AI-Driven Multi-Energy Storage and Conversion: Integrating Hydrogen into Future Power Grids
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 105
Special Issue Editors
Interests: electrification transportation coupled networks; resilience assessment; deep reinforcement learning; graph learning
Special Issues, Collections and Topics in MDPI journals
Interests: electricity markets; renewable-energy distribution systems; multi-energy systems
Special Issues, Collections and Topics in MDPI journals
Interests: flexibility enhancement; energy storage system; electricity–hydrogen–ammonia-coupled system; modeling, planning, and regulation of modern power system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The deep decarbonization of the energy system is driving structural changes in the power, transportation, and industrial sectors. Hydrogen, as a zero-carbon energy carrier, with its large-scale energy storage and flexible conversion capabilities, has become a key link connecting fluctuating renewable energy sources and the future power grid. At the same time, the interaction between electric vehicles and the power grid is reshaping the characteristics of power load. The rapid development of artificial intelligence technology provides transformative tools for the optimized operation and intelligent decision-making of complex energy systems. From electro-hydrogen-coupled hydrogen production stations to intelligent charging/hydrogen refueling networks, how to use AI to achieve coordinated optimization of the electro-hydrogen-transportation multi-network and enhance system resilience in extreme events has become a focus of attention for both academia and industry.
This Special Issue, "AI-Driven Multi-Energy Storage and Conversion: Integrating Hydrogen into Future Power Grids", aims to collect the latest research results that combine artificial intelligence technology with the modeling, simulation, and optimization of multi-energy systems, with a particular focus on the deep coupling of hydrogen energy with the power grid and transportation systems.
Dr. Qiang Xing
Dr. Yunyang Zou
Dr. Wennan Zhuang
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- hydrogen modeling
- grid resilience
- hydrogen storage optimization
- electrolysis for hydrogen production
- hydrogen-electric coupled
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