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Volume 18
Issue 9
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Energies, Volume 18, Issue 9 (May-1 2025) – 263 articles

Cover Story (view full-size image): The presented study focuses on evaluating the mixing properties of gyroid geometry and its modifications as advanced mixing elements in renewable energy technologies. Using numerical simulations, the hydrodynamic characteristics of the gyroid structure and five geometric modifications were analyzed under laminar flow conditions simulating the mixing of water and ethylene glycol. The evaluation was conducted using the parameters mixing index and performance index. The results show that all tested geometries significantly improve the degree of mixing compared to an empty channel. The results of the study demonstrated that, despite similar hydraulic losses among some of the structures, their fluid mixing performance differs, which highlights the importance of targeted geometric design of sheet-gyroid structures. View this paper
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15 pages, 7149 KiB  
Article
Numerical Improvement of Battery Thermal Management Integrating Phase Change Materials with Fin-Enhanced Liquid Cooling
by Bo Wang, Changzhi Jiao and Shiheng Zhang
Energies 2025, 18(9), 2406; https://doi.org/10.3390/en18092406 - 7 May 2025
Viewed by 218
Abstract
Under high-rate charging and discharging conditions, the coupling of phase change materials (PCMs) with liquid cooling proves to be an effective approach for controlling battery pack operating temperature and performance. To address the inherent low thermal conductivity of PCM and enhance heat transfer [...] Read more.
Under high-rate charging and discharging conditions, the coupling of phase change materials (PCMs) with liquid cooling proves to be an effective approach for controlling battery pack operating temperature and performance. To address the inherent low thermal conductivity of PCM and enhance heat transfer from PCM to cooling plates, numerical simulations were conducted to investigate the effects of installing fins between the upper and lower cooling plates on temperature distribution. The results demonstrated that merely adding cooling plates on battery surfaces and filling PCM in inter-cell gaps had limited effectiveness in reducing maximum temperatures during 4C discharge (8A discharge current), achieving only a 1.8 K reduction in peak temperature while increasing the maximum temperature difference to over 10 K. Cooling plates incorporating optimized flow channel configurations in fins, alternating coolant inlet/outlet arrangements, appropriate increases in coolant flow rate (0.5 m/s), and reduced coolant inlet temperature (293.15 K) could maintain battery pack temperatures below 306 K while constraining maximum temperature differences to approximately 5 K during 4C discharge. Although increased flow rates enhanced cooling efficiency, improvements became negligible beyond 0.7 m/s due to inherent limitations in battery and PCM thermal conductivity. Excessively low coolant inlet temperatures (293.15 K) were found to adversely affect maximum temperature difference control during initial discharge phases. While reducing the inlet temperature from 300.65 K to 293.15 K decreased the maximum temperature by 10.1 K, it concurrently increased maximum temperature difference by 0.44 K. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
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15 pages, 2920 KiB  
Article
Battery Health Diagnosis via Neural Surrogate Model: From Lab to Field
by Hojin Cheon, Jihun Jeon, Byungil Jung and Hongseok Kim
Energies 2025, 18(9), 2405; https://doi.org/10.3390/en18092405 - 7 May 2025
Viewed by 195
Abstract
Batteries degrade over time. Such degradation leads to performance loss, but more importantly, safety issues arise. To evaluate the battery degradation, traditional diagnostic techniques rely on model-based or data-driven approaches; however, those methods often require controlled conditions or specific tests, which may not [...] Read more.
Batteries degrade over time. Such degradation leads to performance loss, but more importantly, safety issues arise. To evaluate the battery degradation, traditional diagnostic techniques rely on model-based or data-driven approaches; however, those methods often require controlled conditions or specific tests, which may not be applicable in real fields. In this regard, we propose a deep learning-based method addressing these limitations by accurately modeling batteries using real-world operational data from photovoltaic (PV)-integrated battery energy storage system (BESSs), where charging currents vary dynamically and SOC is capped at 70% by regulation. The proposed method is based on a neural surrogate model for batteries, employing a sequence-to-sequence architecture, which directly captures the dynamic behavior of batteries from operational data, eliminating the need for specialized characterization tests or feature extraction. The proposed model synthesizes the terminal voltage with a mean absolute error of 6.4 mV for lithium–iron–phosphate (LFP) cells and 49 mV for nickel–cobalt–manganese (NCM) battery modules, respectively, which is only 0.4% and 0.29% of the voltage swing. As a health indicator, we also propose the concept of voltage deviation (VD), defined as the deviation between the synthesized and actual terminal voltages. We demonstrate that VD can be evaluated not only in laboratory data but also in field data. Full article
(This article belongs to the Section D: Energy Storage and Application)
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20 pages, 17634 KiB  
Article
Integrating Laboratory-Measured Contact Angles into Time-Dependent Wettability-Adjusted LBM Simulations for Oil–Water Relative Permeability
by Chenglin Liu, Changwei Sun, Ling Dai, Guanqun Wang, Haipeng Shao, Wei Li and Wei Long
Energies 2025, 18(9), 2404; https://doi.org/10.3390/en18092404 - 7 May 2025
Viewed by 163
Abstract
Oil–water relative permeability is essential for reservoir development and enhanced oil recovery (EOR). Traditional core displacement experiments assume static wettability, whereas in real reservoirs, wettability evolves over time due to waterflooding and rock–fluid interactions, significantly altering flow behavior. Existing numerical methods, including conventional [...] Read more.
Oil–water relative permeability is essential for reservoir development and enhanced oil recovery (EOR). Traditional core displacement experiments assume static wettability, whereas in real reservoirs, wettability evolves over time due to waterflooding and rock–fluid interactions, significantly altering flow behavior. Existing numerical methods, including conventional lattice Boltzmann models (LBM), fail to account for these changes and lead to inaccurate predictions. This study integrates laboratory-measured contact angles into a time-dependent wettability-adjusted LBM framework, ensuring real-time wettability updates during simulation. Micro-CT imaging captures oil–water displacement and contact angle evolution at different flooding stages, which are incorporated into the Shan–Chen LBM model. Results show that neglecting the time-dependent wettability overestimates the residual oil saturation and underestimates the water-phase permeability. In contrast, our method reduces the residual oil saturation by up to 35% and expands the two-phase flow region by 15%, aligning closely with experimental observations. This approach enhances the accuracy of relative permeability modeling, providing a more reliable tool for optimizing waterflooding strategies and improving oil recovery efficiency. Full article
(This article belongs to the Special Issue Unconventional Hydrocarbon Resources: Innovations in Occurrence, Exploration and Production)
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24 pages, 7107 KiB  
Article
A Synergistic Planning Framework for Low-Carbon Power Systems: Integrating Coal-Fired Power Plant Retrofitting with a Carbon and Green Certificate Market Coupling Mechanism
by Zifan Tang, Yue Yin, Chao Chen, Changle Liu, Zhuoxun Li and Benyao Shi
Energies 2025, 18(9), 2403; https://doi.org/10.3390/en18092403 - 7 May 2025
Viewed by 168
Abstract
The intensifying impacts of climate change induced by carbon emissions necessitate the implementation of urgent mitigation strategies. Given that the power sector is a major contributor to global carbon emissions, strategic decarbonization planning in this sector is of paramount importance. This study proposes [...] Read more.
The intensifying impacts of climate change induced by carbon emissions necessitate the implementation of urgent mitigation strategies. Given that the power sector is a major contributor to global carbon emissions, strategic decarbonization planning in this sector is of paramount importance. This study proposes a synergistic planning framework for low-carbon power systems that integrates coal-fired power plants (CFPPs) and a carbon and green certificate market coupling mechanism, thereby facilitating a “security–economic–low-carbon” tri-objective transition in power systems. The proposed framework facilitates dynamic decision-making regarding the retrofitting of CFPPs, investments in renewable energy resources, and energy storage systems. By evaluating three distinct CFPP retrofitting pathways, the framework enhances economic efficiency and reduces carbon emissions, achieving reductions of 28.67% in total system costs and 2.96% in CO2 emissions. Implementing the carbon–green certificate market coupling mechanism further unlocks the market value of green certificates, thereby providing economic incentives for clean energy projects and increasing flexibility in the allocation of carbon emission quotas for enterprises. Relative to cases that consider only carbon trading or only green certificate markets, the coupled mechanism reduces the total cost by 10.96% and 15.56%, and decreases carbon emissions by 27.10% and 47.36%, respectively. The collaborative planning framework introduced in this study enhances economic performance, increases renewable energy penetration, and reduces carbon emissions, thus facilitating the low-carbon transition of power systems. Full article
(This article belongs to the Special Issue New Power System Planning and Scheduling)
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30 pages, 4148 KiB  
Article
Energy Potential of Zea mays Grown in Cadmium-Contaminated Soil
by Agata Borowik, Jadwiga Wyszkowska, Magdalena Zaborowska and Jan Kucharski
Energies 2025, 18(9), 2402; https://doi.org/10.3390/en18092402 - 7 May 2025
Viewed by 160
Abstract
Cadmium is a non-essential element for proper plant growth and development and is highly toxic to humans and animals, in part because it inters with calcium-dependent processes in living organisms. For this reason, a study was conducted to assess the potential for producing [...] Read more.
Cadmium is a non-essential element for proper plant growth and development and is highly toxic to humans and animals, in part because it inters with calcium-dependent processes in living organisms. For this reason, a study was conducted to assess the potential for producing maize (Zea mays) biomass in cadmium-contaminated soil for energy purposes. The energy potential of Zea mays was evaluated by determining the heat of combustion (Q), heating value (Hv), and the amount of energy produced from the biomass. Starch, compost, fermented bark, humic acids, molecular sieve, zeolite, sepiolite, expanded clay, and calcium carbonate were assessed as substances supporting biomass production from Zea mays. The accumulation and redistribution of cadmium in the plant were also investigated. The study was conducted in a vegetation hall as part of a pot experiment. Zea mays was grown in uncontaminated soil and in soil contaminated with 15 mg Cd2+ kg−1. A strong toxic effect of cadmium on the cultivated plants was observed, causing a 62% reduction in the biomass of aerial parts and 61% in the roots. However, it did not alter the heat of combustion and heating value of the aerial part biomass, which were 18.55 and 14.98 MJ kg−1 d.m., respectively. Of the nine substances tested to support biomass production, only four (molecular sieve, compost, HumiAgra, and expanded clay) increased the yield of Zea mays grown in cadmium-contaminated soil. The molecular sieve increased aerial part biomass production by 74%, compost by 67%, expanded clay by 19%, and HumiAgra by 15%, but none of these substances completely eliminated the toxic effects of cadmium on the plant. At the same time, the bioaccumulation factor (BAF) of cadmium was higher in the roots (0.21–0.23) than in the aerial parts (0.04–0.03), with the roots showing greater bioaccumulation. Full article
(This article belongs to the Special Issue Sustainable Energy Development in Liquid Waste and Biomass: 2nd Edition)
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17 pages, 2364 KiB  
Article
Battery Health Prediction with Singular Spectrum Analysis and Grey Wolf Optimized Long Short-Term Memory Networks
by Chengti Huang, Na Li, Jianqing Zhu and Shengming Shi
Energies 2025, 18(9), 2401; https://doi.org/10.3390/en18092401 - 7 May 2025
Viewed by 178
Abstract
To tackle the intricate challenges of nonlinearity and non-stationarity in lead-acid battery degradation data, this paper introduces the SG-LSTM model, an innovative approach to battery health prediction. This model uniquely integrates Singular Spectrum Analysis (SSA) and Grey Wolf Optimization (GWO) with Long Short-Term [...] Read more.
To tackle the intricate challenges of nonlinearity and non-stationarity in lead-acid battery degradation data, this paper introduces the SG-LSTM model, an innovative approach to battery health prediction. This model uniquely integrates Singular Spectrum Analysis (SSA) and Grey Wolf Optimization (GWO) with Long Short-Term Memory (LSTM) networks, forming a sophisticated predictive framework. By targeting key degradation features, such as the charging time of multiple voltage rise segments from the charging curve, the model effectively captures critical battery health dynamics. SSA plays a vital role by filtering outliers from these feature sequences, ensuring high-quality data for analysis and enhancing the robustness and accuracy of predictions. The refined data are then processed by a GWO-optimized LSTM network, where GWO’s bio-inspired optimization fine-tunes the LSTM parameters for optimal performance. Experimental results demonstrate that the SG-LSTM model outperforms existing models in prediction accuracy and stability; specifically, SG-LSTM achieves 0.27 RMSE, outperforming LSTM (0.84), SSA-LSTM (0.4), and SSA-BP (0.6). Full article
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30 pages, 12182 KiB  
Article
Electromagnetic Investigation of Innovative Stator–Permanent Magnet Motors
by Mohammad Reza Sarshar, Mohammad Amin Jalali Kondelaji, Pedram Asef and Mojtaba Mirsalim
Energies 2025, 18(9), 2400; https://doi.org/10.3390/en18092400 - 7 May 2025
Viewed by 299
Abstract
Owing to the distinct advantages of stator–permanent magnet (PM) motors over other PM machines, their prominence in high-power-density applications is surging dramatically, capturing growing interest across diverse applications. This article proposes an innovative design procedure for two primary stator–PM motor types, flux switching [...] Read more.
Owing to the distinct advantages of stator–permanent magnet (PM) motors over other PM machines, their prominence in high-power-density applications is surging dramatically, capturing growing interest across diverse applications. This article proposes an innovative design procedure for two primary stator–PM motor types, flux switching and biased flux, yielding 30 novel motor designs. The procedure involves splitting teeth, incorporating a flux reversal effect, and embedding flux barriers into the conventional structure. The analytical reasons behind the novel motors’ architecture are mathematically expressed and verified using finite element analysis (FEA). Through an effective optimisation based on a multi-objective genetic algorithm, various feasible stator/rotor pole combinations are explored, with over 36,000 samples evaluated using FEA coupled with the algorithm. The electromagnetic characteristics of promising motors are analysed, revealing that adding the flux reversal effect and flux barriers, which reduce PM volume while decreasing leakage flux and enhancing air gap flux, improves torque production by up to 68%. Beyond torque enhancement, other electromagnetic parameters, including torque ripple, core loss, and the power factor, are also improved. The proposed motors enhance the PM torque density significantly by about 115% compared to conventional motors and reduce the motor costs. A generalised decision-making process and thermal analysis are applied to the top-performing motors. Additionally, the prototyping measures and considerations are thoroughly discussed. Finally, a comprehensive conclusion is reached. Full article
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56 pages, 13495 KiB  
Review
Advancing Electrochemical Energy Storage: A Review of Electrospinning Factors and Their Impact
by Muhammad Kashif, Sadia Rasul, Mohamedazeem M. Mohideen and Yong Liu
Energies 2025, 18(9), 2399; https://doi.org/10.3390/en18092399 - 7 May 2025
Viewed by 264
Abstract
The imperative for sustainable energy has driven the demand for efficient energy storage systems that can harness renewable resources and store surplus energy for off-peak usage. Among the numerous advancements in energy storage technology, polymeric nanofibers have emerged as promising nanomaterials, offering high [...] Read more.
The imperative for sustainable energy has driven the demand for efficient energy storage systems that can harness renewable resources and store surplus energy for off-peak usage. Among the numerous advancements in energy storage technology, polymeric nanofibers have emerged as promising nanomaterials, offering high specific surface areas that facilitate increased charge storage and enhanced energy density, thereby improving electrochemical performance. This review delves into the pivotal role of nanofibers in determining the optimal functionality of energy storage systems. Electrospinning emerged as a facile and cost-effective method for generating nanofibers with customizable nanostructures, making it attractive for energy storage applications. Our comprehensive review article examines the latest developments in electrospun nanofibers for electrochemical storage devices, highlighting their use as separators and electrode materials. We provide an in-depth analysis of their application in various battery technologies, including supercapacitors, lithium-ion batteries, sodium-ion batteries, potassium-ion batteries, lithium–sulfur batteries, and lithium–oxygen batteries, with a focus on their electrochemical performance. Furthermore, we summarize the diverse fabrication techniques, optimization of key influencing factors, and environmental implications of nanofiber production and their properties. This review aims to offer an inclusive understanding of electrospinning’s role in advancing electrochemical energy storage, providing insights into the factors that drive the performance of these critical materials. Full article
(This article belongs to the Special Issue Research on Advanced Energy Materials for Meeting Global Energy Challenges)
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17 pages, 3398 KiB  
Article
Multilayer Gas-Bearing System and Productivity Characteristics in Carboniferous–Permian Tight Sandstones: Taking the Daning–Jixian Block, Eastern Ordos Basin, as an Example
by Ming Chen, Bo Wang, Haonian Tian, Junyi Sun, Lei Liu, Xing Liang, Benliang Chen, Baoshi Yu and Zhuo Zhang
Energies 2025, 18(9), 2398; https://doi.org/10.3390/en18092398 - 7 May 2025
Viewed by 146
Abstract
The Carboniferous–Permian strata in the Daning–Jixian Block, located on the eastern edge of the Ordos Basin, host multiple sets of tight gas reservoirs. However, systematic research on the characteristics and gas production differences of multilayer tight sandstone gas-bearing systems remains limited. Based on [...] Read more.
The Carboniferous–Permian strata in the Daning–Jixian Block, located on the eastern edge of the Ordos Basin, host multiple sets of tight gas reservoirs. However, systematic research on the characteristics and gas production differences of multilayer tight sandstone gas-bearing systems remains limited. Based on geochemical signatures, reservoir pressure coefficients, and sequence stratigraphy, the tight sandstone gas systems are subdivided into upper and lower systems, separated by regionally extensive Taiyuan Formation limestone. The upper system is further partitioned into four subsystems. Depositional variability from the Benxi Formation to the He 8 Member has generated diverse litho-mineralogical characteristics. The Shan 1 and He 8 Members, deposited in low-energy delta-front subaqueous distributary channels with gentle topography, exhibit lower quartz content (predominantly feldspar lithic sandstone and lithic quartz sand-stone) and elevated lithic fragments, matrix, and clay minerals (particularly chlorite). These factors increase displacement and median pressures, resulting in inferior reservoir quality. By comparing and evaluating the gas production effects under different extraction methods, targeted optimization recommendations are provided to offer both theoretical support and practical guidance for the efficient development of this block. Full article
(This article belongs to the Special Issue Exploration and Development of Unconventional Oil and Gas Resources: Latest Advances and Prospects: 3rd Edition)
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20 pages, 4962 KiB  
Article
Unbalanced Magnetic Pull Calculation in Ironless Axial Flux Motors
by Guoqing Zhu and Jian Luo
Energies 2025, 18(9), 2397; https://doi.org/10.3390/en18092397 - 7 May 2025
Viewed by 137
Abstract
Axial flux motors have gained widespread attention in the field of electric vehicles. The stator may exert a unilateral axial force on the dual rotors under uneven air gaps. The unbalanced magnetic pull can influence the production and processing of the motor, leading [...] Read more.
Axial flux motors have gained widespread attention in the field of electric vehicles. The stator may exert a unilateral axial force on the dual rotors under uneven air gaps. The unbalanced magnetic pull can influence the production and processing of the motor, leading to issues such as vibrations, bearing degradation, reduced lifespan, and torque reduction attributed to the bearings. Accurate evaluation of the unilateral magnetic pull can reduce costs associated with bearing protection. For dual-rotor motors, the axial forces of the rotors act in opposite directions with nearly equal magnitudes, resulting in the catastrophic cancellation of unbalanced magnetic pull calculations. A similar phenomenon may occur between coils, introducing computational errors. To avoid these errors, the stator was selected as the computational target for unilateral axial force calculations. The integration domain was defined to encompass the entire air region containing all windings, rather than summing individual force components. This merged integration approach was mathematically validated through the Maxwell stress tensor method. Finally, the obtained stator axial force closely matched the rotor axial force in magnitude, demonstrating the accuracy of the proposed method. Full article
(This article belongs to the Section E: Electric Vehicles)
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18 pages, 2493 KiB  
Article
Research on Resource Utilization of Bi-Level Non-Cooperative Game Systems Based on Unit Resource Return
by Bo Fu, Peiwen Li and Yi Quan
Energies 2025, 18(9), 2396; https://doi.org/10.3390/en18092396 - 7 May 2025
Viewed by 115
Abstract
In a competitive market, due to differences in the nature of various power generation entities, there is a decline in resource utilization and difficulties in ensuring a return on investment for generating units within the system. A bi-level non-cooperative game model based on [...] Read more.
In a competitive market, due to differences in the nature of various power generation entities, there is a decline in resource utilization and difficulties in ensuring a return on investment for generating units within the system. A bi-level non-cooperative game model based on the Unit Resource Return (URR) is proposed to safeguard the interests and demands of each power generation unit while improving the overall resource utilization rate of the system. Firstly, we construct a comprehensive energy-trading framework for the overall system and analyze the relationship between the Independent System Operator (ISO) and the generation units. Secondly, we propose the Unit Resource Return (URR), inspired by the concept of input-output efficiency in economics. URR evaluates the return on unit resource input by taking the maximum generation potential of each unit as the benchmark. Finally, a bi-level non-cooperative game model is established. In the lower-level non-cooperative game, the generating units safeguard their own interests, while in the upper-level, the ISO adjusts the output allocation and engages in a master–slave game between generating units to ensure the overall operational efficiency of the system. URR is adopted as the ISO’s price-clearing equilibrium criterion, enabling the optimization of both resource profitability and allocation. Ultimately, both the upper and lower-level decision variables reach a Nash equilibrium. The experimental results show that the bi-level non-cooperative game model based on the Unit Resource Return improves the overall resource utilization of the system and enhances the long-term operational motivation of the generating units. Full article
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30 pages, 7926 KiB  
Article
Analysis and Diagnosis of the Stator Turn-to-Turn Short-Circuit Faults in Wound-Rotor Synchronous Generators
by Haotian Mao, Khashayar Khorasani and Yingqing Guo
Energies 2025, 18(9), 2395; https://doi.org/10.3390/en18092395 - 7 May 2025
Viewed by 133
Abstract
In this paper, we introduce a health parameter and estimation algorithm to assess the severity of stator turn-to-turn/inter-turn short-circuit (TTSC) faults in wound-rotor synchronous generators (WRSG). Our methodology establishes criteria for evaluating the severity of stator TTSC faults in WRSG and provides a [...] Read more.
In this paper, we introduce a health parameter and estimation algorithm to assess the severity of stator turn-to-turn/inter-turn short-circuit (TTSC) faults in wound-rotor synchronous generators (WRSG). Our methodology establishes criteria for evaluating the severity of stator TTSC faults in WRSG and provides a specific solution for estimating both the severity of these faults and the resultant power loss. Our assessment methodology directly reflects the intrinsic impact of stator TTSC faults on the WRSG, offering enhanced efficiency, accuracy, and resilience to interference compared with traditional methods in estimating and gauging the TTSC severity. First, we demonstrate that it is impossible to determine the two fault parameters of the WRSG stator TTSC faults solely based on the voltage and current measurements. Subsequently, we introduce a novel health parameter for the WRSG stator TTSC faults and show that for a given generator and load, the dynamics of voltage and current during these faults as well as the resulting power loss are determined by this health parameter. We then detail the characteristics of the proposed health parameter and criteria for evaluating the severity of the WRSG stator TTSC faults. Furthermore, we present an estimation algorithm that is capable of accurately estimating the health parameter and power loss, demonstrating its minimal estimation error. Finally, we provide a comprehensive set of simulation results, including Monte Carlo results, to validate our proposed methodology and illustrate that our approach offers significant improvements in terms of the efficiency, accuracy, and robustness of the WRSG stator TTSC fault detection and isolation (FDI) over conventional methods. Full article
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17 pages, 3408 KiB  
Article
Robust Assessment Method for Hosting Capacity of Distribution Network in Mountainous Areas for Distributed Photovoltaics
by Youzhuo Zheng, Kun Zhou, Yekui Yang, Hanbin Diao, Long Hua, Renzhi Wang, Kang Liu and Qi Guo
Energies 2025, 18(9), 2394; https://doi.org/10.3390/en18092394 - 7 May 2025
Viewed by 127
Abstract
The penetration rate of distributed photovoltaic (PV) in mountainous distribution networks is increasing year by year, and the assessment of distributed PV hosting capacity (PVHC) in distribution networks in mountainous areas is also becoming more and more important. To this end, this paper [...] Read more.
The penetration rate of distributed photovoltaic (PV) in mountainous distribution networks is increasing year by year, and the assessment of distributed PV hosting capacity (PVHC) in distribution networks in mountainous areas is also becoming more and more important. To this end, this paper proposes a robust assessment method for distributed PVHC of flexible distribution networks in mountainous areas. The method utilizes soft open point (SOP) and energy storage to realize the flexible interconnection of distribution networks in mountainous areas, connecting the low-voltage nodes at the end of distribution networks in mountainous areas and improving the overall power quality of distribution networks. Secondly, the output curves of distributed PV output and load demand are analyzed and the distributed PV uncertainty model is drawn, so as to construct a two-layer robust assessment model of distributed PVHC for mountainous flexible distribution networks. Finally, the dual-layer robust assessment model, which cannot be solved directly, is transformed into a solvable mixed-integer linear programming model using the pairwise method, and the effectiveness of this paper’s method is verified by the simulation results of the IEEE 33-node distribution network system. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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27 pages, 2530 KiB  
Review
Recent Advances in Electrified Methane Pyrolysis Technologies for Turquoise Hydrogen Production
by Hossein Rohani, Galina Sudiiarova, Stephen Matthew Lyth and Arash Badakhsh
Energies 2025, 18(9), 2393; https://doi.org/10.3390/en18092393 - 7 May 2025
Viewed by 287
Abstract
The global campaign to reach net zero will necessitate the use of hydrogen as an efficient way to store renewable electricity at large scale. Methane pyrolysis is rapidly gaining traction as an enabling technology to produce low-cost hydrogen without directly emitting carbon dioxide. [...] Read more.
The global campaign to reach net zero will necessitate the use of hydrogen as an efficient way to store renewable electricity at large scale. Methane pyrolysis is rapidly gaining traction as an enabling technology to produce low-cost hydrogen without directly emitting carbon dioxide. It offers a scalable and sustainable alternative to steam reforming whilst being compatible with existing infrastructure. The process most commonly uses thermal energy to decompose methane (CH4) into hydrogen gas (H2) and solid carbon (C). The electrification of this reaction is of great significance, allowing it to be driven by excess renewable electricity rather than fossil fuels, and eliminating indirect emissions. This review discusses the most recent technological advances in electrified methane pyrolysis and the relative merits of the mainstream reactor technologies in this space (plasma, microwave, fluidised bed, and direct resistive heating). This study also examines the economic viability of the process, considering energy costs, and the market potential of both turquoise hydrogen and solid carbon products. Whilst these technologies offer emission-free hydrogen production, challenges such as carbon deposition, reactor stability, and high energy consumption must be addressed for large-scale adoption. Future research should focus on process optimisation, advanced reactor designs, and policy frameworks to support commercialisation. With continued technological innovation and sufficient investment, electrified methane pyrolysis has the potential to become the primary route for sustainable production of hydrogen at industrial scale. Full article
(This article belongs to the Section A5: Hydrogen Energy)
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19 pages, 3200 KiB  
Article
Effects of Ethanol–Diesel Blends on Cylinder Pressure, Ignition Delay, and NOx Emissions in a Diesel Engine
by Krzysztof Górski, Dimitrios Tziourtzioumis, Ruslans Smigins and Rafał Longwic
Energies 2025, 18(9), 2392; https://doi.org/10.3390/en18092392 - 7 May 2025
Viewed by 168
Abstract
This study examined how adding ethanol to diesel fuel affects combustion characteristics, cylinder pressure and NOx emissions in an AVL engine. The research focused on key engine parameters, including autoignition delay, in-cylinder pressure rise rates, the peaks of the mean in-cylinder temperature [...] Read more.
This study examined how adding ethanol to diesel fuel affects combustion characteristics, cylinder pressure and NOx emissions in an AVL engine. The research focused on key engine parameters, including autoignition delay, in-cylinder pressure rise rates, the peaks of the mean in-cylinder temperature and NOx emissions. Three fuel types were tested: pure diesel (DF) and blends with 10 and 20% ethanol by volume (DF10 and DF20). The results obtained indicate that increasing the ethanol content in diesel fuel significantly affects the combustion process of the fuel mixture, particularly in its early stage, reducing the benefits of the pilot fuel injection. Moreover, it was observed that the combustion of the DF20 mixture leads to a substantially higher pressure increase in the cylinder, exceeding the values recorded for pure diesel fuel by approximately 25%. Furthermore, the study revealed that ethanol addition increases the peaks of the mean in-cylinder temperature, with a recorded difference of up to 60 °C between pure diesel fuel and DF20. Since NOx formation is highly temperature-dependent, this temperature rise is likely to result in higher NOx concentration. Additionally, a slight effect of ethanol on increasing the ignition delay angle was observed. This remained minor, and did not exceed approximately 1 CA. These findings highlight the complex relationship between ethanol content in diesel fuel, combustion dynamics, and emissions. They emphasize the need for optimizing the injection process for ethanol–diesel blends to balance the benefits of ethanol addition with potential challenges related to combustion efficiency, engine load and NOx concentration. Full article
(This article belongs to the Special Issue Advances in Fuel Energy)
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24 pages, 8094 KiB  
Article
Optimal Residential Battery Storage Sizing Under ToU Tariffs and Dynamic Electricity Pricing
by Damir Jakus, Joško Novaković, Josip Vasilj and Danijel Jolevski
Energies 2025, 18(9), 2391; https://doi.org/10.3390/en18092391 - 7 May 2025
Viewed by 174
Abstract
The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However, solar power variability and new regulative changes concerning excess solar energy compensation schemes call [...] Read more.
The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However, solar power variability and new regulative changes concerning excess solar energy compensation schemes call for effective energy storage management and sizing to ensure a stable and profitable electricity supply. This paper focuses on optimizing residential battery storage systems under different electricity pricing schemes such as time-of-use tariffs, dynamic pricing, and different excess solar energy compensation schemes. The central question addressed is how different pricing mechanisms and compensation strategies for excess solar energy, as well as varying battery storage investment costs, determine the optimal sizing of battery storage systems. A comprehensive mixed-integer linear programming model is developed to analyze these factors, incorporating various financial and operational parameters. The model is applied to a residential case study in Croatia, examining the impact of monthly net metering/billing, 15 min net billing, and dynamic pricing on optimal battery storage sizing and economic viability. Full article
(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)
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17 pages, 3971 KiB  
Article
Condensation Heat Transfer Efficiency Analysis of Horizontal Double-Sided Enhanced Tubes
by Jianghui Zhang, Junjie Wu, He Zhou, Jiaxiang Yu, Bin Zhang, Wei Li and Yan He
Energies 2025, 18(9), 2390; https://doi.org/10.3390/en18092390 - 7 May 2025
Viewed by 134
Abstract
The enhanced tubes in this study, referred to as E1 and E2, represent significant improvements in the design and performance of smooth tubes. By increasing the surface area on their fin side and optimizing the condensation drainage design, the heat transfer capacity of [...] Read more.
The enhanced tubes in this study, referred to as E1 and E2, represent significant improvements in the design and performance of smooth tubes. By increasing the surface area on their fin side and optimizing the condensation drainage design, the heat transfer capacity of the finned tubes has been further enhanced. These modifications will provide superior thermal management performance for condenser tubes in practical applications, facilitating their widespread use across various engineering fields. In this experiment, R134a was used as the working fluid, with a test section length (L) of 248 mm for the experimental tubes E1 and E2. The experiments were conducted at a saturation temperature of 40 °C, where the refrigerant condensed outside the tube while deionized water circulated inside. The results indicated that, at a heat flux density below 94 kW/m2, the condensation heat transfer coefficient of the E1 tube was 2–5% higher than that of the E2 tube, achieving values that were 11.63–14.42 times and 10.94–14.67 times that of smooth tubes of identical dimensions and materials, respectively. At a heat flux density of 94 kW/m2, the heat transfer coefficient of E2 exceeded that of E1, with E1 exhibiting a more pronounced decline. Under constant water velocity, the heat transfer coefficient outside the tube initially decreased and then increased as the heat flux density rose. The corresponding effective heat transfer area of E1 increased, leading to better overall heat transfer performance compared to E2. Full article
(This article belongs to the Topic Advanced Heat and Mass Transfer Technologies)
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15 pages, 2504 KiB  
Communication
From Climate Risks to Resilient Energy Systems: Addressing the Implications of Climate Change on Indonesia’s Energy Policy
by Agus Setiawan, Dea Mardha Mentari, Dzikri Firmansyah Hakam and Risa Saraswani
Energies 2025, 18(9), 2389; https://doi.org/10.3390/en18092389 - 7 May 2025
Viewed by 172
Abstract
Climate change has presented significant challenges to Indonesia’s energy sector, increasing vulnerabilities in power generation, infrastructure resilience, and energy security. Rising sea levels, extreme weather events, and increasing temperatures disrupt energy systems, highlighting the urgent need to build resilient energy systems. To support [...] Read more.
Climate change has presented significant challenges to Indonesia’s energy sector, increasing vulnerabilities in power generation, infrastructure resilience, and energy security. Rising sea levels, extreme weather events, and increasing temperatures disrupt energy systems, highlighting the urgent need to build resilient energy systems. To support Indonesia’s energy transition, this study addresses a critical gap by providing an integrated analysis of climate resilience, renewable energy policies, and Indonesia’s socio-economic and environmental goals, emphasizing the importance of enabling policies and financial mechanisms. The recommendations mentioned in this study include increasing renewable energy capacity through solar and geothermal projects, modernizing infrastructure to enhance resilience, and adopting decentralized energy systems to reduce dependency on centralized networks. Strengthened governance and stakeholder collaboration are also essential for the successful implementation of energy policies. This study underscores the importance of having comprehensive energy policies to address climate change, promote sustainable development, and help Indonesia achieve its renewable energy targets and long-term goal of net-zero emissions. Full article
(This article belongs to the Special Issue Climate Change in Power and Energy Systems: Challenges, Innovations, and Solutions)
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18 pages, 18892 KiB  
Article
A Bidding Strategy for Power Suppliers Based on Multi-Agent Reinforcement Learning in Carbon–Electricity–Coal Coupling Market
by Zhiwei Liao, Chengjin Li, Xiang Zhang, Qiyun Hu and Bowen Wang
Energies 2025, 18(9), 2388; https://doi.org/10.3390/en18092388 - 7 May 2025
Viewed by 143
Abstract
The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need [...] Read more.
The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article
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22 pages, 4226 KiB  
Article
Analysis of the Possibility of Using CO2 Capture in a Coal-Fired Power Plant
by Łukasz Mika and Karol Sztekler
Energies 2025, 18(9), 2387; https://doi.org/10.3390/en18092387 - 7 May 2025
Viewed by 105
Abstract
Global trends in environmental protection place emphasis on the reduction of CO2 emissions, a key factor in the greenhouse effect. Commercial power generation, mainly based on coal, is the largest emitter of CO2, which justifies work on its reduction. Technologies [...] Read more.
Global trends in environmental protection place emphasis on the reduction of CO2 emissions, a key factor in the greenhouse effect. Commercial power generation, mainly based on coal, is the largest emitter of CO2, which justifies work on its reduction. Technologies involving CO2 capture from flue gases based on adsorption methods are not yet widely used, and therefore, there is a lack of complete data on their impact on power units. With the use of computer simulations, relevant information can be obtained, eliminating the need for costly tests on actual systems. A model of a reference power unit and CO2 separation system based on adsorption methods was developed in the IPSEpro environment. Simulations were carried out, analysing the impact of parameters such as temperature and pressure of the flue gas and of bled steam on the efficiency of the separation system. Optimal adsorption and desorption conditions were determined, and the separation model was then integrated into a power unit. The analysis of CO2 capture in power units indicates that while complete separation of CO2 from the flue gas of an 830 MWe unit is technically feasible, it results in substantial efficiency losses and high energy consumption. Capturing and liquefying CO2 leads to a power output reduction of approximately 358 MWe and a 15.4% decrease in efficiency. Simulation analyses allowed the impact of the CO2 capture system on the operation of the unit to be assessed and the amount of non-emitted gas to be estimated, thus reducing the environmental harm of the power plant. Full article
(This article belongs to the Special Issue Carbon Capture Technologies for Sustainable Energy Production)
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2 pages, 154 KiB  
Correction
Correction: Ciapessoni et al. A Cost–Benefit Analysis Framework for Power System Resilience Enhancement Based on Optimization via Simulation Considering Climate Changes and Cascading Outages. Energies 2023, 16, 5160
by Emanuele Ciapessoni, Diego Cirio and Andrea Pitto
Energies 2025, 18(9), 2386; https://doi.org/10.3390/en18092386 - 7 May 2025
Viewed by 106
Abstract
In the original publication [...] Full article
27 pages, 31117 KiB  
Article
Digital Control Scheme for Class-D Power Amplifier Driving ICP Load Without Matching Network
by Fuchao Lu and Zhengquan Zhang
Energies 2025, 18(9), 2385; https://doi.org/10.3390/en18092385 - 7 May 2025
Viewed by 138
Abstract
Class-D power amplifiers driving variable loads, such as inductively coupled plasma (ICP), typically require an impedance matching network, which has a relatively slow matching speed, generally in the millisecond range. To address this issue, this paper proposes a solution that uses a fully [...] Read more.
Class-D power amplifiers driving variable loads, such as inductively coupled plasma (ICP), typically require an impedance matching network, which has a relatively slow matching speed, generally in the millisecond range. To address this issue, this paper proposes a solution that uses a fully digital control method for Class-D power amplifiers to directly drive ICP loads. This solution eliminates the need for an impedance matching network, reducing the overall output power regulation time to just tens of microseconds. Compared to traditional methods that use a VI probe to detect output power, the proposed method in this paper only requires measuring the resonant current in the loop to control the output power, thereby reducing costs and ensuring that the Class-D power amplifier achieves zero-voltage switching (ZVS) throughout the adjustment process. This paper provides a detailed introduction to the design method of the Class-D power amplifier and the overall digital control scheme and validates them via simulation and experimentation. The Class-D power amplifier prototype was designed using SiC MOSFETs, with a Xilinx ZYNQ-XC7Z100 FPGA as the control board. The output frequency varies around 4 MHz, successfully generating plasma. Full article
(This article belongs to the Section F3: Power Electronics)
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25 pages, 1167 KiB  
Article
Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids
by Alessandro Niccolai, Silvia Trimarchi, Lisa Francesca Barbazza, Alessandro Gandelli, Riccardo Zich, Francesco Grimaccia and Sonia Leva
Energies 2025, 18(9), 2384; https://doi.org/10.3390/en18092384 - 7 May 2025
Viewed by 164
Abstract
The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to [...] Read more.
The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to identify efficient scheduling plans and improve performance. In this paper, a new approach based on evolutionary algorithms (EAs) is designed, implemented, and tested on a real microgrid architecture to evaluate its effectiveness. The proposed approach effectively combines heuristic information with the optimization capabilities of EAs, achieving excellent results with reasonable computational effort. The proposed system is highly flexible, making it applicable to different network architectures and various objective functions. In this work, the optimization algorithm directly manages the microgrid Energy Management System, allowing for a large number of degrees of freedom that can be exploited to achieve highly competitive solutions. This method was compared with a standard scheduling approach, and an average improvement of 11.87% in fuel consumption was achieved. After analyzing the differences between the solutions obtained, the importance of the features introduced with this new approach was demonstrated. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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15 pages, 7706 KiB  
Article
A Computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids
by Ran Li, Wendong Feng, Tianhao Qie, Yulin Liu, Tyrone Fernando, Herbert HoChing Iu and Xinan Zhang
Energies 2025, 18(9), 2383; https://doi.org/10.3390/en18092383 - 7 May 2025
Viewed by 122
Abstract
This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method [...] Read more.
This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method is model-free and eliminates such dependencies. By integrating a recurrent equilibrium network (REN), the controller achieves an enhanced dynamic response and robust steady-state performance, while maintaining a low computational complexity. Moreover, its closed-loop stability can be rigorously verified based on contraction theory and incremental quadratic constraints. To facilitate practical implementation, a design guideline is provided. Experimental results confirm that the proposed method outperforms conventional PI and model predictive controllers in terms of response speed, harmonic suppression, and robustness under parameter variations. Additionally, the algorithm is lightweight enough for real-time execution on embedded platforms, such as a TI DSP. Full article
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17 pages, 6424 KiB  
Article
Implementing Large-Scale CCS in Complex Geologic Reservoirs: Insights from Three Appalachian Basin Case Studies
by Joel Sminchak, Priya Ravi-Ganesh, Randall Hunt, John Hershberger and Brigitte Petras
Energies 2025, 18(9), 2382; https://doi.org/10.3390/en18092382 - 7 May 2025
Viewed by 138
Abstract
This paper presents three design case studies for implementing large-scale geologic carbon storage in the Appalachian Basin region of the midwestern United States. While the Appalachian Basin has a challenging setting for carbon storage, the three case studies detailed in this article demonstrate [...] Read more.
This paper presents three design case studies for implementing large-scale geologic carbon storage in the Appalachian Basin region of the midwestern United States. While the Appalachian Basin has a challenging setting for carbon storage, the three case studies detailed in this article demonstrate that there are realistic options for implementing carbon storage in the basin. Carbonate rock formations, depleted hydrocarbon reservoirs, and moderate-porosity sandstones can be utilized as carbon-storage reservoirs in the Appalachian Basin. While these are not typical concepts for CO2 storage, the storage zones have advantages such as defined trapping mechanisms, multiple caprocks, and defined boundaries that are not always present in thick, permeable sandstones being targeted for many carbon-storage projects. The geologic setting, geotechnical parameters, and hydrologic setting for the three case studies are provided, along with the results of reservoir simulations of the CO2 injection-deployment strategies. The geological rock formations available for CO2 storage in the Appalachian Basin are more localized reservoirs with defined boundaries and finite storage capacities. Simulation results showed that accessing carbon-storage resources in these fields may require wellfields with 2–10 injection wells. However, these fields would have the capacity to inject 1–3 million metric tons of CO2 per year and up to 90 million metric tons of CO2 in total. The CO2 storage resources would fulfill decarbonization goals for many of the natural-gas power plants, cement plants, hydrogen plants, and refineries in the Appalachian Basin region. Full article
(This article belongs to the Special Issue Advances in the Development of Geoenergy: 2nd Edition)
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21 pages, 7819 KiB  
Article
Comprehensive Comparison of Lightning Properties of Insulating Liquids in Relation to Mineral Oil Under Positive Lightning Impulse
by Filip Stuchala and Pawel Rozga
Energies 2025, 18(9), 2381; https://doi.org/10.3390/en18092381 - 7 May 2025
Viewed by 117
Abstract
In this paper, results of comparative studies on the positive lightning impulse breakdown voltage (LIBV) and accelerating voltage (Va) of six insulating liquids of different chemical composition are presented. This paper discusses the behavior of uninhibited naphthenic mineral oil (UMO), inhibited [...] Read more.
In this paper, results of comparative studies on the positive lightning impulse breakdown voltage (LIBV) and accelerating voltage (Va) of six insulating liquids of different chemical composition are presented. This paper discusses the behavior of uninhibited naphthenic mineral oil (UMO), inhibited naphthenic mineral oil (IMO), natural ester (NE), synthetic ester (SE), and two modern dielectric fluids: bio-based hydrocarbon (BIO) and inhibited liquid produced using Gas-to-Liquids (GTL) technology. Measurements are taken in a point-to-sphere electrode system for two selected gap distances: 25 mm (which is suggested by the IEC 60897 standard) and 40 mm. After analyzing the obtained results, it is noted that positive LIBV does not differ significantly between the tested liquids. Noticeable differences are observed, however, for Va. The lowest values of this parameter characterize ester liquids, which is consistent with the common knowledge in this field. In addition, the obtained values of LIBV and Va are used to evaluate the maximum values of electric field intensity through the application of simulations for each specific case based on the finite element method. These simulations confirm that, for a given parameter, maximum electric field stress is on similar level, regardless of the gap distance. This proves that the breakdown and appearance of fast discharges are determined by specific field conditions. Full article
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19 pages, 2912 KiB  
Article
Explainable Clustered Federated Learning for Solar Energy Forecasting
by Syed Saqib Ali, Mazhar Ali, Dost Muhammad Saqib Bhatti and Bong Jun Choi
Energies 2025, 18(9), 2380; https://doi.org/10.3390/en18092380 - 7 May 2025
Viewed by 209
Abstract
Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the [...] Read more.
Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the difficulties posed by heterogeneous clients in traditional federated learning by categorizing related clients according to data characteristics, facilitating more tailored model updates, and improving overall learning efficiency. This paper introduces Explainable Clustered Federated Learning (XCFL), which adds explainability to clustered federated learning. Our method improves performance and explainability by selecting features, clustering clients, training local clients, and analyzing contributions using SHAP values. By incorporating feature-level contributions into cluster and global aggregation, XCFL ensures a more transparent and data-driven model update process. Weighted aggregation by feature contributions improves consumer diversity and decision transparency. Our results show that XCFL outperforms FedAvg and other clustering methods. Our feature-based explainability strategy improves model performance and explains how features affect clustering and model adjustments. XCFL’s improved accuracy and explainability make it a promising solution for heterogeneous and distributed learning environments. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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21 pages, 930 KiB  
Article
An Analysis of the Factors Influencing Energy Consumption Based on the STIRPAT Model: A Case Study of the Western Regions of China
by Yi Zhu, Chao Feng, Xieqihua Liu, Tao Zhang and Xi Wang
Energies 2025, 18(9), 2379; https://doi.org/10.3390/en18092379 - 7 May 2025
Viewed by 156
Abstract
Understanding the factors influencing energy consumption is crucial for sustainable development. This study quantitatively analyzes the factors influencing energy consumption in China’s western regions from 2000 to 2022, employing the STIRPAT model, cointegration analysis, and ridge regression. Focusing on population size, economic development, [...] Read more.
Understanding the factors influencing energy consumption is crucial for sustainable development. This study quantitatively analyzes the factors influencing energy consumption in China’s western regions from 2000 to 2022, employing the STIRPAT model, cointegration analysis, and ridge regression. Focusing on population size, economic development, industrial structure, urbanization, and technological progress, the results reveal significant heterogeneity in their impacts. Urbanization exhibits the strongest positive effect, with a 1% increase leading to a 0.483% rise in energy consumption, followed by economic development and industrial structure. Population growth has a modest positive influence, while technological progress demonstrates a mitigating effect, reducing energy demand. The findings underscore the critical role of urbanization and industrial restructuring in shaping energy consumption patterns. Policy recommendations emphasize optimizing urban layouts, accelerating industrial upgrades, and promoting technological innovation to achieve sustainable energy development in the region. These insights provide a foundation for targeted policies to balance economic growth with energy efficiency in Western China. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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41 pages, 2025 KiB  
Systematic Review
The Energy-Economy Nexus of Advanced Air Pollution Control Technologies: Pathways to Sustainable Development
by Sadiq H. Melhim and Rima J. Isaifan
Energies 2025, 18(9), 2378; https://doi.org/10.3390/en18092378 - 6 May 2025
Viewed by 357
Abstract
Air pollution imposes a substantial economic burden globally, with estimated annual losses exceeding $8.1 trillion due to healthcare costs, lost productivity, infrastructure degradation, and agricultural damage. This review assesses the economic effectiveness of advanced air pollution control technologies within the broader context of [...] Read more.
Air pollution imposes a substantial economic burden globally, with estimated annual losses exceeding $8.1 trillion due to healthcare costs, lost productivity, infrastructure degradation, and agricultural damage. This review assesses the economic effectiveness of advanced air pollution control technologies within the broader context of sustainable energy transitions. Through comparative life-cycle cost-benefit analyses, we evaluate the financial viability, energy efficiency, and policy relevance of innovations such as carbon capture and storage (CCS), AI-driven emissions monitoring, and nanotechnology-enhanced filtration. Among the technologies assessed, CCS presents the most significant capital expenditure (up to $500 million per facility) but offers long-term returns through carbon credits and enhanced oil recovery, yielding up to $30–40 in economic benefits for every $1 invested. AI-based monitoring systems demonstrate strong economic efficiency by reducing energy consumption in industrial operations by up to 15% and improving regulatory compliance at a larger scale. Nanotechnology-enabled filters provide high pollutant capture efficiency and reduce operational resistance, yet face scalability and end-of-life challenges. Additionally, emerging technologies such as bioengineered filters offer promise for low-resource settings but require further economic validation. The integration of these technologies with renewable energy systems, such as hydrogen-powered pollution control units and solar-driven filtration, further amplifies their environmental and economic benefits. By aligning air pollution mitigation with climate and energy goals, this review highlights a pathway for policymakers and industries to achieve both economic resilience and environmental sustainability. The findings underscore that, while upfront costs may be high, strategic investments in advanced pollution control deliver substantial long-term returns across sectors. Full article
(This article belongs to the Section B: Energy and Environment)
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21 pages, 7231 KiB  
Article
Comparing Fast Fourier Transform and Prony Method for Analysing Frequency Oscillation in Real Power System Interconnection
by Didik Fauzi Dakhlan, Joko Muslim, Indra Kurniawan, Kevin Marojahan Banjar-Nahor, Bambang Anggoro Soedjarno and Nanang Hariyanto
Energies 2025, 18(9), 2377; https://doi.org/10.3390/en18092377 - 6 May 2025
Viewed by 240
Abstract
Analysing power system oscillations is essential for maintaining electrical grid stability and reliability. To assess power system oscillations and demonstrate the actual application in a real grid system, this research compares two popular signal processing methods: Prony’s approach and the Fast Fourier Transform [...] Read more.
Analysing power system oscillations is essential for maintaining electrical grid stability and reliability. To assess power system oscillations and demonstrate the actual application in a real grid system, this research compares two popular signal processing methods: Prony’s approach and the Fast Fourier Transform from Phasor Measurement Unit data in the Java Bali (Indonesia) power system interconnection. FFT gives information about the prominent frequency components by representing system oscillations in the frequency domain. Nevertheless, windowing effects and resolution limitations limit it. By fitting exponential functions to time-domain signals, Prony’s approach, on the other hand, excels at precisely estimating the frequency and damping characteristics of oscillatory modes. The accuracy, computational effectiveness, and applicability for the real-time monitoring of both approaches are assessed in this study. Simulation results on both simulated and actual power system data illustrate the benefits and drawbacks of each strategy. The results show that although FFT is helpful for rapid spectral analysis, Prony’s approach offers more thorough mode identification, which makes it especially advantageous for damping evaluations. This study ends with suggestions for choosing the best method for power system stability analysis based on application requirements. Full article
(This article belongs to the Topic Modern Power Systems and Units)
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