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

Suyang Zhou, Di He, Zhiyang Zhang, Zhi Wu, Wei Gu, Junjie Li, Zhe Li and Gaoxiang Wu
Additional contact information
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
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

Sustainability, 2019, vol. 11, issue 23, 1-18

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.

Keywords: hydrogen penetrated energy system; long short-term memory; combined cooling and heat power (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/23/6784/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/23/6784/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:23:p:6784-:d:292370

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().