Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression

Dongsen Li, Kang Qian (), Ciwei Gao, Yiyue Xu, Qiang Xing and Zhangfan Wang
Additional contact information
Dongsen Li: Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Kang Qian: Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Ciwei Gao: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Yiyue Xu: Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Qiang Xing: School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Zhangfan Wang: Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China

Energies, 2024, vol. 17, issue 20, 1-15

Abstract: Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations (WPFs). First, a wavelet packet power decomposition algorithm based on variable frequency entropy improvement is proposed. This algorithm characterizes the energy characteristics of the original wind power in different frequency bands. Second, to minimize WPF and the comprehensive operating cost of EHHS, an optimization model for suppressing wind power in the integrated power and hydrogen system (IPHS) is constructed. Next, considering the real-time and stochastic characteristics of wind power, the wind power smoothing model is transformed into a Markov decision process. A modified proximal policy optimization (MPPO) based on wind power deviation is proposed for training and solving. Based on the DRL agent’s real-time perception of wind power energy characteristics and the IPHS operation status, a WPF smoothing strategy is formulated. Finally, a numerical analysis based on a specific wind farm is conducted. The simulation results based on MATLAB R2021b show that the proposed strategy effectively suppresses WPF and demonstrates excellent convergence stability. The comprehensive performance of the MPPO is improved by 21.25% compared with the proximal policy optimization (PPO) and 42.52% compared with MPPO.

Keywords: electric hydrogen hybrid storage; wind power fluctuations; Markov decision process; deep reinforcement learning; modified proximal policy optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5019/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5019/ (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:jeners:v:17:y:2024:i:20:p:5019-:d:1495256

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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