Energies

18 pages, 2908 KB

Open AccessArticle

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

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)

14 pages, 2310 KB

Open AccessArticle

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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17 pages, 2498 KB

Open AccessArticle

Enhancing the Adsorption Performance of HKUST-1 by Adding NH₄F During Room-Temperature Synthesis for Desulfurization of Fuel Oil

by Jiawei Fu, Xinchun Liu, Yuqing Kong, Ruyu Zhao, Yinyong Sun and Ahmed S. Abou-Elyazed

Energies 2025, 18(20), 5344; https://doi.org/10.3390/en18205344 - 10 Oct 2025

Abstract

Adsorption desulfurization of fuel oil is regarded as one of the most promising technologies for obtaining clean fuel because it can remove refractory sulfur compounds at ambient temperature and pressure. Studies indicate that HKUST-1, as an important type of metal–organic framework (MOF), is [...] Read more.

Adsorption desulfurization of fuel oil is regarded as one of the most promising technologies for obtaining clean fuel because it can remove refractory sulfur compounds at ambient temperature and pressure. Studies indicate that HKUST-1, as an important type of metal–organic framework (MOF), is a potential candidate for adsorption desulfurization of fuel oil. In this work, we report that defective HKUST-1 can be rapidly synthesized at room temperature with the aid of NH₄F and exhibit superior adsorption desulfurization performance compared to conventional HKUST-1 by the solvothermal method. Moreover, the influence of adsorption parameters on the desulfurization performance of HKUST-1 prepared with the aid of NH₄F was investigated. We used 50 mg of HKUST-1-5 synthesized with 5 wt% added NH₄F to adsorb 5 g of model oil with a sulfur concentration of 1000 ppm at 25 °C for 1 h, and the adsorption capacity of the adsorbent reached 23.8 mgS/g, 46.8 mgS/g and 36.8 mgS/g for benzothiophene (BT), dibenzothiophene (DBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT), respectively, which are higher values than those of conventional HKUST-1. Such performance can be mainly attributed to its relatively small particle size and the presence of more unsaturated Cu sites. The results of regeneration experiments show that HKUST-1-5 still maintains excellent adsorption performance after four cycles. These findings highlight the great potential of this material as an efficient adsorbent for adsorption desulfurization of fuel oil. Full article

(This article belongs to the Special Issue Challenges and Opportunities in the Global Clean Energy Transition)

20 pages, 3175 KB

Open AccessArticle

Renewable Energy Storage in a Poly-Generative System Fuel Cell/Electrolyzer, Supporting Green Mobility in a Residential Building

by Giuseppe De Lorenzo, Nicola Briguglio and Antonio S. Vita

Energies 2025, 18(20), 5343; https://doi.org/10.3390/en18205343 - 10 Oct 2025

Abstract

The European Commission, through the REPowerEU plan and the “Fit for 55” package, aims to reduce fossil fuel dependence and greenhouse gas emissions by promoting electric and fuel cell hybrid electric vehicles (EV-FCHEVs). The transition to this mobility model requires energy systems that [...] Read more.

The European Commission, through the REPowerEU plan and the “Fit for 55” package, aims to reduce fossil fuel dependence and greenhouse gas emissions by promoting electric and fuel cell hybrid electric vehicles (EV-FCHEVs). The transition to this mobility model requires energy systems that are able to provide both electricity and hydrogen while reducing the reliance of residential buildings on the national grid. This study analyses a poly-generative (PG) system composed of a Solid Oxide Fuel Cell (SOFC) fed by biomethane, a Photovoltaic (PV) system, and a Proton Exchange Membrane Electrolyser (PEME), with electric vehicles used as dynamic storage units. The assessment is based on simulation tools developed for the main components and applied to four representative seasonal days in Rende (Italy), considering different daily travel ranges of a 30-vehicle fleet. Results show that the PG system provides about 27 kW of electricity, 14.6 kW of heat, and 3.11 kg of hydrogen in winter, spring, and autumn, and about 26 kW, 14 kW, and 3.11 kg in summer; it fully covers the building’s electrical demand in summer and hot water demand in all seasons. The integration of EV batteries reduces grid dependence, improves renewable self-consumption, and allows for the continuous and efficient operation of both the SOFC and PEME, demonstrating the potential of the proposed system to support the green transition. Full article

(This article belongs to the Special Issue Energy Efficiency of the Buildings: 4th Edition)

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23 pages, 14258 KB

Open AccessArticle

Reservoir Characteristics and Shale Oil Enrichment of Shale Laminae in the Chang 7 Member, Ordos Basin

by Mengying Li, Wenzheng Li, Mingfeng Gu, Songtao Wu, Pengwan Wang, Yuce Wang, Quanbin Cao, Zhehang Xu and Yi Hao

Energies 2025, 18(20), 5342; https://doi.org/10.3390/en18205342 (registering DOI) - 10 Oct 2025

Abstract

The laminae of lacustrine shale in China have been systematically identified and characterized by a combination of core/slice observations, mineral compositions, geochemical analysis, pore structure characterization, and oil-bearing evaluation. The shale of the Chang 7 Member, Yanchang Formation, Ordos Basin was examined as [...] Read more.

The laminae of lacustrine shale in China have been systematically identified and characterized by a combination of core/slice observations, mineral compositions, geochemical analysis, pore structure characterization, and oil-bearing evaluation. The shale of the Chang 7 Member, Yanchang Formation, Ordos Basin was examined as an example in the study. Four types of laminae are developed in the Chang 7 Member, including felsic laminae (FQL), clay laminae (CLL), organic matter laminae (OML), and tuff laminae (TUL). The shale reservoirs exhibit significant heterogeneity. Of these, FQL and TUL have superior reservoir characteristics. The pore diameter of TUL is primarily composed of micrometer-sized secondary pores that are generated during the diagenesis process, while mesopore and macropore development are dominant in FQL. The main source laminae in the Chang 7 Member of the Ordos Basin are the OML and CLL, while the main reservoir laminae are the FQL and TUL. Some of the hydrocarbons produced by hydrocarbon generation are stored in the pore space inside the laminae, while the majority migrate to the inorganic pores of the adjacent FQL and TUL. It confirms that OML and CLL afford abundant shale oil, the combination of organic pores and inorganic pores in FQL and TUL serve as reservoir space, and the “clay generation-siliceous reservoir” shale oil enrichment model is established in the Chang 7 Member of Ordos Basin. Full article

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29 pages, 1219 KB

Open AccessReview

Economic Impact Assessment for Positive Energy Districts: A Literature Review

by Marco Volpatti, Andreas Tuerk, Camilla Neumann, Ilaria Marotta, Maria Beatrice Andreucci, Matthias Haase, Francesco Guarino, Rosaria Volpe and Adriano Bisello

Energies 2025, 18(20), 5341; https://doi.org/10.3390/en18205341 - 10 Oct 2025

Abstract

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and [...] Read more.

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges in Zero-Energy Districts)

27 pages, 3596 KB

Open AccessArticle

Feature Selection and Model Fusion for Lithium-Ion Battery Pack SOC Prediction

by Wenqiang Yang, Chong Li, Qinglin Miao, Yonggang Chen and Fuquan Nie

Energies 2025, 18(20), 5340; https://doi.org/10.3390/en18205340 - 10 Oct 2025

Abstract

Accurate prediction of the state of charge (SOC) of a battery pack is essential to improve the operational efficiency and safety of energy storage systems. In this paper, we propose a novel lithium-ion battery (Lib) pack SOC prediction framework that combines redundant control [...] Read more.

Accurate prediction of the state of charge (SOC) of a battery pack is essential to improve the operational efficiency and safety of energy storage systems. In this paper, we propose a novel lithium-ion battery (Lib) pack SOC prediction framework that combines redundant control correlation downscaling with Adaptive Error Variation Weighting Mechanism (AVM) fusion mechanisms. By integrating redundancy feature selection based on correlation analysis with global sensitivity analysis, the dimensionality of the input features was reduced by 81.25%. The AVM merges BiGRU’s ability to model short-term dynamics with Informer’s ability to capture long-term dependencies. This approach allows for complementary information exchange between multiple models. Experimental results indicate that on both monthly and quarterly slice datasets, the RMSE and MAE of the fusion model are significantly lower than those of the single model. In particular, the proposed model shows higher robustness and generalization ability in seasonal generalization tests. Its performance is significantly better than the traditional linear and classical filtering methods. The method provides reliable technical support for accurate estimation of SOC in battery management systems under complex environmental conditions. Full article

30 pages, 2059 KB

Open AccessArticle

China’s Smart Energy Policy Evaluation Based on Policy Modelling Consistency Index

by Rongjiang Cai, Tao Zhang, Xi Wang, Shufang Zhao, Hang Yang and Qixiang Geng

Energies 2025, 18(20), 5339; https://doi.org/10.3390/en18205339 - 10 Oct 2025

Abstract

Against the backdrop of China’s “dual carbon” goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. Traditional qualitative evaluations struggle with subjectivity; therefore we apply the quantitative PMC Index to systematically assess smart energy policies. This research systematically analyzes 16 [...] Read more.

Against the backdrop of China’s “dual carbon” goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. Traditional qualitative evaluations struggle with subjectivity; therefore we apply the quantitative PMC Index to systematically assess smart energy policies. This research systematically analyzes 16 representative Chinese smart energy policies using the PMC model, combined with content analysis. An integrated analytical framework was constructed to examine PMC applications across different energy policy fields. Results demonstrate that China’s smart energy policies achieved excellent performance, with an average PMC score of 7.48 out of 10. Furthermore, 68.75% of policies (11 out of 16) reached the ‘excellent’ level (PMC ≥ 8.0), with Policy “P6” achieving the highest score of 8.88 points. Top-performing policies exhibited strong strategic coordination, clear objectives, and comprehensive supporting measures. The findings reveal a well-structured policy cluster with clear objectives and strong coordination. This mature policy package provides a solid institutional foundation for China’s energy system transformation toward smart and green development, offering valuable insights for energy policy optimization and quantitative assessment methodology improvement. Full article

(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)

29 pages, 5471 KB

Open AccessArticle

Game Theory-Based Bi-Level Capacity Allocation Strategy for Multi-Agent Combined Power Generation Systems

by Zhiding Chen, Yang Huang, Yi Dong and Ziyue Ni

Energies 2025, 18(20), 5338; https://doi.org/10.3390/en18205338 - 10 Oct 2025

Abstract

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) [...] Read more.

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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21 pages, 17448 KB

Open AccessArticle

Deep Reinforcement Learning-Based Optimization of Mobile Charging Station and Battery Recharging Under Grid Constraints

by Atefeh Alirezazadeh and Vahid Disfani

Energies 2025, 18(20), 5337; https://doi.org/10.3390/en18205337 - 10 Oct 2025

Abstract

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a [...] Read more.

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a new and practical solution to meet this growing need. However, the limited energy capacity of MCSs combined with the increasing volume of charging requests underscores the necessity for intelligent and efficient management. This study introduces a comprehensive mathematical framework aimed at optimizing both the deployment of MCSs and the scheduling of their battery recharging using battery swapping technology, while considering grid constraints, using the Deep Q-Network (DQN) algorithm. The proposed model is applied to real-world data from Chattanooga to evaluate its performance under practical conditions. The key goals of the proposed approach are to maximize the profit from fulfilling private EV charging requests, optimize the utilization of MCS battery packages, manage MCS scheduling without causing stress on the power grid, and manage recharging operations efficiently by incorporating photovoltaic (PV) sources at battery charging stations. Full article

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23 pages, 2333 KB

Open AccessArticle

Energy Simulation-Based Assessment of Traditional and Modern Wall Materials for Thermal Performance: A Case Study of a Traditional House in Jordan

by Eman N. Shaqour, Islam A. Alshafei, Ala Abu Taqa, Ahmed Senouci and Ahmed M. Seddik Hassan

Energies 2025, 18(20), 5336; https://doi.org/10.3390/en18205336 - 10 Oct 2025

Abstract

In this study, the energy performance of traditional, modern, and insulated wall assemblies in a heritage residential building in Al Salt city, Jordan, is evaluated using the simulation software DesignBuilder version 7.0.2.004. The case study compares the thermal behavior of traditional thick limestone [...] Read more.

In this study, the energy performance of traditional, modern, and insulated wall assemblies in a heritage residential building in Al Salt city, Jordan, is evaluated using the simulation software DesignBuilder version 7.0.2.004. The case study compares the thermal behavior of traditional thick limestone walls, modern reinforced concrete and block-based walls, and contemporary insulated systems under local climatic conditions. The results show that traditional stone walls exhibit limited energy efficiency and require insulation to meet contemporary standards. However, they perform better than modern concrete walls based on their thermal mass. While concrete walls with inadequate insulation exhibit the poorest performance and are associated with significantly higher energy demand and CO₂ emissions, insulated wall systems that combine stone with insulation layers demonstrate the best thermal performance and achieve substantial reductions in energy use and environmental impact. These findings emphasize the feasibility of upgrading heritage buildings through the integration of modern insulated wall assemblies, which can lead to considerable energy savings and a lowered carbon footprint while simultaneously keeping the architectural identity and cultural value. Full article

(This article belongs to the Special Issue New Technologies and Materials in the Energy Transformation)

20 pages, 4152 KB

Open AccessArticle

A Tie-Line Fault Ride-Through Strategy for PV Power Plants Based on Coordinated Energy Storage Control

by Bo Pan, Feng Xu, Xiangyi Bi, Dong Wan, Zhihua Huang, Jinsong Yang, An Wen and Penghui Shang

Energies 2025, 18(20), 5335; https://doi.org/10.3390/en18205335 - 10 Oct 2025

Abstract

Unplanned islanding and off-grid issues of photovoltaic (PV) power stations caused by tie-line faults have seriously undermined the power supply reliability and operational stability of PV plants. Furthermore, it takes a relatively long time to restore normal operation after an off-grid event, leading [...] Read more.

Unplanned islanding and off-grid issues of photovoltaic (PV) power stations caused by tie-line faults have seriously undermined the power supply reliability and operational stability of PV plants. Furthermore, it takes a relatively long time to restore normal operation after an off-grid event, leading to substantial power losses. To address this problem, this paper proposes a tie-line fault ride-through control strategy based on the coordinated control of on-site energy storage units. After a fault on the tie-line occurs, the control mode of PV inverters is switched to achieve source–load balance, and the control mode of energy storage inverters is switched to VF control mode, which supports the stability of voltage and frequency in the islanded system. Subsequently, the strategy coordinates with the tie-line recloser device to perform synchronous checking and grid reconnection. Simulation results show that, for transient tie-line faults, the proposed method can achieve stable control of the islanded system and grid reconnection within 2 s after a fault on the tie-line occurs. It successfully realizes fault ride-through within the operation time limit of anti-islanding protection, effectively preventing the PV plant from disconnecting from the grid. Finally, a connection scheme for the control strategy of a typical PV plant is presented, providing technical reference for on-site engineering. Full article

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

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18 pages, 1441 KB

Open AccessArticle

Comparison of a Solar Driven Absorption Chiller and Photovoltaic Compression Chiller Under Different Demand Profiles: Technological, Environmental and Economic Performance

by Juan José Roncal-Casano, Javier Rodríguez-Martín, Paolo Taddeo, Javier Muñoz-Antón and Alberto Abánades-Velasco

Energies 2025, 18(20), 5334; https://doi.org/10.3390/en18205334 - 10 Oct 2025

Abstract

HVAC systems are becoming increasingly important around the world due to the increasing need for climatization in recent years. While district heating systems have been used for a long time, district cooling systems tend to be something that is only reserved for large [...] Read more.

HVAC systems are becoming increasingly important around the world due to the increasing need for climatization in recent years. While district heating systems have been used for a long time, district cooling systems tend to be something that is only reserved for large buildings, making decentralized cooling flourish, shaping the idea of considering it as the first choice when it comes to cooling devices, disregarding the efficiency of larger systems. This article compares two technologies for district energy solutions. One option features single-stage absorption chillers using solar thermal technologies (Fresnel collectors) for heat, while the other uses high-efficiency compression chillers with photovoltaic technologies. Parametric studies were used to determine system sizes and considerations were taken to perform such as comparison. This paper concludes that compression chillers are the better option for cooling systems with variable demand while absorption chillers are a good choice for systems with constant demand, like data centers, especially when there is a high-temperature heat source available. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges in Zero-Energy Districts)

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22 pages, 724 KB

Open AccessArticle

State of Health Estimation for Batteries Based on a Dynamic Graph Pruning Neural Network with a Self-Attention Mechanism

by Xuanyuan Gu, Mu Liu and Jilun Tian

Energies 2025, 18(20), 5333; https://doi.org/10.3390/en18205333 - 10 Oct 2025

Abstract

The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy [...] Read more.

The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy and robustness. To address these limitations, this paper proposes a novel dynamic graph pruning neural network with self-attention mechanism (DynaGPNN-SAM) for SOH estimation. The method transforms sequential battery features into graph-structured representations, enabling the explicit modeling of spatial dependencies among operational variables. A self-attention-guided pruning strategy is introduced to dynamically preserve informative nodes while filtering redundant ones, thereby enhancing interpretability and computational efficiency. The framework is validated on the NASA lithium-ion battery dataset, with extensive experiments and ablation studies demonstrating superior performance compared to conventional approaches. Results show that DynaGPNN-SAM achieves lower root mean square error (RMSE) and mean absolute error (MAE) values across multiple batteries, particularly excelling during rapid degradation phases. Overall, the proposed approach provides an accurate, robust, and scalable solution for real-world battery management systems. Full article

(This article belongs to the Special Issue Artificial Intelligence for Battery Health Monitoring and Lifetime Prediction)

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26 pages, 1316 KB

Open AccessArticle

Short-TermPower Demand Forecasting for Diverse Consumer Types Using Customized Machine Learning Approaches

by Asier Diaz-Iglesias, Xabier Belaunzaran and Ane M. Florez-Tapia

Energies 2025, 18(20), 5332; https://doi.org/10.3390/en18205332 - 10 Oct 2025

Abstract

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption [...] Read more.

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability. Full article

(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)

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23 pages, 3468 KB

Open AccessArticle

Research on Wellhead Uplift Prediction for Underground Gas Storage Wells

by Zhaoxi Shen, Jianjun Wang, Gang Zhao, Fatian Guan, Junfeng Cao and Shanpo Jia

Energies 2025, 18(20), 5331; https://doi.org/10.3390/en18205331 - 10 Oct 2025

Abstract

The issue of wellhead uplift in underground gas storage wells not only affects production efficiency but also poses a significant risk of wellhead seal failure, potentially leading to natural gas leakage accidents. This study proposes a systematic analytical framework for predicting wellhead uplift [...] Read more.

The issue of wellhead uplift in underground gas storage wells not only affects production efficiency but also poses a significant risk of wellhead seal failure, potentially leading to natural gas leakage accidents. This study proposes a systematic analytical framework for predicting wellhead uplift in gas storage wells. Initially, based on heat transfer theory and considering the coupled effects of temperature and pressure, a wellbore temperature prediction model was established. This model was tailored to the injection and production operations of gas storage wells, incorporating their specific operational characteristics. Subsequently, a predictive model for wellhead uplift distance was developed, accounting for various cementing conditions under fully cemented well scenarios. The proposed methodology was validated using data from injection and production wells in a gas storage reservoir. Furthermore, an analysis of the impact of injection and production parameters, along with predictions of wellhead uplift heights under different operating conditions, was conducted. The results indicate that the prediction errors relative to measured data are −0.8% and 4.3%, respectively. Gas production volume was identified as the most critical dynamic factor influencing wellhead uplift height. Predictions of wellhead uplift heights under both normal and extreme operating conditions can provide guidance for optimizing operational parameters. The proposed method holds theoretical and practical significance for the integrity management of gas storage wells. Full article

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43 pages, 1429 KB

Open AccessArticle

Is Digital Development the Answer to Energy Poverty? Evidence from China

by Yaofeng Yang, Xiuqing Li, Luping Li, Lan Fang, Yajuan Chen and Nde Ivo Zama

Energies 2025, 18(20), 5330; https://doi.org/10.3390/en18205330 - 10 Oct 2025

Abstract

Energy poverty is one of the major challenges to global sustainable development, while digital development, as a significant trend of the current era, is considered a key pathway to transcend traditional energy governance frameworks. Anchored in provincial panel data spanning 30 regions across [...] Read more.

Energy poverty is one of the major challenges to global sustainable development, while digital development, as a significant trend of the current era, is considered a key pathway to transcend traditional energy governance frameworks. Anchored in provincial panel data spanning 30 regions across China from 2003 to 2023, this study systematically examines the impact and heterogeneity of digital development on energy poverty and further explores the underlying mechanisms and nonlinear characteristics. The findings show that digital development can significantly alleviate energy poverty, and this conclusion remains valid after addressing endogeneity issues and conducting a series of robustness tests. However, the poverty reduction effect of digital development exhibits significant regional heterogeneity: the mitigation effect in central and western regions is significantly stronger than that in eastern regions, the effect in northern regions is higher than that in southern regions, and the effect in energy-disadvantaged regions is better than that in advantageous regions. Additionally, digital development alleviates energy poverty through mediating pathways such as promoting non-agricultural employment, improving human capital levels, and driving technological innovation. Notably, digital development demonstrates threshold effects and quantile heterogeneity in relation to energy poverty, characterized by diminishing marginal returns as digital development progresses; regions with higher levels of energy poverty experience more significant poverty reduction effects from digital development. This research provides a theoretical basis for energy poverty governance under the global energy crisis and offers empirical references for other countries to achieve energy sustainability goals (SDG7) through context-specific digital transformations. Full article

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25 pages, 565 KB

Open AccessArticle

Optimizing Hybrid Renewable Power Plants: A Comparative Analysis of Wind–Solar Configurations for Northeast Brazil

by Isabella Branco Renolphi, Walquiria N. Silva, Luís Felipe Normandia Lourenço, Bruno Z. D. Malta, Thiago S. Andrade and Giovani G. T. Vieira

Energies 2025, 18(20), 5329; https://doi.org/10.3390/en18205329 - 10 Oct 2025

Abstract

The transition to sustainable electricity grids, particularly in countries with high renewable potential, such as Brazil, requires integrated assessments of hybrid and single-source configurations. This study analyzed the technical and economic feasibility of hybrid plants and isolated wind and solar systems in the [...] Read more.

The transition to sustainable electricity grids, particularly in countries with high renewable potential, such as Brazil, requires integrated assessments of hybrid and single-source configurations. This study analyzed the technical and economic feasibility of hybrid plants and isolated wind and solar systems in the Brazilian Northeast, focusing on Macaíba (RN) and Casa Nova (BA), regions characterized by high resource availability. The work addresses a gap in the literature by integrating detailed technical modeling and financial analysis of hybrid configurations, considering both local and operational constraints. Hourly simulations were performed using the HyDesign software (v1.1.0), with optimization based on the ratio between net present value (NPV) and invested capital (CAPEX), covering seven different scenarios by location, including hybrid combinations and systems with solar trackers. The results indicated that systems with solar tracking achieved superior economic performance. In Macaíba, the optimal configuration was the hybrid scenario with trackers, which increased the NPV/CAPEX by 27.69% compared to the relevant baseline. In Casa Nova, the best solution was the pure solar plant with trackers, which increased the NPV/CAPEX by 50.0% compared to fixed solar. Hybridization showed moderate gains in scenarios without tracking. It is concluded that while solar trackers are highly beneficial, the optimal plant configuration (pure solar or hybrid) is site-specific and depends on the local renewable resource profile. Notably, battery storage was not economically justified under the evaluated cost assumptions. The study contributes to the planning of renewable projects in contexts of high source complementarity. Full article

(This article belongs to the Special Issue Modeling, Scheduling, Optimising, and Control of Power Grids and Integrated Energy Systems)

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

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