IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p598-d1578453.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/598/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/3/598/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Coninx, Kristof & Deconinck, Geert & Holvoet, Tom, 2018. "Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics," Applied Energy, Elsevier, vol. 218(C), pages 104-113.
    3. He, Mengjiao & Han, Shuo & Chen, Diyi & Zhao, Ziwen & Jurasz, Jakub & Mahmud, Md Apel & Liu, Pan & Deng, Mingjiang, 2024. "Optimizing cascade Hydropower-VRE hybrid systems: A novel approach addressing whole-process vibration to enhance operational safety," Energy, Elsevier, vol. 304(C).
    4. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    5. Zhang, Menghan & Yang, Zhifang & Lin, Wei & Yu, Juan & Dai, Wei & Du, Ershun, 2021. "Enhancing economics of power systems through fast unit commitment with high time resolution," Applied Energy, Elsevier, vol. 281(C).
    6. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    7. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy," Energy, Elsevier, vol. 311(C).
    8. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    9. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    10. Fürsch, Michaela & Hagspiel, Simeon & Jägemann, Cosima & Nagl, Stephan & Lindenberger, Dietmar & Tröster, Eckehard, 2013. "The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050," Applied Energy, Elsevier, vol. 104(C), pages 642-652.
    11. Ying, Zhi & Geng, Zhen & Zheng, Xiaoyuan & Dou, Binlin & Cui, Guomin, 2022. "Improving water electrolysis assisted by anodic biochar oxidation for clean hydrogen production," Energy, Elsevier, vol. 238(PB).
    12. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    13. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    14. Stinner, Sebastian & Schlösser, Tim & Huchtemann, Kristian & Müller, Dirk & Monti, Antonello, 2017. "Primary energy evaluation of heat pumps considering dynamic boundary conditions in the energy system," Energy, Elsevier, vol. 138(C), pages 60-78.
    15. Yang, Linfeng & Li, Wei & Xu, Yan & Zhang, Cuo & Chen, Shifei, 2021. "Two novel locally ideal three-period unit commitment formulations in power systems," Applied Energy, Elsevier, vol. 284(C).
    16. Alexis Tantet & Philippe Drobinski, 2021. "A Minimal System Cost Minimization Model for Variable Renewable Energy Integration: Application to France and Comparison to Mean-Variance Analysis," Energies, MDPI, vol. 14(16), pages 1-38, August.
    17. 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.
    18. Hatem Abushammala & Muhammad Adil Masood & Salma Taqi Ghulam & Jia Mao, 2023. "On the Conversion of Paper Waste and Rejects into High-Value Materials and Energy," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    19. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    20. Yan, Xiaopeng & Chen, Baijin, 2021. "Analysis of a novel energy-efficient system with 3-D vertical structure for hydraulic press," Energy, Elsevier, vol. 218(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:598-:d:1578453. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.