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Combined economic and technological evaluation of battery energy storage for grid applications

Author

Listed:
  • D. M. Davies

    (University of California San Diego)

  • M. G. Verde

    (University of California San Diego)

  • O. Mnyshenko

    (University of California San Diego)

  • Y. R. Chen

    (University of California San Diego)

  • R. Rajeev

    (University of California San Diego)

  • Y. S. Meng

    (University of California San Diego
    University of California San Diego)

  • G. Elliott

    (University of California San Diego
    University of California San Diego)

Abstract

Batteries will play critical roles in modernizing energy grids, as they will allow a greater penetration of renewable energy and perform applications that better match supply with demand. Applying storage technology is a business decision that requires potential revenues to be accurately estimated to determine the economic viability, which requires models that consider market rules and prices, along with battery and application-specific constraints. Here we use models of storage connected to the California energy grid and show how the application-governed duty cycles (power profiles) of different applications affect different battery chemistries. We reveal critical trade-offs between battery chemistries and the applicability of energy content in the battery and show that accurate revenue measurement can only be achieved if a realistic battery operation in each application is considered. The findings in this work could call for a paradigm shift in how the true economic values of energy storage devices could be assessed.

Suggested Citation

  • D. M. Davies & M. G. Verde & O. Mnyshenko & Y. R. Chen & R. Rajeev & Y. S. Meng & G. Elliott, 2019. "Combined economic and technological evaluation of battery energy storage for grid applications," Nature Energy, Nature, vol. 4(1), pages 42-50, January.
    • Handle: RePEc:nat:natene:v:4:y:2019:i:1:d:10.1038_s41560-018-0290-1
    DOI: 10.1038/s41560-018-0290-1
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    Cited by:

    1. Li, Mo & Yang, Yi & Smith, Timothy M. & Wilson, Elizabeth J., 2020. "Wind can reduce storage-induced emissions at grid scales," Applied Energy, Elsevier, vol. 276(C).
    2. Li, Canbing & Chen, Dawei & Li, Yingjie & Li, Furong & Li, Ran & Wu, Qiuwei & Liu, Xubin & Wei, Juan & He, Shengtao & Zhou, Bin & Allen, Stephen, 2022. "Exploring the interaction between renewables and energy storage for zero-carbon electricity systems," Energy, Elsevier, vol. 261(PA).
    3. Englberger, Stefan & Abo Gamra, Kareem & Tepe, Benedikt & Schreiber, Michael & Jossen, Andreas & Hesse, Holger, 2021. "Electric vehicle multi-use: Optimizing multiple value streams using mobile storage systems in a vehicle-to-grid context," Applied Energy, Elsevier, vol. 304(C).
    4. Shi, Xingping & He, Qing & Lu, Chang & Wang, Tingting & Cui, Shuangshuang & Du, Dongmei, 2023. "Variable load modes and operation characteristics of closed Brayton cycle pumped thermal electricity storage system with liquid-phase storage," Renewable Energy, Elsevier, vol. 203(C), pages 715-730.
    5. Yuqiang Zeng & Fengyu Shen & Buyi Zhang & Jaeheon Lee & Divya Chalise & Qiye Zheng & Yanbao Fu & Sumanjeet Kaur & Sean D. Lubner & Vincent S. Battaglia & Bryan D. McCloskey & Michael C. Tucker & Ravi , 2023. "Nonintrusive thermal-wave sensor for operando quantification of degradation in commercial batteries," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    6. Zhou, Yuekuan, 2022. "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Schauf, Magnus & Schwenen, Sebastian, 2023. "System price dynamics for battery storage," Energy Policy, Elsevier, vol. 183(C).
    8. Ruixue Liu & Guannan He & Xizhe Wang & Dharik Mallapragada & Hongbo Zhao & Yang Shao-Horn & Benben Jiang, 2024. "A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    10. Mathews, Ian & Xu, Bolun & He, Wei & Barreto, Vanessa & Buonassisi, Tonio & Peters, Ian Marius, 2020. "Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging," Applied Energy, Elsevier, vol. 269(C).
    11. Alexandra von Meier & Elizabeth L. Ratnam & Kyle Brady & Keith Moffat & Jaimie Swartz, 2020. "Phasor-Based Control for Scalable Integration of Variable Energy Resources," Energies, MDPI, vol. 13(1), pages 1-14, January.
    12. Jafari, Mehdi & Botterud, Audun & Sakti, Apurba, 2020. "Estimating revenues from offshore wind-storage systems: The importance of advanced battery models," Applied Energy, Elsevier, vol. 276(C).
    13. Chen, Dongwen & Li, Yong & Abbas, Zulkarnain & Li, Dehong & Wang, Ruzhu, 2022. "Network flow calculation based on the directional nodal potential method for meshed heating networks," Energy, Elsevier, vol. 243(C).
    14. Yuhua Xia & Mengzheng Ouyang & Vladimir Yufit & Rui Tan & Anna Regoutz & Anqi Wang & Wenjie Mao & Barun Chakrabarti & Ashkan Kavei & Qilei Song & Anthony R. Kucernak & Nigel P. Brandon, 2022. "A cost-effective alkaline polysulfide-air redox flow battery enabled by a dual-membrane cell architecture," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    15. Zhang, Hongyan & Gao, Shuaizhi & Zhou, Peng, 2023. "Role of digitalization in energy storage technological innovation: Evidence from China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

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