering Multiple Controllable Loads and Carbon Capture Plants," Energies, MDPI, vol. 17(23), pages 1-18, November.
    9. Yibo Jiang & Zhe Wang & Shiqi Bian & Siyang Liao & Huibin Lu, 2024. "Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load," Energies, MDPI, vol. 17(24), pages 1-17, December.
    10. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
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