IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i19p5183-d1761246.html

A Capacity Expansion Model of Hydrogen Energy Storage for Urban-Scale Power Systems: A Case Study in Shanghai

Author

Listed:
  • Chen Fu

    (State Grid Shanghai Economic Research Institute, Shanghai 200235, China)

  • Ruihong Suo

    (State Grid Shanghai Economic Research Institute, Shanghai 200235, China)

  • Lan Li

    (State Grid Shanghai Economic Research Institute, Shanghai 200235, China)

  • Mingxing Guo

    (State Grid Shanghai Economic Research Institute, Shanghai 200235, China)

  • Jiyuan Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Chuanbo Xu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China)

Abstract

With the increasing maturity of renewable energy technologies and the pressing need to address climate change, urban power systems are striving to integrate a higher proportion of low-carbon renewable energy sources. However, the inherent variability and intermittency of wind and solar power pose significant challenges to the stability and reliability of urban power grids. Existing research has primarily focused on short-term energy storage solutions or small-scale integrated energy systems, which are insufficient to address the long-term, large-scale energy storage needs of urban areas with high renewable energy penetration. This paper proposes a mid-to-long-term capacity expansion model for hydrogen energy storage in urban-scale power systems, using Shanghai as a case study. The model employs mixed-integer linear programming (MILP) to optimize the generation portfolios from the present to 2060 under two scenarios: with and without hydrogen storage. The results demonstrate that by 2060, the installed capacity of hydrogen electrolyzers could reach 21.5 GW, and the installed capacity of hydrogen power generators could reach 27.5 GW, accounting for 30% of the total installed capacity excluding their own. Compared to the base scenario, the electricity–hydrogen collaborative energy supply system increases renewable penetration by 11.6% and utilization by 12.9% while reducing the levelized cost of urban comprehensive electricity (LCOUCE) by 2.514 cents/kWh. These findings highlight the technical feasibility and economic advantages of deploying long-term hydrogen storage in urban grids, providing a scalable solution to enhance the stability and efficiency of high-renewable urban power systems.

Suggested Citation

  • Chen Fu & Ruihong Suo & Lan Li & Mingxing Guo & Jiyuan Liu & Chuanbo Xu, 2025. "A Capacity Expansion Model of Hydrogen Energy Storage for Urban-Scale Power Systems: A Case Study in Shanghai," Energies, MDPI, vol. 18(19), pages 1-23, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5183-:d:1761246
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/19/5183/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/19/5183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Wangxin & Han, Qiang & Shang, Wen-Long & Xu, Chengshun, 2024. "Seismic resilience assessment of interdependent urban transportation-electric power system under uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    2. Asghari, Mohammad & Afshari, Hamid & Jaber, Mohamad Y. & Searcy, Cory, 2025. "Strategic analysis of hydrogen market dynamics across collaboration models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    3. Chen, Xianqing & Yang, Lingfang & Dong, Wei & Yang, Qiang, 2024. "Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities," Renewable Energy, Elsevier, vol. 237(PB).
    4. Wang, Chunling & Liu, Chunming & Chen, Jian & Zhang, Gaoyuan, 2024. "Cooperative planning of renewable energy generation and multi-timescale flexible resources in active distribution networks," Applied Energy, Elsevier, vol. 356(C).
    5. Yukta Mehta & Vincent Lo & Vijen Mehta & Kunal Agrawal & Charan Teja Madabathula & Eugene Chang & Jerry Gao, 2025. "Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning," Energies, MDPI, vol. 18(6), pages 1-29, March.
    6. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    7. Wang, Chenyu & Lu, Zhigang & Zhang, Jiangfeng & Guo, Xiaoqiang & Li, Yanlin & Zhang, Jiangyong, 2025. "Planning and economic analysis of low-carbon transition of East China power system considering electricity-hydrogen coupling," Renewable Energy, Elsevier, vol. 249(C).
    8. Liu, Yang & Lv, Mingyun & Sun, Kangwen, 2025. "Comprehensive tradeoff and utilization of airborne renewable energy and uncertain stratospheric wind potential based on reinforcement learning," Energy, Elsevier, vol. 324(C).
    9. Zhang, Huaiyuan & Liao, Kai & Yang, Jianwei & Zheng, Shunwei & He, Zhengyou, 2024. "Frequency-constrained expansion planning for wind and photovoltaic power in wind-photovoltaic-hydro-thermal multi-power system," Applied Energy, Elsevier, vol. 356(C).
    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. Liu, J. & Li, J.W. & Li, X. & Li, Y.P. & Gao, P.P. & Jin, L., 2026. "An optimization model to provide electric power systems’ net zero-carbon emission pathways considering diverse measures under double-side randomness and vagueness," Renewable Energy, Elsevier, vol. 256(PI).
    2. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    3. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.
    4. Liu, Hailiang & Brown, Tom & Andresen, Gorm Bruun & Schlachtberger, David P. & Greiner, Martin, 2019. "The role of hydro power, storage and transmission in the decarbonization of the Chinese power system," Applied Energy, Elsevier, vol. 239(C), pages 1308-1321.
    5. Deng, Xu & Lv, Tao & Meng, Xiangyun & Li, Cong & Hou, Xiaoran & Xu, Jie & Wang, Yinhao & Liu, Feng, 2024. "Assessing the carbon emission reduction effect of flexibility option for integrating variable renewable energy," Energy Economics, Elsevier, vol. 132(C).
    6. Li, Yuan & Zhou, You & Yi, Bo-Wen & Wang, Ya, 2021. "Impacts of the coal resource tax on the electric power industry in China: A multi-regional comprehensive analysis," Resources Policy, Elsevier, vol. 70(C).
    7. Yang, Jingyue & Zhang, Hao & Li, Chenxi & Guo, Pengcheng & Ming, Bo, 2025. "Quantifying the flexibility regulation potential and economic value of pumped storage in extreme scenarios of multi-energy complementary system," Energy, Elsevier, vol. 329(C).
    8. Mei, Haozhou & Wu, Qiong & Ren, Hongbo & Li, Qifen & Gao, Weijun, 2025. "Research status and prospects of regional distribution grid resilience enhancement methods taking into account electrified transportation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    9. Zhang, Hongyu & Deji, Wangzhen & Farinotti, Daniel & Zhang, Da & Huang, Junling, 2024. "The role of Xizang in China's transition towards a carbon-neutral power system," Energy, Elsevier, vol. 313(C).
    10. Shiwei Yu & Chengzhu Gong & Weidong Jia & Li Ma, 2024. "A tripartite evolutionary game model for tradable green certificate transaction strategies in China," Operational Research, Springer, vol. 24(4), pages 1-29, December.
    11. Fang Shi & Xiaodan Li & Ying Cao & Bo Bai, 2023. "The Feasibility Analysis of “Ecological Photovoltaics” from Coal Gangue Mountains," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    12. Jie Wu & Ying Fan & Yan Xia, 2017. "How Can China Achieve Its Nationally Determined Contribution Targets Combining Emissions Trading Scheme and Renewable Energy Policies?," Energies, MDPI, vol. 10(8), pages 1-20, August.
    13. Yao, Xing & Yi, Bowen & Yu, Yang & Fan, Ying & Zhu, Lei, 2020. "Economic analysis of grid integration of variable solar and wind power with conventional power system," Applied Energy, Elsevier, vol. 264(C).
    14. Lv, Chaoxian & Chai, Yuanyuan & Qu, Kaiping & Liang, Rui, 2025. "Multi-entity pricing-enabled supply recovery for electricity-gas energy system with coordinated flexibilities: a hierarchical game approach," Energy, Elsevier, vol. 334(C).
    15. Xuan, Ang & Shen, Xinwei & Luo, Yangfan, 2025. "Bi-level Integrated Electricity and Natural Gas System retrofit planning model considering Carbon Capture, Utilization and Storage," Applied Energy, Elsevier, vol. 385(C).
    16. Laha, Priyanka & Chakraborty, Basab, 2021. "Low carbon electricity system for India in 2030 based on multi-objective multi-criteria assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Yang, Dongfeng & Zhan, Tong & Liu, Xiaojun & Jiang, Chao & Huang, Gang & Wang, Hui, 2025. "Scenario information gap planning for electricity-gas-hydrogen integrated energy systems considering the impact of grid interaction locations," Energy, Elsevier, vol. 335(C).
    18. Wang, Can & Wang, Zhen & Chu, Sihu & Ma, Hui & Yang, Nan & Zhao, Zhuoli & Lai, Chun Sing & Lai, Loi Lei, 2024. "A two-stage underfrequency load shedding strategy for microgrid groups considering risk avoidance," Applied Energy, Elsevier, vol. 367(C).
    19. Kou, Qiaoyuan & Zhang, Hongli & Wang, Cong & Meng, Yue, 2026. "Probabilistic resilience assessment framework for cyber-physics multi-microgrids systems considering uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 268(C).
    20. Cheng, Chuntian & Chen, Fu & Li, Gang & Ristić, Bora & Mirchi, Ali & Qiyu, Tu & Madani, Kaveh, 2018. "Reform and renewables in China: The architecture of Yunnan's hydropower dominated electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 682-693.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jeners:v:18:y:2025:i:19:p:5183-:d:1761246. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.