An optimization scheduling strategy for hydrogen-based integrated energy systems using multi-agent deep reinforcement learning

[Display omitted] •An optimized scheduling strategy for hydrogen based IES was proposed using MADRL.•Variable temperature PEMFC mechanism is considered to improve energy supply flexibility.•Time series segment is used to improve the integrity of state information.•Decision variable grouping is used...

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Bibliographic Details
Published in:	Energy conversion and management Vol. 326
Main Authors:	Zhang, Lei, He, Ye, Hatziargyriou, Nikos D., Wu, Hongbin, Han, Pingping
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.02.2025
Subjects:	administrative management algorithms case studies electricity energy conversion fuels heat hydrogen Hydrogen energy utilization Integrated energy systems issues and policy Multi agent deep reinforcement learning Multi-objective optimization Optimal scheduling time series analysis uncertainty wind Multi agent deep reinforcement learning Multi-objective optimization Hydrogen energy utilization Integrated energy systems Optimal scheduling
ISSN:	0196-8904
Online Access:	Get full text
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