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A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network

Author

Listed:
  • Suyang Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Di He

    (School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China)

  • Zhiyang Zhang

    (School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China)

  • Zhi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China)

  • Junjie Li

    (Chongqing Electric Power Research Institute, Chongqing 400041, China)

  • Zhe Li

    (Chongqing Electric Power Research Institute, Chongqing 400041, China)

  • Gaoxiang Wu

    (Chongqing Electric Power Research Institute, Chongqing 400041, China)

Abstract

Intra-day control and scheduling of energy systems require high-speed computation and strong robustness. Conventional mathematical driven approaches usually require high computation resources and have difficulty handling system uncertainties. This paper proposes two data-driven scheduling approaches for hydrogen penetrated energy system (HPES) operational scheduling. The two data-driven approaches learn the historical optimization results calculated out using the mixed integer linear programing (MILP) and conditional value at risk (CVaR), respectively. The intra-day rolling optimization mechanism is introduced to evaluate the proposed data-driven scheduling approaches, MILP data-driven approach and CVaR data-driven approach, along with the forecasted renewable generation and load demands. Results show that the two data-driven approaches have lower intra-day operational costs compared with the MILP based method by 1.17% and 0.93%. In addition, the combined cooling and heating plant (CCHP) has a lower frequency of changing the operational states and power output when using the MILP data-driven approach compared with the mathematical driven approaches.

Suggested Citation

  • Suyang Zhou & Di He & Zhiyang Zhang & Zhi Wu & Wei Gu & Junjie Li & Zhe Li & Gaoxiang Wu, 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6784-:d:292370
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    References listed on IDEAS

    as
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