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Energies, Volume 18, Issue 20 (October-2 2025) – 46 articles

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25 pages, 947 KB  
Systematic Review
Systematic Review of Biomass Supercritical Water Gasification for Energy Production
by Filipe Neves, Armando A. Soares and Abel Rouboa
Energies 2025, 18(20), 5374; https://doi.org/10.3390/en18205374 (registering DOI) - 12 Oct 2025
Abstract
Due to the growing global population, rising energy demands, and the environmental impacts of fossil fuel use, there is an urgent need for sustainable energy sources. Biomass conversion technologies have emerged as a promising solution, particularly supercritical water gasification (SCWG), which enables efficient [...] Read more.
Due to the growing global population, rising energy demands, and the environmental impacts of fossil fuel use, there is an urgent need for sustainable energy sources. Biomass conversion technologies have emerged as a promising solution, particularly supercritical water gasification (SCWG), which enables efficient energy recovery from wet and dry biomass. This systematic review, following PRISMA 2020 guidelines, analyzed 51 peer-reviewed studies published between 2015 and 2025. The number of publications has increased over the decade, reflecting rising interest in SCWG for energy production. Research has focused on six biomass feedstock categories, with lignocellulosic and wet biomasses most widely studied. Reported energy efficiencies ranged from ~20% to >80%, strongly influenced by operating conditions and system integration. Integrating SCWG with solid oxide fuel cells, organic Rankine cycles, carbon capture and storage, or solar input enhanced both energy recovery and environmental performance. While SCWG demonstrates lower greenhouse gas emissions than conventional methods, many studies lacked comprehensive life cycle or economic analyses. Common limitations include high energy demand, modeling simplifications, and scalability challenges. These trends highlight both the potential and the barriers to advancing SCWG as a viable biomass-to-energy technology. Full article
18 pages, 1035 KB  
Article
Short-Term Probabilistic Prediction of Photovoltaic Power Based on Bidirectional Long Short-Term Memory with Temporal Convolutional Network
by Weibo Yuan, Jinjin Ding, Li Zhang, Jingyi Ni and Qian Zhang
Energies 2025, 18(20), 5373; https://doi.org/10.3390/en18205373 (registering DOI) - 12 Oct 2025
Abstract
To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, [...] Read more.
To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration. Full article
(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)
16 pages, 1963 KB  
Article
SHAP-Enhanced Artificial Intelligence Machine Learning Framework for Data-Driven Weak Link Identification in Regional Distribution Grid Power Supply Reliability
by Yu Zhang, Jinyue Shi, Shicheng Huang, Liang Geng, Zexiong Wang, Hao Sun, Qingguang Yu, Ding Liu, Xin Yao, Weihua Zuo, Min Guo and Xiaoyu Che
Energies 2025, 18(20), 5372; https://doi.org/10.3390/en18205372 (registering DOI) - 12 Oct 2025
Abstract
Reliability assessment of power systems is essential for ensuring the secure and stable operation of power grids, and identifying weak links constitutes a critical step in enhancing system reliability. Traditional deterministic methods are limited in their ability to capture the complex, nonlinear relationships [...] Read more.
Reliability assessment of power systems is essential for ensuring the secure and stable operation of power grids, and identifying weak links constitutes a critical step in enhancing system reliability. Traditional deterministic methods are limited in their ability to capture the complex, nonlinear relationships between component failures and overall system risk. To overcome this limitation, this paper proposes an explainable machine learning-based approach for identifying weak components in power systems. Specifically, a set of contingency scenarios is constructed through enumeration, and a random forest regression model is trained to map transmission line outage events to the amount of system load curtailment. The trained model is then interpreted using SHapley Additive exPlanations (SHAP) values. By aggregating these values, the global reliability contribution of each component is quantified. The proposed method is validated on the IEEE 57-bus system, and the results demonstrate its effectiveness and feasibility. This research offers a data-driven framework for translating system-level reliability metrics into device-level quantitative attributions, thereby enabling interpretable identification of weak links. Full article
(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)
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34 pages, 6332 KB  
Article
Optimal Sizing of an Off-Grid Hybrid Energy System with Metaheuristics and Meteorological Forecasting Based on Wavelet Transform and Long Short-Term Memory Networks
by Yamilet González Cusa, José Hidalgo Suárez, Jorge Laureano Moya Rodríguez, Tulio Hernández Ramírez, Silvio A. B. Vieira de Melo and Ednildo Andrade Torres
Energies 2025, 18(20), 5371; https://doi.org/10.3390/en18205371 (registering DOI) - 12 Oct 2025
Abstract
This study proposes an integrated framework for the optimal sizing of off-grid hybrid energy systems, combining photovoltaic panels, wind turbines, battery storage, a diesel generator, and an inverter. The methodology uniquely integrates long-term meteorological forecasting through a hybrid approach based on the Discrete [...] Read more.
This study proposes an integrated framework for the optimal sizing of off-grid hybrid energy systems, combining photovoltaic panels, wind turbines, battery storage, a diesel generator, and an inverter. The methodology uniquely integrates long-term meteorological forecasting through a hybrid approach based on the Discrete Wavelet Transform and Long Short-Term Memory networks, together with metaheuristic optimization techniques (Particle Swarm Optimization and Genetic Algorithm), to minimize the system’s total annual cost. A case study was conducted in Guanambi, Brazil, using ten years (2012–2021) of hourly data on wind speed, solar irradiance, and ambient temperature. Forecasting results show that the hybrid Discrete Wavelet Transform–Long Short-Term Memory model outperforms the conventional Long Short-Term Memory approach, reducing error metrics and improving predictive accuracy. In the optimization stage, Particle Swarm Optimization consistently achieved lower costs and more stable convergence compared to the Genetic Algorithm. The optimal configuration comprised 450 photovoltaic panels, 10 wind turbines, 66 lithium iron phosphate battery, and 1 diesel generator, yielding a total annual cost of $105,381.17, a cost of energy of $0.1243/kWh, and minimal diesel dependence ($8825.89 annually). The proposed framework demonstrates robustness, economic viability, and applicability for providing sustainable and reliable electricity in isolated regions with high renewable energy potential. Full article
20 pages, 6756 KB  
Article
Potential Impacts of Climate Change on South China Sea Wind Energy Resources Under CMIP6 Future Climate Projections
by Yue Zhuo and Bo Hong
Energies 2025, 18(20), 5370; https://doi.org/10.3390/en18205370 (registering DOI) - 12 Oct 2025
Abstract
Wind is an important renewable energy source, and even minor variations in wind speed will significantly impact wind power generation. The objective of this study was to systematically assess the impacts of climate change on wind energy resources in the South China Sea [...] Read more.
Wind is an important renewable energy source, and even minor variations in wind speed will significantly impact wind power generation. The objective of this study was to systematically assess the impacts of climate change on wind energy resources in the South China Sea (SCS) under future climate projections. To achieve this, we employed a multi-model ensemble approach based on Coupled Model Intercomparison Project Phase 6 (CMIP6) data under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results demonstrated that, in comparison with scatterometer wind data, the CMIP6 historical results (1995–2014) showed good performance in capturing the spatiotemporal distribution of wind power density (WPD) in the SCS. There were regional discrepancies in the central SCS due to the complex monsoon-driven wind dynamics. Future projections revealed an overall increase in annual mean wind power density (WPD) across the entire SCS by the mid-21st century (2046–2065) and late 21st century (2080–2099). The seasonal analyses indicated significant WPD increases in summer, especially in the northern SCS and the region adjacent to the Kalimantan strait. The increase in summer (>40 × 10−4 m/s/year under SSP5-8.5) is about triple that in winter. In the late 21st century, an increase in WPD exceeding 10% can be generally anticipated under the SSP2-4.5 and SSP5-8.5 scenarios in all seasons. The extreme wind in the northern and central SCS will further increase by 5% under the three scenarios, which will add an extra extreme load to wind turbines and related marine facilities. These assessments are essential for wind farm planning and long-term energy production evaluations in the SCS. Based on the findings in this study, specific areas of concern can be targeted to conduct localized downscaling analyses and risk assessments. Full article
(This article belongs to the Special Issue Recent Developments in Offshore Renewable Energies: Wind, Wave, Tidal, and Ocean Energy)
17 pages, 6529 KB  
Article
Temperature Field Analysis and Experimental Verification of Mining High-Power Explosion-Proof Integrated Variable-Frequency Permanent Magnet Motor
by Xiaojun Wang, Gaowei Tian, Qingqing Lü, Kun Zhao, Xuandong Wu, Liquan Yang and Guangxi Li
Energies 2025, 18(20), 5369; https://doi.org/10.3390/en18205369 (registering DOI) - 12 Oct 2025
Abstract
An efficient cooling configuration is critical for ensuring the safe operation of electrical machines and is key for optimizing the iterative design of motors. To improve the heat dissipation performance of high-power, explosion-proof, integrated variable-frequency permanent magnet motors used in mining and reduce [...] Read more.
An efficient cooling configuration is critical for ensuring the safe operation of electrical machines and is key for optimizing the iterative design of motors. To improve the heat dissipation performance of high-power, explosion-proof, integrated variable-frequency permanent magnet motors used in mining and reduce the risk of permanent magnet demagnetization, this study considers a 1600 kW mining explosion-proof variable-frequency permanent magnet motor as its research object. Based on the zigzag-type water channel structure of the frame, a novel rotor-cooling scheme integrating axial–radial ventilation structures and axial flow fans was proposed. The temperature field of the motor was simulated and analyzed using a fluid–thermal coupling method. Under rated operating conditions, the flow characteristics of the frame water channel and the temperature distribution law inside the motor were compared when the water supply flow rates were 5.4, 4.8, 4.2, 3.6, 3, 2.4, and 1.8 m3/h, respectively, and the relationship between the motor temperature rise and the variation in water flow rate was revealed. A production prototype was developed, and temperature rise tests were conducted for verification. The test results were in good agreement with the simulation calculation results, thereby confirming the accuracy of the simulation calculation method. The results provide an important reference for enterprises in the design optimization and upgrading of high-power explosion-proof integrated variable-frequency permanent-magnet motors. Full article
(This article belongs to the Special Issue Advanced Technology in Permanent Magnet Motors)
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18 pages, 2922 KB  
Article
Enhancing Yazd’s Combined Cycle Power Plant Performance Through Concentrated Solar Power Integration
by Alireza Moradmand, M. Soltani, Saeid Ziaei Tabatabaei, Arash Haghparast Kashani, Mohammad Golmohammad, Alireza Mahmoudpour and Mohammad Bandehee
Energies 2025, 18(20), 5368; https://doi.org/10.3390/en18205368 (registering DOI) - 12 Oct 2025
Abstract
Combined Cycle Power Plants (CCPP) suffer from drops in power and efficiency due to summer time ambient conditions. This power reduction is especially important in regions with extreme summer ambient conditions. Given the substantial investment and labor involved in the establishment and operation [...] Read more.
Combined Cycle Power Plants (CCPP) suffer from drops in power and efficiency due to summer time ambient conditions. This power reduction is especially important in regions with extreme summer ambient conditions. Given the substantial investment and labor involved in the establishment and operation of these power plants, mitigating power loss using various methods emerges as a promising solution. In this context, the integration of Concentrated Solar Power (CSP) technologies has been proposed in this research not primarily to improve the overall performance efficiency of power plants as other recent studies entail, but to ensure continuous power generation throughout summer days, improving stability. This research aims to address this issue by conducting an extensive study covering the different scenarios in which Concentrated Solar Power (CSP) can be integrated into the power plant. Multiple scenarios for integration were defined including CSP integration in the Heat Recovery Steam Generator, CSP-powered chiller for Gas Turbine Compressor Cooling and Gas Turbine Combustion Chamber Preheating using CSP, and scenarios with inlet air fog cooling and hybrid scenarios were studied. This systematic analysis resulted in the selection of the scenario where the CSP is integrated into the combined cycle power plant in the HRSG section as the best case. The selected scenario was benchmarked against its equivalent model operating in Seville’s ambient conditions. By comparing the final selected model, both Yazd and Seville experience a noticeable boost in power and efficiency while reaching the maximum integration capacity at different reflector lengths (800 m for Seville and 900 m for Yazd). However, both cities reach their minimum fuel consumption at an approximate 300 m total reflector length. Full article
(This article belongs to the Special Issue Solar Thermal Heat and Power Technology: Developments and Applications)
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18 pages, 573 KB  
Article
Green Growth’s Unintended Burden: The Distributional and Well-Being Impacts of China’s Energy Transition
by Li Liu and Jichuan Sheng
Energies 2025, 18(20), 5367; https://doi.org/10.3390/en18205367 (registering DOI) - 11 Oct 2025
Abstract
Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating [...] Read more.
Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating provincial-level energy metrics with a decade-long household panel survey (CFPS), we employ a fixed-effects model to provide a holistic assessment of the policy’s effects on household well-being. The analysis reveals a stark trade-off: a 10% increase in clean energy adoption generates significant non-monetary well-being gains, equivalent to a 190,000 CNY annual income rise, primarily through improved environmental quality and cleaner cooking fuel access. However, these benefits are partially offset by rising energy costs. Our heterogeneity analysis reveals a clear regressive burden: the transition significantly increases energy expenditures for rural and low-income households, while having a negligible or even cost-reducing effect on their urban and high-income counterparts. Our findings demonstrate that while the energy transition promotes aggregate welfare, its benefits are unevenly distributed, potentially exacerbating energy poverty and inequality. This underscores a critical development challenge: green growth is not automatically inclusive. We argue that for the energy transition to be truly pro-poor, it must be accompanied by robust social protection mechanisms, such as targeted subsidies, to shield the most vulnerable from the adverse economic shocks of the policy. Full article
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21 pages, 2719 KB  
Article
Physio-Mechanical Properties and Meso-Scale Damage Mechanism of Granite Under Thermal Shock
by Kai Gao, Jiamin Wang, Chi Liu, Pengyu Mu and Yun Wu
Energies 2025, 18(20), 5366; https://doi.org/10.3390/en18205366 (registering DOI) - 11 Oct 2025
Abstract
Clarifying the differential effects of temperature gradient and temperature change rate on the evolution of rock fractures and damage mechanism under thermal shock is of great significance for the development and utilization of deep geothermal resources. In this study, granite samples at different [...] Read more.
Clarifying the differential effects of temperature gradient and temperature change rate on the evolution of rock fractures and damage mechanism under thermal shock is of great significance for the development and utilization of deep geothermal resources. In this study, granite samples at different temperatures (20 °C, 150 °C, 300 °C, 450 °C, 600 °C, and 750 °C) were subjected to rapid cooling treatment with liquid nitrogen. After the thermal treatment, a series of tests were conducted on the granite, including wave velocity test, uniaxial compression experiment, computed tomography scanning, and scanning electron microscopy test, to explore the influence of thermal shock on the physical and mechanical parameters as well as the meso-structural damage of granite. The results show that with the increase in heat treatment temperature, the P-wave velocity, compressive strength, and elastic modulus of granite gradually decrease, while the peak strain gradually increases. Additionally, the failure mode of granite gradually transitions from brittle failure to ductile failure. Through CT scanning experiments, the spatial distribution characteristics of the pore–fracture structure of granite under the influence of different temperature gradients and temperature change rates were obtained, which can directly reflect the damage degree of the rock structure. When the heat treatment temperature is 450 °C or lower, the number of thermally induced cracks in the scanned sections of granite is relatively small, and the connectivity of the cracks is poor. When the temperature exceeds 450 °C, the micro-cracks inside the granite develop and expand rapidly, and there is a gradual tendency to form a fracture network, resulting in a more significant effect of fracture initiation and permeability enhancement of the rock. The research results are of great significance for the development and utilization of hot dry rock and the evaluation of thermal reservoir connectivity and can provide useful references for rock engineering involving high-temperature thermal fracturing. Full article
19 pages, 1643 KB  
Article
Experimental Studies on Diesel Deterioration: Accelerated Oxidation in a Reaction Vessel and Thermogravimetric Analysis
by Nan Li, Mingchang Wang, Pengpeng Li, Shuping Che, Xingyu Liang, Yinhui Che, Jia Yan and Yongdi He
Energies 2025, 18(20), 5365; https://doi.org/10.3390/en18205365 (registering DOI) - 11 Oct 2025
Abstract
Accelerated oxidation experiments on Chinese 0# diesel fuel were performed with a self-designed aging reactor system. Five experimental conditions covering pressures ranging from atmospheric pressure to 0.8 MPa, temperatures ranging from room temperature (25 °C) to 80 °C, and their synergistic effects were [...] Read more.
Accelerated oxidation experiments on Chinese 0# diesel fuel were performed with a self-designed aging reactor system. Five experimental conditions covering pressures ranging from atmospheric pressure to 0.8 MPa, temperatures ranging from room temperature (25 °C) to 80 °C, and their synergistic effects were adopted to simulate the long-term oxidation of diesel fuel. The extent of deterioration was evaluated based on the measurement of three key indicators, i.e., oxidation stability, wear scar diameter, and viscosity. Thermogravimetric analysis (TGA) tests were performed, and the measured thermogravimetric (TG) curves and derivative thermogravimetric (DTG) curves were used to evaluate the effects of reactor material, heating rate, bath gas, and reactive gas on the deterioration and vaporization processes of diesel fuel. Based on a comparison of the deterioration indicators of diesel fuel collected from the accelerated oxidation experiments and oil depots serving actual operating emergency diesel generators (EDGs), a rapid assessment method of real-time diesel deterioration was explored. Based on the experimental observations, the affecting mechanisms of the increases in temperature and oxygen partial pressure were discussed. Two test methods of accelerated oxidation, with the respective conditions of 0.8 MPa/80 °C and atmospheric pressure/80 °C, were proposed, which could effectively compress the time needed for long-term storage simulations (e.g., 200 h lab aging equals three years of actual operation). The optional temperature and pressure windows for acceleration oxidation were confirmed (40–80 °C/0.3–0.8 MPa). These results are valuable for the further understanding of the processes of deterioration and vaporization of diesel fuel. Full article
24 pages, 5446 KB  
Article
Modeling of Residual Stress, Plastic Deformation, and Permanent Warpage Induced by the Resin Molding Process in SiC-Based Power Modules
by Giuseppe Mirone, Luca Corallo, Raffaele Barbagallo and Giuseppe Bua
Energies 2025, 18(20), 5364; https://doi.org/10.3390/en18205364 (registering DOI) - 11 Oct 2025
Abstract
A critical aspect in the design of power electronics packages is the prediction of their mechanical response under severe thermomechanical loads and the consequent structural damage. For this purpose, finite element (FE) simulations are used to estimate the mechanical performance and reliability under [...] Read more.
A critical aspect in the design of power electronics packages is the prediction of their mechanical response under severe thermomechanical loads and the consequent structural damage. For this purpose, finite element (FE) simulations are used to estimate the mechanical performance and reliability under operational conditions, typically alternate high voltages/currents resulting in thermal gradients. When simulations are performed, it is common practice to consider the as-received package to be in a stress-free state. Namely, residual stresses and plastic deformation induced by the manufacturing processes are neglected. In this study, an advanced FE modeling approach is proposed to assess the structural consequences of the encapsulating resin curing, typical in the production of silicon carbide (SiC)-based power electronics modules for electric vehicles. This work offers a general modeling framework that can be further employed to simulate the effects of thermal gradients induced by the production process on the effective shape and residual stresses of the as-received package for other manufacturing stages, such as metal brazing, soldering processes joining copper and SiC, and, to lower extents, the application of polyimide on top of passivation layers. The obtained results have been indirectly validated with experimental data from literature. Full article
(This article belongs to the Special Issue Reliability of Power Electronics Devices and Converter Systems: 2nd Edition)
32 pages, 1075 KB  
Article
Forecasting the Power Generation of a Solar Power Plant Taking into Account the Statistical Characteristics of Meteorological Conditions
by Vitalii Kuznetsov, Valeriy Kuznetsov, Zbigniew Ciekanowski, Valeriy Druzhinin, Valerii Tytiuk, Artur Rojek, Tomasz Grudniewski and Viktor Kovalenko
Energies 2025, 18(20), 5363; https://doi.org/10.3390/en18205363 (registering DOI) - 11 Oct 2025
Abstract
The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by [...] Read more.
The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
47 pages, 1628 KB  
Review
Energy Dissipation and Efficiency Challenges of Cryogenic Sloshing in Aerospace Propellant Tanks: A Systematic Review
by Alih John Eko, Xuesen Zeng, Mazahar Peerzada, Tristan Shelley, Jayantha Epaarachchi and Cam Minh Tri Tien
Energies 2025, 18(20), 5362; https://doi.org/10.3390/en18205362 (registering DOI) - 11 Oct 2025
Abstract
Cryogenic propellant sloshing presents significant challenges in aerospace systems, inducing vehicle instability, structural fatigue, energy losses, and complex thermal management issues. This review synthesizes experimental, analytical, and numerical advances with an emphasis on energy dissipation and conversion efficiency in propellant storage and transfer. [...] Read more.
Cryogenic propellant sloshing presents significant challenges in aerospace systems, inducing vehicle instability, structural fatigue, energy losses, and complex thermal management issues. This review synthesizes experimental, analytical, and numerical advances with an emphasis on energy dissipation and conversion efficiency in propellant storage and transfer. Recent developments in computational fluid dynamics (CFD) and AI-driven digital-twin frameworks are critically examined alongside the influences of tank materials, baffle configurations, and operating conditions. Unlike conventional fluids, cryogenic propellants in microgravity and within composite overwrapped pressure vessels (COPVs) exhibit unique thermodynamic and dynamic couplings that remain only partially characterized. Prior reviews have typically treated these factors in isolation; here, they are unified through an integrated perspective linking cryogenic thermo-physics, reduced-gravity hydrodynamics, and fluid–structure interactions. Persistent research limitations are identified in the areas of data availability, model validation, and thermo-mechanical coupling fidelity, underscoring the need for scalable multi-physics approaches. This review’s contribution lies in consolidating these interdisciplinary domains while outlining a roadmap toward experimentally validated, AI-augmented digital-twin architectures for improved energy efficiency, reliability, and propellant stability in next-generation aerospace missions. Full article
19 pages, 5164 KB  
Article
Hierarchical Optimization Strategy Considering Regulation of Electric-Fused Magnesium High-Energy-Consuming Load and Deep Peak Regulation of Thermal Power
by Kexin Ren, Yibo Wang, Shunjiang Wang, Chuang Liu and Xudong Zhao
Energies 2025, 18(20), 5361; https://doi.org/10.3390/en18205361 (registering DOI) - 11 Oct 2025
Abstract
The randomness and volatility of wind power increase peak regulation pressure, leading to wind curtailment despite the deep peak regulation efforts of thermal power units. By integrating conventional power source dispatch and high-energy-consuming load configuration, a two-layer optimization model is developed to maximize [...] Read more.
The randomness and volatility of wind power increase peak regulation pressure, leading to wind curtailment despite the deep peak regulation efforts of thermal power units. By integrating conventional power source dispatch and high-energy-consuming load configuration, a two-layer optimization model is developed to maximize wind curtailment absorption and minimize thermal power deep peak regulation costs. The model first analyzes the fused magnesium load’s operating characteristics and its dispatch-participation model, then combines with the thermal power deep peak regulation model for hierarchical joint peak regulation. Applying the method to an actual regional system via CPLEX shows that it reduces wind curtailment, optimizes thermal power deep peak regulation, and improves power generation economic efficiency. Full article
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32 pages, 3615 KB  
Article
Development of a Hybrid Expert Diagnostic System for Power Transformers Based on the Integration of Computational and Measurement Complexes
by Ivan Beloev, Mikhail Evgenievich Alpatov, Marsel Sharifyanovich Garifullin, Ilgiz Fanzilevich Galiev, Shamil Faridovich Rakhmankulov, Iliya Iliev and Ylia Sergeevna Valeeva
Energies 2025, 18(20), 5360; https://doi.org/10.3390/en18205360 (registering DOI) - 11 Oct 2025
Abstract
The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of [...] Read more.
The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of PT: 1—insulating (liquid and solid insulation); 2—electromagnetic (windings, magnetic conductor); 3—voltage regulation; and 4—high-voltage inputs. Computational complexes and modules of the system are connected with the real object of power grids, 110/10 kV substation, which interact with each other and contain a relational database of retrospective offline data of the PT “life cycle” (including test and measurement results), supplemented by online monitoring data of the main subsystems, corrected by high-precision test measurements; analytical complex, in which the work of calculation modules of the operational state of PT subsystems is supplemented by predictive analytics and machine learning modules; and a knowledge base, sections of which are regularly updated and supplemented. The system architecture is tested at industrial facilities in terms of online transformer diagnostics based on dissolved gas analysis (DGA) data. Additionally, a theoretical model of diagnostics based on the electromagnetic characteristics of the transformer, which takes into account distorted and nonlinear modes of its operation, is presented. The scientific significance of the work consists of the presentation of the following new provisions: Methodology and algorithm for diagnostics of electromagnetic parameters of ST, taking into account nonlinearity and non-sinusoidality of winding currents and voltages; formation of optimal client–service architecture of training models of hybrid system based on the processes of data storage and management; and modification of the moth–flame algorithm to optimize the smoothing coefficient in the process of training a probabilistic neural network Full article
(This article belongs to the Section F: Electrical Engineering)
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20 pages, 4326 KB  
Article
Analysis and Enhancement of HQT and ENTSO-E Synthetic Inertia Criteria Using the Unison U151 Wind Turbine
by Yong Cheol Kang, Kicheol Kang, Youngsun Lee and Kyu-Ho Kim
Energies 2025, 18(20), 5359; https://doi.org/10.3390/en18205359 (registering DOI) - 11 Oct 2025
Abstract
Synthetic inertia (SI) enables wind turbine generators (WTGs) to support frequency stability by releasing stored kinetic energy during disturbances. Existing grid-code requirements, such as those of Hydro-Québec TransÉnergie (HQT) and ENTSO-E/Nord Pool, improve the first frequency nadir but often aggravate a second frequency [...] Read more.
Synthetic inertia (SI) enables wind turbine generators (WTGs) to support frequency stability by releasing stored kinetic energy during disturbances. Existing grid-code requirements, such as those of Hydro-Québec TransÉnergie (HQT) and ENTSO-E/Nord Pool, improve the first frequency nadir but often aggravate a second frequency dip (SFD) or risk rotor over-deceleration (OD) when the boost magnitude is large. This paper proposes an enhanced SI requirement that retains the stepwise boost-and-hold structure but replaces the time-based ramp-down with a rotor-speed-dependent recovery, followed by a smooth transition back to maximum power point tracking (MPPT). The proposed scheme was validated using an electromagnetic transient model of the Unison U151 wind turbine (4.569 MW, inertia constant 9.68 s), designed for Korea’s low-wind conditions. Five case studies at wind speeds of 5 and 7 m/s with varying boost levels confirmed that all methods yield identical first nadirs for a given boost, but only the proposed approach consistently maintained a higher second nadir, stabilized rotor dynamics, and prevented repeated dips. These results demonstrate that rotor-speed-dependent SI requirements, when combined with high-inertia turbines, can enhance frequency stability while protecting turbine operation, offering practical guidance for future grid-code revisions. Full article
(This article belongs to the Collection Advanced Control and Applications of Power Electronics and Power Converters)
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17 pages, 6434 KB  
Article
UAV and 3D Modeling for Automated Rooftop Parameter Analysis and Photovoltaic Performance Estimation
by Wioleta Błaszczak-Bąk, Marcin Pacześniak, Artur Oleksiak and Grzegorz Grunwald
Energies 2025, 18(20), 5358; https://doi.org/10.3390/en18205358 (registering DOI) - 11 Oct 2025
Abstract
The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, [...] Read more.
The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article
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27 pages, 3092 KB  
Article
Energy Audit of Road Lighting Installations as a Tool for Improving Efficiency and Visual Safety Conditions
by Marek Kurkowski, Tomasz Popławski, Henryk Wachta and Dominik Węclewski
Energies 2025, 18(20), 5357; https://doi.org/10.3390/en18205357 (registering DOI) - 11 Oct 2025
Abstract
This study presents an analysis of the condition of street lighting based on a selected typical installation in one of the 1459 rural communes in Poland. The analysis was carried out on the basis of publicly available statistical data, local government reports, and [...] Read more.
This study presents an analysis of the condition of street lighting based on a selected typical installation in one of the 1459 rural communes in Poland. The analysis was carried out on the basis of publicly available statistical data, local government reports, and information contained in national and European strategic documents. During the analysis, numerous irregularities and differences in the quality and energy efficiency of the lighting infrastructure were indicated. It was found that outdated sodium luminaires with high energy consumption, low durability, and limited luminous efficacy are used in many cases, which generates significant operating costs and negatively affects the environment. The authors emphasize that a lack of regular and professional lighting audits leads to the suboptimal use of energy resources, an insufficient level of road safety, and failure to adapt lighting to current technical standards and the needs of road users. A lighting audit is a key tool for diagnosing the technical condition, efficiency, and compliance of installations with relevant regulations and recommendations. It also allows for the identification of potential savings and determining the directions of modernization and implementation of energy-saving technologies, such as LED luminaires and intelligent control systems.The presented analysis demonstrates that energy audits are an effective tool for confirming efficiency improvements and enhancing visual safety conditions through better compliance with photometric standards (luminance, lighting uniformity). Direct accident statistics were not within the scope of this study. Full article
(This article belongs to the Section F: Electrical Engineering)
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21 pages, 6867 KB  
Article
The Effect of Cherry Stone Addition to Sawdust on the Pelletization Process and Fuel Pellet Quality
by Sławomir Obidziński, Paweł Cwalina, Małgorzata Kowczyk-Sadowy, Aneta Sienkiewicz, Jacek Mazur and Paweł Braun
Energies 2025, 18(20), 5356; https://doi.org/10.3390/en18205356 (registering DOI) - 11 Oct 2025
Abstract
This study presents the results of research on the pelleting process of pine sawdust with the addition of cherry stone waste, which was carried out using a flat-die pellet press in the context of fuel pellet production. The findings indicate that increasing the [...] Read more.
This study presents the results of research on the pelleting process of pine sawdust with the addition of cherry stone waste, which was carried out using a flat-die pellet press in the context of fuel pellet production. The findings indicate that increasing the proportion of crushed cherry stones in the sawdust mixture from 10% to 20% reduced the pelletizer’s power demand by approximately 14% (from 3.35 to 2.86 kW) and by around 24% (from 3.79 to 2.86 kW), compared with the compaction of sawdust alone. The incorporation of 10% crushed cherry stone waste into pine sawdust slightly improved the kinetic strength of the pellets, increasing it by about 2% (from 94.6 to 96.60%). However, raising the cherry stone content further to 20% resulted in a moderate decrease in kinetic strength, by approximately 5% (from 96.60 to 91.37%). A similar trend was observed for pellet density: the addition of cherry stones (10–20%) slightly reduced the density by about 5.5% (from 1312.02 to 1241.65 kg·m−3), accompanied by a small decrease in bulk density. This study also confirmed the high calorific potential of crushed cherry stones, with a heat of combustion of 24.418 MJ·kg−1 (dry basis) and a net calorific value of 22.326 MJ·kg−1. Their incorporation at levels of 10–20% into sawdust mixtures increased the heat of combustion of the pellets by 0.42–0.84% (from 19.959 MJ·kg−1 for sawdust alone at 15% moisture content to 20.042 MJ·kg−1 with a 10% addition and 20.126 MJ·kg−1 with a 20% addition). Moreover, the inclusion of cherry stone waste in the mixture had a beneficial effect on combustion performance, lowering emissions of harmful compounds such as CO, NO, and SO2, due to the higher combustion temperature achieved. Consequently, the use of cherry stone waste as an additive to sawdust not only enhances the energetic and environmental performance of pellets but also provides an effective pathway for the management of large quantities of fruit industry residues. Full article
(This article belongs to the Special Issue Advances in Bio-Energy: Biofuel and Other Value-Added Bioproducts, Production Technology, and Environmental Impact Systems Components)
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17 pages, 4602 KB  
Article
Experimental Investigation of Hydraulic Fracturing Damage Mechanisms in the Chang 7 Member Shale Reservoirs, Ordos Basin, China
by Weibo Wang, Lu Bai, Peiyao Xiao, Zhen Feng, Meng Wang, Bo Wang and Fanhua Zeng
Energies 2025, 18(20), 5355; https://doi.org/10.3390/en18205355 (registering DOI) - 11 Oct 2025
Abstract
The Chang 7 member of the Ordos Basin hosts abundant shale oil and gas resources and plays a vital role in the development of unconventional energy. This study investigates differences in damage evolution and underlying mechanisms between representative shale oil and shale gas [...] Read more.
The Chang 7 member of the Ordos Basin hosts abundant shale oil and gas resources and plays a vital role in the development of unconventional energy. This study investigates differences in damage evolution and underlying mechanisms between representative shale oil and shale gas reservoir cores from the Chang 7 member under fracturing fluid hydration. A combination of high-temperature expansion tests, nuclear magnetic resonance (NMR), and micro-computed tomography (Micro-CT) was used to systematically characterize macroscopic expansion behavior and microscopic pore structure evolution. Results indicate that shale gas cores undergo faster expansion and higher imbibition rates during hydration (reaching stability in 10 h vs. 23 h for shale oil cores), making them more vulnerable to water-lock damage, while shale oil cores exhibit slower hydration but more pronounced pore structure reconstruction. After 72 h of immersion in fracturing fluid, both core types experienced reduced pore volumes and structural reorganization; however, shale oil cores demonstrated greater capacity for pore reconstruction, with a newly formed pore volume fraction of 34.5% compared to 24.6% for shale gas cores. NMR and Micro-CT analyses reveal that hydration is not merely a destructive process but a dynamic “damage–reconstruction” evolution. Furthermore, the addition of clay stabilizers effectively mitigates water sensitivity and preserves pore structure, with 0.7% identified as the optimal concentration. The research results not only reveal the differential response law of fracturing fluid damage in the Chang 7 shale reservoir but also provide a theoretical basis and technical support for optimizing fracturing fluid systems and achieving differential production increases. Full article
(This article belongs to the Section H: Geo-Energy)
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18 pages, 5708 KB  
Article
Directly Heated Solid Media Thermal Energy Storage System for Heat Supply in Battery Electric Vehicles: A Holistic Evaluation
by Thorsten Ott and Volker Dreißigacker
Energies 2025, 18(20), 5354; https://doi.org/10.3390/en18205354 (registering DOI) - 11 Oct 2025
Abstract
Battery electric vehicles (BEVs) play a key role in reducing CO2 emissions and enabling a climate-neutral economy. However, they suffer from reduced range in cold conditions due to electric cabin heating. Electrically heated thermal energy storage (TES) systems can decouple heat generation [...] Read more.
Battery electric vehicles (BEVs) play a key role in reducing CO2 emissions and enabling a climate-neutral economy. However, they suffer from reduced range in cold conditions due to electric cabin heating. Electrically heated thermal energy storage (TES) systems can decouple heat generation from demand, thereby preventing a loss of range. For this purpose, a novel concept based on a directly electrically heated ceramic solid media TES is investigated, aiming to achieve high storage density while enabling both high charging and discharging powers. To assess the feasibility of the proposed TES concept in BEVs, a holistic evaluation of central aspects is conducted, including experimental characterization for material selection, experimental investigations on electrical contacting, and simulations of the electrothermal charging and thermal discharging processes under vehicle-relevant conditions. As a result of the material characterization, a promising material—a silicon carbide-based composite—was identified, which meets the electrothermal requirements under typical household charging conditions and allows reliable operation with silver-metallized electrodes. Design studies with this material show gravimetric energy densities—including thermal insulation demand—exceeding 100 Wh/kg, storage utilization of up to 90%, and fast charging within 25 min, while offering 5 kW at flexible temperature levels for cabin heating during thermal discharging. These results show that the basic prerequisites for such storage systems are met, while further development—particularly in terms of material improvements—remains necessary. Full article
(This article belongs to the Section E: Electric Vehicles)
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38 pages, 18472 KB  
Article
Bend–Twist Coupling for Small Wind Turbines: A Blade Design Methodology to Enhance Power Generation
by Juan Pablo Vanegas-Alzate, María Antonia Restrepo-Madrigal, José Luis Torres-Madroñero, César Nieto-Londoño, Germán Alberto Barragán de los Rios, Jorge Mario Tamayo-Avendaño, Julián Sierra-Pérez, Joham Alvarez-Montoya and Daniel Restrepo-Montoya
Energies 2025, 18(20), 5353; https://doi.org/10.3390/en18205353 (registering DOI) - 11 Oct 2025
Abstract
Small-scale wind turbines (SWTs) represent a promising solution for the energy transition and the decentralization of electricity generation in non-interconnected areas. Conventional strategies to improve SWT performance often rely on active pitch control, which, while effective at rated conditions, is too costly and [...] Read more.
Small-scale wind turbines (SWTs) represent a promising solution for the energy transition and the decentralization of electricity generation in non-interconnected areas. Conventional strategies to improve SWT performance often rely on active pitch control, which, while effective at rated conditions, is too costly and complex for small systems. An alternative is passive pitch control through bend–twist coupling in the blade structure, which enables self-regulation and improved power generation. This work proposes a novel blade design methodology for a 5 kW SWT that integrates passive bend–twist coupling with conventional pitch adjustment, thereby creating a hybrid passive–active control strategy. The methodology encompasses the definition of aerodynamic blade geometry, laminate optimization via genetic algorithms combined with finite element analysis, and experimental characterization of composite materials. Aerodynamic–structural interactions are studied using one-way fluid–structure simulations, with responses analyzed through the blade element momentum method to assess turbine performance. The results indicate that the proposed design enhances power generation by about 4%. The study’s originality lies in integrating optimization, structural tailoring, and material testing, offering one of the first demonstrations of combined passive–active pitch control in SWTs, and providing a cost-effective route to improve efficiency and reliability in decentralized renewable energy systems. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy in Light of Energy Transition Processes—2nd Edition)
24 pages, 1597 KB  
Article
A Comparative Study of Electricity Sales Forecasting Models Based on Different Feature Decomposition Methods
by Shichong Chen, Yushu Zhang, Xiaoteng Ma, Xu Yang, Junyi Shi and Haoyang Ji
Energies 2025, 18(20), 5352; https://doi.org/10.3390/en18205352 (registering DOI) - 11 Oct 2025
Abstract
Accurate forecasting of electricity sales holds significant practical importance. On the one hand, it helps to implement and achieve the annual goals of power companies, and on the other hand, it helps to control the balance of enterprise profits. This study was conducted [...] Read more.
Accurate forecasting of electricity sales holds significant practical importance. On the one hand, it helps to implement and achieve the annual goals of power companies, and on the other hand, it helps to control the balance of enterprise profits. This study was conducted in China using data from the State Grid Corporation (Henan, Fujian, and national data) from the Wind database. Based on collected data such as electricity sales, this study addresses the limitations of the existing literature, which mostly employs a single feature decomposition method for forecasting. We simultaneously apply three decomposition techniques—seasonal adjustment decomposition (X13), empirical mode decomposition (EMD), and discrete wavelet transform (DWT)—to decompose electricity sales into multiple components. Subsequently, we model each component using the ADL, SARIMAX, and LSTM models, synthesize the component-level forecasts, and realize the comparison of electricity sales forecasting models based on different feature decomposition methods. The findings reveal (1) forecasting performance based on feature decomposition generally outperforms direct forecasting without decomposition; (2) different regions may benefit from different decomposition methods—EMD is more suitable for regions with high sales volatility, while DWT is preferable for more stable regions; and (3) among the forecasting models, ADL performs better than SARIMAX, while LSTM yields the least accurate results when combined with decomposition methods. Full article
(This article belongs to the Section C: Energy Economics and Policy)
25 pages, 1520 KB  
Article
Deep Learning-Based Classification of Transformer Inrush and Fault Currents Using a Hybrid Self-Organizing Map and CNN Model
by Heungseok Lee, Sang-Hee Kang and Soon-Ryul Nam
Energies 2025, 18(20), 5351; https://doi.org/10.3390/en18205351 (registering DOI) - 11 Oct 2025
Abstract
Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a [...] Read more.
Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Electrical Power Systems)
12 pages, 2809 KB  
Article
High-Efficiency Multistage Charge Pump Rectifiers Design
by Ying Wang, Ce Wang and Shiwei Dong
Energies 2025, 18(20), 5350; https://doi.org/10.3390/en18205350 (registering DOI) - 11 Oct 2025
Abstract
This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent [...] Read more.
This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent limitations of cascading Cockcroft–Walton topologies with class-F load networks, a novel ground plane isolation technique was developed, which utilizes the reverse-side metallization of the circuit board. A 5.8 GHz two-stage Cockcroft–Walton voltage multiplier rectifier was fabricated and characterized. Measurement results demonstrate that the circuit achieves a maximum output voltage of 7.4 V and a peak conversion efficiency of 70.5% with an input power of only 30 mW, while maintaining stable performance across varying load conditions. A comparison with a two-stage Dickson rectifier reveals that the Cockcroft–Walton rectifier exhibits superior output voltage and conversion efficiency. The proposed architecture delivers significant improvements in power conversion efficiency and voltage multiplication capability compared to conventional designs, establishing a new benchmark for low-power wireless energy harvesting applications. Full article
(This article belongs to the Special Issue Design, Modelling and Analysis for Wireless Power Transfer Systems)
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27 pages, 18801 KB  
Article
Hydrogen Production Plant Retrofit for Green H2: Experimental Validation of a High-Efficiency Retrofit of an Alkaline Hydrogen Plant Using an Isolated DC Microgrid
by Rogerio Luiz da Silva Junior, Filipe Tavares Carneiro, Leonardo Bruno Garcial Campanhol, Guilherme Gemi Pissaia, Tales Gottlieb Jahn, Angel Ambrocio Quispe, Carina Bonavigo Jakubiu, Daniel Augusto Cantane, Leonardo Sostmeyer Mai, Jose Alfredo Valverde and Fernando Marcos Oliveira
Energies 2025, 18(20), 5349; https://doi.org/10.3390/en18205349 (registering DOI) - 11 Oct 2025
Abstract
Given the climate change observed in the past few decades, sustainable development and the use of renewable energy sources are urgent. In this scenario, hydrogen production through electrolyzers is a promising renewable source and energy vector because of its ultralow greenhouse emissions and [...] Read more.
Given the climate change observed in the past few decades, sustainable development and the use of renewable energy sources are urgent. In this scenario, hydrogen production through electrolyzers is a promising renewable source and energy vector because of its ultralow greenhouse emissions and high energy content. Hydrogen can be used in a variety of applications, from transportation to electricity generation, contributing to the diversification of the energy matrix. In this context, this paper presents an autonomous isolated DC microgrid system for generating and storing electrical energy to be exclusively used for feeding an electrolyzer hydrogen production plant, which has been retrofitted for green hydrogen production. Experimental verification was performed at Itaipu Parquetec, which consists of an alkaline electrolysis unit directly integrated with a battery energy storage system and renewable sources (e.g., photovoltaic and wind) by using an isolated DC microgrid concept based on DC/DC and AC/DC converters. Experimental results revealed that the new electrolyzer DC microgrid increases the system’s overall efficiency in comparison to the legacy thyristor-based power supply system by 26%, and it autonomously controls the energy supply to the electrolyzer under optimized conditions with an extremely low output current ripple. Another advantage of the proposed DC microgrid is its ability to properly manage the startup and shutdown process of the electrolyzer plant under power generation outages. This paper is the result of activities carried out under the R&D project of ANEEL program No. PD-10381-0221/2021, entitled “Multiport DC-DC Converter and IoT System for Intelligent Energy Management”, which was conducted in partnership with CTG-Brazil. Full article
(This article belongs to the Section A5: Hydrogen Energy)
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50 pages, 2689 KB  
Review
Inkjet Printing for Batteries and Supercapacitors: State-of-the-Art Developments and Outlook
by Juan C. Rubio and Martin Bolduc
Energies 2025, 18(20), 5348; https://doi.org/10.3390/en18205348 (registering DOI) - 11 Oct 2025
Abstract
Inkjet printing enables contactless deposition onto fragile substrates for printed energy-storage devices and supports flexible batteries and supercapacitors with reduced material use. This review examines multilayer and interdigital architectures and analyzes how ink rheology, droplet formation, colloidal interactions, and the printability window govern [...] Read more.
Inkjet printing enables contactless deposition onto fragile substrates for printed energy-storage devices and supports flexible batteries and supercapacitors with reduced material use. This review examines multilayer and interdigital architectures and analyzes how ink rheology, droplet formation, colloidal interactions, and the printability window govern performance. For batteries, reported inkjet-printed electrodes commonly deliver capacities of ~110–150 mAh g−1 for oxide cathodes at C/2–1 C, with coulombic efficiency ≥98% and stability over 102–103 cycles; silicon anodes reach ~1.0–2.0 Ah g−1 with efficiency approaching 99% under stepwise formation. Typical current densities are ~0.5–5 mA cm−2 depending on areal loading, and multilayer designs with optimized drying and parameter tuning can yield rate and discharge behavior comparable to cast films. For supercapacitors, inkjet-printed microdevices report volumetric capacitances in the mid-hundreds of F cm−3, translating to ~9–34 mWh cm−3 and ~0.25–0.41 W cm−3, with 80–95% retention after 10,000 cycles and coulombic efficiency near 99%. In solid-state configurations, stability is enhanced, although often accompanied by reduced areal capacitance. Although solids loading is lower than in screen printing, precise material placement together with thermal or photonic sintering enables competitive capacity, rate capability, and cycle life while minimizing waste. The review consolidates practical guidance on ink formulation, printability, and defect control and outlines opportunities in greener chemistries, oxidation-resistant metallic systems, and scalable high-throughput printing. Full article
(This article belongs to the Special Issue Power Electronics Technology and Application)
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19 pages, 5654 KB  
Article
Analysis of the Influence of Structural Defects on the Insulation of GIL Basin Insulator Under AC Electric Field
by Zhuoran Yang, Yue Wang, Jian Liu, Hongze Li, Lixiang Lv and Xiaolong Li
Energies 2025, 18(20), 5347; https://doi.org/10.3390/en18205347 (registering DOI) - 11 Oct 2025
Abstract
Basin insulator is a critical component of gas-insulated transmission line (GIL) systems. Air gap defects and surface crack defects may form in basin insulators due to casting, installation, or transport processes. This phenomenon poses a significant threat to long-term safety and stability and [...] Read more.
Basin insulator is a critical component of gas-insulated transmission line (GIL) systems. Air gap defects and surface crack defects may form in basin insulators due to casting, installation, or transport processes. This phenomenon poses a significant threat to long-term safety and stability and may even lead to partial discharges. This study establishes a simulation model of a GIL system-incorporating insulator to systematically analyze the influence patterns of various defects on the insulation characteristics of the basin insulator. Meanwhile, an equation predicting the relationship between defect size and maximum electric field strength is derived. The research revealed the following: For short air gap defects near the conductor, increasing length reduces their impact on the surrounding electric field, with the radius having minimal effect; for long air gap defects near the conductor, increasing length amplifies their influence. Smooth air gap defects distant from the conductor show negligible variation in maximum electric field strength with increasing length, while unsmooth air gap defects exhibit more pronounced effects at shorter lengths. Under identical conditions, unsmooth air gap defects demonstrate greater influence on the electric field than smooth ones. For elliptical surface defects, variations in radius show the strongest distortion. The degree of influence from surface crack defects correlates directly with their proximity to the conductor. These findings provide critical diagnostic criteria for assessing the insulation performance of basin insulator under damaged conditions. Full article
(This article belongs to the Special Issue Advanced Dielectric Materials, Sensing, Monitoring, and Diagnostic Technologies for High-Voltage Equipment in New Power Systems)
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17 pages, 2920 KB  
Article
Frequency Domain Reflectometry for Power Cable Defect Localization: A Comparative Study of FFT and IFFT Methods
by Wenbo Zhu, Baojun Hui, Jianda Li, Tao Han, Linjie Zhao and Shuai Hou
Energies 2025, 18(20), 5346; https://doi.org/10.3390/en18205346 - 10 Oct 2025
Abstract
At present, the development of power cables shows three notable trends: higher voltage, longer distance and more complex environments. Against this backdrop, the limitations of traditional detection techniques in locating local defects have become increasingly apparent. Frequency Domain Reflectometry (FDR) has garnered sustained [...] Read more.
At present, the development of power cables shows three notable trends: higher voltage, longer distance and more complex environments. Against this backdrop, the limitations of traditional detection techniques in locating local defects have become increasingly apparent. Frequency Domain Reflectometry (FDR) has garnered sustained research attention both domestically and internationally due to its high sensitivity and accuracy in detecting localized defects. This paper aims to compare the defect localization effectiveness of the Fast Fourier Transform (FFT) method and the Inverse Fast Fourier Transform (IFFT) method within FDR. First, the differences between the two methods are analyzed from a theoretical perspective. Then, field tests are conducted on cables of varying voltage levels and lengths, with comparisons made using parameters such as full width at half maximum (FWHM) and signal-to-noise ratio (SNR). The results indicate that the FFT method is more suitable for low-interference or short cables, while the IFFT method is more suitable for high-noise, high-resolution, or long cables. Full article
(This article belongs to the Special Issue Advanced Techniques for Power Transmission, Distribution and Transformation Equipment)
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14 pages, 2310 KB  
Article
Quantifying the Need for Synthetic Inertia in the UK Grid: Empirical Evidence from Frequency Demand and Generation Data
by Sid-Ali Amamra
Energies 2025, 18(20), 5345; https://doi.org/10.3390/en18205345 - 10 Oct 2025
Abstract
The increasing integration of inverter-based renewable energy sources is displacing conventional synchronous generation, resulting in a progressive reduction in system inertia and heightened challenges to frequency stability. This study presents a detailed empirical analysis of the UK electricity grid over a representative 24 [...] Read more.
The increasing integration of inverter-based renewable energy sources is displacing conventional synchronous generation, resulting in a progressive reduction in system inertia and heightened challenges to frequency stability. This study presents a detailed empirical analysis of the UK electricity grid over a representative 24 h period, utilizing high-resolution datasets that capture grid frequency, energy demand, generation mix, and wholesale market prices. An inertia proxy is developed based on the share of synchronous generation, enabling quantitative assessment of its relationship with the Rate of Change of Frequency (RoCoF). Through the application of change point detection and unsupervised clustering algorithms, the analysis identifies critical renewable penetration thresholds beyond which frequency stability significantly deteriorates. These findings underscore the increasing importance of synthetic inertia in maintaining grid resilience under high renewable scenarios. The results offer actionable insights for system operators aiming to enhance frequency control strategies and contribute to the formulation of policy and technical standards regarding synthetic inertia provision in future low-inertia power systems. Full article
(This article belongs to the Special Issue Modern Smart Grid Technologies for Power Grids and Electric Energy Power Networks)
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