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Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China

Author

Listed:
  • Xinyang Guo

    (Huazhong University of Science and Technology
    Harvard University)

  • Xinyu Chen

    (Huazhong University of Science and Technology
    Harvard University)

  • Xia Chen

    (Huazhong University of Science and Technology)

  • Peter Sherman

    (Harvard University
    Harvard University)

  • Jinyu Wen

    (Huazhong University of Science and Technology)

  • Michael McElroy

    (Harvard University
    Harvard University)

Abstract

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world’s largest wind power market.

Suggested Citation

  • Xinyang Guo & Xinyu Chen & Xia Chen & Peter Sherman & Jinyu Wen & Michael McElroy, 2023. "Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37536-3
    DOI: 10.1038/s41467-023-37536-3
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    References listed on IDEAS

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    1. Xi Lu & Michael B. McElroy & Wei Peng & Shiyang Liu & Chris P. Nielsen & Haikun Wang, 2016. "Challenges faced by China compared with the US in developing wind power," Nature Energy, Nature, vol. 1(6), pages 1-6, June.
    2. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    3. Han, Xingning & Chen, Xinyu & McElroy, Michael B. & Liao, Shiwu & Nielsen, Chris P. & Wen, Jinyu, 2019. "Modeling formulation and validation for accelerated simulation and flexibility assessment on large scale power systems under higher renewable penetrations," Applied Energy, Elsevier, vol. 237(C), pages 145-154.
    4. Pérez, Beatriz & Mínguez, Roberto & Guanche, Raúl, 2013. "Offshore wind farm layout optimization using mathematical programming techniques," Renewable Energy, Elsevier, vol. 53(C), pages 389-399.
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    Cited by:

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    2. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    3. Jiang, Ziyue & Han, Jingzuo & Li, Yetong & Chen, Xinyu & Peng, Tianduo & Xiong, Jianliang & Shu, Zhan, 2023. "Charging station layout planning for electric vehicles based on power system flexibility requirements," Energy, Elsevier, vol. 283(C).
    4. Xing Su & Xudong Wang & Wanli Xu & Liqian Yuan & Chunhua Xiong & Jinmao Chen, 2024. "Offshore Wind Power: Progress of the Edge Tool, Which Can Promote Sustainable Energy Development," Sustainability, MDPI, vol. 16(17), pages 1-22, September.
    5. Han, Zhixin & Fang, Debin & Yang, Peiwen & Lei, Leyao, 2023. "Cooperative mechanisms for multi-energy complementarity in the electricity spot market," Energy Economics, Elsevier, vol. 127(PB).
    6. Sun, Yanwei & Ai, Hongying & Li, Ying & Wang, Run & Ma, Renfeng, 2024. "Data-driven large-scale spatial planning framework for determining size and location of offshore wind energy development: A case study of China," Applied Energy, Elsevier, vol. 367(C).
    7. Xue Zhou & Yajian Ke & Jianhui Zhu & Weiwei Cui, 2023. "Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting," Sustainability, MDPI, vol. 16(1), pages 1-26, December.

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