Energies

21 pages, 5544 KB

Open AccessArticle

Revealing Guangdong’s Bridging Role in Embodied Energy Flows Through International and Domestic Trade

by Qiqi Liu, Yu Yang, Yi Liu and Xiaoying Qian

Energies 2025, 18(21), 5607; https://doi.org/10.3390/en18215607 (registering DOI) - 24 Oct 2025

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a [...] Read more.

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

(This article belongs to the Special Issue Energy Security, Transition, and Sustainable Development)

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22 pages, 319 KB

Open AccessArticle

Integrated Spatiotemporal Life Cycle Assessment Framework for Hydroelectric Power Generation in Brazil

by Vanessa Cardoso de Albuquerque, Rodrigo Flora Calili, Maria Fatima Ludovico de Almeida, Rodolpho Albuquerque, Tarcisio Castro and Rafael Kelman

Energies 2025, 18(21), 5606; https://doi.org/10.3390/en18215606 (registering DOI) - 24 Oct 2025

Abstract

This study proposes and empirically validates a spatiotemporal life cycle assessment (LCA) framework for hydroelectric power generation applied to the Sinop Hydroelectric Power Plant in Brazil. Unlike conventional LCA, which assumes spatial and temporal homogeneity, the framework incorporates annual temporal discretisation and geographically [...] Read more.

This study proposes and empirically validates a spatiotemporal life cycle assessment (LCA) framework for hydroelectric power generation applied to the Sinop Hydroelectric Power Plant in Brazil. Unlike conventional LCA, which assumes spatial and temporal homogeneity, the framework incorporates annual temporal discretisation and geographically differentiated impacts across all phases of assessment. The methodology combines the Enhanced Structural Path Analysis (ESPA) method with temporal modeling and region-specific inventory data. The results indicate that environmental impacts peak in the fourth year of the ‘Construction and Assembly’ stage, primarily due to the intensive production of concrete and steel. A spatial analysis shows that these impacts extend beyond Brazil, with notable contributions from international supply chains. By identifying temporal and geographical hotspots, the framework offers a refined understanding of impact dynamics and drivers. Uncertainty analysis further demonstrates that temporal discretisation significantly affects impact attribution, with the ‘Construction and Assembly’ stage results varying by up to ±15%, depending on scheduling assumptions. Overall, the study advances the LCA methodology while offering robust empirical evidence to guide sustainable decision-making in Brazil’s power sector and to inform global debates on low-carbon energy transitions. Full article

(This article belongs to the Section A: Sustainable Energy)

27 pages, 2085 KB

Open AccessArticle

A Digital Twin for Real-Time and Predictive Optimization of Electric Vehicle Charging in Microgrids Integrating Renewable Energy Sources

by Tancredi Testasecca, Francesco Bellesini, Diego Arnone and Marco Beccali

Energies 2025, 18(21), 5605; https://doi.org/10.3390/en18215605 (registering DOI) - 24 Oct 2025

Abstract

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, [...] Read more.

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, despite extensive theoretical studies, few practical implementations have been carried out. In response, this paper presents a digital twin of a microgrid designed specifically for optimizing the charging schedules of an electric vehicle fleet, with the goal of maximizing photovoltaic self-consumption. Machine learning algorithms are utilized to forecast vehicle energy consumption, and various heuristic optimization methods are applied to determine optimal charging schedules. The system incorporates an interactive dashboard, enabling users to input specific preferences or delegate charging decisions to a real-time optimizer. Additionally, a user-centric decision support system was developed to provide recommendations on optimal vehicle connection timings and heat pump setpoints. Certain algorithms failed to converge on a feasible optimal solution, even after 340 s and over 500 generations, particularly within high-production scenarios. Conversely, using the GWO-WOA algorithm, optimal charging schedules are computed in less than 25 s, balancing photovoltaic power exports under varying weather conditions. Furthermore, K-Means was identified as the most effective clustering technique, achieving a Silhouette Score of up to 0.57 with four clusters. This configuration resulted in four distinct velocity ranges, within which energy consumption varied by up to 5.8 kWh/100 km, depending on the vehicle's velocity. Finally, the facility managers positively assessed the usability of the DT dashboard and the effectiveness of the decision support system. Full article

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

17 pages, 1547 KB

Open AccessArticle

Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems

by Md Musabbir Hossain and Wei Sun

Energies 2025, 18(21), 5604; https://doi.org/10.3390/en18215604 (registering DOI) - 24 Oct 2025

Abstract

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple [...] Read more.

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids—2nd Edition)

21 pages, 1672 KB

Open AccessArticle

Experimental Study on the Heat Dissipation of Photovoltaic Panels by Spiral Coil Cold Plates

by Ruofei Tian, Yan Liu and Shuailing Ma

Energies 2025, 18(21), 5603; https://doi.org/10.3390/en18215603 (registering DOI) - 24 Oct 2025

Abstract

Photovoltaic/Thermal (PV/T) systems are a technology designed to simultaneously convert solar energy into both electrical and thermal energy. The overall conversion efficiency of these systems can be significantly enhanced by effectively cooling the photovoltaic (PV) module. To this end, this paper presents a [...] Read more.

Photovoltaic/Thermal (PV/T) systems are a technology designed to simultaneously convert solar energy into both electrical and thermal energy. The overall conversion efficiency of these systems can be significantly enhanced by effectively cooling the photovoltaic (PV) module. To this end, this paper presents a comparative experimental study of a PV panel under three distinct configurations: operating with a no cold plate, with an ordinary cold plate, and with a spiral coil cold plate. The system’s photo-thermoelectric efficiency was evaluated by measuring key parameters, including the PV panel’s surface temperature, electrical power output, and the water tank temperature. The results indicate that the spiral coil configuration demonstrated a marked superiority in temperature regulation over the baseline case, achieving a maximum temperature reduction of 13.8 °C and an average reduction of 10.74 °C. Furthermore, a stable temperature drop exceeding 10 °C was maintained for 74.07% of the experimental duration. When compared to the ordinary cold plate, the spiral coil configuration continued to exhibit superior performance, delivering maximum and average temperature drops of 3.6 °C and 2.16 °C, respectively, while sustaining a cooling advantage of over 2 °C for 66.67% of the test period. These findings conclusively demonstrate that the spiral coil cold plate is the most effective configuration for enhancing the system’s overall performance. Full article

27 pages, 5817 KB

Open AccessArticle

Design Optimisation of Legacy Francis Turbine Using Inverse Design and CFD: A Case Study of Bérchules Hydropower Plant

by Israel Enema Ohiemi and Aonghus McNabola

Energies 2025, 18(21), 5602; https://doi.org/10.3390/en18215602 (registering DOI) - 24 Oct 2025

Abstract

The lack of detailed design information in legacy hydropower plants creates challenges for modernising their ageing turbine components. This research advances a digitalisation approach which combines inverse design methodology (IDM) with multi-objective genetic algorithms (MOGA) and computational fluid dynamics (CFD) to digitally reconstruct [...] Read more.

The lack of detailed design information in legacy hydropower plants creates challenges for modernising their ageing turbine components. This research advances a digitalisation approach which combines inverse design methodology (IDM) with multi-objective genetic algorithms (MOGA) and computational fluid dynamics (CFD) to digitally reconstruct and optimise the Bérchules Francis turbine runner and guide vane geometries using limited available legacy data, avoiding invasive techniques. A two-stage optimisation process was conducted. The first stage of runner blade optimisation achieved a 22.7% reduction in profile loss and a 16.8% decrease in secondary flow factor while raising minimum pressure from −877,325.5 Pa to −132,703.4 Pa. Guide vane optimisation during Stage 2 produced additional performance gains through a 9.3% reduction in profile loss and a 20% decrease in secondary flow factor and a minimum pressure increase to +247,452.1 Pa which represented an 183% improvement. The CFD validation results showed that the final turbine efficiency reached 93.7% while producing more power than the plant’s rated 942 kW. The sensitivity analysis revealed that leading edge loading at mid-span and normal chord proved to be the most significant design parameters affecting pressure loss and flow behaviour metrics. The research proves that legacy turbines can be digitally restored through hybrid optimisation and CFD workflows, which enables data-driven refurbishment design without needing complete component replacement. Full article

(This article belongs to the Special Issue Energy Security, Transition, and Sustainable Development)

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23 pages, 9347 KB

Open AccessArticle

Influence of Scenarios for Space Heating and Domestic Hot Water in Buildings on the Winter Electricity Demand of Switzerland in 2050

by Krisztina Kelevitz, Michel Haller, Matthias Frommelt and Boris Meier

Energies 2025, 18(21), 5601; https://doi.org/10.3390/en18215601 (registering DOI) - 24 Oct 2025

Abstract

Switzerland’s energy transition toward net-zero greenhouse gas emissions by 2050 presents a critical challenge in managing winter electricity demand, particularly due to the widespread electrification of space heating and domestic hot water. In this study, we assess how targeted measures in the building [...] Read more.

Switzerland’s energy transition toward net-zero greenhouse gas emissions by 2050 presents a critical challenge in managing winter electricity demand, particularly due to the widespread electrification of space heating and domestic hot water. In this study, we assess how targeted measures in the building sector can influence heat demand and thereby also the winter electricity gap. To this end, we extended the existing PowerCheck simulation tool by incorporating a detailed bottom-up representation of the Swiss building stock. We model hourly heat and electricity demand across 60 building categories, defined by climate zone, usage type, and insulation standard. Twelve future scenarios are developed based on variations in four key parameters: building renovation rate, hot water heat recovery, heat sources used by heat pumps, and ambient temperature trends. Our results indicate that renovation of old buildings to current insulation standards has by far the greatest effect out of the studied parameters. Increasing the annual thermal renovation rate of building shells from the currently planned 1.1% to 2% can reduce the winter electricity gap from 10.7 TWh to 6.0 TWh, a 44% reduction. Conversely, achieving only a low renovation rate of 0.5% could increase the gap to 13.9 TWh. Additional measures, such as greater use of ground-source instead of air-source heat pumps and implementation of hot water recovery, offer further potential for reduction. These findings underscore the importance of early and sustained investment in thermal renovation of building shells for achieving Switzerland’s 2050 net-zero climate targets. Full article

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

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21 pages, 2879 KB

Open AccessArticle

Prediction of Coal Calorific Value Based on Coal Quality-Derived Indicators and Support Vector Regression Method

by Xin Wang, Dahu Li, Youxiang Jiao, Yibin Yang and Zhao Cao

Energies 2025, 18(21), 5600; https://doi.org/10.3390/en18215600 (registering DOI) - 24 Oct 2025

Abstract

This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, [...] Read more.

This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FC_ad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R² > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. Full article

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15 pages, 4903 KB

Open AccessArticle

Protective Coating for Zinc Electrodes of Zinc–Air Battery in a Neutral Electrolyte

by Sonia Bagheri, Benedetto Bozzini, Carola Esposito Corcione, Raffaella Striani and Claudio Mele

Energies 2025, 18(21), 5599; https://doi.org/10.3390/en18215599 (registering DOI) - 24 Oct 2025

Abstract

This work introduces a novel approach to enhancing the performance of zinc anodes in zinc–air batteries through a photopolymerizable organic–inorganic hybrid coating. Electrochemical tests were conducted in a neutral NaCl electrolyte, selected to minimize electrolyte carbonation, anode corrosion, and zinc dendrite formation. The [...] Read more.

This work introduces a novel approach to enhancing the performance of zinc anodes in zinc–air batteries through a photopolymerizable organic–inorganic hybrid coating. Electrochemical tests were conducted in a neutral NaCl electrolyte, selected to minimize electrolyte carbonation, anode corrosion, and zinc dendrite formation. The behavior of bare and coated zinc electrodes was investigated using linear sweep voltammetry, electrochemical impedance spectroscopy (EIS), potentiostatic measurements, galvanostatic discharge tests, and charge-discharge tests, while morphological and structural characterizations were carried out by Atomic Force Microscopy (AFM), Raman spectroscopy, and X-ray Diffraction (XRD). The results confirmed that the hybrid coating acts as a corrosion-resistant barrier, enhancing the reversibility and stability of zinc electrodes through a barrier mechanism. Charge–discharge tests further confirmed the improved performance of the coated electrode, obtaining at a current density of 1 mA/cm², a coulombic efficiency of 92.61% and a capacity retention of 90.18%, respectively, after 16 cycles. These findings highlight the effectiveness of the photopolymerizable hybrid coating in improving the durability and rechargeability of zinc–air batteries. Full article

(This article belongs to the Special Issue Advances in Materials for Electrochemical Energy Applications 2024)

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24 pages, 6905 KB

Open AccessArticle

A Virtual Power Plant Load Forecasting Approach Using COM Encoding and BiLSTM-Att-KAN

by Yong Zhu, Liangyi Pu, Di Yang, Tun Kang, Chao Liang, Mingzhi Peng and Chao Zhai

Energies 2025, 18(21), 5598; https://doi.org/10.3390/en18215598 (registering DOI) - 24 Oct 2025

Abstract

Virtual Power Plant (VPP) is capable of aggregating and intelligently coordinating diverse distributed energy resources, among which the accuracy of load forecasting is a key factor in ensuring their regulation capability. To address the periodicity and complex nonlinear fluctuations of electricity load data, [...] Read more.

Virtual Power Plant (VPP) is capable of aggregating and intelligently coordinating diverse distributed energy resources, among which the accuracy of load forecasting is a key factor in ensuring their regulation capability. To address the periodicity and complex nonlinear fluctuations of electricity load data, this study introduces a Cyclic Order Mapping (COM) encoding method, which maps weekly and intraday sequences into continuous ordered variables on the unit circle, thereby effectively preserving load periodic features. On the basis of the COM encoding, a novel forecasting model is proposed by integrating Bidirectional Long Short-Term Memory (BiLSTM) networks, an efficient self-attention mechanism, and the Kolmogorov–Arnold Network (KAN). This model is termed BiLSTM-Att-KAN. Comparative and ablation experiments were conducted to assess the scientific validity and predictive accuracy of the proposed approach. The results confirm its superiority, achieving a Root Mean Square Error (RMSE) of 141.403, a Mean Absolute Error (MAE) of 106.687, and a coefficient of determination (R²) of 0.962. These findings demonstrate the effectiveness of the proposed model in enhancing load forecasting performance for VPP applications. Full article

(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)

15 pages, 451 KB

Open AccessArticle

The Effect of Enzymatic Disintegration Using Cellulase and Lysozyme on the Efficiency of Methane Fermentation of Sewage Sludge

by Bartłomiej Macherzyński, Małgorzata Wszelaka-Rylik, Anna Marszałek and Elżbieta Popowska-Nowak

Energies 2025, 18(21), 5597; https://doi.org/10.3390/en18215597 (registering DOI) - 24 Oct 2025

Abstract

This study presents a novel approach to intensifying the anaerobic digestion of sewage sludge through enzymatic pretreatment using hydrolytic enzymes—cellulase and lysozyme. It aims to determine how enzymatic activation affects the efficiency of methane fermentation, defined as the degree of organic matter decomposition [...] Read more.

This study presents a novel approach to intensifying the anaerobic digestion of sewage sludge through enzymatic pretreatment using hydrolytic enzymes—cellulase and lysozyme. It aims to determine how enzymatic activation affects the efficiency of methane fermentation, defined as the degree of organic matter decomposition and yield and composition of biogas. An experiment was carried out under mesophilic conditions over 20 days, analyzing the physicochemical properties of sludge, biogas production, methane content, and sanitary parameters. The addition of cellulase and lysozyme significantly enhanced process efficiency, increasing both the rate of organic matter degradation and biogas yield. The highest biogas production values (0.73 L·g⁻¹ d.m. for cellulase and 0.72 L·g⁻¹ d.m. for lysozyme) were obtained at a 4% (w/w) enzyme concentration, with a corresponding increase in the degree of organic matter decomposition to 78.7% and 80.0%, respectively. The produced biogas contained 58–61% methane, exceeding the values observed in the control sample, which indicates a positive effect of enzymatic activation on methane selectivity. Enhanced biogas production was attributed to improved hydrolysis of complex organic compounds, resulting in greater substrate bioavailability for methanogenic microorganisms. Moreover, methane fermentation led to the complete elimination of E. coli from all supernatants, confirming the hygienization potential of the process. The results of this study indicate that enzymatic pretreatment may serve as a viable strategy to improve both the energy efficiency and hygienic safety of anaerobic digestion processes, with relevance for future optimization and full-scale wastewater treatment applications. Full article

(This article belongs to the Special Issue Nutrient and Energy Recovery from Municipal and Industrial Wastewater)

19 pages, 1895 KB

Open AccessArticle

Study on Superconducting Magnetic Energy Storage for Large Subway Stations with Multiple Lines

by Wenjing Mo, Boyang Shen, Xiaoyuan Chen, Yu Chen and Lin Fu

Energies 2025, 18(21), 5596; https://doi.org/10.3390/en18215596 (registering DOI) - 24 Oct 2025

Abstract

With accelerating urbanization, subway stations, as high-energy-consumption sectors, face significant challenges in maintaining power supply stability and ensuring power quality. This paper proposed a novel voltage compensation solution utilizing superconducting magnetic energy storage (SMES) to suppress voltage fluctuations in the traction system of [...] Read more.

With accelerating urbanization, subway stations, as high-energy-consumption sectors, face significant challenges in maintaining power supply stability and ensuring power quality. This paper proposed a novel voltage compensation solution utilizing superconducting magnetic energy storage (SMES) to suppress voltage fluctuations in the traction system of a large subway station with multiple lines, which was caused by frequent acceleration and regenerative braking of multiple subway trains. Using the MATLAB/Simulink platform, a model of the traction power system with SMES for a large subway station with multiple lines was constructed. Appropriate control methods and hierarchical control strategies were used to suppress voltage fluctuations in both single-line and multi-line configurations at subway stations. The technical advantages of SMES in rapid response and efficient charging/discharging were explored. Overall, results show SMES with the novel control strategies can effectively suppress voltage fluctuations on both single- and triple-line configurations, validating the feasibility in mitigating voltage fluctuations and enhancing regenerative braking energy utilization. Full article

(This article belongs to the Special Issue Application of the Superconducting Technology in Energy System)

20 pages, 1690 KB

Open AccessArticle

Hybrid Drive Simulation Architecture for Power Distribution Based on the Federated Evolutionary Monte Carlo Algorithm

by Dongli Jia, Xiaoyu Yang, Wanxing Sheng, Keyan Liu, Tingyan Jin, Xiaoming Li and Weijie Dong

Energies 2025, 18(21), 5595; https://doi.org/10.3390/en18215595 (registering DOI) - 24 Oct 2025

Abstract

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization [...] Read more.

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization (FEMCO) algorithm for distribution network optimization. The model-driven module employs spectral clustering to decompose the network into multiple autonomous subsystems and performs distributed reconstruction through gradient descent. The data-driven module, built upon Long Short-Term Memory (LSTM) networks, learns temporal dependencies between load curves and operational parameters to enhance predictive accuracy. These two modules are fused via a Random Forest ensemble, while FEMCO jointly leverages Monte Carlo global sampling, Federated Learning-based distributed training, and Genetic Algorithm-driven evolutionary optimization. Simulation studies on the IEEE 33 bus distribution system demonstrate that the proposed framework reduces power losses by 25–45% and voltage deviations by 75–85% compared with conventional Genetic Algorithm and Monte Carlo approaches. The results confirm that the proposed hybrid architecture effectively improves convergence stability, optimization precision, and adaptability, providing a scalable solution for the intelligent operation and distributed control of modern power distribution systems. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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16 pages, 3072 KB

Open AccessArticle

Identification of Wide-Range-Frequency Oscillations in Power Systems Based on Improved PSO-VMD

by Heran Kang, Wenyi Li, Bin He and Zitao Chen

Energies 2025, 18(21), 5594; https://doi.org/10.3390/en18215594 (registering DOI) - 24 Oct 2025

Abstract

With the continuous advancement of China’s new-type power system construction, wide-range-frequency oscillation accidents in the power grid have become frequent, characterized by multiple modal components and a wide frequency range. Due to the nonlinearity and coupled time-varying characteristic of wide-range-frequency oscillations, it is [...] Read more.

With the continuous advancement of China’s new-type power system construction, wide-range-frequency oscillation accidents in the power grid have become frequent, characterized by multiple modal components and a wide frequency range. Due to the nonlinearity and coupled time-varying characteristic of wide-range-frequency oscillations, it is difficult to accurately identify parameters. Therefore, this paper proposes an improved particle swarm optimization (PSO)–variational mode decomposition (VMD) method for identifying wide-range-frequency oscillations. First, through improved PSO, the number of modal and secondary penalty factors of the VMD are self-optimized. Energy loss is used as the fitness function, and optimum is achieved through dynamic adjustment of the inertial factor of the particle swarm algorithm. Second, the wide-range-frequency oscillation signal undergoes VMD based on the number of obtained modals and the secondary penalty factor. The effective and noisy modal components are separated using the correlation coefficient approach, and signal reconstruction is utilized to reduce noise. Finally, simulation examples were used to verify the feasibility and effectiveness of the method proposed in this paper. Simulation results demonstrate that the proposed method can capture all wide-band-frequency oscillation information of the signal with an identification error below 2%. It provides a theoretical basis and technical support for wide-band-frequency oscillation traceability and mitigation. Full article

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25 pages, 1868 KB

Open AccessArticle

AI-Powered Digital Twin Co-Simulation Framework for Climate-Adaptive Renewable Energy Grids

by Kwabena Addo, Musasa Kabeya and Evans Eshiemogie Ojo

Energies 2025, 18(21), 5593; https://doi.org/10.3390/en18215593 (registering DOI) - 24 Oct 2025

Abstract

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive [...] Read more.

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article

(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)

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20 pages, 2995 KB

Open AccessArticle

Numerical Study of Liquid Hydrogen Internal Flow in Liquid Hydrogen Storage Tank

by Xiang Li, Qun Wei, Lianyan Yu, Xiaobin Zhang, Yiting Zou, Yongcheng Zhu, Yanbo Peng, Daolin Wang, Zexian Zhu, Xianlei Chen, Yalei Zhao, Chengxu Tu and Fubing Bao

Energies 2025, 18(21), 5592; https://doi.org/10.3390/en18215592 (registering DOI) - 24 Oct 2025

Abstract

As a key zero-carbon energy carrier, the accurate measurement of liquid hydrogen flow in its industrial chain is crucial. However, the ultra-low temperature, ultra-low density and other properties of liquid hydrogen can introduce calibration errors. To enhance the measurement accuracy and reliability of [...] Read more.

As a key zero-carbon energy carrier, the accurate measurement of liquid hydrogen flow in its industrial chain is crucial. However, the ultra-low temperature, ultra-low density and other properties of liquid hydrogen can introduce calibration errors. To enhance the measurement accuracy and reliability of liquid hydrogen flow, this study investigates the heat and mass transfer within a 1 m³ non-vented storage tank during the calibration process of a liquid hydrogen flow standard device that integrates combined dynamic and static gravimetric methods. The vertical tank configuration was selected to minimize the vapor–liquid interface area, thereby suppressing boil-off gas generation and enhancing pressure stability, which is critical for measurement accuracy. Building upon research on cryogenic flow standard devices as well as tank experiments and simulations, this study employs computational fluid dynamics (CFD) with Fluent 2024 software to numerically simulate liquid hydrogen flow within a non-vented tank. The thermophysical properties of hydrogen, crucial for the accuracy of the phase-change simulation, were implemented using high-fidelity real-fluid data from the NIST Standard Reference Database, as the ideal gas law is invalid under the cryogenic conditions studied. Specifically, the Lee model was enhanced via User-Defined Functions (UDFs) to accurately simulate the key phase-change processes, involving coupled flash evaporation and condensation during liquid hydrogen refueling. The simulation results demonstrated good agreement with NASA experimental data. This study systematically examined the effects of key parameters, including inlet flow conditions and inlet liquid temperature, on the flow characteristics of liquid hydrogen entering the tank and the subsequent heat and mass transfer behavior within the tank. The results indicated that an increase in mass flow rate elevates tank pressure and reduces filling time. Conversely, a decrease in the inlet liquid hydrogen temperature significantly intensifies heat and mass transfer during the initial refueling stage. These findings provide important theoretical support for a deeper understanding of the complex physical mechanisms of liquid hydrogen flow calibration in non-vented tanks and for optimizing calibration accuracy. Full article

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14 pages, 4042 KB

Open AccessArticle

Visualizing and Quantifying Fluid Flow in Multi-Coal Reservoirs Using Three-Dimensional Monitoring Data

by Anxu Ding, Cui Xiao, Jiang Xu, Shoujian Peng, Liang Wang and Li Jia

Energies 2025, 18(21), 5591; https://doi.org/10.3390/en18215591 (registering DOI) - 24 Oct 2025

Abstract

To investigate the three-dimensional spatial distribution characteristics of fluids during the combined production of coalbed methane from multi-coal reservoirs (MCR), a physical simulation test platform was established, and a quantitative characterization parameter calculation principle for fluid migration was developed. The influence of fluid [...] Read more.

To investigate the three-dimensional spatial distribution characteristics of fluids during the combined production of coalbed methane from multi-coal reservoirs (MCR), a physical simulation test platform was established, and a quantitative characterization parameter calculation principle for fluid migration was developed. The influence of fluid pressure difference and in situ stress difference on the three-dimensional spatial distribution of fluids and their quantitative characterization parameters was analyzed. The results indicate that the dynamic pressure equilibrium between the coal reservoir and the wellbore forces fluids from high-pressure reservoirs to intrude into low-pressure reservoirs, altering the flow state of fluids in the latter. Consequently, the relative flow velocity in the low-pressure reservoir becomes negative, with the relative deflection angle approaching 180°, while the relative flow velocity in the high-pressure reservoir remains positive. An increase in the relative flow rate of 0.08 and 0.007 corresponds to a 1 MPa increase in fluid pressure difference and geostress difference, respectively. During the co-production of coalbed methane from MCR, the existing pressure difference and in situ stress difference between reservoirs modify the fluid migration patterns, leading to fluid interaction and interference effects. This results in centrifugal flow patterns in low-pressure reservoirs and centripetal flow patterns in high-pressure reservoirs. Compared to in situ stress difference, the fluid pressure difference exerts a more significant influence on the fluid migration patterns. Full article

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17 pages, 1919 KB

Open AccessArticle

Determination of Voltage Margin Decision Boundaries via Logistic Regression for Distribution System Operations

by Jun-Hyuk Nam, Dong-Il Cho, Yun-Jin Cho and Won-Sik Moon

Energies 2025, 18(21), 5590; https://doi.org/10.3390/en18215590 - 24 Oct 2025

Abstract

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and [...] Read more.

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

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

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19 pages, 3328 KB

Open AccessArticle

Comparison of PID and Adaptive Algorithms in Diesel Engine Speed Control

by Paweł Magryta, Mirosław Wendeker, Arkadiusz Gola and Monika Andrych-Zalewska

Energies 2025, 18(21), 5589; https://doi.org/10.3390/en18215589 - 24 Oct 2025

Abstract

This study experimentally compares classical PID and three adaptive control strategies (including a novel adaptive control strategy developed by the authors) for stabilizing the crankshaft speed of a diesel engine (ADCR Euro 4). The tests were performed on a dynamometer with alternator-induced step [...] Read more.

This study experimentally compares classical PID and three adaptive control strategies (including a novel adaptive control strategy developed by the authors) for stabilizing the crankshaft speed of a diesel engine (ADCR Euro 4). The tests were performed on a dynamometer with alternator-induced step loads. All tests were performed at a constant engine crankshaft speed using National Instruments instrumentation and custom LabVIEW-based software for real-time monitoring. Metrics included response time (RT), overshoot (OV), and steady-state error (SSE), each based on ten repetitions with reported standard deviations. Results show that the competitive adaptive algorithm reduced RT by ~20%, OV by ~15%, and SSE by ~10% compared to PID. These results confirm that adaptive control, especially the competitive strategy, provides high precision and fast disturbance rejection, bridging the gap between simulation-based studies and industrial diesel engine applications. These results highlight the potential of adaptive control in applications such as air–fuel ratio control, turbocharger pressure control, knock detection, and fuel optimization. Full article

(This article belongs to the Special Issue Internal Combustion Engines: Research and Applications—3rd Edition)

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20 pages, 4133 KB

Open AccessArticle

Numerical Investigation of the Non-Uniform Distribution of Outlet Parameters in a Radial Wave Rotor Combustor

by Jize Liang, Erlei Gong, Jianzhong Li, Qian Yao and Wu Jin

Energies 2025, 18(21), 5588; https://doi.org/10.3390/en18215588 - 24 Oct 2025

Abstract

The non-uniform distribution of outlet parameters in a Radial Wave Rotor Combustor (RWRC) significantly impacts downstream component performance. This study aims to investigate the spatial-temporal characteristics of temperature and pressure at the RWRC outlet. Three-dimensional unsteady Reynolds-Averaged Navier–Stokes (RANS) simulations were coupled with [...] Read more.

The non-uniform distribution of outlet parameters in a Radial Wave Rotor Combustor (RWRC) significantly impacts downstream component performance. This study aims to investigate the spatial-temporal characteristics of temperature and pressure at the RWRC outlet. Three-dimensional unsteady Reynolds-Averaged Navier–Stokes (RANS) simulations were coupled with a User-Defined Function (UDF) to efficiently model the combustion process. The Shear-Stress Transport (SST) k-ω turbulence model was employed. Quantitative metrics including Relative Standard Deviation (RSD) and Christiansen Uniformity Coefficient (CUC) were introduced to evaluate the non-uniformity. The results reveal significant temporal fluctuations and spatial non-uniformity in both temperature and pressure. At 3000 r/min, the average temperature fluctuation amplitude and uniformity coefficient are 0.38 and 0.9, respectively, while the pressure fluctuation amplitude reaches 0.44. Crucially, temperature distribution expands circumferentially with increasing speed, whereas pressure distribution remains locally concentrated and is less sensitive to speed variations. This study provides a quantitative assessment of outlet non-uniformity in RWRC, highlighting the distinct behaviors between temperature and pressure distributions. The findings offer critical insights for the design and optimization of wave rotors and their integration with downstream components. Full article

(This article belongs to the Topic New Advances in Membrane Technology and Its Contribution to Sustainability)

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30 pages, 5764 KB

Open AccessArticle

Control and Modeling Framework for Balanced Operation and Electro-Thermal Analysis in Three-Level T-Type Neutral Point Clamped Inverters

by Ahmed H. Okilly, Cheolgyu Kim, Do-Wan Kim and Jeihoon Baek

Energies 2025, 18(21), 5587; https://doi.org/10.3390/en18215587 - 24 Oct 2025

Abstract

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive [...] Read more.

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive control (MPC) with space vector pulse width modulation (SVPWM). The particle swarm optimization (PSO) algorithm is used to methodically tune the MPC cost function weights for minimization, while achieving a balance between output current tracking, stabilization of the neutral-point voltage, and, consequently, a uniform distribution of thermal stress. The proposed SVPWM-MPC algorithm selects optimal switching states, which are then utilized in a chip-level loss model coupled with a Cauer RC thermal network to predict transient chip-level junction temperatures dynamically. The proposed framework is executed in MATLAB R2024b and validated with experiments, and the SemiSel industrial thermal simulation tool, demonstrating both control effectiveness and accuracy of the electro-thermal model. The results demonstrate that the proposed control method can sustain neutral-point voltage imbalance of less than 0.45% when operating at 25% load and approximately 1% under full load working conditions, while accomplishing a uniform junction temperature profile in all inverter legs across different working conditions. Moreover, the results indicate that the proposed control and modeling structure is an effective and common-sense way to perform coordinated electrical and thermal management, effectively allowing for predesign and reliability testing of high-power TNPC inverters. Full article

(This article belongs to the Special Issue Power Electronics Technology and Application)

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24 pages, 3609 KB

Open AccessArticle

Experimental Characterization and Modelling of a Humidification–Dehumidification (HDH) System Coupled with Photovoltaic/Thermal (PV/T) Modules

by Giovanni Picotti, Riccardo Simonetti, Luca Molinaroli and Giampaolo Manzolini

Energies 2025, 18(21), 5586; https://doi.org/10.3390/en18215586 - 24 Oct 2025

Abstract

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components [...] Read more.

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of

44 ° C

, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

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18 pages, 6011 KB

Open AccessArticle

From Data-Rich to Data-Scarce: Spatiotemporal Evaluation of a Hybrid Wavelet-Enhanced Deep Learning Model for Day-Ahead Wind Power Forecasting Across Greece

by Ioannis Laios, Dimitrios Zafirakis and Konstantinos Moustris

Energies 2025, 18(21), 5585; https://doi.org/10.3390/en18215585 (registering DOI) - 24 Oct 2025

Abstract

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored [...] Read more.

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

(This article belongs to the Special Issue Machine Learning in Renewable Energy Resource Assessment)

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21 pages, 7623 KB

Open AccessArticle

Study on CO₂ Induced Gas Channeling in Tight Gas Reservoirs and Optimization of Injection Production Parameters

by Haijun Yan, Gang Cheng, Jianlin Guo, Runxi Wang, Bo Ning, Xinglong Wang, He Yuan and Huaxun Liu

Energies 2025, 18(21), 5584; https://doi.org/10.3390/en18215584 - 23 Oct 2025

Abstract

Tight gas reservoirs are characterized by low porosity, low permeability, and strong heterogeneity. CO₂ flooding, as an important approach for enhancing gas recovery while achieving carbon sequestration, is often restricted by gas channeling. Based on the sandstone reservoir parameters of the Shihezi [...] Read more.

Tight gas reservoirs are characterized by low porosity, low permeability, and strong heterogeneity. CO₂ flooding, as an important approach for enhancing gas recovery while achieving carbon sequestration, is often restricted by gas channeling. Based on the sandstone reservoir parameters of the Shihezi Formation in the Ordos Basin, a two-dimensional fracture–matrix coupled numerical model was developed to systematically investigate the effects of fracture number, fracture inclination, fracture width, injection pressure, and permeability contrast on gas breakthrough time and sweep efficiency. A second-order regression model was further established using response surface methodology (RSM). The results show that a moderate fracture density can extend breakthrough time and improve sweep efficiency, while permeability contrast is the fundamental factor controlling gas channeling risk. When the contrast increases from 0.7 to 9.9, the breakthrough efficiency decreases from 88.5% to 68.9%. The response surface analysis reveals significant nonlinear interactions, including the coupled effects of fracture number with fracture width, injection pressure, and inclination angle. Under the optimized conditions, the breakthrough time can be extended to 46,984 h, with a corresponding sweep efficiency of 87.7%. These findings provide a quantitative evaluation method and engineering optimization guidance for controlling CO₂ channeling in tight gas reservoirs. Full article

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20 pages, 3045 KB

Open AccessArticle

Analyzing the Influence of Load Current on the Thermal RC Network Response of Melting-Type Fuses Used in Battery Electric Vehicles

by Oliver Makan and Kai-Peter Birke

Energies 2025, 18(21), 5583; https://doi.org/10.3390/en18215583 - 23 Oct 2025

Abstract

High-voltage fuses are critical safety components in electric vehicle (EV) battery systems, yet their thermal behavior under charging currents remains insufficiently characterized. This study develops and validates a physics-based thermal resistor-capacitor (RC) network model of a high-voltage melting fuse, accounting for copper elements, [...] Read more.

High-voltage fuses are critical safety components in electric vehicle (EV) battery systems, yet their thermal behavior under charging currents remains insufficiently characterized. This study develops and validates a physics-based thermal resistor-capacitor (RC) network model of a high-voltage melting fuse, accounting for copper elements, quartz sand filling, and polyester casing. Experimental accelerated life tests and current step load profiles were performed in a climate chamber at 70 °C, with temperature measurements at the fuse terminals. The RC model was constructed using material properties and geometry-derived parameters, including three copper element sections, one quartz sand node, and one case node. A discretized state–space formulation was implemented to simulate the transient thermal behavior. The results reveal distinct dynamic and stationary characteristics, with thermal time constants varying strongly between fuse sections. Comparisons with experimental data demonstrate that the proposed model captures both rise time and steady-state behavior, with deviations attributable to contact resistances and parasitic effects. The findings highlight that charging currents in practical profiles typically remain below 50% of fuse current ratings, leaving optimization potential for higher permissible currents, faster charging, and reduced downtime while maintaining safety. The outcome of this model is highly relevant for lifetime prediction models. Full article

(This article belongs to the Collection "Electric Vehicles" Section: Review Papers)

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17 pages, 943 KB

Open AccessArticle

Harmonic Mitigation and Energy Savings in 13.2 kV Distribution Feeders via P–Q-Based Shunt Active Filters and Luminaire Retrofit

by Brandon Condemaita and Milton Ruiz

Energies 2025, 18(21), 5582; https://doi.org/10.3390/en18215582 - 23 Oct 2025

Abstract

This article designs and validates a P-Q-based shunt active power filter (SAPF) to mitigate voltage harmonics in EERSA’s 13.2 kV feeder 1500080T03. A CYMDIST feeder model, calibrated with field measurements, reveals worst-case voltage THD up to 9.48% due to legacy high-pressure sodium (HPS) [...] Read more.

This article designs and validates a P-Q-based shunt active power filter (SAPF) to mitigate voltage harmonics in EERSA’s 13.2 kV feeder 1500080T03. A CYMDIST feeder model, calibrated with field measurements, reveals worst-case voltage THD up to 9.48% due to legacy high-pressure sodium (HPS) street lighting. Co-simulation with a MATLAB/Simulink R2024b, controller guides the sizing of a 150 kVA SAPF at Substation 8. Simulations reduce peak THD at a representative node from 9.48% to 1.51%; replacing HPS with LEDs further improves efficiency while lowering distortion. The retrofit complies with IEEE Std 519-2022, enhances supply reliability, and yields an internal rate of return above 17%, indicating a technically and financially attractive solution for Latin American distribution networks. Full article

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21 pages, 10106 KB

Open AccessArticle

Distributed Hierarchical Control with Cost Optimization and Priority-Based Dispatch for Workplace EV Charging: A Field Study

by Anna Malkova, Simone Striani, Jan Martin Zepter and Mattia Marinelli

Energies 2025, 18(21), 5581; https://doi.org/10.3390/en18215581 - 23 Oct 2025

Abstract

Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart [...] Read more.

Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations. Full article

(This article belongs to the Special Issue Energy Management and Control System of Electric Vehicles)

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31 pages, 7154 KB

Open AccessArticle

Enhancing Rural Electrification in Tigray: A Geospatial Approach to Hybrid Wind-Solar Site Selection

by Tsige Gebregergs Tesfay and Mulu Bayray Kahsay

Energies 2025, 18(21), 5580; https://doi.org/10.3390/en18215580 - 23 Oct 2025

Abstract

Renewable energy sources offer a promising future, backed by mature technologies and a viable pathway toward sustainable energy systems. However, careful planning is necessary to efficiently utilize these resources, especially during site selection. Many rural areas lack access to grid electricity, making off-grid [...] Read more.

Renewable energy sources offer a promising future, backed by mature technologies and a viable pathway toward sustainable energy systems. However, careful planning is necessary to efficiently utilize these resources, especially during site selection. Many rural areas lack access to grid electricity, making off-grid hybrid wind-solar power an attractive solution. In the Tigray region of Ethiopia, no such research has been conducted before. This study aims to identify suitable sites for hybrid wind-solar power for rural electrification using Geographic Information System (GIS), Analytic Hierarchy Process, and Monte Carlo simulation. The criteria fall into three categories: Climate, Topography, and Infrastructure, prioritized through pairwise comparisons by thirteen experts from five organizations engaged in renewable energy research, planning, and operations. Monte Carlo simulation was used for sensitivity analysis to address uncertainties in expert judgments and validate the rankings. The spatial analysis reveals 6470 km² as highly suitable for off-grid solar, 76 km² for off-grid wind with predominant easterly winds, and 177 km² as most favorable for hybrid generation. Areas of good suitability measure 447 km² for wind, 44,128 km² for solar, and 16,695 km² for hybrid systems. Based on this assessment, techno-economic analysis quantified the Levelized Cost of Energy (LCOE) under varying solar–wind shares and battery autonomy days. The analysis shows a minimum LCOE of $0.23/kWh with one-day storage and $0.58/kWh with three-day storage, indicating shorter autonomy is more cost-effective while longer autonomy enhances reliability. Sensitivity analysis shows financial parameters, particularly discount rate and battery capital cost, dominate system economics. Full article

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

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41 pages, 4380 KB

Open AccessArticle

A Two-Layer HiMPC Planning Framework for High-Renewable Grids: Zero-Exchange Test on Germany 2045

by Alexander Blinn, Joshua Bunner and Fabian Kennel

Energies 2025, 18(21), 5579; https://doi.org/10.3390/en18215579 - 23 Oct 2025

Abstract

High-renewables grids are planned in min but judged in milliseconds; credible studies must therefore resolve both horizons within a single model. Current adequacy tools bypass fast frequency dynamics, while detailed simulators lack multi-hour optimization, leaving investors without a unified basis for sizing storage, [...] Read more.

High-renewables grids are planned in min but judged in milliseconds; credible studies must therefore resolve both horizons within a single model. Current adequacy tools bypass fast frequency dynamics, while detailed simulators lack multi-hour optimization, leaving investors without a unified basis for sizing storage, shifting demand, or upgrading transfers. We present a two-layer Hierarchical Model Predictive Control framework that links 15-min scheduling with 1-s corrective action and apply it to Germany’s four TSO zones under a stringent zero-exchange stress test derived from the NEP 2045 baseline. Batteries, vehicle-to-grid, pumped hydro and power-to-gas technologies are captured through aggregators; a decentralized optimizer pre-positions them, while a fast layer refines setpoints as forecasts drift; all are subject to inter-zonal transfer limits. Year-long simulations hold frequency within ±2 mHz for 99.9% of hours and below ±10 mHz during the worst multi-day renewable lull. Batteries absorb sub-second transients, electrolyzers smooth surpluses, and hydrogen turbines bridge week-long deficits—none of which violate transfer constraints. Because the algebraic core is modular, analysts can insert new asset classes or policy rules with minimal code change, enabling policy-relevant scenario studies from storage mandates to capacity-upgrade plans. The work elevates predictive control from plant-scale demonstrations to system-level planning practice. It unifies adequacy sizing and dynamic-performance evaluation in a single optimization loop, delivering an open, scalable blueprint for high-renewables assessments. The framework is readily portable to other interconnected grids, supporting analyses of storage obligations, hydrogen roll-outs and islanding strategies. Full article

19 pages, 684 KB

Open AccessArticle

The Impact of the Common Agricultural Policy on Energy Efficiency in Agriculture: Between Farmer Support and Sustainable Development in the Visegrad Group

by Piotr Kułyk and Waldemar Sługocki

Energies 2025, 18(21), 5578; https://doi.org/10.3390/en18215578 - 23 Oct 2025

Abstract

This study examines the relationship between energy efficiency in agricultural production and its determinants, considering technological, economic, political, and social factors. The aim was to determine the impact of the CAP on the energy efficiency of agricultural production, as well as technological, market, [...] Read more.

This study examines the relationship between energy efficiency in agricultural production and its determinants, considering technological, economic, political, and social factors. The aim was to determine the impact of the CAP on the energy efficiency of agricultural production, as well as technological, market, and social changes. The impact of time effects was also taken into account. The study focuses on the four Visegrad Group countries over the 2004–2023 period. Both fixed-effects and dynamic panel models were employed to capture structural changes over time. The significance of agriculture, as a result of structural transformations, is relatively small and hovers around 3% in these countries. The CAP was found to have a significant impact on the energy efficiency of agricultural production. However, it was not the amount of support but rather its structure that played a crucial role, particularly environmental support (0.04). The inertia effect was also of fundamental importance (0.41—elasticity in the inertia model). The total value of transfers, especially in the long term, proved to be a discouraging factor for this process. Market conditions, including energy prices (0.456), structural changes in farms (0.016), and labor input (−0.04), were also significant factors. However, it was not so much the size of support but rather the structure of support that was crucial. The total value of transfers, especially in the long term, was a demotivator for this process. Market conditions, including energy prices, structural changes on farms, and labor inputs, were also important factors. A key recommendation for agricultural financial support policy is to focus support more on environmental and low-emission issues, which are linked to improving the energy efficiency of production while maintaining its growth. Transfers related to the growing importance of renewable energy sources and support for rural development, which do not yield beneficial effects in the considered scope, require increased conditionality. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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Energies, Volume 18, Issue 21 (November-1 2025) – 56 articles

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