IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0320734.html
   My bibliography  Save this article

Capacity optimization strategy for gravity energy storage stations considering the impact of new power systems

Author

Listed:
  • Can Lv
  • Jun He
  • Jingjing Ma
  • Yukun Yang
  • Fan Liu
  • Wentao Huang

Abstract

The integration of renewable energy sources, such as wind and solar power, into the grid is essential for achieving carbon peaking and neutrality goals. However, the inherent variability and unpredictability of these energy sources pose significant challenges to power system stability. Advanced energy storage systems (ESS) are critical for mitigating these challenges, with gravity energy storage systems (GESS) emerging as a promising solution due to their scalability, economic viability, and environmental benefits. This paper proposes a multi-objective economic capacity optimization model for GESS within a novel power system framework, considering the impacts on power network stability, environmental factors, and economic performance. The model is solved using an enhanced Grasshopper Optimization Algorithm (W-GOA) incorporating a whale spiral motion strategy to improve convergence and solution accuracy. Simulations on the IEEE 30-node system demonstrate that GESS reduces peak-to-valley load differences by 36.1% and curtailment rates by 42.3% (wind) and 18.7% (PV), with a 15% lower levelized cost than CAES. The results indicate that GESS effectively mitigates peak load pressures, stabilizes the grid, and provides a cost-effective solution for integrating high shares of renewable energy. This study highlights the potential of GESS as a key component in future low-carbon power systems, offering both technical and economic advantages over traditional energy storage technologies.

Suggested Citation

  • Can Lv & Jun He & Jingjing Ma & Yukun Yang & Fan Liu & Wentao Huang, 2025. "Capacity optimization strategy for gravity energy storage stations considering the impact of new power systems," PLOS ONE, Public Library of Science, vol. 20(4), pages 1-20, April.
  • Handle: RePEc:plo:pone00:0320734
    DOI: 10.1371/journal.pone.0320734
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320734
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0320734&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0320734?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Emrani, Anisa & Berrada, Asmae & Bakhouya, Mohamed, 2022. "Optimal sizing and deployment of gravity energy storage system in hybrid PV-Wind power plant," Renewable Energy, Elsevier, vol. 183(C), pages 12-27.
    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. Tongu, Daiki & Obara, Shin'ya, 2024. "Formation temperature range expansion and energy storage properties of CO2 hydrates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Rajabzadeh, Hamed & Babazadeh, Reza, 2022. "A game-theoretic approach for power pricing in a resilient supply chain considering a dual channel biorefining structure and the hybrid power plant," Renewable Energy, Elsevier, vol. 198(C), pages 1082-1094.
    3. Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.
    4. Shaojun Wang & Hao Xiao & Zhaoquan Zhao & Dezhao Li & Dong Hu & Qi Hu & Chen Shen & Xingyu Zhang & Jiahao Hu & Cheng Chi & Xin Cheng & Wei Zhang & Erjun Bu & Chenxu Zhao & An Wang & Lu Wang, 2025. "Grid Peak Shaving and Energy Efficiency Improvement: Advances in Gravity Energy Storage Technology and Research on Its Efficient Application," Energies, MDPI, vol. 18(4), pages 1-44, February.
    5. Mingyi Liu & Bin Zhang & Jiaqi Wang & Han Liu & Jianxing Wang & Chenghao Liu & Jiahui Zhao & Yue Sun & Rongrong Zhai & Yong Zhu, 2023. "Optimal Configuration of Wind-PV and Energy Storage in Large Clean Energy Bases," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    6. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    7. Sun, Liangliang & Peng, Jiayu & Dinçer, Hasan & Yüksel, Serhat, 2022. "Coalition-oriented strategic selection of renewable energy system alternatives using q-ROF DEMATEL with golden cut," Energy, Elsevier, vol. 256(C).
    8. Huang, Nantian & Zhao, Xuanyuan & Guo, Yu & Cai, Guowei & Wang, Rijun, 2023. "Distribution network expansion planning considering a distributed hydrogen-thermal storage system based on photovoltaic development of the Whole County of China," Energy, Elsevier, vol. 278(C).
    9. Ayuso-Virgili, Gerard & Christakos, Konstantinos & Lande-Sudall, David & Lümmen, Norbert, 2024. "Measure-correlate-predict methods to improve the assessment of wind and wave energy availability at a semi-exposed coastal area," Energy, Elsevier, vol. 309(C).
    10. Mohammadali Kiehbadroudinezhad & Adel Merabet & Ahmed G. Abo-Khalil & Tareq Salameh & Chaouki Ghenai, 2022. "Intelligent and Optimized Microgrids for Future Supply Power from Renewable Energy Resources: A Review," Energies, MDPI, vol. 15(9), pages 1-21, May.
    11. Berrada, Asmae, 2022. "Financial and economic modeling of large-scale gravity energy storage system," Renewable Energy, Elsevier, vol. 192(C), pages 405-419.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0320734. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.