Energies

44 pages, 3067 KB

Open AccessFeature PaperArticle

Optimization of Green Hydrogen Production via Direct Seawater Electrolysis Powered by Hybrid PV-Wind Energy: Response Surface Methodology

by Sandile Mtolo, Emmanuel Kweinor Tetteh, Nomcebo Happiness Mthombeni, Katleho Moloi and Sudesh Rathilal

Energies 2025, 18(19), 5328; https://doi.org/10.3390/en18195328 - 9 Oct 2025

Viewed by 421

Abstract

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational [...] Read more.

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

(This article belongs to the Special Issue Green Hydrogen Energy Production)

► Show Figures

Figure 1

22 pages, 4427 KB

Open AccessArticle

Higher-Order Dynamic Mode Decomposition to Identify Harmonics in Power Systems

by Aboubacar Abdou Dango, Innocent Kamwa, Himanshu Grover, Alexia N’Dori and Alireza Masoom

Energies 2025, 18(19), 5327; https://doi.org/10.3390/en18195327 - 9 Oct 2025

Viewed by 377

Abstract

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics [...] Read more.

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 4th Edition)

► Show Figures

Figure 1

23 pages, 4862 KB

Open AccessArticle

Rapid Temperature Prediction Model for Large-Scale Seasonal Borehole Thermal Energy Storage Unit

by Donglin Zhao, Mengying Cui, Shuchuan Yang, Xiao Li, Junqing Huo and Yonggao Yin

Energies 2025, 18(19), 5326; https://doi.org/10.3390/en18195326 - 9 Oct 2025

Viewed by 375

Abstract

The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods [...] Read more.

The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods for calculating the average temperature of the storage unit. This limitation hinders accurate assessment of the thermal charging and discharging states. Furthermore, some models involve complex computations and exhibit low operational efficiency, failing to meet the practical engineering demands for rapid prediction and response. To address these challenges, this study first develops a thermal response model for the average temperature of the storage unit based on the finite line source theory and further proposes a simplified engineering algorithm for predicting the storage unit temperature. Subsequently, two-dimensional discrete convolution and Fast Fourier Transform (FFT) techniques are introduced to accelerate the solution of the storage unit temperature distribution. Finally, the model’s accuracy is validated against practical engineering cases. The results indicate that the single-point temperature engineering algorithm yields a maximum relative error of only 0.3%, while the average temperature exhibits a maximum relative error of 1.2%. After employing FFT, the computation time of both single-point and average temperature engineering algorithms over a 10-year simulation period is reduced by more than 90%. When using two-dimensional discrete convolution to calculate the temperature distribution of the storage unit, expanding the input layer from 200 × 200 to 400 × 400 and the convolution kernel from 25 × 25 to 51 × 51 reduces the time required for temperature superposition calculations to approximately 0.14–0.82% of the original time. This substantial improvement in computational efficiency is achieved without compromising accuracy. Full article

(This article belongs to the Section G: Energy and Buildings)

► Show Figures

Figure 1

16 pages, 1936 KB

Open AccessArticle

Simplified Mechanisms of Nitrogen Migration Paths for Ammonia-Coal Co-Combustion Reactions

by Yun Hu, Fang Wu, Guoqing Chen, Wenyu Cheng, Baoju Han, Kexiang Zuo, Xinglong Gao, Jianguo Liu and Jiaxun Liu

Energies 2025, 18(19), 5325; https://doi.org/10.3390/en18195325 - 9 Oct 2025

Viewed by 283

Abstract

Ammonia–coal co-combustion has emerged as a promising strategy for reducing carbon emissions from coal utilization, although its underlying reaction mechanisms remain insufficiently understood. The Chemkin simulation of zero-dimensional homogeneous reaction model and entrained flow reaction model was employed here, and the ROP (rate [...] Read more.

Ammonia–coal co-combustion has emerged as a promising strategy for reducing carbon emissions from coal utilization, although its underlying reaction mechanisms remain insufficiently understood. The Chemkin simulation of zero-dimensional homogeneous reaction model and entrained flow reaction model was employed here, and the ROP (rate of production) and sensitivity analysis was performed for analyzing in-depth reaction mechanisms. The nitrogen conversion pathways were revealed, and the mechanisms were simplified. Based on simplified mechanisms, molecular-level reaction pathways and thermochemical conversion networks of nitrogen-containing precursors were established. The results indicate that NO emissions peak at a 30% co-firing ratio, while N₂O formation increases steadily. The NH radical facilitates NO reduction to N₂O, with NH + NO → N₂O + H identified as the dominant pathway. Enhancing NNH formation and suppressing NCO intermediates are key to improving nitrogen conversion to N₂. This paper quantifies the correlation between NO_x precursors such as HCN and NH₃ and intermediates such as NCO and NNH during ammonia–coal co-firing and emphasizes the important role of N₂O. These insights offer a molecular-level foundation for designing advanced ammonia–coal co-combustion systems aimed at minimizing NO_x emissions. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

► Show Figures

Figure 1

27 pages, 1549 KB

Open AccessArticle

Thermal Modernization for Sustainable Cities: Environmental and Economic Impacts in Central Urban Areas

by Piotr Sobierajewicz and Piotr Dzikowski

Energies 2025, 18(19), 5324; https://doi.org/10.3390/en18195324 - 9 Oct 2025

Viewed by 253

Abstract

Maintaining a high-quality urban environment remains a critical yet challenging issue in modern cities, particularly in densely built and historically significant central areas. In response, the European Green Deal initiative aims to promote sustainable urban development. This study presents a multi-criteria assessment methodology [...] Read more.

Maintaining a high-quality urban environment remains a critical yet challenging issue in modern cities, particularly in densely built and historically significant central areas. In response, the European Green Deal initiative aims to promote sustainable urban development. This study presents a multi-criteria assessment methodology for evaluating urban environments, with a focus on prioritizing thermal renovations of buildings to achieve substantial environmental improvements. The research adopts a centrifugal strategy, targeting buildings with the poorest energy performance for phased renovation efforts. Using the model city of Gubin, Poland, as a case study, the assessment proceeds through five stages: evaluating technical wear (Stages I–II), estimating replacement values and renovation costs (Stages III–IV), and finally, quantifying environmental benefits from energy efficiency upgrades (Stage V). Findings reveal that buildings in the lowest energy class (Class G) require investments of 111–193% of their replacement value but can deliver CO₂ emissions reduced to 1/6.2 of the original level (an approximate 84% reduction). The primary contribution of this paper is the development and application of a novel multi-criteria assessment methodology for evaluating urban environments, specifically designed to prioritize thermal renovations in central urban areas to achieve significant environmental and economic benefits. The study provides valuable economic and environmental indicators that can guide the formulation of pro-environmental urban policies and support strategic decision-making in cities with dense populations and aging infrastructure. Full article

► Show Figures

Figure 1

27 pages, 1075 KB

Open AccessArticle

A New Method to Design Resilient Wide-Area Damping Controllers for Power Systems

by Murilo E. C. Bento

Energies 2025, 18(19), 5323; https://doi.org/10.3390/en18195323 - 9 Oct 2025

Viewed by 295

Abstract

Operating power systems has become challenging due to the complexity of these systems. Stability studies are essential to ensure that a system operates under suitable conditions. Low-frequency oscillation modes (LFOMs) are one of the main branches of system angular stability studies and are [...] Read more.

Operating power systems has become challenging due to the complexity of these systems. Stability studies are essential to ensure that a system operates under suitable conditions. Low-frequency oscillation modes (LFOMs) are one of the main branches of system angular stability studies and are often studied in small-signal stability. Many LFOMs in the system may have low and insufficient damping rates, negatively affecting the operation of power systems. Different control strategies have been proposed, such as the Wide-Area Damping Controller (WADC), to adequately and easily dampen these LFOMs. The operating principle of a WADC requires the reception of remote and synchronized signals from system PMUs through communication channels. However, WADCs are subject to communication failures and cyberattacks that compromise their proper operation. This paper proposes a multi-objective optimization model whose variables are the WADC parameters and the objective function guarantees the previously desired and high damping rates for the system under normal conditions and when there are permanent communication failures caused by a Denial-of-Service attack. The design method uses Linear Quadratic Regulator theory, where the parameters of this method are tuned by a bio-inspired algorithm. The studies were performed in the IEEE 68-bus system, considering a set of different operating points. The results achieved in the modal and time domain analysis confirm the successful and robust design of the WADC. Full article

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

► Show Figures

Figure 1

18 pages, 2046 KB

Open AccessArticle

A Flow-Based Approach for the Optimal Location and Sizing of Hydrogen Refueling Stations Along a Highway Corridor

by Salvatore Micari, Antonino Salvatore Scardino, Giuseppe Napoli, Luciano Costanzo, Orlando Marco Belcore and Antonio Polimeni

Energies 2025, 18(19), 5322; https://doi.org/10.3390/en18195322 - 9 Oct 2025

Viewed by 322

Abstract

The development of hydrogen refueling infrastructure plays a strategic role in enabling the decarbonization of the transport sector, especially along major freight and passenger corridors such as the Trans-European Transport Network (TEN-T). Despite the growing interest in hydrogen mobility, existing methodologies for the [...] Read more.

The development of hydrogen refueling infrastructure plays a strategic role in enabling the decarbonization of the transport sector, especially along major freight and passenger corridors such as the Trans-European Transport Network (TEN-T). Despite the growing interest in hydrogen mobility, existing methodologies for the optimal location of hydrogen refueling stations (HRS) remain fragmented and often overlook operational dynamics. Following a review of the existing literature on HRS location models and approaches, this study highlights key methodological gaps that hinder effective infrastructure planning. In response, a two-stage framework is proposed, combining a flow-based location model with a stochastic queueing approach to determine both the optimal placement of HRS and the number of dispensers required at each site. The method is applied to a real segment of the TEN-T network in Northern Italy. The results demonstrate the flexibility of the model in accommodating different hydrogen vehicle penetration scenarios and its utility as a decision-support tool for public authorities and infrastructure planners. Full article

(This article belongs to the Special Issue Renewable Energy and Hydrogen Energy Technologies)

► Show Figures

Figure 1

24 pages, 12411 KB

Open AccessArticle

RANS-Based Aerothermal Database of LS89 Transonic Turbine Cascade Under Adiabatic and Cooled Wall Conditions

by Davide Fornasari, Stefano Regazzo, Ernesto Benini and Francesco De Vanna

Energies 2025, 18(19), 5321; https://doi.org/10.3390/en18195321 - 9 Oct 2025

Viewed by 417

Abstract

Modern gas turbines for aeroengines operate at ever-increasing inlet temperatures to maximize thermal efficiency, power, output and thrust, subjecting turbine blades to severe thermal and mechanical stresses. To ensure component durability, effective cooling strategies are indispensable, yet they strongly influence the underlying aerothermal [...] Read more.

Modern gas turbines for aeroengines operate at ever-increasing inlet temperatures to maximize thermal efficiency, power, output and thrust, subjecting turbine blades to severe thermal and mechanical stresses. To ensure component durability, effective cooling strategies are indispensable, yet they strongly influence the underlying aerothermal behavior, particularly in transonic regimes where shock–boundary layer interactions are critical. In this work, a comprehensive Reynolds-Averaged Navier–Stokes (RANS) investigation is carried out on the LS89 transonic turbine cascade, considering both adiabatic and cooled wall conditions. Three operating cases, spanning progressively higher outlet Mach numbers (0.84, 0.875, and 1.020), are analyzed using multiple turbulence closures. To mitigate the well-known model dependence of RANS predictions, a model-averaging strategy is introduced, providing a more robust prediction framework and reducing the uncertainty associated with single-model results. A systematic mesh convergence study is also performed to ensure grid-independent solutions. The results show that while wall pressure and isentropic Mach number remain largely unaffected by wall cooling, viscous near-wall quantities and wake characteristics exhibit a pronounced sensitivity to the wall-to-recovery temperature ratio. To support further research and model benchmarking, the complete RANS database generated in this work is released as an open-source resource and made publicly. Full article

(This article belongs to the Special Issue Advancements in Gas Turbine Aerothermodynamics)

► Show Figures

Figure 1

15 pages, 12388 KB

Open AccessArticle

Evaluating a New Prototype of Plant Microbial Fuel Cell: Is the Electrical Performance Affected by Carbon Pellet Layering and Urea Treatment?

by Ilaria Brugellis, Marco Grassi, Piero Malcovati and Silvia Assini

Energies 2025, 18(19), 5320; https://doi.org/10.3390/en18195320 - 9 Oct 2025

Viewed by 361

Abstract

Plant Microbial Fuel Cells (PMFCs) represent a promising technology that uses electroactive bacteria to convert the chemical energy in organic matter into electrical energy. The addition of carbon pellet on electrodes may increase the specific surface area for colonization via bacteria. Use of [...] Read more.

Plant Microbial Fuel Cells (PMFCs) represent a promising technology that uses electroactive bacteria to convert the chemical energy in organic matter into electrical energy. The addition of carbon pellet on electrodes may increase the specific surface area for colonization via bacteria. Use of nutrients such as urea could enhance plant growth. Our study aims to address the following questions: (1) Does carbon pellet layering affect the electrical performance of PMFCs? (2) Does urea treatment of the plants used to feed the PMFCs affect the electrical performance? A new prototype of PMFC has been tested: the plant pot is on the top, drainage water percolates to the tub below, containing the Microbial Fuel Cells (MFCs). To evaluate the best layering setup, two groups of MFCs were constructed: a “Double layer” group (with carbon pellet both on the cathode and on the anode), and a “Single layer” group (with graphite only on the cathode). All MFCs were plant-fed by Spathiphyllum lanceifolium L leachate. After one year, each of the previous two sets has been divided into two subsets: one wetted with percolate from plants fertilized with urea, and the other with percolate from unfertilized plants. Open circuit voltage (mV), short circuit peak current, and short circuit current after 5 s (mA) produced values that were measured on a weekly basis. PMFCs characterized by a “Single layer” group performed better than the “Double layer” group most times, in terms of higher and steadier values for voltage and calculated power. Undesirable results regarding urea treatment suggest the use of less concentrated urea solution. The treatment may provide consistency but appears to limit voltage and peak values, particularly in the “Double layer” configuration. Full article

(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)

► Show Figures

Figure 1

12 pages, 2322 KB

Open AccessReview

High-Efficiency, Lightweight, and Reliable Integrated Structures—The Future of Fuel Cells and Electrolyzers

by Jun Zhang, Runjin Deng, Yanyan Wang, Conggan Ma, Zhaojie Shen, Yitao Shen, Stuart M. Holmes and Zhaoqi Ji

Energies 2025, 18(19), 5319; https://doi.org/10.3390/en18195319 - 9 Oct 2025

Viewed by 536

Abstract

The high efficiency, light weight, and reliability of hydrogen energy electrochemical equipment are among the future development directions. Membrane electrode assemblies (MEAs) and electrolyzers, as key components, have structures and strengths that determine the efficiency of their power generation and the hydrogen production [...] Read more.

The high efficiency, light weight, and reliability of hydrogen energy electrochemical equipment are among the future development directions. Membrane electrode assemblies (MEAs) and electrolyzers, as key components, have structures and strengths that determine the efficiency of their power generation and the hydrogen production efficiency of electrolyzers. This article summarizes the evolution of membrane electrode and electrolyzer structures, and their power and efficiency in recent years, highlighting the significant role of integrated structures in promoting proton transport and enhancing performance. Finally, it proposes the development direction of integrating electrolyte and electrode manufacturing using phase-change methods. Full article

(This article belongs to the Special Issue Next-Generation Fuel Cells: Innovations in Materials and Performance)

► Show Figures

Figure 1

25 pages, 16014 KB

Open AccessArticle

Photo-Set: A Proposed Dataset and Benchmark for Physics-Based Cybersecurity Monitoring in Photovoltaic Systems

by Afroz Mokarim, Giovanni Battista Gaggero, Giulio Ferro, Michela Robba, Paola Girdinio and Mario Marchese

Energies 2025, 18(19), 5318; https://doi.org/10.3390/en18195318 - 9 Oct 2025

Viewed by 328

Abstract

Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, [...] Read more.

Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

► Show Figures

Figure 1

16 pages, 2594 KB

Open AccessArticle

Gas Injection Gravity Miscible Displacement Development of Fractured-Vuggy Volatile Oil Reservoir in the Fuman Area of the Tarim Basin

by Xingliang Deng, Wei Zhou, Zhiliang Liu, Yao Ding, Chao Zhang and Liming Lian

Energies 2025, 18(19), 5317; https://doi.org/10.3390/en18195317 - 9 Oct 2025

Viewed by 346

Abstract

This study investigates gas injection gravity miscible flooding to enhance oil recovery in fractured-vuggy volatile oil reservoirs of the Fuman area, Tarim Basin. The Fuman 210 reservoir, containing light oil with high maturity, large column heights, and strong fracture control, provides favorable conditions [...] Read more.

This study investigates gas injection gravity miscible flooding to enhance oil recovery in fractured-vuggy volatile oil reservoirs of the Fuman area, Tarim Basin. The Fuman 210 reservoir, containing light oil with high maturity, large column heights, and strong fracture control, provides favorable conditions for gravity-driven flooding. Laboratory tests show that natural gas and CO₂ achieve miscibility, while N₂ reaches near-miscibility. Mixed gas injection, especially at a natural gas to nitrogen ratio of 1:4, effectively lowers minimum miscibility pressure and enhances displacement efficiency. Full-diameter core experiments confirm that miscibility improves oil washing and expands the sweep volume. Based on these results, a stepped three-dimensional well network was designed, integrating shallow injection with deep production. Optimal parameters were determined: injection rates of 50,000–100,000 m³/day per well and stage-specific injection–production ratios (1.2–1.5 early, 1.0–1.2 middle, 0.8–1.0 late). Field pilots validated the method, maintaining stable production for seven years and achieving a recovery factor of 30.03%. By contrast, conventional development relies on depletion and limited water flooding, and dry gas injection yields only 12.6%. Thus, the proposed approach improves recovery by 17.4 percentage points. The novelty of this work lies in establishing the feasibility of mixed nitrogen–natural gas miscible flooding for ultra-deep fault-controlled carbonate reservoirs and introducing an innovative stepped well network model. These findings provide new technical guidance for large-scale application in similar reservoirs. Full article

(This article belongs to the Special Issue Advancing Oil and Gas Resource Exploration, Extraction, and Utilization for Sustainability)

► Show Figures

Figure 1

19 pages, 2847 KB

Open AccessArticle

Dynamic Modelling of the Natural Gas Market in Colombia in the Framework of a Sustainable Energy Transition

by Derlyn Franco, Juan C. Osorio and Diego F. Manotas

Energies 2025, 18(19), 5316; https://doi.org/10.3390/en18195316 - 9 Oct 2025

Viewed by 402

Abstract

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with [...] Read more.

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with lower carbon intensity than other fossil fuels, existing reserves could be depleted by 2030 if no new discoveries are made. To assess this risk, a System Dynamics model was developed to project supply and demand under alternative transition pathways. The model integrates: (1) GDP, urban population growth, and adoption of clean energy, (2) the behavior of six major consumption sectors, and (3) the role of gas-fired thermal generation relative to NCRES output and hydroelectric availability, influenced by the El Niño river-flow variability. The novelty and contribution of this study lie in the integration of supply and demand within a unified System Dynamics framework, allowing for a holistic understanding of the Colombian natural gas market. The model explicitly incorporates feedback mechanisms such as urbanization, vehicle replacement, and hydropower variability, which are often overlooked in traditional analyses. Through the evaluation of twelve policy scenarios that combine hydrogen, wind, solar, and new gas reserves, the study provides a comprehensive view of potential energy transition pathways. A comparative analysis with official UPME projections highlights both consistencies and divergences in long-term forecasts. Furthermore, the quantification of demand coverage from 2026 to 2033 reveals that while current reserves can satisfy demand until 2026, the expansion of hydrogen, wind, and solar sources could extend full coverage until 2033; however, ensuring long-term sustainability ultimately depends on the discovery and development of new reserves, such as the Sirius-2 well. Full article

► Show Figures

Figure 1

20 pages, 4466 KB

Open AccessArticle

SA-STGCN: A Spectral-Attentive Spatio-Temporal Graph Convolutional Network for Wind Power Forecasting with Wavelet-Enhanced Multi-Scale Learning

by Yakai Yang, Zhenqing Liu and Zhongze Yu

Energies 2025, 18(19), 5315; https://doi.org/10.3390/en18195315 - 9 Oct 2025

Viewed by 395

Abstract

Wind power forecasting remains a major challenge for renewable energy integration, as conventional models often perform poorly when confronted with complex atmospheric dynamics. This study addresses the problem by developing a Spectral-Attentive Spatio-Temporal Graph Convolutional Network (SA-STGCN) designed to capture the intricate temporal [...] Read more.

Wind power forecasting remains a major challenge for renewable energy integration, as conventional models often perform poorly when confronted with complex atmospheric dynamics. This study addresses the problem by developing a Spectral-Attentive Spatio-Temporal Graph Convolutional Network (SA-STGCN) designed to capture the intricate temporal and spatial dependencies of wind systems. The approach first applies wavelet transform decomposition to separate volatile wind signals into distinct frequency components, enabling more interpretable representation of rapidly changing conditions. A dynamic temporal attention mechanism is then employed to adaptively identify historical patterns that are most relevant for prediction, moving beyond the fixed temporal windows used in many existing methods. In addition, spectral graph convolution is conducted in the frequency domain to capture farm-wide spatial correlations, thereby modeling long-range atmospheric interactions that conventional localized methods overlook. Although this design increases computational complexity, it proves critical for representing wind variability. Evaluation on real-world datasets demonstrates that SA-STGCN achieves substantial accuracy improvements, with a mean absolute error of 1.52 and a root mean square error of 2.31. These results suggest that embracing more expressive architectures can yield reliable forecasting performance, supporting the stable integration of wind power into modern energy systems. Full article

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

► Show Figures

Figure 1

19 pages, 4365 KB

Open AccessArticle

Enhancing Load Stratification in Power Distribution Systems Through Clustering Algorithms: A Practical Study

by Williams Mendoza-Vitonera, Xavier Serrano-Guerrero, María-Fernanda Cabrera, John Enriquez-Loja and Antonio Barragán-Escandón

Energies 2025, 18(19), 5314; https://doi.org/10.3390/en18195314 - 9 Oct 2025

Viewed by 316

Abstract

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, [...] Read more.

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor (

L F

), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average

L F

of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Data Analysis Technologies in Modern Energy Systems)

► Show Figures

Figure 1

34 pages, 2388 KB

Open AccessArticle

Safe Reinforcement Learning for Buildings: Minimizing Energy Use While Maximizing Occupant Comfort

by Mohammad Esmaeili, Sascha Hammes, Samuele Tosatto, David Geisler-Moroder and Philipp Zech

Energies 2025, 18(19), 5313; https://doi.org/10.3390/en18195313 - 9 Oct 2025

Viewed by 598

Abstract

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and [...] Read more.

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

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

► Show Figures

Figure 1

27 pages, 539 KB

Open AccessReview

Low-Carbon Hydrogen Production and Use on Farms: European and Global Perspectives

by Andrzej Kuranc, Agnieszka Dudziak and Tomasz Słowik

Energies 2025, 18(19), 5312; https://doi.org/10.3390/en18195312 - 9 Oct 2025

Viewed by 466

Abstract

This article examines the growing potential of low-emission hydrogen as an innovative solution supporting the decarbonization of the agricultural sector. It discusses its potential applications on farms, including as an energy source for powering agricultural machinery, producing fertilizers, and storing energy from renewable [...] Read more.

This article examines the growing potential of low-emission hydrogen as an innovative solution supporting the decarbonization of the agricultural sector. It discusses its potential applications on farms, including as an energy source for powering agricultural machinery, producing fertilizers, and storing energy from renewable sources. Within the European context, it considers actions arising from the European Green Deal and the “Fit for 55” strategy, which promote the development of hydrogen infrastructure and support research into low-emission technologies. The article also discusses global initiatives and trends in the development of the hydrogen economy, pointing to international cooperation, investment, and the need for technology standardization. It highlights the challenges related to cost, infrastructure, and scalability, as well as the opportunities hydrogen offers for a sustainable and energy-efficient agriculture of the future. Full article

(This article belongs to the Special Issue Advances in Hydrogen Production in Renewable Energy Systems)

► Show Figures

Figure 1

31 pages, 5080 KB

Open AccessArticle

Deep Learning Models Applied Flowrate Estimation in Offshore Wells with Electric Submersible Pump

by Josenílson G. Araújo, Hellockston G. Brito, Marcus V. Galvão, Carla Wilza S. P. Maitelli and Adrião D. Doria Neto

Energies 2025, 18(19), 5311; https://doi.org/10.3390/en18195311 - 9 Oct 2025

Viewed by 396

Abstract

To address the persistent challenge of reliable real-time flowrate estimation in complex offshore oil production systems using Electric Submersible Pumps (ESPs), this study proposes a hybrid modeling approach that integrates a first-principles hydrodynamic model with Long Short-Term Memory (LSTM) neural networks. The aim [...] Read more.

To address the persistent challenge of reliable real-time flowrate estimation in complex offshore oil production systems using Electric Submersible Pumps (ESPs), this study proposes a hybrid modeling approach that integrates a first-principles hydrodynamic model with Long Short-Term Memory (LSTM) neural networks. The aim is to enhance prediction accuracy across five offshore wells (A through E) in Brazil, particularly under conditions of limited or noisy sensor data. The methodology encompasses exploratory data analysis, preprocessing, model development, training, and validation using high-frequency operational data, including active power, frequency, and pressure, all collected at one-minute intervals. The LSTM architectures were tailored to the operational stability of each well, ranging from simpler configurations for stable wells to more complex structures for transient systems. Results indicate that prediction accuracy is strongly correlated with operational stability: LSTM models achieved near-perfect forecasts in stable wells such as Well E, with minimal residuals, and effectively captured cyclical patterns in unstable wells such as Well B, albeit with greater error dispersion during abrupt transients. The model also demonstrated adaptability to planned interruptions, as observed in Well A. Statistical validation using ANOVA, Levene’s test, and Tukey’s HSD confirmed significant performance differences (α < 0.01) among the wells, underscoring the importance of well-specific model tuning. This study confirms that the LSTM-based hybrid approach is a robust and scalable solution for real-time flowrate forecasting in digital oilfields, supporting production optimization and fault detection, while laying the groundwork for future advances in adaptive and interpretable modeling of complex petroleum systems. Full article

(This article belongs to the Special Issue Modern Aspects of the Design and Operation of Electric Machines)

► Show Figures

Figure 1

28 pages, 7808 KB

Open AccessArticle

Evaluation of Development Performance and Adjustment Strategies for High Water-Cut Reservoirs Based on Flow Diagnostics: Application in the QHD Oilfield

by Yifan He, Yishan Guo, Li Wu, Liangliang Jiang, Shouliang Wang, Shangshu Ning and Zhihong Kang

Energies 2025, 18(19), 5310; https://doi.org/10.3390/en18195310 - 8 Oct 2025

Viewed by 363

Abstract

Offshore reservoirs in the high water-cut stage present significant development challenges, including declining production, complex remaining oil distribution, and the inadequacy of conventional evaluation methods to capture intricate flow dynamics. To overcome these limitations, this study introduces a novel approach based on flow [...] Read more.

Offshore reservoirs in the high water-cut stage present significant development challenges, including declining production, complex remaining oil distribution, and the inadequacy of conventional evaluation methods to capture intricate flow dynamics. To overcome these limitations, this study introduces a novel approach based on flow diagnostics for performance evaluation and potential adjustment. The method integrates key metrics such as time-of-flight (TOF) and the dynamic Lorenz coefficient, supported by reservoir engineering principles and numerical simulation, to construct a multi-parameter evaluation system. This system, which also incorporates injection–production communication volume and inter-well fluid allocation factors, precisely quantifies and visualizes waterflood displacement processes and sweep efficiency. Applied to the QHD32 oilfield, this framework was used to establish specific thresholds for operational adjustments. These include criteria for infill drilling (waterflooded ratio < 45%, remaining oil thickness > 6 m, TOF > 200 days), conformance control (TOF < 50 days, dynamic Lorenz coefficient > 0.5), and artificial lift optimization (remaining oil thickness ratio > 2/3, TOF > 200 days). Field validation confirmed the efficacy of this approach: an additional cumulative oil production of 165,600 m³ was achieved from infill drilling in the C29 well group, while displacement adjustments in the B03 well group increased oil production by 2.2–3.8 tons/day, demonstrating a significant enhancement in waterflooding performance. This research provides a theoretical foundation and a technical pathway for the refined development of offshore heavy oil reservoirs at the ultra-high water-cut stage, offering a robust framework for the sustainable management of analogous reservoirs worldwide. Full article

(This article belongs to the Special Issue Advances in Unconventional Reservoirs and Enhanced Oil Recovery)

► Show Figures

Figure 1

38 pages, 3155 KB

Open AccessArticle

Analysis of Vibration Comfort and Vibration Energy Distribution in the Child Restraint System-Base Configuration

by Damian Frej

Energies 2025, 18(19), 5309; https://doi.org/10.3390/en18195309 - 8 Oct 2025

Viewed by 287

Abstract

This study presents the results of an experimental evaluation of ride comfort for children transported in child restraint systems (CRS) during passages over speed bumps, with particular emphasis on the energy contained in vibrations. The tests were carried out under real operating conditions [...] Read more.

This study presents the results of an experimental evaluation of ride comfort for children transported in child restraint systems (CRS) during passages over speed bumps, with particular emphasis on the energy contained in vibrations. The tests were carried out under real operating conditions using two vehicles with different suspension characteristics and three loading levels corresponding to different stages of child development. Vertical accelerations were recorded at key points of the vehicle–seat system and subsequently analyzed in accordance with ISO 2631-1. Based on the vibration signals, root mean square acceleration (RMS), vibration dose value (VDV), seat effective amplitude transmissibility (SEAT), and root mean quad (RMQ) indices were calculated, enabling not only the assessment of discomfort levels but also the estimation of mechanical energy transmitted through the seat structure. The results showed that, depending on the type of vehicle, bump geometry, and load mass, the vibration energy can be significant and, in many cases, corresponds to levels classified as “severe” or “extreme discomfort.” At the same time, this energy constitutes a potential power source for low-power sensors in “smart seat” systems, such as those monitoring the child’s posture or environmental conditions. The findings highlight the need to consider vibration comfort criteria and the potential for vibration energy harvesting in the design and homologation of CRS, which aligns with the concept of sustainable transport and the development of energy self-sufficient technologies. Full article

(This article belongs to the Section B: Energy and Environment)

► Show Figures

Figure 1

17 pages, 2833 KB

Open AccessArticle

Research on the Influence of Transformer Winding on Partial Discharge Waveform Propagation

by Kaining Hou, Zhaoyang Kang, Dongxin He, Fuqiang Ren and Qingquan Li

Energies 2025, 18(19), 5308; https://doi.org/10.3390/en18195308 - 8 Oct 2025

Viewed by 282

Abstract

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation [...] Read more.

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types. Full article

(This article belongs to the Special Issue Fault Diagnosis and Simulations for Power Transformers, Converter Transformers, and High-Frequency Transformers)

► Show Figures

Figure 1

22 pages, 7067 KB

Open AccessArticle

New Evaluation System for Extra-Heavy Oil Viscosity Reducer Effectiveness: From 1D Static Viscosity Reduction to 3D SAGD Chemical–Thermal Synergy

by Hongbo Li, Enhui Pei, Chao Xu and Jing Yang

Energies 2025, 18(19), 5307; https://doi.org/10.3390/en18195307 - 8 Oct 2025

Viewed by 401

Abstract

To overcome the production bottleneck induced by the high viscosity of extra-heavy oil and resolve the issues of limited efficiency in traditional thermal oil recovery methods (including cyclic steam stimulation (CSS), steam flooding, and steam-assisted gravity drainage (SAGD)) as well as the fragmentation [...] Read more.

To overcome the production bottleneck induced by the high viscosity of extra-heavy oil and resolve the issues of limited efficiency in traditional thermal oil recovery methods (including cyclic steam stimulation (CSS), steam flooding, and steam-assisted gravity drainage (SAGD)) as well as the fragmentation of existing viscosity reducer evaluation systems, this study establishes a multi-dimensional evaluation system for the effectiveness of viscosity reducers, with stage-averaged remaining oil saturation as the core benchmarks. A “1D static → 2D dynamic → 3D synergistic” progressive sequential experimental design was adopted. In the 1D static experiments, multi-gradient concentration tests were conducted to analyze the variation law of the viscosity reduction rate of viscosity reducers, thereby screening out the optimal adapted concentration for subsequent experiments. For the 2D dynamic experiments, sand-packed tubes were used as the experimental carrier to compare the oil recovery efficiencies of ultimate steam flooding, viscosity reducer flooding with different concentrations, and the composite process of “steam flooding → viscosity reducer flooding → secondary steam flooding”, which clarified the functional value of viscosity reducers in dynamic displacement. In the 3D synergistic experiments, slab cores were employed to simulate the SAGD development process after multiple rounds of cyclic steam stimulation, aiming to explore the regulatory effect of viscosity reducers on residual oil distribution and oil recovery factor. This novel evaluation system clearly elaborates the synergistic mechanism of viscosity reducers, i.e., “chemical empowerment (emulsification and viscosity reduction, wettability alteration) + thermal amplification (steam carrying and displacement, steam chamber expansion)”. It fills the gap in the existing evaluation chain, which previously lacked a connection from static performance to dynamic displacement and further to multi-process synergistic adaptation. Moreover, it provides quantifiable and implementable evaluation criteria for steam–chemical composite flooding of extra-heavy oil, effectively releasing the efficiency-enhancing potential of viscosity reducers. This study holds critical supporting significance for promoting the efficient and economical development of extra-heavy oil resources. Full article

► Show Figures

Figure 1

24 pages, 2257 KB

Open AccessArticle

Hybrid Renewable Energy Systems: Integration of Urban Mobility Through Metal Hydrides Solution as an Enabling Technology for Increasing Self-Sufficiency

by Lorenzo Bartolucci, Edoardo Cennamo, Stefano Cordiner, Vincenzo Mulone and Alessandro Polimeni

Energies 2025, 18(19), 5306; https://doi.org/10.3390/en18195306 - 8 Oct 2025

Viewed by 349

Abstract

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most [...] Read more.

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

(This article belongs to the Special Issue Energy Saving, Efficiency, and Conversion in Renewable and Sustainable Energy)

► Show Figures

Figure 1

31 pages, 7893 KB

Open AccessArticle

A Capacity Optimization Method of Ship Integrated Power System Based on Comprehensive Scenario Planning: Considering the Hydrogen Energy Storage System and Supercapacitor

by Fanzhen Jing, Xinyu Wang, Yuee Zhang and Shaoping Chang

Energies 2025, 18(19), 5305; https://doi.org/10.3390/en18195305 - 8 Oct 2025

Viewed by 290

Abstract

Environmental pollution caused by shipping has always received great attention from the international community. Currently, due to the difficulty of fully electrifying medium- and large-scale ships, the hybrid energy ship power system (HESPS) will be the main type in the future. Considering the [...] Read more.

Environmental pollution caused by shipping has always received great attention from the international community. Currently, due to the difficulty of fully electrifying medium- and large-scale ships, the hybrid energy ship power system (HESPS) will be the main type in the future. Considering the economic and long-term energy efficiency of ships, as well as the uncertainty of the output power of renewable energy units, this paper proposes an improved design for an integrated power system for large cruise ships, combining renewable energy and a hybrid energy storage system. An energy management strategy (EMS) based on time-gradient control and considering load dynamic response, as well as an energy storage power allocation method that considers the characteristics of energy storage devices, is designed. A bi-level power capacity optimization model, grounded in comprehensive scenario planning and aiming to optimize maximum return on equity, is constructed and resolved by utilizing an improved particle swarm optimization algorithm integrated with dynamic programming. Based on a large-scale cruise ship, the aforementioned method was investigated and compared to the conventional planning approach. It demonstrates that the implementation of this optimization method can significantly decrease costs, enhance revenue, and increase the return on equity from 5.15% to 8.66%. Full article

► Show Figures

Figure 1

16 pages, 1276 KB

Open AccessArticle

Discourse vs. Decarbonisation: Tracking the Alignment Between EU Climate Rhetoric and National Energy Patterns

by Olena Pavlova, Oksana Liashenko, Kostiantyn Pavlov, Marek Rutkowski, Artur Kornatka, Tetiana Vlasenko and Mykola Halei

Energies 2025, 18(19), 5304; https://doi.org/10.3390/en18195304 - 8 Oct 2025

Viewed by 357

Abstract

This study examines the alignment between the European Union’s climate policy rhetoric and the actual fossil fuel consumption behaviours of its Member States. By combining long-term and short-term time-series data with machine learning classification techniques, the analysis captures dynamic national energy trends and [...] Read more.

This study examines the alignment between the European Union’s climate policy rhetoric and the actual fossil fuel consumption behaviours of its Member States. By combining long-term and short-term time-series data with machine learning classification techniques, the analysis captures dynamic national energy trends and decarbonisation signals. Key innovations include the use of slope acceleration metrics and the identification of label reversals to detect volatility, acceleration, or stagnation in transition trajectories. The results show that, while some countries such as France and Denmark demonstrate consistent structural progress, others show deceleration or reversal, particularly in the use of gas and liquid fuels. This indicates that the relationship between EU-level policy ambition and national implementation is asymmetric and conditionally aligned. This study concludes that ongoing empirical monitoring and targeted diagnostics are essential to prevent conflating symbolic commitments with material change, and provides practical insights for improving climate policy accountability and adaptability across the EU. Full article

► Show Figures

Figure 1

22 pages, 3920 KB

Open AccessArticle

An Applied Study on Predicting Natural Gas Prices Using Mixed Models

by Shu Tang, Dongphil Chun and Xuhui Liu

Energies 2025, 18(19), 5303; https://doi.org/10.3390/en18195303 - 8 Oct 2025

Viewed by 278

Abstract

Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and [...] Read more.

Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and NYMEX datasets, the models are trained on long historical periods and tested under multi-step horizons. The results show that all hybrid models significantly outperform the traditional moving average benchmark, achieving R² values above 95% for one-step-ahead forecasts and maintaining an accuracy of over 87% at longer horizons. CNN-BiLSTM-Attention performs best in short-term prediction due to its ability to capture bidirectional dependencies, while TCN-based models demonstrate greater robustness over extended horizons due to their effective modeling of long-range temporal structures. These findings confirm the advantages of deep learning hybrids in energy forecasting and emphasize the importance of horizon-sensitive evaluation. This study contributes methodological innovation and provides practical insights for market participants, with future directions including the integration of macroeconomic and climatic factors, exploration of advanced architectures such as Transformers, and probabilistic forecasting for uncertainty quantification. Full article

► Show Figures

Figure 1

17 pages, 3686 KB

Open AccessArticle

Study of Superconducting Fault Current Limiter Functionality in the Presence of Long-Duration Short Circuits

by Sylwia Hajdasz, Adam Kempski, Krzysztof Solak and Jacek Rusinski

Energies 2025, 18(19), 5302; https://doi.org/10.3390/en18195302 - 8 Oct 2025

Viewed by 287

Abstract

In this paper, superconducting fault current limiter (SFCL) operation in the presence of a long-duration fault is presented. The SFCL device utilizes second-generation high-temperature superconducting (2G HTS) tapes, which exhibit zero resistance under normal operating conditions. When the current exceeds the critical threshold [...] Read more.

In this paper, superconducting fault current limiter (SFCL) operation in the presence of a long-duration fault is presented. The SFCL device utilizes second-generation high-temperature superconducting (2G HTS) tapes, which exhibit zero resistance under normal operating conditions. When the current exceeds the critical threshold specific to the superconducting tape, then it undergoes a transition to a resistive state—a phenomenon known as quenching. As a consequence, this leads to introducing impedance into the circuit, effectively limiting the magnitude of the fault current. Additionally, this transition dissipates electrical energy as heat within the material. The generated energy corresponds to the product of the voltage drop across the quenched region and the current flowing through it during the fault duration. In specific configurations of the power system, it is expected that the SFCL should limit the fault current for an extended period of time. In such a situation, a certain amount of energy will be generated, and it must be verified that the tape loses its properties or parameters (e.g., lowering the critical current value) or is destroyed. Therefore, experimental tests of the tapes were conducted for various short-circuit current, voltage drop, and short-circuit duration values to assess the effect of the amount of generated energy on the 2G HTS tape. Additionally, recommendations are presented on how to protect the SFCL during long-lasting short circuits. Full article

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

► Show Figures

Figure 1

24 pages, 810 KB

Open AccessArticle

Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition

by Ali Ragab Ali, Kolawole Iyiola and Ahmad Alzubi

Energies 2025, 18(19), 5301; https://doi.org/10.3390/en18195301 - 8 Oct 2025

Viewed by 278

Abstract

This study addresses a significant gap in the existing literature by introducing novel perspectives. First, it provides a comprehensive assessment of the impact of ESG sustainability and information and communication technology (ICT) on energy transition using updated quarterly data from 2002 Q3 to [...] Read more.

This study addresses a significant gap in the existing literature by introducing novel perspectives. First, it provides a comprehensive assessment of the impact of ESG sustainability and information and communication technology (ICT) on energy transition using updated quarterly data from 2002 Q3 to 2024 Q4. Second, it uniquely integrates climate policy uncertainty (CPU) and financial development (FD) as core explanatory variables, which have been largely neglected in prior research. Third, this study applies advanced quantile-based methodologies, including the Quantile Autoregressive Distributed Lag (QARDL) model and Quantile Cointegration (QC) techniques, to enhance empirical rigor and ensure policy relevance across the entire conditional distribution. The results showed that at lower quantiles (τ = 0.05–0.30), FD positively influences ET, supporting early-stage clean energy adoption. ICT shows a short-term negative effect (τ = 0.05–0.40). Based on these findings, policymakers should strengthen financial development to accelerate clean energy adoption at early stages, while addressing the short-term negative impacts of ICT by promoting supportive digital and energy policies that align technology use with sustainability goals. Full article

(This article belongs to the Special Issue Financial Development and Energy Consumption Nexus—Third Edition)

► Show Figures

Figure 1

22 pages, 3656 KB

Open AccessArticle

Design and Experimental Validation of a Cluster-Based Virtual Power Plant with Centralized Management System in Compliance with IEC Standard

by Putu Agus Aditya Pramana, Akhbar Candra Mulyana, Khotimatul Fauziah, Hafsah Halidah, Sriyono Sriyono, Buyung Sofiarto Munir, Yusuf Margowadi, Dionysius Aldion Renata, Adinda Prawitasari, Annisaa Taradini, Arief Kurniawan and Kholid Akhmad

Energies 2025, 18(19), 5300; https://doi.org/10.3390/en18195300 - 7 Oct 2025

Viewed by 430

Abstract

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design [...] Read more.

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

(This article belongs to the Section F2: Distributed Energy System)

► Show Figures

Figure 1

20 pages, 2263 KB

Open AccessReview

Alternative Fuels for General Aviation Piston Engines: A Comprehensive Review

by Florentyna Morawska, Paula Kurzawska-Pietrowicz, Remigiusz Jasiński and Andrzej Ziółkowski

Energies 2025, 18(19), 5299; https://doi.org/10.3390/en18195299 - 7 Oct 2025

Viewed by 655

Abstract

This review synthesizes recent research on alternative fuels for piston-engine aircraft and related propulsion technologies. Biofuels show substantial promise but face technological, economic, and regulatory barriers to widespread adoption. Among liquid options, biodiesel offers a high cetane number and strong lubricity yet suffers [...] Read more.

This review synthesizes recent research on alternative fuels for piston-engine aircraft and related propulsion technologies. Biofuels show substantial promise but face technological, economic, and regulatory barriers to widespread adoption. Among liquid options, biodiesel offers a high cetane number and strong lubricity yet suffers from poor low-temperature flow and reduced combustion efficiency. Alcohol fuels (bioethanol, biomethanol) provide high octane numbers suited to high-compression engines but are limited by hygroscopicity and phase-separation risks. Higher-alcohols (biobutanol, biopropanol) combine favorable heating values with stable combustion and emerge as particularly promising candidates. Biokerosene closely matches conventional aviation kerosene and can function as a drop-in fuel with minimal engine modifications. Emissions outcomes are mixed across studies: certain biofuels reduce NO_x or CO, while others elevate CO₂ and HC, underscoring the need to optimize combustion and advance second- to fourth-generation biofuel production pathways. Beyond biofuels, hydrogen engines and hybrid-electric systems offer compelling routes to lower emissions and improved efficiency, though they require new infrastructure, certification frameworks, and cost reductions. Demonstrated test flights with biofuels, synthetic fuels, and hydrogen confirm technical feasibility. Overall, no single option fully replaces aviation gasoline today; instead, a combined trajectory—biofuels alongside hydrogen and hybrid-electric propulsion—defines a pragmatic medium- to long-term pathway for decarbonizing general aviation. Full article

(This article belongs to the Special Issue Internal Combustion Engine Performance 2025)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Energies, Volume 18, Issue 19 (October-1 2025) – 286 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI