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Multi-Time Scale Probabilistic Production Simulation of Wind-Solar Hydrogen Integrated Energy System Considering Hydrogen Storage

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  • معلومة اضافية
    • بيانات النشر:
      Editorial Office of Journal of Shanghai Jiao Tong University, 2024.
    • الموضوع:
      2024
    • Collection:
      LCC:Engineering (General). Civil engineering (General)
      LCC:Chemical engineering
      LCC:Naval architecture. Shipbuilding. Marine engineering
    • نبذة مختصرة :
      The uncertainty of wind power output and the difficulty of power storage restrict the development of new energy. As a high-quality secondary energy, hydrogen energy is green and pollution-free and has a high energy density. In order to cope with the volatility and randomness of new energy output, this paper proposes a multi-time scale probabilistic production simulation method for wind-solar hydrogen integrated energy system considering hydrogen storage. First, thermal energy recovery is considered in the hydrogen storage system model, and the wind-solar hydrogen integrated energy system model including electrothermal hydrogen multiple energy storage is constructed. Then, multi-time scale probabilistic production simulation is conducted for the wind-solar hydrogen integrated energy system, and the system maintenance arrangement and hydrogen storage seasonal distribution scheme are obtained through medium and long-term production simulation. The simulation results are taken as the boundary for short-term production simulation to achieve the cooperation of electrothermal hydrogen multiple energy storage and to smooth the random fluctuation of wind and solar power. Finally, an IEEE-RTS79 node example is given to verify that the proposed method can improve the reliability, flexibility and low-carbon feature of system operation.
    • File Description:
      electronic resource
    • ISSN:
      1006-2467
    • Relation:
      https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-6-881.shtml; https://doaj.org/toc/1006-2467
    • الرقم المعرف:
      10.16183/j.cnki.jsjtu.2022.379
    • الرقم المعرف:
      edsdoj.20e2f7d87e2648e59a373454580983e9