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Who Wins the Energy Race? Artificial Intelligence for Smarter Energy Use in Logistics and Supply Chain Management
A Critical Review of Green Hydrogen Production by Electrolysis: From Technology and Modeling to Performance and Cost
A Large Eddy Simulation-Based Power Forecast Approach for Offshore Wind Farms
Integrating Fish-Friendly Hydropower Solutions with the Nature Restoration Policy Through River Barrier Modification
Biogas Production in Agriculture: Technological, Environmental, and Socio-Economic Aspects

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 16.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second 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

21 pages, 2047 KB

Open AccessArticle

Thermographic Diagnosis of Corrosion-Driven Contact Degradation in Power Equipment Using Infrared Imaging and Color-Channel Decomposition

by Milton Ruiz and Carlos Betancourt

Energies 2026, 19(3), 766; https://doi.org/10.3390/en19030766 (registering DOI) - 1 Feb 2026

This study presents a measurement–modeling pathway for diagnosing corrosion-driven contact degradation in power equipment using infrared thermography and color-channel analysis. Thermal data were acquired with a Fluke Ti450 (LWIR, 7.5–14

μ m

) under typical high-altitude, temperate conditions in Quito, Ecuador. Radiometric parameters [...] Read more.

This study presents a measurement–modeling pathway for diagnosing corrosion-driven contact degradation in power equipment using infrared thermography and color-channel analysis. Thermal data were acquired with a Fluke Ti450 (LWIR, 7.5–14

μ m

) under typical high-altitude, temperate conditions in Quito, Ecuador. Radiometric parameters (emissivity, distance, ambient/reflected temperature, and humidity) are reported explicitly, and images are processed with a reproducible pipeline that combines adaptive thresholding, morphology, and region-of-interest statistics, including

Δ T

relative to a reference region. A worked example links an observed hotspot to emissivity-corrected temperature and discusses qualitative implications for the effective contact resistance

R_{eff}

. Uncertainty is summarized through a per-case template that propagates

u_{Δ T}

to

u (R_{eff})

and Weibull characteristic life

η

. Environmental influences (solar load, wind, and emissivity variability) are acknowledged and mitigated. Two field cases illustrate the approach to substation assets. Because the dataset comprises single-visit inspections, formal parameter estimation (e.g., EIS-validated

R_{eff}

and full Weibull/Arrhenius fits) is reserved for longitudinal follow-up. By making radiometry, processing steps, and limitations explicit, the study reduces ambiguity in the transition from temperature contrast to physics-based interpretation and supports auditable maintenance decisions. Full article

(This article belongs to the Section F: Electrical Engineering)

24 pages, 5385 KB

Open AccessArticle

Design of an Electrical Power Take-Off for a Wave Energy Converter for Hydrogen Production

by Andrea Toscani, Davide Spaggiari and Nicola Delmonte

Energies 2026, 19(3), 765; https://doi.org/10.3390/en19030765 (registering DOI) - 1 Feb 2026

In this work, the design of a linear electric generator and an electronic power converter to be integrated into a system to produce hydrogen by exploiting sea waves’ motion is described. The results of Finite Element Method (FEM) simulations for the design of [...] Read more.

In this work, the design of a linear electric generator and an electronic power converter to be integrated into a system to produce hydrogen by exploiting sea waves’ motion is described. The results of Finite Element Method (FEM) simulations for the design of the linear induction generator are presented and discussed along with relevant considerations that will guide the future fabrication of the prototype. Additionally, a novel topological approach for the AC/DC converter, necessary to interface the linear generator with the electrolyzer, is presented. The proposed architecture is able to manage the voltages produced by the harvesting system which are characterized by random phase and amplitude that vary significantly over time. By converting every individual voltage into a unidirectional current before combining them, the proposed converter prevents internal losses due to destructive interactions and increases the overall efficiency of the harvesting process. Moreover, thanks to the specially designed feed-forward corrective action, which mathematically inverts the transfer function of the converter, it is possible to obtain a stable output voltage, even in case of large variations in the input signals. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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30 pages, 6718 KB

Open AccessArticle

Data-Driven Site Selection Based on CO₂ Injectivity in the San Juan Basin

by Donna Christie Essel, William Ampomah, Najmudeen Sibaweihi and Dung Bui

Energies 2026, 19(3), 764; https://doi.org/10.3390/en19030764 (registering DOI) - 1 Feb 2026

CO₂ injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO₂ injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. [...] Read more.

CO₂ injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO₂ injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. The framework is applied to the Entrada Formation in the San Juan Basin, a laterally extensive sandstone unit with limited structural complexity across most of the basin, except for localized uplift in the Hogback region. A numerical model was calibrated to perform sensitivity analysis to identify the dominant parameters influencing injectivity. A dataset of these parameters generated through experimental design informs the development of several ML-based proxies and the best model is selected based on error metrics. These metrics include coefficient of determination (R²), mean absolute error (MAE), and mean squared error (MSE). The effective permeability-thickness product was obtained by the Peaceman’s well model, fractional flow slope, and Dykstra–Parsons coefficient were identified as the most influential parameters impacting the objective function. Train–test and blind test validation identified the Ridge model as the best, achieving an R² ≈ 0.994. The Ridge model which was used to map the Entrada Formation closely matches field-based correlations in the literature, confirming both its physical validity and the Entrada Formation’s strong injectivity potential, with slight deviations explained by the inclusion of additional parameters. This study reduces dependence on computationally intensive simulations while improving prediction accuracy. By benchmarking against established correlations, it enhances model reliability across diverse reservoir conditions. The proposed framework enables rapid, data-driven well placement and feasibility evaluations, streamlining decision-making for CO₂ storage projects. Full article

(This article belongs to the Collection Feature Papers in Carbon Capture, Utilization, and Storage)

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27 pages, 4201 KB

Open AccessArticle

Circular Economy and Energy Transition: Research Trends, Knowledge Structure, and Future Directions

by Sai-Leung Ng and Chih-Yuan Chen

Energies 2026, 19(3), 763; https://doi.org/10.3390/en19030763 (registering DOI) - 1 Feb 2026

The circular economy offers effective strategies to support the transition from fossil fuels to renewable energy. However, research at the nexus of the circular economy and energy transition remains fragmented across disciplines and lacks a systematic and integrative overview of its intellectual structure [...] Read more.

The circular economy offers effective strategies to support the transition from fossil fuels to renewable energy. However, research at the nexus of the circular economy and energy transition remains fragmented across disciplines and lacks a systematic and integrative overview of its intellectual structure and thematic evolution. To address this gap, this study conducts a large-scale bibliometric analysis of 2977 journal articles published between 2008 and 2025 to examine the development, knowledge structure, and global distribution of this field. Performance analysis and scientific mapping are employed to evaluate research output, subject areas, thematic structures, intellectual foundations, journal dissemination, and international collaborations. The results indicate that the circular economy–energy transition nexus is a rapidly growing and multidisciplinary field. It is anchored by conceptual and policy-oriented works and complemented by applied studies on waste management, bioenergy, and decarbonization technologies that directly relate to energy production, conversion, and system efficiency. The geographical distribution shows a multi-pillar but uneven research landscape, with Europe and China emerging as leading contributors, while other regions remain comparatively underrepresented, shaped by regional priorities and collaborative networks. The study highlights emerging research gaps and future directions, offering insights into how circular economy strategies such as resource circularity and waste-to-energy applications can contribute to sustainable and equitable energy transitions and inform future energy-focused research agendas in the context of low-carbon transformation. Full article

(This article belongs to the Special Issue Circular Economy in Energy Infrastructure)

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16 pages, 6330 KB

Open AccessArticle

Convergent Annular Thermoelectric Generator with Fish-Fin-like Heat Exchange

by Ning Wang, Zirui Zhang, Jiahao Li, Jianxiang Cheng, Hongzhi Jia, Bo Dai and Dawei Zhang

Energies 2026, 19(3), 762; https://doi.org/10.3390/en19030762 (registering DOI) - 1 Feb 2026

To address the critical challenge of low thermoelectric conversion efficiency in high-temperature, highly turbulent waste heat recovery, a novel fish-fin convergent annular thermoelectric generator (FF-CATEG) device is proposed. An annular contraction-type thermal conduction ceramic component is designed along the axial gradient direction, with [...] Read more.

To address the critical challenge of low thermoelectric conversion efficiency in high-temperature, highly turbulent waste heat recovery, a novel fish-fin convergent annular thermoelectric generator (FF-CATEG) device is proposed. An annular contraction-type thermal conduction ceramic component is designed along the axial gradient direction, with fish-fin-like fins and thermocouple annular arrays introduced on the inner and outer walls of the ceramic, respectively. Therefore, the directional transport through the cross-coupling of fluid kinetic energy and thermal energy is achieved, significantly improving the thermoelectric conversion efficiency of the proposed structure. Experimental validation demonstrates that the optimized FF-CATEG attains a maximum net output power of 6.17 W at a pipe contraction angle of 3.5° and a fin coverage of 13.44%. With a temperature difference of 320 K and a waste heat fluid velocity of 14.5 m/s, the thermoelectric conversion efficiency is enhanced to 3.97%, representing a substantial 39.3% improvement compared to the finless configuration. This study presents a new approach for recovering waste heat from turbulent flows. Full article

(This article belongs to the Section J: Thermal Management)

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14 pages, 6484 KB

Open AccessArticle

Short-Term Electricity Price Forecasting via a Reinforcement Learning-Based Dynamic Soft Ensemble Strategy

by Yan Wang, Yongxi Zhao, Kun Liang and Hong Fan

Energies 2026, 19(3), 761; https://doi.org/10.3390/en19030761 (registering DOI) - 1 Feb 2026

To address the high volatility of spot market prices and the feature extraction limitations of single models, a short-term electricity price forecasting method based on a reinforcement learning dynamic soft ensemble strategy is proposed. First, a complementary dual-branch architecture is constructed: the CNN-LSTM-Attention [...] Read more.

To address the high volatility of spot market prices and the feature extraction limitations of single models, a short-term electricity price forecasting method based on a reinforcement learning dynamic soft ensemble strategy is proposed. First, a complementary dual-branch architecture is constructed: the CNN-LSTM-Attention branch mines local temporal features, while the Transformer branch captures long-range global dependencies. Second, the Q-learning algorithm is introduced to model weight optimization as a Markov Decision Process. An intelligent agent perceives fluctuation states to adaptively allocate weights, overcoming the rigidity of traditional ensembles. Case studies on PJM market data demonstrate that the proposed model outperforms advanced benchmarks in MAE and RMSE metrics. Notably, prediction accuracy is significantly improved during price spikes and negative price periods. The results verify that the strategy effectively copes with market concept drift, supporting reliable bidding and risk mitigation. Full article

(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 5th Edition)

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25 pages, 2993 KB

Open AccessArticle

Joint Forecasting of Energy Consumption and Generation in P2P Networks Using LSTM–CNN and Transformers

by Kandel L. Yandar, Oscar Revelo Sánchez and Manuel Bolaños Gonzales

Energies 2026, 19(3), 760; https://doi.org/10.3390/en19030760 (registering DOI) - 1 Feb 2026

Electric energy is an essential resource in modern society; however, most current distribution systems are centralized and dependent on fossil fuels, posing risks of shortages and a potential energy crisis. The transition to renewable sources represents a sustainable alternative, though it introduces challenges [...] Read more.

Electric energy is an essential resource in modern society; however, most current distribution systems are centralized and dependent on fossil fuels, posing risks of shortages and a potential energy crisis. The transition to renewable sources represents a sustainable alternative, though it introduces challenges associated with intermittency and generation variability. In this context, peer-to-peer (P2P) networks and artificial intelligence (AI) emerge as strategies to promote decentralization, self-management, and efficiency in energy operation. This research proposes an AI-based knowledge discovery model to predict electricity generation and consumption in a P2P network. The study was developed in four phases: exploration of AI techniques for energy prediction; analysis of the most widely used techniques in the Knowledge Discovery in Databases (KDD) process; construction of the predictive model; and validation using real energy generation and consumption data from renewable energy sources. The LSTM–CNN and Transformer models achieved an R² greater than 80% and mean absolute errors (MAE) of less than 0.02 kWh, demonstrating high prediction accuracy. The results confirm that integrating the KDD approach with deep LSTM–CNN and Transformer architectures significantly improves energy management in P2P networks, providing a solid foundation for the development of innovative and sustainable electrical systems. Full article

(This article belongs to the Special Issue The Impact of Artificial Intelligence on Modern Energy Systems)

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20 pages, 5989 KB

Open AccessArticle

UV and Visible Radiation Characteristics of Thermoacoustic Instabilities in an Ammonia–Methane Premixed Swirl-Stabilized Combustor

by Junhui Ma, Xianglan Fu, Dongqi Chen, Le Chang, Lingxue Wang, Yingchen Shi, Haocheng Wen and Bing Wang

Energies 2026, 19(3), 759; https://doi.org/10.3390/en19030759 (registering DOI) - 31 Jan 2026

Ammonia (NH₃) is a promising carbon-free energy carrier for low-carbon power generation. However, in turbulent ammonia–methane (NH₃-CH₄) premixed swirling flames, operating at lean conditions to limit NO_X, emissions can trigger strong thermoacoustic oscillations. This study [...] Read more.

Ammonia (NH₃) is a promising carbon-free energy carrier for low-carbon power generation. However, in turbulent ammonia–methane (NH₃-CH₄) premixed swirling flames, operating at lean conditions to limit NO_X, emissions can trigger strong thermoacoustic oscillations. This study investigates thermoacoustic oscillatory instability in an NH₃-CH₄ swirl-stabilized combustor using the chemiluminescence of CH*, OH*, and NH* over a wide range of ammonia fuel fraction (X_NH3). Combined spectral measurements and 2D chemiluminescence imaging are employed to obtain the global emission characteristics and spatial distributions of OH* and NH* in the UV band and CH* in the visible band. A custom-designed intensified CMOS (ICMOS) camera based on a high-gain UV–visible image intensifier with direct coupling is developed to enable sensitive OH* and NH* imaging (gain > 10⁴). Frequency analysis of continuous CH* imaging, together with morphology-based principal component analysis and k-means clustering of 46 image features, shows that oscillatory combustion occurs for X_NH3 < 0.40, whereas X_NH3 ≥ 0.40 leads to multimode, stable combustion. As X_NH3 increases, OH* and NH* fields progressively decouple from CH*, becoming more elongated and shifting downstream. These results demonstrate that UV radical chemiluminescence provides indispensable information on NH₃ reaction zones and should be combined with CH* diagnostics for reliable thermoacoustic analysis and control in practical NH₃-fueled combustion systems. Full article

(This article belongs to the Topic Theoretical, Numerical and Experimental Studies on Clean Energy and Combustion, 2nd Edition)

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28 pages, 7322 KB

Open AccessArticle

Numerical and Experimental Investigation of Blade Outlet Angle Effects on Flow Characteristics and Energy Losses in a Vortex Pump

by Lingyan He, Xiaofu Fan, Jianfa Li, Changliang Ye, Xuesong Li, Ziyang Niu and Chongshan Li

Energies 2026, 19(3), 758; https://doi.org/10.3390/en19030758 (registering DOI) - 31 Jan 2026

The blade outlet angle is a critical design parameter of vortex pump impellers, exerting a significant influence on the pump’s hydraulic performance and internal flow characteristics. In this study, numerical simulations combined with experimental validation were conducted to investigate a vortex pump, with [...] Read more.

The blade outlet angle is a critical design parameter of vortex pump impellers, exerting a significant influence on the pump’s hydraulic performance and internal flow characteristics. In this study, numerical simulations combined with experimental validation were conducted to investigate a vortex pump, with three impellers featuring blade outlet angles of 50°, 60°, and 65° analyzed based on the SST k–ω turbulence model. To quantify irreversible energy losses, entropy production theory was adopted, while the Liutex method was utilized to characterize rigid-body vorticity. The results demonstrate that increasing the blade outlet angle leads to a reduction in head under both small-flow-rate and design-flow-rate conditions, impairs flow uniformity, strengthens vortex structures, and elevates total entropy production—with turbulent dissipation being the dominant contributor to energy losses. Additionally, larger outlet angles enhance the sensitivity of internal flow structures to off-design operating conditions. These findings offer valuable guidance for the optimization of impeller design and the development of energy-efficient vortex pumps. Full article

23 pages, 1492 KB

Open AccessArticle

Technical Indicators for the Assessment of Hard Coal Mine Exhaust Shafts in Terms of Ventilation Methane Processing

by Krzysztof Kaczmarczyk, Dominik Bałaga, Michał Siegmund, Krzysztof Nieśpiałowski, Marek Kalita, Marzena Iwaniszyn, Anna Pawlaczyk-Kurek, Anna Gancarczyk, Jacek Skiba, Robert Hildebrandt, Jerzy Krawczyk, Piotr Ostrogórski, Bartłomiej Bezak and Bożena Gajdzik

Energies 2026, 19(3), 757; https://doi.org/10.3390/en19030757 (registering DOI) - 31 Jan 2026

Methane (CH₄) is one of the most important greenhouse gases, and substantially impacts climate change. Over a 20-year period, its global warming potential (GWP) is approximately 80 times higher than that of carbon dioxide (CO₂). One of the significant [...] Read more.

Methane (CH₄) is one of the most important greenhouse gases, and substantially impacts climate change. Over a 20-year period, its global warming potential (GWP) is approximately 80 times higher than that of carbon dioxide (CO₂). One of the significant sources of methane emissions is the hard coal mining industry, particularly regarding the release of methane with mine ventilation air. Methane released from coal seams during mining operations and discharged into the atmosphere through exhaust shafts is referred to as VAM (Ventilation Air Methane). In the context of the European Union’s climate policy, activities aimed at reducing and utilizing VAM emissions are gaining increasing importance. One initiative supporting the development of such solutions is the research project ProVAM (Reduction of Ventilation Air Methane Emissions in the Coal Mining Transformation Process), implemented by a consortium of scientific and industrial institutions from EU member states. The project focuses on developing guidelines and selecting technologies dedicated to the utilization of VAM. This article presents a methodology for assessing parameters associated with VAM emissions and provides a characterization of the selected mine exhaust shafts analyzed within the ProVAM project. Key technical factors affecting the feasibility of using oxidation technologies to reduce methane emissions from hard coal mining are identified. Full article

(This article belongs to the Special Issue Advances in Extraction and Utilization of Coal and Shale Gas)

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29 pages, 2138 KB

Open AccessArticle

A Dual-Layer Scheduling Method for Virtual Power Generation with an Integrated Regional Energy System

by Zhaojun Gong, Zhiyuan Zhao, Pengfei Li, Jiafeng Song, Zhile Yang, Yuanjun Guo, Linxin Zhang, Zunyao Wang, Jian Guo, Xiaoran Zheng and Zhenhua Wei

Energies 2026, 19(3), 756; https://doi.org/10.3390/en19030756 (registering DOI) - 31 Jan 2026

An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES [...] Read more.

An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness. Full article

54 pages, 2046 KB

Open AccessReview

Data-Driven Tools and Methods for Low-Carbon Industrial Parks: A Scoping Review of Industrial Symbiosis and Carbon Capture with Practitioner Insights

by Zheng Grace Ma, Joy Dalmacio Billanes and Bo Nørregaard Jørgensen

Energies 2026, 19(3), 755; https://doi.org/10.3390/en19030755 - 30 Jan 2026

Industrial symbiosis and carbon capture are increasingly recognized as critical strategies for reducing emissions and resource consumption in industrial parks. However, existing research remains fragmented across tools, methods, and case-specific applications, providing limited guidance for effective real-world deployment of data-driven approaches. This study [...] Read more.

Industrial symbiosis and carbon capture are increasingly recognized as critical strategies for reducing emissions and resource consumption in industrial parks. However, existing research remains fragmented across tools, methods, and case-specific applications, providing limited guidance for effective real-world deployment of data-driven approaches. This study addresses this gap through a PRISMA-guided scoping review of 116 publications, complemented by a targeted practitioner survey conducted within the IEA IETS Task 21 initiative to assess practical relevance and adoption challenges. The review identifies a broad landscape of data-driven tools, ranging from high-technology-readiness simulation and optimization platforms to emerging visualization and matchmaking solutions. While the literature demonstrates substantial methodological maturity, the combined evidence reveals a persistent gap between tool availability and effective implementation. Key barriers include fragmented and non-standardized data infrastructures, confidentiality constraints, limited stakeholder coordination, and weak policy and market incentives. Based on the integrated analysis of literature and practitioner insights, the paper proposes a conceptual framework that links tools and methods with data infrastructure, stakeholder governance, policy, and market enablers, and implementation contexts. The findings highlight that improving data governance, interoperability, and collaborative implementation pathways is as critical as advancing analytical capabilities. The study concludes by outlining focused directions for future research, including AI-enabled optimization, standardized data-sharing frameworks, and coordinated pilot projects to support scalable low-carbon industrial transformation. Full article

(This article belongs to the Special Issue Advances in Energy, Industry, and Low-Carbon Systems: Science, Technology, and Policy)

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26 pages, 2287 KB

Open AccessArticle

A Finite Control Set–Model Predictive Control Method for Hybrid AC/DC Microgrid Operation with PV, Wind Generation, and Energy Storage System

by Muhammad Nauman Malik, Qianyu Zhao and Shouxiang Wang

Energies 2026, 19(3), 754; https://doi.org/10.3390/en19030754 - 30 Jan 2026

The global transition towards decentralized, decarbonized energy systems worldwide must include robust methods for controlling hybrid AC/DC microgrids to integrate diverse renewables and storage technologies effectively. This paper presents a Finite Control Set–Model Predictive Control (FCS-MPC) architecture for coordinated control of a hybrid [...] Read more.

The global transition towards decentralized, decarbonized energy systems worldwide must include robust methods for controlling hybrid AC/DC microgrids to integrate diverse renewables and storage technologies effectively. This paper presents a Finite Control Set–Model Predictive Control (FCS-MPC) architecture for coordinated control of a hybrid microgrid comprising photovoltaic and wind generation, along with an energy storage system and MATLAB/Simulink component-level modeling. The islanded and grid-connected modes of operation are seamlessly simulated at the component level, ensuring maximum power point tracking and stability. The method has been experimentally validated through dynamic simulations across a range of operating conditions, demonstrating good performance: PV and wind MPPT efficiency > 99%, DC-link voltage control with < 2% overshoot, AC voltage THD < 3%, and efficient grid synchronization. It is superior to conventional PID and sliding mode control in terms of dynamic response, voltage deviation (reduced compared to before), and power quality. The proposed FCS-MPC is an all-in-one solution to enhance the stability, reliability, and efficiency of modern hybrid microgrids. Full article

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

24 pages, 1303 KB

Open AccessArticle

The Impact of Electric Vehicle Hosting Factors on Distribution Network Performance Using an Impedance-Based Heuristic Approach

by Abdullah Alrashidi, Nora Elayaat, Adel A. Abou El-Ela, Ashraf Fahmy, Ismail Hafez, Tamer Attia and Abdelazim Salem

Energies 2026, 19(3), 753; https://doi.org/10.3390/en19030753 (registering DOI) - 30 Jan 2026

The fast adoption of electric vehicles (EVs) and the integration of renewable distributed generators (DGs) provide significant operational issues for radial distribution networks (RDNs), notably in terms of power losses, voltage variations, and system stability. This paper investigates the optimal placement and sizing [...] Read more.

The fast adoption of electric vehicles (EVs) and the integration of renewable distributed generators (DGs) provide significant operational issues for radial distribution networks (RDNs), notably in terms of power losses, voltage variations, and system stability. This paper investigates the optimal placement and sizing of EV charging stations (EVCSs) and DGs under varying EV hosting factors (EV-HFs). An impedance matrix-based load flow method is developed, and a derived analytical formula for power loss calculation is proposed to improve computational efficiency. A weighted multi-objective function is developed to reduce active power losses and voltage variations while optimizing the voltage stability index and the yearly cost savings from energy loss. The optimization is performed using a deterministic heuristic procedure that incrementally adjusts the location and size of EVCSs and DGs until no further improvement in the fitness function is achieved. This stepwise approach provides fast convergence with low computational effort compared to population-based metaheuristics. The methodology is used on the IEEE 33-bus system under different loading conditions and EV-HFs. The results reveal that for 40% and 60% EV-HFs, active power losses decreased by about 57% compared with the basic case, while the minimum bus voltage improved from 0.9148 pu to 0.9654 pu and 0.9641 pu. The economic analysis demonstrates annual savings of up to USD 473,550, with a payback period between 7 and 8 years. These findings emphasize the need of integrated EVCS and DG planning in improving future distribution systems’ technical and economic performance. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, 2nd Edition)

15 pages, 2847 KB

Open AccessArticle

Theoretical Study and Resistance Reduction Performance of a Pipeline-Type Grounding Grid in Seepage-Proof Pumped Storage Power Stations

by Wanqin Ding, Fengrun Wang, Yang Lv, Wendong Wang, Kun Zhao and Hailiang Lu

Energies 2026, 19(3), 752; https://doi.org/10.3390/en19030752 - 30 Jan 2026

Pumped storage power stations commonly adopt impermeable linings at reservoir bottoms to reduce seepage losses. However, these linings significantly weaken the current dissipation capability of grounding grids, particularly in high-resistivity bedrock areas. To address this problem, a pipeline-type grounding grid (PTGG) with seepage [...] Read more.

Pumped storage power stations commonly adopt impermeable linings at reservoir bottoms to reduce seepage losses. However, these linings significantly weaken the current dissipation capability of grounding grids, particularly in high-resistivity bedrock areas. To address this problem, a pipeline-type grounding grid (PTGG) with seepage holes is proposed for installation beneath impermeable reservoir basins. By enabling controlled water seepage, the PTGG increases bedrock moisture content and reduces its electrical resistivity, thereby improving grounding performance. A coupled seepage–resistivity–grounding model is established by integrating multiphase flow simulation in porous media with grounding impedance calculations using CDEGS. Simulation results indicate that controlled seepage can reduce the effective resistivity of initially dry bedrock from approximately 38,000 Ω·m to about 500–2000 Ω·m within the primary current-dissipation zone. For a typical pumped storage power station, the proposed PTGG reduces the overall grounding resistance by approximately 11.3–14.0% within 0.5–2 years of operation. Parametric analyses show that decreasing the spacing of seepage holes from 10 m to 1 m significantly enhances resistance reduction, whereas the influence of hole diameter (5–20 cm) on grounding resistance is relatively minor when the spacing is fixed. These results demonstrate that the PTGG provides an effective and site-specific resistance reduction solution for impermeable basin pumped storage power stations, where conventional grounding measures exhibit limited effectiveness. Full article

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28 pages, 1973 KB

Open AccessReview

Refuse-Derived Fuel (RDF) for Low-Carbon Waste-to-Energy: Advances in Preparation Technologies, Thermochemical Behavior, and High-Efficiency Combustion Systems

by Hao Jiao, Jingzhe Li, Xijin Cao, Zhiliang Zhang, Yingxu Liu, Di Wang, Ka Li, Wei Zhang and Lin Gong

Energies 2026, 19(3), 751; https://doi.org/10.3390/en19030751 - 30 Jan 2026

Refuse-derived fuel (RDF) presents a viable strategy to concurrently address the challenges of municipal solid waste management and the need for alternative energy. In this context, the present review systematically synthesizes recent advances in RDF preparation, combustion behavior, and efficient utilization technologies. The [...] Read more.

Refuse-derived fuel (RDF) presents a viable strategy to concurrently address the challenges of municipal solid waste management and the need for alternative energy. In this context, the present review systematically synthesizes recent advances in RDF preparation, combustion behavior, and efficient utilization technologies. The study examines the full chain of RDF production—including waste selection, mechanical/optical/magnetic sorting, granulation, briquetting, and chemical modification—highlighting how pretreatment technologies influence fuel homogeneity, calorific value, and emissions. The thermochemical conversion characteristics of RDF are systematically analyzed, covering the mechanism differences among slow pyrolysis, fast pyrolysis, flash pyrolysis, pyrolysis mechanisms, catalytic pyrolysis, fragmentation behavior, volatile release patterns, and kinetic modeling using Arrhenius and model-free isoconversional methods (e.g., FWO). Special attention is given to co-firing and high-efficiency combustion technologies, including ultra-supercritical boilers, circulating fluidized beds, and rotary kilns, where fuel quality, ash fusion behavior, slagging, bed agglomeration, and particulate emissions determine operational compatibility. Integrating recent findings, this review identifies the key technical bottlenecks—feedstock variability, chlorine/sulfur release, heavy-metal contaminants, ash-related issues, and the need for standardized RDF quality control. Emerging solutions such as AI-assisted sorting, catalytic upgrading, optimized co-firing strategies, and advanced thermal conversion systems (oxy-fuel, chemical looping, supercritical steam cycles) are discussed within the broader context of carbon reduction and circular economy transitions. Overall, RDF represents a scalable, flexible, and high-value waste-to-energy pathway, and the review provides insights into future research directions, system optimization, and policy frameworks required to support its industrial deployment. Full article

(This article belongs to the Section I1: Fuel)

16 pages, 1520 KB

Open AccessArticle

Numerical Simulation Study of Multi-Component Discontinuous Chemical Flooding

by Zhijie Wei, Yongzheng Cui, Yanchun Su, Jian Zhang and Wensheng Zhou

Energies 2026, 19(3), 750; https://doi.org/10.3390/en19030750 - 30 Jan 2026

Discontinuous phase flooding (such as polymer microspheres) is an important method for enhancing oil recovery. With the hydration swelling and elastic properties, a unique “migration–entrapment–remigration” discontinuous flow behavior is identified during flooding. And a more pronounced conformance control effect is observed in high-permeability [...] Read more.

Discontinuous phase flooding (such as polymer microspheres) is an important method for enhancing oil recovery. With the hydration swelling and elastic properties, a unique “migration–entrapment–remigration” discontinuous flow behavior is identified during flooding. And a more pronounced conformance control effect is observed in high-permeability flow channels and deeper reservoir regions compared to continuous phase flooding. These complex seepage mechanisms pose significant challenges to reservoir numerical simulation. Based upon a chemical reaction framework, a multi-component mathematical model comprising oil, gas, water, pre-discontinuous phase, and discontinuous phase components is developed in this study. The discontinuous phase is generated through chemical reactions involving the pre-discontinuous phase. A minimum reaction porosity is first introduced in the chemical reaction process to enhance the discontinuous phase generation in high-permeability regions. A threshold pressure is incorporated into the discontinuous phase equation for the “migration–entrapment–remigration” discontinuous flow characteristics. The model is subsequently solved using a fully implicit finite volume method. A new numerical simulator implementing this approach is developed in C++. Validation through physical experiments confirms the method’s accuracy. The discontinuous migration process of “migration–entrapment–remigration” is clearly reflected through the injection pressure fluctuations during simulation. Mechanistic models and field-scale simulations both confirm that discontinuous phase flooding significantly enhances oil recovery efficiency, outperforming both water flooding and continuous phase flooding. The novel reaction specification enhances conformance control in high-permeability channels, as demonstrated by the simulation results. The proposed model accurately captures the migration characteristics of the discontinuous phase and holds important practical value for reservoirs with discontinuous phase flooding. Full article

(This article belongs to the Section H1: Petroleum Engineering)

27 pages, 2251 KB

Open AccessArticle

Economic Energy Consumption Strategy Considering Multimodal Energy Under the Base Station Cluster of Multi-Device Communication Private Networks

by Yan Zhong, Xuchong Yin, Chenguang Wu and Gang Xu

Energies 2026, 19(3), 749; https://doi.org/10.3390/en19030749 - 30 Jan 2026

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number [...] Read more.

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number of base stations deployed across substations and distribution networks has led to rising electricity expenditure, making cost-effective energy supply a critical challenge. To reduce the operating costs of base station clusters and enhance the economic efficiency of power supply, this paper proposes a multimodal power consumption optimization method that coordinates wind energy, solar energy, and energy storage based on user interaction behavior. First, considering user interaction characteristics and the complementarity of multiple energy sources, a dual-layer cellular network architecture consisting of macro- and micro-base stations is constructed. This architecture incorporates grid power purchases, wind power generation, and photovoltaic energy. An optimization model is then developed, which includes both equipment operation constraints and energy interaction constraints. Second, the key factors influencing energy consumption are analyzed using operational research methods. The existence of an optimal solution for the energy consumption function is demonstrated based on the Weierstrass optimization theorem. An energy-saving strategy for base stations under user group access is then derived using Karush–Kuhn–Tucker (KKT) conditions. Through spatio-temporal (ST) dynamic analysis, the coupling relationships among wind power, solar energy, energy storage, and grid electricity purchases are quantified. Based on this analysis, a multimodal cost optimization scheme utilizing dynamic bandwidth allocation is proposed. Simulation results demonstrate that, compared with traditional single-source power supply models and representative existing optimization schemes, the proposed multimodal energy scheduling framework can significantly reduce the operating cost of base station clusters while maintaining communication performance. Full article

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26 pages, 2444 KB

Open AccessArticle

Optimized Elbow Design for Hydrogen Pipeline Using Multi-Objective Genetic Algorithm

by Ho-Jin Choi and Younjea Kim

Energies 2026, 19(3), 748; https://doi.org/10.3390/en19030748 - 30 Jan 2026

In 90° elbows, abrupt turning induces strong secondary flow, separation, and turbulence, increasing pressure loss and degrading velocity uniformity. A hydrogen pipeline elbow is optimized by combining a nature-inspired cross-section with a guide vane, while tuning vane position/angle and geometric radii/offsets using a [...] Read more.

In 90° elbows, abrupt turning induces strong secondary flow, separation, and turbulence, increasing pressure loss and degrading velocity uniformity. A hydrogen pipeline elbow is optimized by combining a nature-inspired cross-section with a guide vane, while tuning vane position/angle and geometric radii/offsets using a multi-objective genetic algorithm (MOGA). Three-dimensional CFD is performed for compressible gaseous hydrogen using the Peng–Robinson equation of state and the SST k–ω turbulence model. Design points are generated by Latin hypercube sampling, and response surface models based on non-parametric regression (NPR) and genetic aggregation (GA) guide the search. Relative to the reference elbow, the GA-based optimum improves velocity uniformity by 5.825% and reduces the total pressure-drop coefficient by 0.470%; the NPR-based optimum yields 4.021% and 0.229%, respectively. Flow-field analysis shows reduced separation area, axial vorticity, turbulent kinetic energy, and dissipation, indicating suppressed secondary flow and smoother turning. These gains translate to lower pumping power and enhanced energy efficiency, supporting cost-effective deployment of carbon-neutral hydrogen infrastructure. Full article

(This article belongs to the Section A5: Hydrogen Energy)

18 pages, 1523 KB

Open AccessArticle

A Two-Stage Optimization Design of Jacket Structures for Offshore Wind Turbines with Integrated Parallel System Verification

by Jiawei Yu, Yujia Tang and Bin Wang

Energies 2026, 19(3), 747; https://doi.org/10.3390/en19030747 - 30 Jan 2026

This paper presents a novel two-stage optimization framework for offshore wind turbine jacket structures that integrates gradient-based optimization with comprehensive system verification. The methodology addresses the challenge of balancing structural efficiency with reliability through sequential optimization and validation phases. Applied to a 5 [...] Read more.

This paper presents a novel two-stage optimization framework for offshore wind turbine jacket structures that integrates gradient-based optimization with comprehensive system verification. The methodology addresses the challenge of balancing structural efficiency with reliability through sequential optimization and validation phases. Applied to a 5 MW reference turbine, the framework achieved a 34% reduction in steel mass while maintaining all structural performance requirements. The optimized structure preserves its fundamental natural frequency at 0.294 Hz within the required soft–stiff frequency band, effectively avoiding resonance with rotor excitations. Structural verification demonstrates significant improvements in joint performance, with a 42.35% reduction in the Maximum unity check value of joint shear. Dynamic analysis confirms consistent performance of OWT under the operational cases before and after optimization, with particular sensitivity to structural modifications observed during parked conditions due to absent operational damping. Full article

(This article belongs to the Topic Wind, Wave and Tidal Energy Technologies in China)

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