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Advances in Hydrogen Energy Systems Integration, Modeling and Optimization

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 10276

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Special Issue Editors

Dr. Yue Qiu

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Guest Editor

School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: integrated energy system; hydrogen; energy–transportation integration

Dr. Suyang Zhou

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Guest Editor

School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: integrated energy systems; artificial intelligence; demand-side management

Dr. Shichang Cui

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Guest Editor

School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: integrated energy systems; stochastic optimization; power market

Dr. Qirun Sun

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Guest Editor

School of Electrical and Power Engineering, Hohai University, Nanjing 211106, China
Interests: integrated energy systems; resilience; distribution system planning

Special Issue Information

Dear Colleagues,

With the rapid global energy transition and the pursuit of carbon neutrality, hydrogen energy has emerged as a strategic clean energy carrier. It enables low- to zero-carbon emissions and links power, industry, and transportation, enhancing energy system flexibility and reliability. However, challenges remain in the efficient integration and optimization of hydrogen systems.

This Special Issue highlights advancements in hydrogen energy system integration, modeling, and optimization, fostering interdisciplinary collaboration and innovation. We invite original research contributions on topics such as the following:

- Integration and optimization of green hydrogen production and storage;

- Multi-energy systems coupling hydrogen and renewables;

- Optimization and scheduling of hydrogen systems across timescales;

- Cross-sectoral applications in power, industry, and transportation;

- Economic evaluation and business model innovation;

- AI-driven planning and optimization of hydrogen systems.

Combining theory, simulation, and case studies, this Special Issue aims to provide insights for building efficient, economical, and secure hydrogen infrastructures, supporting a global transition toward low-carbon, intelligent, and resilient energy systems.

Dr. Yue Qiu
Dr. Suyang Zhou
Dr. Shichang Cui
Dr. Qirun Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

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

integration of hydrogen energy systems
multi-energy complementarity optimization
green hydrogen production technologies
low-carbon energy transition
applications of hydrogen energy

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Published Papers (9 papers)

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Research

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27 pages, 4380 KB

Open AccessArticle

Adaptive Working Condition-Based Fault Location Method for Low-Voltage Distribution Grids Using Progressive Transfer Learning and Time-Frequency Analysis

by Fengqian Xu, Zhenyu Wu, Yong Zheng, Jianfeng Zheng, Zhiming Qiao, Lun Xu, Dongli Xu and Haitao Liu

Processes 2025, 13(12), 3873; https://doi.org/10.3390/pr13123873 - 1 Dec 2025

Viewed by 195

Abstract

Data-driven fault location methods based on deep learning offer strong feature learning and nonlinear mapping capabilities; however, in low-voltage distribution grids (LVDG) the scarcity of high-rate sampling devices and the variability introduced by distributed renewable generation lead to data insufficiency and data imbalance, which reduce the accuracy of deep-learning-based fault location. To address this, this paper proposes an adaptive working condition-based fault location method that integrates S-transform-enhanced feature extraction with progressive transfer learning. The method clusters working conditions using k-means on a 21-dimensional indicator set covering load, photovoltaic, and voltage. For each condition, a CNN is trained on the corresponding data, and the S-transform extracts distinctive time-frequency signatures from limited measurements to separate fault points at similar distances from the feeder head. Then, progressive transfer learning with Euclidean distance-based domain adaptation migrates effective parameters from data-rich conditions to data-scarce ones through fine-tuning and medium-tuning, thereby addressing the degradation of fault-location accuracy in scenarios with limited data. Experimental validation on a 400 V LVDG demonstrates superior performance, achieving 99.80% fault location accuracy and 99.72% fault type classification. The S-transform enhancement improves fault location by 6.63%, while transfer learning maintains 96% accuracy in edge conditions using only 200 samples. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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17 pages, 14670 KB

Open AccessArticle

Multi-Scale Graph Learning with Seasonal and Trend Awareness Electricity Load Forecasting

by Zijian Hu, Ye Ji, Honghua Xu, Hong Zhu and Lei Wei

Processes 2025, 13(12), 3865; https://doi.org/10.3390/pr13123865 - 30 Nov 2025

Viewed by 230

Abstract

Accurate electricity load forecasting underpins smart-grid operation and broader economic planning. Yet multivariate load series are driven by weather, economic activity, and seasonal effects whose intertwined, scale-dependent dynamics make forecasting challenging. While graph neural networks (GNNs) capture spatio-temporal dependencies, they often underrepresent multi-scale structure. MTSGNN (multi-scale trend–seasonal GNN) is introduced to bridge this gap. MTSGNN couples a multi-scale trend–seasonal GNN module with a Hawkes-enhanced temporal decoder. The former decomposes load signals into multiple temporal scales and models cross-variable interactions at each scale, while the latter embeds a Hawkes process to capture decaying and self-/mutually exciting temporal influences. To effectively combine long-term trends with periodic variations, a Trend–Seasonal Spatio-Temporal Fusion mechanism is proposed, which jointly learns and integrates trend and seasonal representations across both space and time. MTSGNN is designed for multi-step load forecasting with a historical window of 120 time steps and a prediction horizon of 120 future steps. Evaluations on five real-world datasets demonstrate that MTSGNN consistently surpasses existing approaches for multi-step power load prediction, establishing a new benchmark in forecasting accuracy. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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19 pages, 3005 KB

Open AccessArticle

Coordinated FRT Control for Paralleled Grid-Following and Grid-Forming Generators Connected to Weak Grid

by Tao Tan, Shengli He, Yuqin Gao, Hao Xiao and Xia Shen

Processes 2025, 13(12), 3816; https://doi.org/10.3390/pr13123816 - 26 Nov 2025

Viewed by 237

Abstract

The combination of grid-forming (GFM) and grid-following (GFL) distributed renewable resources (DERs) can leverage their complementary functionalities to achieve superior resilience, reliability, and power quality compared to systems employing a single control strategy. Several studies have focused on the steady-state power coordinated control under stiff power grids, while the transient interaction and coordinated fault ride-through (FRT) issue between the parallel GMF and GFL DERs under weak power grids remains underexplored. To fill this gap, the transient interaction model of the hybrid system under weak grids is developed to guide the stability enhancement-oriented controller design. It is revealed that the GFM DER should help to enhance the GFL DER under transient state since the latter’s PLL has a high probability of lose lock under a weak grid. Moreover, a coordinated FRT control is proposed according to the coupling mechanism. The GMF DER has no need to switch the operation modes, while the system frequency deviation and voltage inrush could be reduced by 0.2% and 40% compared with conventional methods. Finally, simulation verifications based on PSCAD/EMTDC are provided to validate the correctness of the theoretical analysis and the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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22 pages, 6862 KB

Open AccessArticle

Control Strategy for Enhancing Frequency Support Capability of Renewable Energy Plants Under Asymmetric Grid Voltage Dips

by Penghan Li, Xiaowei Ma, Zhuojun Jiang, Meng Wang, Chao Huo, Ying Wang, Guankun Zhao, Keqiang Tai, Dan Sun and Heng Nian

Processes 2025, 13(11), 3524; https://doi.org/10.3390/pr13113524 - 3 Nov 2025

Viewed by 391

Abstract

With the increasing penetration of renewable energy generation, large-scale voltage dips may cause significant active power deficits and threaten system frequency stability. To address the issue, this article proposes a two-stage control strategy to enhance the frequency support capability of renewable energy plants by maximizing converter utilization during asymmetric grid voltage dips. First, a qualitative analysis of converter active power capacity considering current capacity constraints under grid faults is conducted to establish the basis for mitigating system-wide active power deficits. Second, individual phase current constraints are formulated for converters under asymmetric voltage conditions to achieve full utilization of converter capacity. Based on this, a two-stage control strategy for renewable energy plants is proposed, where plant-level convex optimization models for both pre-fault and post-fault conditions are established. By optimally allocating current references of converters within the plants, the requirement of grid codes is satisfied, and the overall frequency support capability of plants is effectively improved. Simulation results demonstrate that the proposed strategy raises the system frequency nadir from 49.58 Hz to 49.66 Hz under a minor fault and from 49.06 Hz to 49.11 Hz under a severe fault, confirming its effectiveness in enhancing the frequency support capability of renewable energy plants. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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14 pages, 2105 KB

Open AccessArticle

A Unified Control Strategy Integrating VSG and LVRT for Current-Source PMSGs

by Yang Yang, Zaijun Wu, Xiangjun Quan, Junjie Xiong, Zijing Wan and Zetao Wei

Processes 2025, 13(11), 3432; https://doi.org/10.3390/pr13113432 - 25 Oct 2025

Viewed by 585

Abstract

The growing penetration of renewable energy has reduced system inertia and damping, threatening grid stability. This paper proposes a novel control strategy that seamlessly integrates virtual synchronous generator (VSG) emulation with low-voltage ride-through (LVRT) capability for direct-drive permanent magnet synchronous generators (PMSGs). The unified control framework enables simultaneous inertia support during frequency disturbances and compliant reactive current injection during voltage sags—eliminating mode switching. Furthermore, the proposed strategy has been validated through both a single-machine model and actual wind farm topology. Results demonstrate that the strategy successfully achieves VSG control functionality while simultaneously meeting LVRT requirements. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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29 pages, 5215 KB

Open AccessArticle

Decarbonization of Lithium Battery Plant: A Planning Methodology Considering Manufacturing Chain Flexibilities

by Anlan Chen, Yue Qiu, Ruonan Li, Wennan Zhuang, Zhizhen Li, Peng Xia, Bo Yuan, Gang Lu, Yingxiang Wang and Suyang Zhou

Processes 2025, 13(10), 3360; https://doi.org/10.3390/pr13103360 - 20 Oct 2025

Viewed by 421

Abstract

The rising penetration of electric vehicles is driving huge demand for lithium batteries, making low-carbon manufacturing a critical objective. This goal is challenged by insufficient production scheduling flexibility and the neglect of carbon-reduction technologies. To address these challenges, this paper develops a low-carbon planning methodology for lithium battery plant energy systems by leveraging manufacturing chain flexibilities. First, a lithium battery energy–carbon material modeling approach is developed that accounts for process production delays and intermediate product storage to capture schedulable process energy consumption patterns. A nitrogen–oxygen coupling production framework is introduced to facilitate oxygen-enriched combustion technology application, while energy recovery pathways are incorporated given the high energy consumption of the formation stage. Subsequently, a process scheduling-driven planning model for lithium battery industrial integrated energy systems (IIES) is developed. Finally, the planning model is validated through four contrasting case studies and systematically evaluated using multi-criteria decision analysis (MCDA). The results demonstrate three principal conclusions: (1) incorporating process scheduling effectively enhances process energy flexibility and reduces total system costs by 19.4%, with MCDA closeness coefficient improving from 0.257 to 0.665; (2) oxygen-enriched combustion increases maximum combustion and carbon capture (CCS) rates from 90% to 95%, reducing carbon tax to 40.5% of the baseline; (3) energy recovery on the basis of process scheduling further reduces costs and carbon emissions, with battery recovery achieving an additional 30.2% cost reduction compared to 24.1% for heat recovery, and MCDA identifies this integrated approach as the optimal solution with a closeness coefficient of 0.919. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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23 pages, 3488 KB

Open AccessArticle

Robust Distribution System State Estimation with Physics-Constrained Heterogeneous Graph Embedding and Cross-Modal Attention

by Siyan Liu, Zhuang Tang, Bo Chai and Ziyu Zeng

Processes 2025, 13(10), 3073; https://doi.org/10.3390/pr13103073 - 25 Sep 2025

Viewed by 561

Abstract

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that context, we develop a deep learning framework that leverages General Attributed Multiplex Heterogeneous Network Embedding to explicitly encode the multiplex, heterogeneous structure of distribution networks and to support inductive learning that adapts to dynamic topology. A cross-modal attention mechanism further models fine-grained interactions between input measurements and node/edge attributes, enabling the capture of nonlinear correlations essential for accurate state estimation. To ensure physical feasibility, soft power-flow residuals are incorporated into training as a physics-constrained regularization, guiding predictions toward consistency with grid operation. Extensive studies on IEEE/CIGRE 14-, 70-, and 179-bus systems show that the proposed method surpasses conventional weighted least squares and representative neural baselines in accuracy, convergence speed, and computational efficiency while exhibiting strong robustness to measurement noise and topological uncertainty. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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26 pages, 2059 KB

Open AccessArticle

Integration and Development Path of Smart Grid Technology: Technology-Driven, Policy Framework and Application Challenges

by Tao Wei, Haixia Li and Junfeng Miao

Processes 2025, 13(8), 2428; https://doi.org/10.3390/pr13082428 - 31 Jul 2025

Cited by 1 | Viewed by 4012

Abstract

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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Review

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28 pages, 2976 KB

Open AccessReview

Catalytic Combustion Hydrogen Sensors for Vehicles: Hydrogen-Sensitive Performance Optimization Strategies and Key Technical Challenges

by Biyi Huang, Yi Wang, Chao Wang, Lijian Wang and Shubin Yan

Processes 2025, 13(8), 2384; https://doi.org/10.3390/pr13082384 - 27 Jul 2025

Viewed by 3108

Abstract

As an efficient and low-carbon renewable energy source, hydrogen plays a strategic role in the global energy transition, particularly in the transportation sector. However, the flammable and explosive nature of hydrogen makes leakage risks in enclosed environments a core challenge for the safe promotion of hydrogen fuel cell vehicles. Catalytic combustion sensors are ideal choices due to their high sensitivity and long lifespan. Nevertheless, they face technical bottlenecks under vehicle operational conditions, such as high-power consumption caused by elevated working temperatures, slow response rates, weak anti-interference capabilities, and catalyst poisoning. This paper systematically reviews the research status of catalytic combustion hydrogen sensors for vehicle applications, summarizes technical difficulties and development strategies from the perspectives of hydrogen-sensitive material design and integration processes, and provides theoretical references and technical guidance for the development of catalytic combustion hydrogen sensors suitable for vehicle use. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

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