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Hydrogen Carriers for Renewable Microgrid System Applications
Artificial Intelligence and Digital Twins for Bioclimatic Building Design: Innovations in Sustainability and Efficiency
Power-to-Heat and Seasonal Thermal Energy Storage: Pathways Toward a Low-Carbon Future for District Heating
Deep Neural Network-Based Optimal Power Flow for Active Distribution Systems with High Photovoltaic Penetration
Fuzzy Logic Estimation of Coincidence Factors for EV Fleet Charging Infrastructure Planning in Residential Buildings

Journal Description

Energies

Energies is a peer-reviewed, open access journal of related scientific research, technology development, engineering policy and management studies related to the general field of energy (from technologies of energy supply, conversion, dispatch and final use to the physical and chemical processes behind such technologies), and is published semimonthly online by MDPI. The European Biomass Industry Association (EUBIA), Association of European Renewable Energy Research Centres (EUREC), Institute of Energy and Fuel Processing Technology (ITPE), International Society for Porous Media (InterPore), CYTED and others are affiliated with Energies and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, RePEc, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.2 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Sections: published in 41 topical sections.
Testimonials: See what our editors and authors say about Energies.
Companion journals for Energies include: Energy Storage and Applications and Bioresources and Bioproducts.
Journal Cluster of Energy and Fuels: Energies, Batteries, Hydrogen, Biomass, Electricity, Wind, Fuels, Gases, Solar, ESA and Methane.

Impact Factor: 3.2 (2024); 5-Year Impact Factor: 3.1 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1996-1073

Latest Articles

19 pages, 2424 KB

Open AccessArticle

A Multi-Time Scale Optimal Dispatch Strategy for Green Ammonia Production Using Wind–Solar Hydrogen Under Renewable Energy Fluctuations

by Yong Zheng, Shaofei Zhu, Dexue Yang, Jianpeng Li, Fengwei Rong, Xu Ji and Ge He

Energies 2025, 18(24), 6518; https://doi.org/10.3390/en18246518 (registering DOI) - 12 Dec 2025

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale [...] Read more.

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

(This article belongs to the Special Issue Advances in Green Hydrogen Production Technologies)

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28 pages, 3764 KB

Open AccessArticle

Robust Optimal Dispatch of Microgrid Considering Flexible Demand-Side

by Pengcheng Pan, Wenjie Yang and Zhongkun Li

Energies 2025, 18(24), 6516; https://doi.org/10.3390/en18246516 - 12 Dec 2025

Objective To address the uncertainty in power grid scheduling caused by the output variability of distributed energy resources (DERs) in microgrids, as well as the limitations of stochastic optimization relying on accurate probability distributions and the overly conservative nature of robust optimization leading [...] Read more.

Objective To address the uncertainty in power grid scheduling caused by the output variability of distributed energy resources (DERs) in microgrids, as well as the limitations of stochastic optimization relying on accurate probability distributions and the overly conservative nature of robust optimization leading to insufficient economic performance, this paper proposes a disseminated robust optimization method for microgrid operation that considers flexible demand-side resources. Methods First, to address the uncertainty in the forecasting of wind and solar power scenarios, this paper launches a two-stage distributionally robust optimization (DRO) model based on a Kullback–Leibler (KL) divergence ambiguity set using a min–max–min framework. Then, the Column-and-Constraint Generation (C&CG) algorithm is employed to decouple the model for an iterative solution. Finally, simulation case studies are directed to validate the effectiveness of the proposed model. Results The demand response-based optimization model projected in the paper effectively enhances the flexibility of the Microgrid. Compared to robust optimization, this model reduces the daily operating cost by 2.86%. Although the cost is slightly higher (4.88%) than that of stochastic optimization, it achieves a balance between economy and robustness by optimizing the expected value under the worst-case probability distribution. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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21 pages, 1642 KB

Open AccessArticle

A Robust Wind Power Forecasting Framework for Non-Stationary Signals via Decomposition and Metaheuristic Optimization

by Weiping Duan, Zhirong Zhang, Anjie Zhong and Zhongyi Tang

Energies 2025, 18(24), 6515; https://doi.org/10.3390/en18246515 - 12 Dec 2025

Accurate wind power forecasting is crucial for the secure and efficient integration of renewable energy into the power grid. However, the inherent intermittency and non-stationary nature of wind power pose significant challenges to prediction models. To address these issues, this paper proposes a [...] Read more.

Accurate wind power forecasting is crucial for the secure and efficient integration of renewable energy into the power grid. However, the inherent intermittency and non-stationary nature of wind power pose significant challenges to prediction models. To address these issues, this paper proposes a novel hybrid forecasting framework named VMD-IPCA-IHSO-FSRVFL. This model synergistically combines variational mode decomposition (VMD), incremental principal component analysis (IPCA) for feature selection, an improved holistic swarm optimization (IHSO) algorithm, and a feature space-regularized random vector functional link (FSRVFL) network. The VMD first decomposes the complex original wind power signal into several stable sub-sequences to simplify the prediction task. The IPCA then identifies and selects the most relevant features, reducing data redundancy and noise. Subsequently, the IHSO algorithm is employed to automatically optimize the hyperparameters of the FSRVFL model, enhancing its performance and convergence speed. Finally, the optimized FSRVFL, a computationally efficient semi-supervised learning model, performs the final prediction. The proposed model was validated using four seasonal datasets from a Chinese offshore wind farm. Experimental results demonstrate that our VMD-IPCA-IHSO-FSRVFL model significantly outperforms other benchmark models, including BP, ELM, RVFL, and their variants, across all evaluation metrics (MSE, RMSE, MAE, and R²). The findings confirm that the integration of signal decomposition, effective feature selection, and intelligent parameter optimization substantially improves forecasting accuracy and stability under different seasonal conditions. This study provides a robust and effective solution for wind power prediction, offering valuable insights for wind farm operation and grid management. Full article

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18 pages, 3642 KB

Open AccessArticle

Analysis of Useful Energy Demand for Heating Purposes in a Building with a Self-Supporting Polystyrene Structure in a Temperate Climate

by Krzysztof Wąs, Grzegorz Nawalany and Miroslav Žitňák

Energies 2025, 18(24), 6514; https://doi.org/10.3390/en18246514 - 12 Dec 2025

This article presents an analysis of the useful energy demand for heating purposes in a single-family, single-storey building with a self-supporting polystyrene structure, which is a relatively niche solution, in relation to a traditional masonry structure with similar partition thickness. The structures considered [...] Read more.

This article presents an analysis of the useful energy demand for heating purposes in a single-family, single-storey building with a self-supporting polystyrene structure, which is a relatively niche solution, in relation to a traditional masonry structure with similar partition thickness. The structures considered met the requirements for passive buildings. The analysis was performed on three locations in Europe with a temperate climate, i.e., Kołobrzeg in Poland, Vienna in Austria, and Essen in Germany. The research showed significant savings in the energy demand of the polystyrene structure compared to the masonry structure for each location, ranging from 38% to 52%. Similarly, the heating period was 21% to 38% shorter in individual locations. This shows that polystyrene construction allows for a significant reduction in building energy demand, leading to lower operating costs. Full article

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

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31 pages, 5985 KB

Open AccessArticle

From Roof to Grid: A Case Study on the Technical and Economic Performance of a 27 kWp Solar PV System at University Campus

by Bipu Alam Emon, Md Shafiul Alam, Md Shafiullah and Imil Hamda Imran

Energies 2025, 18(24), 6513; https://doi.org/10.3390/en18246513 - 12 Dec 2025

Bangladesh’s electricity use is growing rapidly, but it has limited fossil fuel reserves. This disadvantage makes it harder for the country to provide people in densely populated cities with access to reliable energy. Solar photovoltaic (PV) electricity could solve these problems by making [...] Read more.

Bangladesh’s electricity use is growing rapidly, but it has limited fossil fuel reserves. This disadvantage makes it harder for the country to provide people in densely populated cities with access to reliable energy. Solar photovoltaic (PV) electricity could solve these problems by making the grid less dependent on fossil fuels, cutting carbon emissions, and encouraging businesses and institutions to switch to cleaner energy sources. This study designs and simulates a 27 kWp grid-connected solar photovoltaic (PV) system for the University of Asia Pacific (UAP) in Dhaka, Bangladesh. The system has 80 SunPower SPR-MAX2-340 modules and one Sunways STT-30KTL-P inverter. It is expected to generate 36,412 kWh of electricity every year with a performance ratio (PR) of 82.42%. The economic analysis indicates that the system is financially profitable, with a levelized cost of energy (LCOE) of 0.0613 USD/kWh and a payback period of 5 years. The environmental assessment also states that the system will reduce emissions by 503.0 tCO₂ over its lifetime. The results indicate that solar PV systems in cities in Bangladesh could be a long-term solution for meeting energy needs. The overall results show that grid-connected solar PV systems can be a viable, long-term solution for meeting Bangladesh’s urban energy needs. Full article

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14 pages, 1856 KB

Open AccessArticle

Enhancing Robustness in Photoacoustic Detection of Dissolved Acetylene in Transformer Oil: Temperature Effects on Resonance Frequency and Suppression Using the Perturbation Observation Method

by Heli Ni, Jiajia Wang, Xinye Wu, Jinxuan Song, Zhicheng Wu, Lin He and Qiaogen Zhang

Energies 2025, 18(24), 6512; https://doi.org/10.3390/en18246512 - 12 Dec 2025

Photoacoustic spectroscopy is a promising method for detecting dissolved acetylene (C₂H₂) in transformer oil, facilitating early fault diagnosis in power transformers. However, temperature variations significantly influence the resonance frequency of the photoacoustic cell, potentially reducing detection accuracy. This study [...] Read more.

Photoacoustic spectroscopy is a promising method for detecting dissolved acetylene (C₂H₂) in transformer oil, facilitating early fault diagnosis in power transformers. However, temperature variations significantly influence the resonance frequency of the photoacoustic cell, potentially reducing detection accuracy. This study investigates the temperature effects on the first-order longitudinal acoustic mode of a resonant photoacoustic cell using finite element simulations with thermo-viscous acoustics. The results show that as the temperature increases, the resonant frequency increases linearly and the sound pressure amplitude decreases, consistent with analytical models. To enhance system robustness, a perturbation observation method is proposed, treating operating frequency as the independent variable and acoustic pressure as the dependent variable. Time-domain simulations validate its effectiveness in tracking resonance frequency shifts under varying temperatures, ensuring reliable detection. Future work should focus on improving frequency resolution, noise filtering, and adaptive step-size optimization for practical applications. Full article

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16 pages, 2984 KB

Open AccessArticle

Fuzzy-Logic-Based Novel Aggregation Method for Federated Learning: Application to a Solar PV Energy Generation System

by Koksal Erenturk, Azeddine Draou and Abdulrahman AlKassem

Energies 2025, 18(24), 6511; https://doi.org/10.3390/en18246511 - 12 Dec 2025

Federated learning enables collaborative model training across decentralized devices, preserving data privacy by keeping sensitive information localized. Federated learning facilitates the development of robust machine learning models by leveraging diverse datasets distributed across numerous clients, without necessitating data centralization. Federated learning frameworks utilize [...] Read more.

Federated learning enables collaborative model training across decentralized devices, preserving data privacy by keeping sensitive information localized. Federated learning facilitates the development of robust machine learning models by leveraging diverse datasets distributed across numerous clients, without necessitating data centralization. Federated learning frameworks utilize a suite of aggregation algorithms, such as weighted averaging (FedAvg), proximal optimization (FedProx), and adaptive optimization (FedOpt), to perform a convergent synthesis of locally computed model parameter vectors, thus enabling distributed model refinement while maintaining data locality. Fuzzy logic provides a computational framework for approximate reasoning, enabling the modeling of imprecise and uncertain information through the utilization of fuzzy sets and linguistic variables. This study introduces the development of a novel fuzzy-logic-driven aggregation mechanism, FedFZY, for a federated learning approach. This implemented methodology eliminates the requirement for complex mathematical operations and derivative applications. To validate the efficacy and observe the operational behavior of the proposed methodology, the FedFZY method has been deployed on a photovoltaic solar energy generation system, comprising 14 clients. The obtained results have been subjected to comparative analysis with alternative aggregation methodologies, yielding demonstrably successful outcomes. Full article

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12 pages, 3631 KB

Open AccessArticle

A Study on the Lithium-Ion Battery Fire Prevention Diagnostic Technique Based on Time-Resolved Partial Discharge Algorithm

by Wen-Cheng Jin, Chang-Won Kang, Soon-Hyung Lee, Kyung-Min Lee and Yong-Sung Choi

Energies 2025, 18(24), 6510; https://doi.org/10.3390/en18246510 - 12 Dec 2025

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, [...] Read more.

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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12 pages, 444 KB

Open AccessArticle

From Energy-Intensive to Net-Zero Ready: A Campus Sustainability Transition at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia

by Walied Alfraidi

Energies 2025, 18(24), 6509; https://doi.org/10.3390/en18246509 - 12 Dec 2025

The transition to net-zero energy solutions in university campuses is essential for advancing sustainability and enhancing energy efficiency. This paper presents a mathematical optimization model for implementing net-zero energy strategies in Saudi Arabian universities, focusing on Imam Mohammad Ibn Saud Islamic University (IMSIU) [...] Read more.

The transition to net-zero energy solutions in university campuses is essential for advancing sustainability and enhancing energy efficiency. This paper presents a mathematical optimization model for implementing net-zero energy strategies in Saudi Arabian universities, focusing on Imam Mohammad Ibn Saud Islamic University (IMSIU) as a case study. An administration building within IMSIU campus, using real operational data with daily peak loads of 900 kW, are analyzed to determine optimal configurations of renewable and storage systems. Simulation results show that optimally deploying a 3,500 kW photovoltaic array integrated with a 560 kW/2,800 kWh battery energy storage system can effectively meet building-level energy demands and achieve seasonal net-zero balance during both winter and summer periods. The model demonstrates a substantial reduction in grid dependency while promoting the integration of renewable energy resources, showing strong alignment with the targets of the Saudi Green Initiative and national pathways for accelerating renewable energy deployment and energy sustainability. The findings provide a scalable and replicable framework for universities seeking to transition toward net-zero readiness, promoting sustainability in higher education and supporting the broader national goal of carbon-neutral development. Full article

(This article belongs to the Special Issue Innovations and Recent Trends in Power Systems: Smart Grids, Energy Storage Systems and Electric Vehicle Integrations)

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1 pages, 121 KB

Open AccessCorrection

Correction: Menyhart, J. Electric Vehicles and Energy Communities: Vehicle-to-Grid Opportunities and a Sustainable Future. Energies 2025, 18, 854

by Jozsef Menyhart

Energies 2025, 18(24), 6508; https://doi.org/10.3390/en18246508 - 12 Dec 2025

In the original publication [...] Full article

1 pages, 125 KB

Open AccessCorrection

Correction: Balitskii et al. Steel Hydrogen-Induced Degradation Diagnostics for Turbo Aggregated Rotor Shaft Repair Technologies. Energies 2025, 18, 4368

by Alexander I. Balitskii, Valerii O. Kolesnikov, Maria R. Havrilyuk, Valentina O. Balitska, Igor V. Ripey, Marcin A. Królikowski and Tomasz K. Pudlo

Energies 2025, 18(24), 6507; https://doi.org/10.3390/en18246507 - 12 Dec 2025

In the original publication [...] Full article

22 pages, 1393 KB

Open AccessReview

Biogas Upgrading and Bottling Technologies: A Critical Review

by Yolanda Mapantsela and Patrick Mukumba

Energies 2025, 18(24), 6506; https://doi.org/10.3390/en18246506 - 12 Dec 2025

Biogas upgrading and bottling represent essential processes in transforming raw biogas produced via the anaerobic digestion of organic waste into high-purity biomethane (≥95% CH₄), a renewable energy source suitable for applications in cooking, transportation, and electricity generation. Upgrading technologies, such as [...] Read more.

Biogas upgrading and bottling represent essential processes in transforming raw biogas produced via the anaerobic digestion of organic waste into high-purity biomethane (≥95% CH₄), a renewable energy source suitable for applications in cooking, transportation, and electricity generation. Upgrading technologies, such as membrane separation, pressure swing adsorption (PSA), water and chemical scrubbing, and emerging methods, like cryogenic distillation and supersonic separation, play a pivotal role in removing impurities like CO₂, H₂S, and moisture. Membrane and hybrid systems demonstrate high methane recovery (>99.5%) with low energy consumption, whereas chemical scrubbing offers superior gas purity but is limited by high operational complexity and cost. Challenges persist around material selection, safety standards, infrastructure limitations, and environmental impacts, particularly in rural and off-grid contexts. Bottled biogas, also known as bio-compressed natural gas (CNG), presents a clean, portable alternative to fossil fuels, contributing to energy equity, greenhouse gases (GHG) reduction, and rural development. The primary aim of this research is to critically analyze and review the current state of biogas upgrading and bottling systems, assess their technological maturity, identify performance optimization challenges, and evaluate their economic and environmental viability. The research gap identified in this study demonstrates that there is no comprehensive comparison of biogas upgrading technologies in terms of energy efficiency, price, scalability, and environmental impact. Few studies directly compare these technologies across various operational contexts (e.g., rural vs. urban, small vs. large scale). Additionally, the review outlines insights into how biogas can replace fossil fuels in transport, cooking, and electricity generation, contributing to decarbonization goals. Solutions should be promoted that reduce methane emissions, lower operational costs, and optimize resource use, aligning with climate targets. This synthesis highlights the technological diversity, critical barriers to scalability, and the need for robust policy mechanisms to accelerate the deployment of biogas upgrading solutions as a central component of a low-carbon, decentralized energy future. Full article

(This article belongs to the Special Issue Advanced Bioenergy, Biomass and Waste Conversion Technologies: 2nd Edition)

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14 pages, 3441 KB

Open AccessArticle

Improved Biomethane Potential by Substrate Augmentation in Anaerobic Digestion and Biodigestate Utilization in Meeting Circular Bioeconomy

by Wame Bontsi, Nhlanhla Othusitse, Amare Gessesse and Lesedi Lebogang

Energies 2025, 18(24), 6505; https://doi.org/10.3390/en18246505 - 12 Dec 2025

Waste generated from agricultural activities is anticipated to increase in the future, especially in less developed countries, and this could cause environmental health risks if these wastes are not well managed. The anaerobic digestion (AD) by co-digesting organic waste is a technology used [...] Read more.

Waste generated from agricultural activities is anticipated to increase in the future, especially in less developed countries, and this could cause environmental health risks if these wastes are not well managed. The anaerobic digestion (AD) by co-digesting organic waste is a technology used to produce biogas while utilizing biodigestate as a biofertilizer; however, AD requires a lot of water to be efficient, which could pose water challenges to arid areas. This study evaluated biogas production under semi-dry conditions by augmenting the process with a high-water content wild melon and determined the nutrient composition of the resultant biodigestate. Batch studies of AD were performed to evaluate methane potential of the different animal waste using an online and standardized Automatic Methane Potential Test System (AMPTS) II light for approximately 506 h (21 days) at 38 °C. The highest biomethane potential (BMP) determined for mono and co-substrate digestion was 29.5 NmL CH₄/g VS (CD) and 63.3 NmL CH₄/g VS (CMWM), respectively, which was calculated from AMPTS biomethane yield of 3166.2 NmL (CD) and 1480.6 NmL (CMWM). Water-displacement method was also used to compare biogas yield in wet and semi-dry AD. The results showed high biogas yield of 8480 mL for CM (mono-substrate) and 10,975 mL for CMCC in wet AD. Semi-dry AD was investigated by replacing water with a wild melon (WM), and the highest biogas production was 8000 mL from the CMCC combination augmented with WM. Generally, in wet AD, co-digestion was more effective in biogas production than mono-substrate AD. The biodigestate from different substrate combinations were also evaluated for nutrient composition using X-ray Fluorescence (XRF) analysis, and all the samples contained fair amount of essential nutrients such as calcium (Ca), phosphorus (P), potassium (K) and microelements such as chloride (Cl), magnesium (Mn), iron (Fe), zinc (Zn). This study successfully implemented semi-dry AD from co-digested animal wastes to produce biogas as an energy solution and biofertilizer for crop production, thereby creating a closed-loop system that supports a circular bioeconomy. In addition, the study confirmed that lowering the water content in the AD process is feasible without compromising substantial biogas production. This technology, when optimized and well implemented, could provide sustainable biogas production in areas with water scarcity, therefore making the biogas production process accessible to rural communities. Full article

(This article belongs to the Special Issue Waste-to-Energy and the Circular Economy: Towards Sustainable Resource Management)

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35 pages, 3111 KB

Open AccessArticle

Enhancing Shaft Voltage Mitigation with Diffusion Models: A Comprehensive Review for Industrial Electric Motors

by Zuhair Abbas, Arifa Zahir and Jin Hur

Energies 2025, 18(24), 6504; https://doi.org/10.3390/en18246504 - 11 Dec 2025

Industrial electric motors powered by variable frequency drives (VFDs) offer better controllability as compared to the conventional sinusoid-fed motors. However, the switching transients of VFDs induce shaft voltage in electric motors, which can lead to bearing failure. This may cause the machine to [...] Read more.

Industrial electric motors powered by variable frequency drives (VFDs) offer better controllability as compared to the conventional sinusoid-fed motors. However, the switching transients of VFDs induce shaft voltage in electric motors, which can lead to bearing failure. This may cause the machine to shut down and pose a serious threat to the system’s reliability. Several shaft voltage mitigation strategies are suggested in the literature, including insulated bearings, grounding brushes, copper shields, and filters. Although mitigation strategies have been extensively studied, shaft voltage signal processing remains relatively underexplored. This review introduces diffusion models (DMs), a new generative learning technique, as an effective solution for processing shaft voltage signals. These models are good at reducing noise, handling uncertainty, and capturing complex patterns over time. DMs offer robust performance under dynamic conditions as compared to traditional machine learning (ML) and deep learning (DL) techniques. In summary, the review outlines the sources and causes of shaft voltage, its existing mitigation strategies, and the theory behind DMs for shaft voltage analysis. Thus, by combining insights from electrical engineering and artificial intelligence (AI), this work addresses an important gap in the existing literature and provides a strong path forward for improving the reliability of industrial motor systems. Full article

(This article belongs to the Special Issue Advanced Structures, Fault Diagnosis and Tolerant Control of Permanent Magnet Synchronous Motors: 2nd Edition)

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19 pages, 7066 KB

Open AccessArticle

Improvement and Validation of Transient Analysis Code FRTAC for Liquid Metal-Cooled Fast Reactors

by Jian Hong, Bo Kuang, Lixia Ren, Yuping Zhou, Xintong Zhao, Xiaochen Xu, Shirui Li and Wenjun Hu

Energies 2025, 18(24), 6503; https://doi.org/10.3390/en18246503 - 11 Dec 2025

Transient safety analysis is a critical aspect of ensuring the safe design of Liquid Metal-cooled Fast Reactors (LMRs), relying heavily on advanced system analysis programs. To this end, the China Institute of Atomic Energy (CIAE) independently developed the Fast Reactor Transient Analysis Code [...] Read more.

Transient safety analysis is a critical aspect of ensuring the safe design of Liquid Metal-cooled Fast Reactors (LMRs), relying heavily on advanced system analysis programs. To this end, the China Institute of Atomic Energy (CIAE) independently developed the Fast Reactor Transient Analysis Code (FRTAC) system analysis code for LMRs, which has been applied to the safety analysis of several reactor types. However, long-term use has revealed certain limitations, such as complex control system modeling and numerical dissipation from the first-order numerical scheme. This study analyzes the current limitations of the code and carries out systematic improvements and validation. The main improvements include enhancing the system compilation architecture and refactoring functional modules to improve computational efficiency, scalability, and usability; introducing a second-order accurate numerical scheme based on a limiter to reduce numerical dissipation in the convection term while ensuring computational stability; and optimizing the solution procedure to accommodate the new architecture and algorithms. The improved code’s computational stability and accuracy were validated using the Edwards blowdown experiment and the Energy Technology Engineering Center (ETEC) once-through steam generator steady-state test, respectively. The validation results show that the improved code maintains excellent numerical stability in problems with rapid transient pressure changes. In steady-state convective heat transfer problems, the computational accuracy and grid convergence are significantly improved, with the relative deviation of the water-side outlet temperature reduced from −3.56% to −0.59%. Under the same computational conditions, the computational efficiency was increased by up to 36.1%. The results of this study will provide a more accurate and efficient system analysis code for the transient safety analysis of LMRs. Full article

(This article belongs to the Special Issue Thermal Hydraulics and Safety Research for Nuclear Reactors)

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23 pages, 982 KB

Open AccessArticle

Development of a Digital Twin of a DC Motor Using NARX Artificial Neural Networks

by Victor Busher, Valeriy Kuznetsov, Zbigniew Ciekanowski, Artur Rojek, Tomasz Grudniewski, Natalya Druzhinina, Vitalii Kuznetsov, Mykola Tryputen, Petro Hubskyi and Alibek Batyrbek

Energies 2025, 18(24), 6502; https://doi.org/10.3390/en18246502 - 11 Dec 2025

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. [...] Read more.

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor. Full article

(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)

21 pages, 10533 KB

Open AccessArticle

Differential Mechanisms of Tight Sandstone Reservoirs and Their Impact on Gas-Bearing Characteristics in the Shaximiao Formation, Southwestern Sichuan Basin

by Xiaojuan Wang, Ke Pan, Zaiquan Yang, Xu Guan, Shuangling Chen, Dongxia Chen, Lan Li, Yilin Liang, Maosen Wang, Kaijun Tan and Qiaochu Wang

Energies 2025, 18(24), 6501; https://doi.org/10.3390/en18246501 - 11 Dec 2025

To identify the principal controls on gas-bearing property heterogeneity in tight reservoirs of the Shaximiao Formation in the southwestern Sichuan Basin, this study systematically examines pore structure characteristics and their influence on reservoir quality through an integrated approach incorporating cast thin sections, X-ray [...] Read more.

To identify the principal controls on gas-bearing property heterogeneity in tight reservoirs of the Shaximiao Formation in the southwestern Sichuan Basin, this study systematically examines pore structure characteristics and their influence on reservoir quality through an integrated approach incorporating cast thin sections, X-ray diffraction (XRD), high-pressure mercury injection (HPMI), and parameters such as homogeneity and variation coefficients. The research has indicated that the following findings: (1) The reservoir lithology in the study area is predominantly lithic arkose, with pore types dominated by residual intergranular pores and intragranular dissolution pores, and pore-throat radii ranging from 5 nm to 1 μm. (2) The disparity in reservoir quality is attributed to two primary factors. Firstly, diverse sediment provenance directions and varying mineral compositions directly influence the internal pore structure of the reservoirs. Secondly, differences in diagenetic minerals lead to heterogeneity in pore space development. Specifically, early carbonate cementation in the Pingluoba reservoir occluded porosity, resulting in poor physical properties. In the Yanjinggou reservoir, clay mineral cementation and pore-filling activities significantly reduced reservoir quality. In contrast, the presence of chlorite coatings in the Baimamiao and Guanyinsi reservoirs helped preserve primary porosity, contributing to superior reservoir properties. (3) The variation in gas content between different gas reservoirs is primarily attributed to differences in reservoir heterogeneity on a planar scale, whereas the gas content variation within different intervals of the same gas reservoir is controlled by differences in pore structure among various sand units. Furthermore, gas content heterogeneity within the same interval of a single reservoir results from variations in sand body thickness and connectivity. Full article

(This article belongs to the Special Issue Exploration and Development of Unconventional Oil and Gas Resources: Latest Advances and Prospects: 3rd Edition)

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26 pages, 4974 KB

Open AccessArticle

Controller Hardware-in-the-Loop Simulation of SOFC-GT Hybrid System

by Yuandong Liu, Chen Yang, Hailin Jiang and Huai Wang

Energies 2025, 18(24), 6500; https://doi.org/10.3390/en18246500 - 11 Dec 2025

The solid oxide fuel cell–gas turbine (SOFC-GT) hybrid system is confronted with challenges related to system integration and coordinated control. In this study, a Controller Hardware-in-the-Loop Simulation (C-HILS) platform is constructed to validate its digital solutions. The C-HILS platform integrates the Advanced Process [...] Read more.

The solid oxide fuel cell–gas turbine (SOFC-GT) hybrid system is confronted with challenges related to system integration and coordinated control. In this study, a Controller Hardware-in-the-Loop Simulation (C-HILS) platform is constructed to validate its digital solutions. The C-HILS platform integrates the Advanced Process Simulation System (APROS), LabVIEW 2020 programming software, NI PXI hardware, and a distributed control system (DCS). Specifically, bidirectional data transmission between the simulation software and the DCS is facilitated through LabVIEW and PXI, leveraging the OLE for Process Control (OPC) protocol and physical Input and Output (I/O) channels. The dynamic SOFC-GT model developed in APROS demonstrates good consistency with design values, with relative errors below 4%. The DCS configuration employs PID controllers to achieve control over total power, SOFC fuel utilization, and gas turbine rotational speed. Experiments under transient conditions reveal that, despite discrepancies in dynamic responses between C-HILS and full-digital simulations, both can achieve stable control. This C-HILS platform effectively integrates virtual models with physical hardware, offering a reliable environment for verifying SOFC-GT control strategies and digital solutions, and thus facilitating the digital transformation of energy systems. Full article

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21 pages, 2486 KB

Open AccessArticle

Characterizing the Spatial Variability of Thermodynamic Properties for Heterogeneous Soft Rock Using Random Field Theory and Copula Statistical Method

by Tao Wang, Wen Nie, Xuemin Zeng, Guoqing Zhou and Ying Xu

Energies 2025, 18(24), 6499; https://doi.org/10.3390/en18246499 - 11 Dec 2025

Studying the thermodynamic properties of soft rocks is critical for geothermal energy extraction, as it elucidates their temperature-dependent mechanical behaviors and heat transfer mechanisms, thereby optimizing reservoir stimulation, enhancing extraction efficiency, and ensuring long-term operational stability. Owing to the intricate geothermal settings and [...] Read more.

Studying the thermodynamic properties of soft rocks is critical for geothermal energy extraction, as it elucidates their temperature-dependent mechanical behaviors and heat transfer mechanisms, thereby optimizing reservoir stimulation, enhancing extraction efficiency, and ensuring long-term operational stability. Owing to the intricate geothermal settings and interconnected physicochemical processes, the thermodynamic properties exhibit pronounced spatial heterogeneity and interdependencies. Concurrently, constraints imposed by technical and economic limitations result in scarce practical field survey and experimental data on these properties, severely hampering comprehensive assessments of geothermal energy potential and exploitation feasibility. To evaluate the spatial variability of thermodynamic properties for heterogeneous soft rock using limited data, the thermal conductivity (TC), heat capacity (HC), and thermal diffusivity (TD) were measured. A new Copula statistical method is used to analyze thermodynamic properties under limited measurement data. Spatial variability in heterogeneous soft rocks is quantified using random field theory. The methodology’s reliability is confirmed through cross-validation against theoretical predictions, empirical measurements, and simulation outputs. The analysis framework of thermodynamic variability characteristics has been presented by stability point analysis and linear regression analysis processes. The variance reduction function, scale of fluctuation, autocorrelation distances, and autocorrelation structure of thermodynamic properties for heterogeneous soft rock are analyzed and discussed. This study can provide scientific data for thermal energy analysis and geothermal reservoir modification specifically applicable to soft rock formations with diagenetic and tectonic histories similar to those investigated in the Weishan Lake area. Full article

(This article belongs to the Section J2: Thermodynamics)

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23 pages, 4464 KB

Open AccessArticle

Diagnosis of Cascaded Open/Short-Circuit Fault in Three-Phase Inverter Using Two-Stage Interval Sliding Mode Observer

by Cen Chen, He Du, Xuerong Ye, Xiaowen Nie, Chunqing Wang and Guofu Zhai

Energies 2025, 18(24), 6498; https://doi.org/10.3390/en18246498 - 11 Dec 2025

A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for [...] Read more.

A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for hybrid OC and SC faults. To address these issues, this paper proposes a diagnosis method for cascaded switch open/short-circuit fault in a three-phase inverter based on a two-stage interval sliding mode observer (ISMO). First, by establishing a mixed logic dynamic (MLD) model considering open- and short-circuit faults, the different fault operating states of the three-phase inverter can be fully characterized. Furthermore, a two-stage cascaded ISMO was designed. The pre-stage ISMO rapidly detects abnormal status and fault phase, while the post-stage ISMO accurately isolates OC and SC faults. After diagnosis, the corresponding fault identification of the observer is set for the next fault diagnosis, achieving the sequential diagnosis of cascaded faults. The proposed diagnosis method was tested to validate its effectiveness. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)

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