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A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network.
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- المؤلفون: Zhou, Suyang1 (AUTHOR) ; He, Di2 (AUTHOR); Zhang, Zhiyang2 (AUTHOR); Wu, Zhi1 (AUTHOR) ; Gu, Wei1,2 (AUTHOR); Li, Junjie3 (AUTHOR) ; Li, Zhe3 (AUTHOR); Wu, Gaoxiang3 (AUTHOR)
- المصدر:
Sustainability (2071-1050). Dec2019, Vol. 11 Issue 23, p6784. 1p.
- الموضوع:
- معلومة اضافية
- نبذة مختصرة :
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. [ABSTRACT FROM AUTHOR]
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