Energies

22 pages, 9636 KB

Open AccessArticle

Quantitative Assessment of Free and Adsorbed Shale Oil in Kerogen Pores Using Molecular Dynamics Simulations and Experiment Characterization

by Yuhao Guo, Liqiang Sima, Liang Wang, Song Tang, Jun Li, Wujun Jin, Bowen Liu and Bojie Li

Energies 2025, 18(21), 5695; https://doi.org/10.3390/en18215695 (registering DOI) - 29 Oct 2025

Abstract

Understanding the microscopic occurrence states of shale oil—particularly the distribution between adsorbed and free phases—is essential for optimizing the development of unconventional reservoirs. In this study, we propose an integrated methodology that combines experimental techniques with molecular dynamics simulations to investigate shale oil [...] Read more.

Understanding the microscopic occurrence states of shale oil—particularly the distribution between adsorbed and free phases—is essential for optimizing the development of unconventional reservoirs. In this study, we propose an integrated methodology that combines experimental techniques with molecular dynamics simulations to investigate shale oil behavior within kerogen nanopores. Specifically, pyrolysis–gas chromatography–mass spectrometry (PY-GC-MS), solid-state ¹³C nuclear magnetic resonance (¹³C NMR), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) were performed to construct a representative kerogen molecular model based on shale samples from the Lianggaoshan Formation in the Sichuan Basin. Grand Canonical Monte Carlo (GCMC) simulations and a theoretical occurrence model were applied to quantify the adsorption characteristics of n-dodecane under varying pore sizes, temperatures, and pressure. The results show that temperature exerts a stronger influence than pore diameter on adsorption capacity, with adsorption decreasing by over 50% at higher temperatures, and pressure has a limited effect on the adsorption amount of dodecane molecules. This study offers a robust workflow for evaluating shale oil occurrence states in complex pore systems and provides guidance for thermal stimulation strategies in tight oil reservoirs. Full article

18 pages, 1622 KB

Open AccessArticle

Simulation of the Hydrogen Railway Engine Performance Under Different Load Conditions and Control Parameters

by Petro Dumenko, Andriy Prokhorenko and Ruslans Smigins

Energies 2025, 18(21), 5694; https://doi.org/10.3390/en18215694 (registering DOI) - 29 Oct 2025

Abstract

The article examines the use of hydrogen fuel as an alternative to traditional diesel fuel for internal combustion engines (ICE) in railway applications. The main objective of the study is to analyze the operational consumption of hydrogen fuel based on the mathematical modeling [...] Read more.

The article examines the use of hydrogen fuel as an alternative to traditional diesel fuel for internal combustion engines (ICE) in railway applications. The main objective of the study is to analyze the operational consumption of hydrogen fuel based on the mathematical modeling of the working cycle of the EMD 12-645E3C engine installed on CIE 071 locomotives used in freight and passenger service. The article provides information on the design features of the EMD 12-645E3C engine, its technical parameters, and the results of bench tests. The indicator parameters of the engine at various controller positions are determined and analyzed, and the results of mathematical modeling of its operation on hydrogen fuel are presented. Particular attention is paid to changes in indicator parameters, including the maximum combustion pressure and the peak gas temperature in the cylinder, as well as comparing the mass consumption of diesel and hydrogen fuel. The study results demonstrate that the use of hydrogen allows the engine to maintain effective power across all operational modes while simultaneously reducing energy costs up to 8%. In this case, the pressure and temperature of the gases in the cylinder increased by 3–6% and 5–8%. Recommendations are also provided regarding technical challenges associated with transitioning to hydrogen fuel, including the modernization of the combustion chamber, fuel system, and safety system. Full article

32 pages, 3083 KB

Open AccessArticle

Comparative Analysis of Passive Thermal Solutions for Building Resilience Under Future Climate Scenarios

by José Pedro Teixeira, Pedro Dinho da Silva, Luís Carvalho Pires and Pedro Dinis Gaspar

Energies 2025, 18(21), 5693; https://doi.org/10.3390/en18215693 (registering DOI) - 29 Oct 2025

Abstract

The intensification of thermal extremes increases the need for strategies that protect indoor comfort and reduce the energy demand of active systems. This study employs EnergyPlus dynamic simulations to evaluate how passive thermal design solutions for heating and cooling can minimize indoor temperature [...] Read more.

The intensification of thermal extremes increases the need for strategies that protect indoor comfort and reduce the energy demand of active systems. This study employs EnergyPlus dynamic simulations to evaluate how passive thermal design solutions for heating and cooling can minimize indoor temperature fluctuations. The analysis covers multiple locations to identify the most effective techniques for improving indoor thermal performance and energy efficiency. Results demonstrate that passive thermal strategies offer a sustainable and efficient approach to adapting buildings to extreme temperature variations, thereby reducing dependence on mechanical systems. The greatest reduction in energy demand is achieved by increasing the envelope’s thermal mass, particularly in hot and temperate climates. Enhanced insulation and green roofs are more effective in cold and humid climates. In addition, solar control measures, such as external shading and reduced glazing areas, help lower indoor temperatures in high-thermal-radiation regions. Full article

22 pages, 1138 KB

Open AccessReview

Scaling Up Non-Thermal Plasma Technology for Water and Wastewater Treatment: Opportunities and Challenges

by Benjamin Morenas, Sidra Saqib, Ahmad Mukhtar, Jonathan Stromberg and Sarah Wu

Energies 2025, 18(21), 5692; https://doi.org/10.3390/en18215692 (registering DOI) - 29 Oct 2025

Abstract

Emerging contaminants such as per- and polyfluoroalkyl substances (PFASs) pose significant challenges for conventional wastewater treatment technologies. Non-thermal plasma (NTP) has gained attention as a promising advanced oxidation process capable of degrading persistent pollutants via hydrated electrons and reactive oxygen/nitrogen species under ambient [...] Read more.

Emerging contaminants such as per- and polyfluoroalkyl substances (PFASs) pose significant challenges for conventional wastewater treatment technologies. Non-thermal plasma (NTP) has gained attention as a promising advanced oxidation process capable of degrading persistent pollutants via hydrated electrons and reactive oxygen/nitrogen species under ambient conditions. This review summarizes recent progress in the application and scale-up of NTP for water treatment, with a focus on reactor configurations, degradation mechanisms, and energy efficiency. Key plasma reactor types—including dielectric barrier discharge, corona discharge, plasma jets, and gliding arc discharge—are evaluated for their suitability in large-scale applications. Pilot-scale studies addressing pharmaceuticals, dyes, and PFASs are reviewed to assess scalability, cost, and operational viability. Although NTP systems consistently achieve >80% contaminant removal, optimizing energy use and maintaining performance across complex water matrices remain critical challenges. Hybrid systems integrating NTP with ozonation, ultrafiltration, or cavitation show potential to improve treatment efficacy and reduce energy demands. Future research priorities include reactor design optimization, contaminant-specific plasma tuning, and technoeconomic analysis to support the translation of NTP technologies from lab-scale innovation to field-scale implementation. Full article

(This article belongs to the Special Issue Advances in Wastewater Treatment, 2nd Edition)

20 pages, 2077 KB

Open AccessArticle

Assessing the Thermal Storage Potential of Timber and Hybrid Activated Slabs: A Simulation-Based Comparison of Different Construction Types

by Andrea Agner and Doris Österreicher

Energies 2025, 18(21), 5691; https://doi.org/10.3390/en18215691 (registering DOI) - 29 Oct 2025

Abstract

Thermally activated building systems (TABS) rely on high thermal mass materials, such as concrete, which perform well thermally but have a high carbon footprint. This study systematically investigates the thermal behavior of bio-based materials—spruce, pine, beech, and oak—in TABS using numerical simulations, comparing [...] Read more.

Thermally activated building systems (TABS) rely on high thermal mass materials, such as concrete, which perform well thermally but have a high carbon footprint. This study systematically investigates the thermal behavior of bio-based materials—spruce, pine, beech, and oak—in TABS using numerical simulations, comparing them with conventional and hybrid materials like concrete and clay. A total of 120 variants were simulated with different pipe diameters, spacing, embedment depths, and inlet temperatures. Thermal properties, particularly thermal conductivity and specific heat capacity, significantly influenced component activation efficiency. Concrete exhibited a characteristic cooling time of 71 h at an inlet temperature of 26 °C (pipe diameter 16 mm), while pine reached 80 h under the same conditions. The use of capillary tube mats extended the cooling times to 75 h for concrete and 92 h for pine. Although concrete provides the best thermal performance, certain bio-based materials achieve comparable results under optimized conditions. Hybrid systems with mineral components offer additional potential for improvement. These findings demonstrate that ecologically sustainable component activation using bio-based materials is feasible with only moderate efficiency losses compared to mineral-based systems, provided system parameters are appropriately adapted. Full article

(This article belongs to the Special Issue Energy Efficiency and Energy Saving in Buildings)

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14 pages, 2069 KB

Open AccessArticle

Validation of 4TP Impedance Bridge Developed at GUM

by Marian Kampik, Krzysztof Musioł, Adam Ziółek, Jolanta Jursza and Maciej Koszarny

Energies 2025, 18(21), 5690; https://doi.org/10.3390/en18215690 (registering DOI) - 29 Oct 2025

Abstract

Impedance methods are gaining importance each year and have recently become an essential tool for diagnosing batteries, capacitors, and fuel cells. In this article we present the results of validating the digital impedance bridge developed by the National Metrology Institute (GUM) in Warsaw, [...] Read more.

Impedance methods are gaining importance each year and have recently become an essential tool for diagnosing batteries, capacitors, and fuel cells. In this article we present the results of validating the digital impedance bridge developed by the National Metrology Institute (GUM) in Warsaw, Poland. The comparison involved new impedance standards recently developed by GUM and the Silesian University of Technology (SUT), as well as standards developed by the Swiss National Metrology Institute (METAS). The comparison of the standard resistors and capacitors demonstrated agreement between the measurement results obtained with the Polish and Swiss sampling bridges, with a deviation of 5 µΩ/Ω in the frequency range from 500 Hz to 5 kHz. Full article

28 pages, 1187 KB

Open AccessArticle

An Empirical Investigation of Explosive Price Behavior in U.S. State-Level Electricity Markets Across Consumer Segments

by Merve Mert Saritas, Gokhan Konat, Levent Dalyanci and Veli Yilanci

Energies 2025, 18(21), 5689; https://doi.org/10.3390/en18215689 (registering DOI) - 29 Oct 2025

Abstract

This study investigates speculative bubbles in U.S. state-level electricity markets across commercial, industrial, and residential segments. Using monthly data (2005–2025) from the U.S. Energy Information Administration and employing the Generalized Supremum Augmented Dickey–Fuller test, evidence of localized explosive price behavior was observed predominantly [...] Read more.

This study investigates speculative bubbles in U.S. state-level electricity markets across commercial, industrial, and residential segments. Using monthly data (2005–2025) from the U.S. Energy Information Administration and employing the Generalized Supremum Augmented Dickey–Fuller test, evidence of localized explosive price behavior was observed predominantly in Florida, Hawaii, Pennsylvania, and Oregon, among others. These bubbles, often tied to market disruptions such as fuel price volatility and post-pandemic recovery, were mainly short-lived and region-specific. The findings highlight the need for tailored, state-specific regulatory strategies to address unique market dynamics, ensuring stability amidst the ongoing energy transition. Full article

(This article belongs to the Special Issue Assessment of Energy and Electricity Market: Trends and Future Perspectives)

19 pages, 7899 KB

Open AccessArticle

Development of DC–DC Converters for Fuel-Cell Hybrid Power Systems in a Lift–Cruise Unmanned Aerial Vehicle

by Min-Gwan Gwon, Ki-Chang Lee and Jang-Mok Kim

Energies 2025, 18(21), 5688; https://doi.org/10.3390/en18215688 (registering DOI) - 29 Oct 2025

Abstract

Lift–cruise-type unmanned aerial vehicles (UAVs) powered by hydrogen fuel cells often integrate secondary energy storage devices to improve responsiveness to load fluctuations during different flight phases, which necessitates an efficient energy management strategy that optimizes power allocation among multiple power sources. This paper [...] Read more.

Lift–cruise-type unmanned aerial vehicles (UAVs) powered by hydrogen fuel cells often integrate secondary energy storage devices to improve responsiveness to load fluctuations during different flight phases, which necessitates an efficient energy management strategy that optimizes power allocation among multiple power sources. This paper presents an innovative fuel cell DC–DC converter (FDC) design for the hybrid power system of a lift–cruise-type UAV comprising a multi-stack fuel cell system and a battery. The novelty of this work lies in the development of an FDC suitable for a multi-stack fuel cell system through a dual-input single-output converter structure and a control algorithm. To integrate inputs supplied from two hydrogen fuel cell stacks into a single output, a controller with a single voltage controller–dual current controller structure was applied, and its performance was verified through simulations and experiments. Load balancing was maintained even under input asymmetry, and fault-tolerant performance was evaluated by analyzing the FDC output waveform under a simulated single-stack input failure. Furthermore, under the assumed flight scenarios, the results demonstrate that stable and efficient power supply is achieved through power-supply mode switching and application of a power distribution algorithm. Full article

(This article belongs to the Collection Advanced Control and Applications of Power Electronics and Power Converters)

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19 pages, 1541 KB

Open AccessArticle

Cable Outer Sheath Defect Identification Using Multi-Scale Leakage Current Features and Graph Neural Networks

by Musong Lin, Hankun Wei, Xukai Duan, Zhi Li, Qiang Fu and Yong Liu

Energies 2025, 18(21), 5687; https://doi.org/10.3390/en18215687 (registering DOI) - 29 Oct 2025

Abstract

The outer sheath of power cables is prone to mechanical damage and environmental stress during long-term operation, and early defects are often difficult to detect accurately using conventional methods. To address this challenge, this paper proposes an outer sheath defect identification method based [...] Read more.

The outer sheath of power cables is prone to mechanical damage and environmental stress during long-term operation, and early defects are often difficult to detect accurately using conventional methods. To address this challenge, this paper proposes an outer sheath defect identification method based on leakage current features and graph neural networks. An electro–thermal coupling physical model was first proposed to simulate the electric field distribution and thermal effects under typical defects, thereby revealing the mechanisms by which defects influence leakage current and harmonic components. A power-frequency high-voltage experimental platform was then constructed to collect leakage current signals under conditions such as scratches, indentations, moisture, and chemical corrosion. Multi-scale frequency band features were extracted using wavelet packet decomposition to construct correlation graphs, which were further modeled through a combination of graph convolutional networks and long short-term memory networks for spatiotemporal analysis. Experimental results demonstrate that the proposed method effectively improves defect type and severity identification. By integrating physical mechanism analysis with data-driven modeling, this approach provides a feasible pathway for condition monitoring and refined operation and maintenance of cable outer sheaths. Full article

24 pages, 3101 KB

Open AccessArticle

Spatio-Temporal Feature Fusion-Based Hybrid GAT-CNN-LSTM Model for Enhanced Short-Term Power Load Forecasting

by Jia Huang, Qing Wei, Tiankuo Wang, Jiajun Ding, Longfei Yu, Diyang Wang and Zhitong Yu

Energies 2025, 18(21), 5686; https://doi.org/10.3390/en18215686 (registering DOI) - 29 Oct 2025

Abstract

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network [...] Read more.

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network (GAT) dynamically captures spatial correlations via adaptive node weighting, resolving static topology constraints; a CNN-LSTM module extracts multi-scale temporal features—convolutional kernels decompose load fluctuations, while bidirectional LSTM layers model long-term trends; and a gated fusion mechanism adaptively weights and fuses spatio-temporal features, suppressing noise and enhancing sensitivity to critical load periods. Experimental validations on multi-city datasets show significant improvements: the model outperforms baseline models by a notable margin in error reduction, exhibits stronger robustness under extreme weather, and maintains superior stability in multi-step forecasting. This study concludes that the hybrid model balances spatial topological analysis and temporal trend modeling, providing higher accuracy and adaptability for STLF in complex power grid environments. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

32 pages, 2290 KB

Open AccessArticle

Research on Parameter Identification for Primary Frequency Regulation of Steam Turbine Based on Improved Bayesian Optimization-Whale Optimization Algorithm

by Wei Li, Weizhen Hou, Siyuan Wen, Yang Jiang, Jiaming Sun and Chengbing He

Energies 2025, 18(21), 5685; https://doi.org/10.3390/en18215685 (registering DOI) - 29 Oct 2025

Abstract

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm [...] Read more.

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR. Full article

(This article belongs to the Section F1: Electrical Power System)

21 pages, 3892 KB

Open AccessArticle

Study on Energy-Saving Potential Based on Heat and Moisture Transfer Characteristics During Fresh Air Introduction in Deep Underground Engineering

by Jiangyan Ma, Xu Zhou, Lin Huang, Baoshun Deng, Lei He, Xiaoling Cao and Shuang Qiu

Energies 2025, 18(21), 5684; https://doi.org/10.3390/en18215684 (registering DOI) - 29 Oct 2025

Abstract

The goal of this paper is to clarify the heat–moisture coupled regulation mechanism of deep-buried underground air tunnels and to address the research gaps in the heat–moisture coupled transfer between airflow and surrounding rock. This paper established a 560 m deep ventilation shaft [...] Read more.

The goal of this paper is to clarify the heat–moisture coupled regulation mechanism of deep-buried underground air tunnels and to address the research gaps in the heat–moisture coupled transfer between airflow and surrounding rock. This paper established a 560 m deep ventilation shaft with a diameter of 5 m focused on the heat–moisture coupled transfer of “surrounding rock—air tunnel—airflow” to investigate the airflow characteristics; analyze the heat and moisture changes of the tunnel surface and airflow, as well as the energy storage characteristics of the surrounding rock; and compare the induced airflow characteristics across four typical cities in China. The results show the following: there is an “inlet effect” in the deep-buried air tunnel; the wall temperature becomes basically stable after 200 m from the entrance, while a greater depth is required for the stable section of humidity; in summer, the airflow temperature decreases by more than 1 °C and the enthalpy decreases by 3.5 kJ/kg; in addition, the ground temperature in Guangzhou is relatively high, resulting in a limited effect on adjusting the intake airflow. This study aims to provide support for the energy-saving design of fresh air systems in deep-buried underground buildings. Full article

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23 pages, 3930 KB

Open AccessReview

A Review of the Recent Advances in CH₄ Recovery from CH₄ Hydrate in Porous Media by CO₂ Replacement

by Yingfei Wang, Weizhong Li, Xiangen Wu and Bo Dong

Energies 2025, 18(21), 5683; https://doi.org/10.3390/en18215683 (registering DOI) - 29 Oct 2025

Abstract

With increasing attention paid to the development of natural gas hydrates, various mining methods have been studied. CO₂-CH₄ hydrate replacement has become one of the key research topics in the field of natural gas hydrate mining because it can overcome [...] Read more.

With increasing attention paid to the development of natural gas hydrates, various mining methods have been studied. CO₂-CH₄ hydrate replacement has become one of the key research topics in the field of natural gas hydrate mining because it can overcome the disadvantage of traditional mining methods that easily lead to reservoir collapse and realize CO₂ sequestration while extracting CH₄. However, complex heat and mass transfer, as well as fluid migration, are involved in CO₂-CH₄ hydrate in situ replacement, and this method has the drawbacks of slower reaction rates and a lower replacement efficiency compared to traditional methods. Therefore, a substantial amount of experimental and simulation research is still needed to advance this method. This paper reviews the current research on CH₄ recovery from CH₄ hydrate by CO₂ replacement. The main CO₂-CH₄ hydrate replacement mechanisms are summarized according to whether the hydrate cage structure is disrupted. Numerical simulation studies based on the above replacement mechanisms are introduced and compared in detail. The effects of various replacement methods, such as soaking replacement and dynamic replacement, as well as factors including the presence of initial water, reservoir permeability, temperature, and pressure on the replacement reaction, are summarized. Additionally, existing pore-scale replacement studies are reviewed, highlighting the necessity of pore-scale research on CO₂-CH₄ hydrate replacement reactions, pointing out the shortcomings of current pore-scale studies, and proposing suggestions for future research directions. This work provides a reference for the development of the CO₂-CH₄ hydrate replacement method and the realization of its industrial applications. Full article

(This article belongs to the Special Issue Advanced Solutions for Carbon Capture, Storage, and Utilization)

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25 pages, 2625 KB

Open AccessArticle

Modeling of Induction Motor Response to Voltage Sags with Re-Acceleration Analysis

by Marina Konuhova

Energies 2025, 18(21), 5682; https://doi.org/10.3390/en18215682 (registering DOI) - 29 Oct 2025

Abstract

This paper analyzes the behavior of a three-phase induction motor (IM) during voltage sags in the supply network and its subsequent re-acceleration following voltage recovery. A dynamic mathematical model based on the two-axis (d,q) representation of the IM is developed, taking [...] Read more.

This paper analyzes the behavior of a three-phase induction motor (IM) during voltage sags in the supply network and its subsequent re-acceleration following voltage recovery. A dynamic mathematical model based on the two-axis (d,q) representation of the IM is developed, taking into account variations in supply voltage, electromagnetic torque, and stator currents over time. The model enables a detailed assessment of motor stability and transient behavior when the supply voltage falls below nominal levels. The analysis covers sag depths of 0.9–0.5

U_{N}

and interruption durations of 0.14 s and 1.14 s, quantifying stator currents and electromagnetic torque both at the instant of the dip and within the first cycles after recovery. Particular attention is given to identifying the conditions under which the IM may fail to re-accelerate or transition into generator mode, depending on the depth and duration of the voltage sag and the type of mechanical load. The study includes simulations for a 0.75 kW IM under both constant and variable torque conditions, as well as different types and durations of short-circuit faults in the supply system. Results show that sag duration has little effect at sag onset but strongly influences recovery inrush and torque oscillations; shorter interruptions yield lower recovery currents. The findings provide practical insights for the design of more robust power supply infrastructures and the refinement of motor control and protection strategies. Full article

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27 pages, 2423 KB

Open AccessArticle

Nodal Marginal Price Decomposition Mechanism for the Hydrogen Energy Market Considering Hydrogen Transportation Characteristics

by Shouheng Li, Wei Yang, Kangkang Wang and Anan Zhang

Energies 2025, 18(21), 5681; https://doi.org/10.3390/en18215681 (registering DOI) - 29 Oct 2025

Abstract

With the growing significance of hydrogen in the global energy transition, research on its pricing mechanisms has become increasingly crucial. Focusing on hydrogen markets predominantly supplied by electrolytic production, this study proposes a nodal marginal hydrogen price decomposition algorithm that explicitly incorporates the [...] Read more.

With the growing significance of hydrogen in the global energy transition, research on its pricing mechanisms has become increasingly crucial. Focusing on hydrogen markets predominantly supplied by electrolytic production, this study proposes a nodal marginal hydrogen price decomposition algorithm that explicitly incorporates the time-delay dynamics inherent in hydrogen transmission. A four-dimensional price formation framework is established, comprising the energy component, network loss component, congestion component, and time-delay component. To address the nonconvex optimization challenges arising in the market-clearing model, an improved second-order cone programming method is introduced. This method effectively reduces computational complexity through the reconstruction of time-coupled constraints and reformulation of the Weymouth equation. On this basis, the analytical expression of the nodal marginal hydrogen price is rigorously derived, elucidating how transmission dynamics influence each price component. Empirical studies using a modified Belgian 20-node system demonstrate that the proposed pricing mechanism dynamically adapts to load variations, with hydrogen prices exhibiting a strong correlation with electricity cost fluctuations. The results validate the efficacy and superiority of the proposed approach in hydrogen energy market applications. This study provides a theoretical foundation for designing efficient and transparent pricing mechanisms in emerging hydrogen markets. Full article

(This article belongs to the Special Issue New Power System Planning and Scheduling)

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10 pages, 217 KB

Open AccessEditorial

Progress and Policy Considerations to Achieve Energy Transition and Carbon Mitigation

by Lei Shen, Ayman Elshkaki, Delong Li, Shuai Zhong, Xinyi Wu and Xueyue Hu

Energies 2025, 18(21), 5680; https://doi.org/10.3390/en18215680 (registering DOI) - 29 Oct 2025

Abstract

The global paradigm shift toward carbon neutrality represents one of the most complex and consequential socioeconomic transformations of the twenty-first century [...] Full article

(This article belongs to the Collection Energy Transition Towards Carbon Neutrality)

24 pages, 3749 KB

Open AccessArticle

Study on Nanostructure and Oxidation Reactivity of Diesel Engine Exhaust Particulates Burning Methanol/F-T Diesel

by Yan Hua, Junjun Jin, Meijuan Zhang, Jialong Zhu, Ruina Li and Shuai Liu

Energies 2025, 18(21), 5679; https://doi.org/10.3390/en18215679 (registering DOI) - 29 Oct 2025

Abstract

In this study, the exhaust particulates of a diesel engine burning methanol/F-T diesel blends were collected. The nanostructure and oxidation reactivity of the particulates were explored using the Brunauer–Emmett–Teller (BET) method, high-resolution transmission electron microscope (HRTEM), and thermogravimetric analysis (TGA), and the relationship [...] Read more.

In this study, the exhaust particulates of a diesel engine burning methanol/F-T diesel blends were collected. The nanostructure and oxidation reactivity of the particulates were explored using the Brunauer–Emmett–Teller (BET) method, high-resolution transmission electron microscope (HRTEM), and thermogravimetric analysis (TGA), and the relationship between them was assessed via the partial least squares (PLS) and variable importance in the projection (PLS-VIP). The results showed that particulates from methanol/F-T diesel combustion were aggregates composed of several primary particles, and the distribution range of particulate half pore width (

R

) was 8~76 nm. As the methanol mixture ratio increased, the mean

R

of particulates decreased, and the particulates′ total pore volume (

V_{p}

), specific surface area (

S_{B E T}

), and the fractal dimension (

D_{f}

) increased. Compared with F-T diesel, methanol/F-T diesel blends particulates showed more disordered structure with a smaller diameter (

d_{p}

) of primary particles, a shorter fringe length (

L_{a}

), a wider separation distance (

d

), and a larger tortuosity (

T_{f}

). With increasing the methanol mixture ratio, it was also found that the amount of soluble organic fraction (SOF) of particulates increased, while oxidation characteristic temperature and the apparent activation energy (

E_{a}

) reduced. The correlation coefficients of

E_{a}

with

T_{f}

and

D_{f}

were 0.99 and 0.98, respectively, by the linear fitting, illustrating that they showed the strongest linear relationship with the reactivity among the discussed nanostructure parameters. The VIP values of

D_{f}

,

T_{f}

,

V_{p}

, and d, with

E_{a}

obtained by the PLS and PLS-VIP, were greater than 1, indicating that they were the chief factors influencing particulate reactivity. Full article

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24 pages, 3845 KB

Open AccessArticle

Energy Management System-Based Multi-Objective Nizar Optimization Algorithm Considering Grid Power and Battery Degradation Cost

by Hasan Wahhab Salih Rabee and Doaa Mohsin Majeed

Energies 2025, 18(21), 5678; https://doi.org/10.3390/en18215678 (registering DOI) - 29 Oct 2025

Abstract

A microgrid (MG) topology combines various kinds of resources like solar photovoltaic (PV) systems, wind turbines (WTs), energy storage systems, and the conventional utility grid. These different resources need to be coordinated in an optimal way to keep the power balanced, reduce the [...] Read more.

A microgrid (MG) topology combines various kinds of resources like solar photovoltaic (PV) systems, wind turbines (WTs), energy storage systems, and the conventional utility grid. These different resources need to be coordinated in an optimal way to keep the power balanced, reduce the operational cost, and make the system resilient to any kind of failures. Therefore, an efficient energy management system (EMS) is essential in an MG system to provide suitable and reliable operation under different weather and demand load conditions. In this paper, a new EMS-based multi-objective Nizar Optimization Algorithm (NOA) is proposed. The suggested EMS aims to improve the power quality problem caused by the unpredictable nature of renewable energy sources and then minimize the grid power and battery degradation costs. By leveraging the adaptability of the NOA, the applied EMS method simply optimizes the allocation and energy sharing of the resources in a grid-connected MG. The proposed EMS was verified in simulation using MATLAB software. The performance of the proposed EMS was tested under different weather conditions, and the obtained results have been compared with those obtained in the existing methods. The obtained results indicate that the proposed EMS based on the NOA is capable of adjusting the multi-source energy allocation with minimal grid costs and the battery degradation issue. The proposed NOA indicates robust performance with total cost savings varying from USD 17 to USD 34 compared to other optimizers, as well as a great reduction in degradation cost, up to 27% improvement over the conventional methods. Finally, the proposed EMS offers several advantages over the conventional methods, including the improved dynamic system, faster convergence, lower operational costs, and higher energy efficiency. Full article

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19 pages, 1994 KB

Open AccessArticle

IVCLNet: A Hybrid Deep Learning Framework Integrating Signal Decomposition and Attention-Enhanced CNN-LSTM for Lithium-Ion Battery SOH Prediction and RUL Estimation

by Yulong Pei, Hua Huo, Yinpeng Guo, Shilu Kang and Jiaxin Xu

Energies 2025, 18(21), 5677; https://doi.org/10.3390/en18215677 (registering DOI) - 29 Oct 2025

Abstract

Accurate prediction of the degradation trajectory and estimation of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the reliability and safety of modern energy storage systems. However, many existing approaches rely on deep or highly complex models to achieve [...] Read more.

Accurate prediction of the degradation trajectory and estimation of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the reliability and safety of modern energy storage systems. However, many existing approaches rely on deep or highly complex models to achieve high accuracy, often at the cost of computational efficiency and practical applicability. To tackle this challenge, we propose a novel hybrid deep-learning framework, IVCLNet, which predicts the battery’s state-of-health (SOH) evolution and estimates RUL by identifying the end-of-life threshold (SOH = 80%). The framework integrates Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Variational Mode Decomposition (VMD), and an attention-enhanced Long Short-Term Memory (LSTM) network. IVCLNet leverages a cascade decomposition strategy to capture multi-scale degradation patterns and employs multiple indirect health indicators (HIs) to enrich feature representation. A lightweight Convolutional Block Attention Module (CBAM) is embedded to strengthen the model’s perception of critical features, guiding the one-dimensional convolutional layers to focus on informative components. Combined with LSTM-based temporal modeling, the framework ensures both accuracy and interpretability. Extensive experiments conducted on two publicly available lithium-ion battery datasets demonstrated that IVCLNet significantly outperforms existing methods in terms of prediction accuracy, robustness, and computational efficiency. The findings indicate that the proposed framework is promising for practical applications in battery health management systems. Full article

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