Special Issues

Revolution in Energy Systems: Hydrogen and Beyond

Submission Deadline: 01 June 2025 View: 370 Submit to Special Issue

Guest Editors

Dr. Zhichao Zhang

Email: zhichao.zhang@northumbria.ac.uk

Affiliation: Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

Homepage:

Research Interests: Hydrogen energy, Biofuels, Combustion, Engine systems 

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Prof. Dr. Jie Ji

Email: jijie@hyit.edu.cn

Affiliation: Electric Engineering, Huaiyin Institute of Technology, Huaian, 223001, China 

Homepage:

Research Interests: Green Energy, Energy system, Control system

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Summary

The energy sector is undergoing a transformative period, with a growing emphasis on sustainability, decarbonization, and the integration of renewable energy sources. Hydrogen, as a clean and versatile energy carrier, stands at the forefront of this revolution. It holds the potential to decarbonize hard-to-abate sectors such as transportation, industry, and power generation. The "Revolution in Energy Systems: Hydrogen and Beyond" special issue aims to bring together cutting-edge research that explores the role of hydrogen in the energy transition and beyond, focusing on innovative technologies, policy frameworks, and market dynamics that will shape the future of energy systems.

 

The aim of this special issue is to provide a comprehensive platform for scholars, researchers, and industry experts to share their insights and findings on the latest developments in hydrogen energy and its broader implications for the energy sector. 


Topics of interest of this Special Issue include, but are not limited to:

· Hydrogen Production Technologies: Including electrolysis, steam methane reforming, thermochemical processes, and innovative methods such as green hydrogen production via renewable energy sources.

· Hydrogen Storage and Transportation: Focusing on advances in hydrogen storage materials, transportation infrastructure, and the critical aspects of safety, efficiency, and cost-effectiveness.

· Hydrogen Economy and Market Dynamics: Covering economic models, market analyses, and policy frameworks that support the development of a hydrogen-based economy.

· Hydrogen in Decarbonization: Exploring the role of hydrogen in decarbonizing various sectors, including its integration with carbon capture and storage (CCS) technologies.

· Hydrogen Fuel Cells and Applications: Developments in fuel cell technologies and their applications in transportation, power generation, and other sectors.

· Comparative Energy Systems Analysis: A comparative study of hydrogen with other energy carriers, focusing on sustainability, efficiency, environmental impact, and energy return on investment.

· Hydrogen and Renewable Energy Integration: Strategies for integrating hydrogen with solar, wind, and other renewable energy sources to enhance energy system flexibility and reliability.

· Policy and Regulation in the Hydrogen Sector: Analysis of policy instruments and regulatory frameworks that support the development, deployment, and commercialization of hydrogen technologies.

· Load Forecasting and Hydrogen Demand: Assessing the future demand for hydrogen in various applications and developing forecasting models to guide infrastructure planning and investment.


Keywords

Hydrogen Energy; Renewable Integration; Decarbonization Strategies; Energy Storage Technologies; Hydrogen Economy; Green Hydrogen Production; Sustainable Energy Systems

Published Papers


  • Open Access

    ARTICLE

    Smart Grid Peak Shaving with Energy Storage: Integrated Load Forecasting and Cost-Benefit Optimization

    Cong Zhang, Chutong Zhang, Lei Shen, Renwei Guo, Wan Chen, Hui Huang, Jie Ji
    Energy Engineering, Vol.122, No.5, pp. 2077-2097, 2025, DOI:10.32604/ee.2025.064175
    (This article belongs to the Special Issue: Revolution in Energy Systems: Hydrogen and Beyond)
    Abstract This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method. The solution involves a hybrid prediction framework based on an improved grey regression neural network (IGRNN), which combines grey prediction, an improved BP neural network, and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy. Additionally, an improved cuckoo search (ICS) algorithm is designed to empower the neural network model, incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation, achieving… More >

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