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Volume 19
Issue 13
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Energies, Volume 19, Issue 13 (July-1 2026) – 278 articles

Cover Story (view full-size image): This paper evaluates residential energy management systems (EMSs) that combine on-site renewable generation and battery energy storage in an all-electric house. Using a calibrated gray-box model of the Archetype Sustainable House in Vaughan, Ontario, the study compares rule-based, optimization-based, machine-learning, predictive, and transactive control strategies under the same operating assumptions. The results show a clear trade-off between simplicity and performance. Machine-learning control achieved the strongest annual savings, improving cost performance by about 15–22% over deterministic control. Predictive and transactive methods also showed promise but were subject to practical limitations. Overall, the study shows that the most realistic near-term path is a gradual move from simple battery control to more adaptive and connected residential energy systems. View this paper
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26 pages, 9673 KB  
Article
Evaluation of Steady-State Volumetric Heating Load Methods Considering Building Envelope Type and Window-to-Wall Ratio
by Bayaraa Batsuuri and Haksung Lee
Energies 2026, 19(13), 3224; https://doi.org/10.3390/en19133224 - 7 Jul 2026
Viewed by 362
Abstract
Recent studies have shown that building-envelope characteristics, including thermal mass and window-related solar gains, can significantly influence heating load behavior. However, their implications for the applicability and accuracy of simplified volumetric heating load methods remain insufficiently understood. Therefore, this study evaluates the applicability [...] Read more.
Recent studies have shown that building-envelope characteristics, including thermal mass and window-related solar gains, can significantly influence heating load behavior. However, their implications for the applicability and accuracy of simplified volumetric heating load methods remain insufficiently understood. Therefore, this study evaluates the applicability and accuracy of steady-state volumetric heating load methods under varying envelope conditions. Representative residential building models with different envelope types and window-to-wall ratios (WWRs) were analyzed. Heating loads were calculated using a normative volumetric approach based on tabulated specific heat-loss coefficients (Method 1) and a recalculated approach incorporating envelope characteristics and solar gains (Method 2). A comparison at hourly, peak-load, seasonal, and load-duration-curve (LDC) levels revealed that the performance of steady-state volumetric methods is strongly dependent on both envelope characteristics and the level of heating load assessment. Method 1 consistently overestimated seasonal heating demand by 41–218%, whereas Method 2 substantially reduced the error. However, Method 2 still underestimated annual heating demand in highly insulated buildings (up to 39%) and peak heating load (up to 63%). These findings indicate that the applicability of volumetric methods depends on both envelope characteristics and the level of heating load assessment. The results highlight the importance of accounting for envelope-dependent thermal-mass effects when applying volumetric methods to hourly, peak-load, seasonal, and LDC-based analyses. Full article
(This article belongs to the Section B: Energy and Environment)
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30 pages, 362 KB  
Article
Which Energy-Transition Policies Improve Energy Security? Evidence from Policy-Instrument Decomposition and Cross-Country Panel Models
by Bartosz Kozicki, Nataliya Stoyanets, Grigor Nazaryan, Marcin Jurgilewicz, Aleksandra Skrabacz and Oleksii Havrylenko
Energies 2026, 19(13), 3223; https://doi.org/10.3390/en19133223 - 7 Jul 2026
Viewed by 354
Abstract
Energy security has become a central policy challenge because decarbonisation must be achieved without weakening the reliability, affordability and resilience of national energy systems. This article examines whether and how energy transition policies contribute to national energy security, with particular attention to aggregate [...] Read more.
Energy security has become a central policy challenge because decarbonisation must be achieved without weakening the reliability, affordability and resilience of national energy systems. This article examines whether and how energy transition policies contribute to national energy security, with particular attention to aggregate policy stringency, individual policy instruments, and renewable electricity deployment. The analysis uses a panel of 49 countries over 23 observed years between 2000 and 2023, excluding 2002, comprising 1127 country-year observations, and applies two-way fixed-effects models with Driscoll–Kraay standard errors. The aggregate Energy Policy Stringency Index has a positive but statistically insignificant coefficient in the contemporaneous model (0.421) and remains insignificant with one-, two- and three-year lags (0.187, 0.128 and –0.026, respectively). Renewable electricity generation is consistently positive and significant, with coefficients ranging from 0.071 to 0.090, indicating that actual renewable deployment is more closely associated with energy security than formal policy stringency. Policy-instrument decomposition shows that fossil fuel excise taxes have the strongest positive association, with coefficients from 1.554 to 1.082 in full-instrument models and from 1.614 to 1.077 in one-by-one robustness checks. Air emission standards have delayed positive effects, while some renewable-support and cross-sectoral tools show mixed results, indicating dependence on design and system readiness. Full article
(This article belongs to the Special Issue Sustainable Energy & Society—2nd Edition)
53 pages, 8996 KB  
Article
Hierarchical GA–LP Framework with Explainable AI and Clustering for Generating and Interpreting Diverse Feasible Solutions in Net-Zero Energy Systems: An Illustrative Case Study
by Ryosuke Gotoh, Wataru Sato, Yuuri Nagase and Tomohiro Mizukami
Energies 2026, 19(13), 3222; https://doi.org/10.3390/en19133222 - 7 Jul 2026
Viewed by 387
Abstract
The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse [...] Read more.
The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including selected fuel-price assumptions, technology-cost conditions, equipment capacities, end-use electrification rates, CO2-capture installation rates, and CO2-storage limits, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into illustrative archetypes. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The framework, under the assumed conditions, generates diverse feasible solutions, identifies influential cost-related conditions and their interactions, and organizes the generated solution set into five illustrative archetypes. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty. Full article
(This article belongs to the Special Issue Energy Informatics and Intelligent Systems: IoT, AI, and Machine Learning Applications)
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20 pages, 3094 KB  
Article
Distributionally Robust Coordinated Maintenance and Dispatch in Multi-Energy Systems with Electricity, Heat, and Hydrogen Carriers: A Wasserstein-Metric Framework
by Anurag Gautam, Pitshou Ntambu Bokoro, Gulshan Sharma and Rajesh Kumar
Energies 2026, 19(13), 3221; https://doi.org/10.3390/en19133221 - 7 Jul 2026
Viewed by 432
Abstract
The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very [...] Read more.
The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very operationally challenged. This paper proposes a Distributionally Robust Optimization (DRO) based on a Wasserstein-metric ambiguity set, which simultaneously optimizes the annual maintenance schedules and short-term operational dispatch across MESs. The ambiguity set is constructed using joint samples of forecast errors for the three carriers’ demand, allowing for a data-driven worst-case distribution approach that mitigates the excessive conservatism typically associated with conventional robust optimization (CRO). The penalties are explicitly enforced for load and renewable energy curtailments across each of the MESs with source-specific value-of-lost-load coefficients. The Wasserstein radius is improved by sensitivity analysis, obtaining a θ value of 0.20 as the cost reduction radius for a 40% RES penetration. Five RES penetration levels are implemented here on the IEEE 39-bus New England network, with CHP, electrolyzer, fuel cell, thermal storage, and hydrogen storage. The DRO reduces the total annual system cost by 56% compared to CRO, while reducing the unbalanced energy. 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—2nd Edition)
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28 pages, 6230 KB  
Article
Steady-State Analysis of Voltage Deviations and Three-Phase Imbalance in Distribution Networks Considering Spatiotemporal Coupling of Source-Load Uncertainties
by Shifeng Zhang, Xiao Chang, Min Zhang and Le Gao
Energies 2026, 19(13), 3220; https://doi.org/10.3390/en19133220 - 7 Jul 2026
Viewed by 323
Abstract
To address the deep spatiotemporal coupling of source-load dual uncertainties attributed to the high penetration of distributed photovoltaics (PVs) and electric vehicles (EVs) into distribution grids, and the difficulty of analyzing composite disturbances using traditional methods, this paper proposes a voltage quality analysis [...] Read more.
To address the deep spatiotemporal coupling of source-load dual uncertainties attributed to the high penetration of distributed photovoltaics (PVs) and electric vehicles (EVs) into distribution grids, and the difficulty of analyzing composite disturbances using traditional methods, this paper proposes a voltage quality analysis method that considers spatiotemporal coupling of source-load uncertainty, focusing on steady-state voltage deviation and three-phase imbalance problems. First, a probabilistic model of PV generation is constructed using the beta distribution combined with Monte Carlo-based scenario reduction, and high-precision forecasting of EV charging loads is achieved by an attention-based convolutional neural network and long short-term memory network. Second, multi-scenario spatiotemporal power flow calculations are conducted on the distribution network to analyze the complementary effects of voltage deviation and three-phase imbalance under the hybrid integration of PV and EV. Finally, gray wolf optimization-based variational mode decomposition is introduced to adaptively decompose the source-load power. This reveals the intrinsic mechanisms where low-frequency components dominate the fundamental amplitude variations of bus voltages, while high-frequency components exert a significant impact on voltage quality. Simulation results demonstrate that the proposed method can effectively analyze the spatiotemporal coupling of source-load uncertainties, providing technical support for the comprehensive management of voltage quality in distribution networks. Full article
(This article belongs to the Special Issue Reliability of Power Electronics Devices and Converter Systems: 2nd Edition)
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35 pages, 6526 KB  
Article
Effects of Roof Material and Rear Ventilation Gap on Rooftop PV Modules in Tropical Conditions
by Nam Quyen Nguyen, Hristo Ivanov Beloev, Huy Bich Nguyen and Van Lanh Nguyen
Energies 2026, 19(13), 3219; https://doi.org/10.3390/en19133219 - 7 Jul 2026
Viewed by 348
Abstract
Solar energy has become one of the most important renewable energy sources for reducing dependence on conventional fossil-based energy systems. Rooftop photovoltaic (PV) installations play a key role in the expansion of solar energy, particularly in tropical countries such as Vietnam. This study [...] Read more.
Solar energy has become one of the most important renewable energy sources for reducing dependence on conventional fossil-based energy systems. Rooftop photovoltaic (PV) installations play a key role in the expansion of solar energy, particularly in tropical countries such as Vietnam. This study experimentally investigates the effects of roof material, rear ventilation gap, PV technology, solar irradiance, and wind speed on the power conversion efficiency (PCE) of rooftop PV modules under tropical climatic conditions in Ho Chi Minh City, Vietnam. Three roof types (concrete, tiled, and corrugated metal), three rear ventilation gaps (10, 30, and 50 cm), and two PV technologies (monocrystalline and polycrystalline) were evaluated under real operating conditions. The results indicate that increased module temperature significantly reduces power output and PCE, even under high solar irradiance. PV modules installed on corrugated metal roofs exhibited the highest operating temperatures and the lowest efficiencies, whereas concrete and tiled roofs provided more favorable thermal conditions. Increasing the rear ventilation gap enhanced convective cooling, with the 30–50 cm configurations showing superior heat dissipation compared with the 10 cm configuration, particularly for corrugated metal roofs. The experimentally determined heat transfer coefficient ranged from 23.48 to 67.64 W m−2 K−1, exceeding the theoretical wind-based coefficient (16.86–17.22 W m−2 K−1), thereby indicating the contribution of mixed convection, radiative exchange, and roof–module thermal interactions. Monocrystalline modules consistently achieved slightly higher efficiencies than polycrystalline modules. The findings provide practical guidance for optimizing rooftop PV installations and improving energy yield in tropical climates. Full article
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34 pages, 16521 KB  
Article
Distributed Downhole Electric Heating as a Thermal-Control Element in Deep Steam-Assisted Gravity Drainage: Experimental Operating-Window Analysis for Heavy-Oil Recovery
by Kadyrzhan Zaurbekov, Seitzhan Zaurbekov, Sergey Trebukhov, Boris V. Malozyomov and Nikita V. Martyushev
Energies 2026, 19(13), 3218; https://doi.org/10.3390/en19133218 - 7 Jul 2026
Viewed by 305
Abstract
Steam-assisted gravity drainage (SAGD) is constrained in deep heavy-oil reservoirs by wellbore heat losses, delayed steam-chamber development and high steam–oil ratio (SOR). This study develops an experimentally parameterized reduced-order screening framework for thermocable-assisted SAGD, formulated as a digital-twin prototype that couples heat transfer, [...] Read more.
Steam-assisted gravity drainage (SAGD) is constrained in deep heavy-oil reservoirs by wellbore heat losses, delayed steam-chamber development and high steam–oil ratio (SOR). This study develops an experimentally parameterized reduced-order screening framework for thermocable-assisted SAGD, formulated as a digital-twin prototype that couples heat transfer, temperature-dependent viscosity, chamber-growth geometry and energy-efficiency indicators. The formulation is evaluated within an experimentally parameterized screening matrix covering steam temperature, oil viscosity, permeability, depth, cable power and early heating time. The graphical dependencies are presented in a unified publication format and supplemented by heat-balance, chamber-field, sensitivity and operating-window analyses. For the reference experimental case, thermocable support increases oil rate from 84.1 to 96.1 t/day and reduces SOR from 2.70 to 2.30 t/t. The cable heat input is small relative to useful steam heat; therefore, its effect is interpreted through local compensation of downstream heat deficit and longitudinal temperature stabilization rather than through bulk energy addition. The strongest sensitivity is associated with steam rate, oil viscosity and depth, whereas cable power shows a beneficial but saturating effect. The proposed reduced-order digital-twin prototype is intended for feasibility screening, preliminary operating-window selection and prioritization of candidate regimes for detailed thermal-reservoir simulation and subsequent field-scale validation. Full article
(This article belongs to the Special Issue Petroleum and Natural Gas Engineering: 2nd Edition)
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46 pages, 6448 KB  
Review
Solutions Based on Active Disturbance Rejection Control Applied for Electric Drives—A Review
by Grzegorz Kaczmarczyk, Jan Kupycz, Danton Diego Ferreira and Marcin Kaminski
Energies 2026, 19(13), 3217; https://doi.org/10.3390/en19133217 - 7 Jul 2026
Viewed by 559
Abstract
Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, [...] Read more.
Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions. Full article
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33 pages, 65191 KB  
Article
Frequency-Adaptive Current Control with Kalman Filter-Based Observer for Multiple Grid-Connected Inverters Under Harsh Grid Distortion
by Seung-Yong Yeo, Min Kang, Luong Duc-Tai Cu and Kyeong-Hwa Kim
Energies 2026, 19(13), 3216; https://doi.org/10.3390/en19133216 - 7 Jul 2026
Viewed by 321
Abstract
As renewable energy source-based distributed generation is more widely connected to the grid, stable current control and power quality improvement in grid-connected inverters (GCIs) become more important. To satisfy increasing power demand, multi-inverter systems connected to the grid in parallel are being widely [...] Read more.
As renewable energy source-based distributed generation is more widely connected to the grid, stable current control and power quality improvement in grid-connected inverters (GCIs) become more important. To satisfy increasing power demand, multi-inverter systems connected to the grid in parallel are being widely adopted. However, parallel operation may degrade current quality and stability because of inverter interactions under harsh grid conditions. In particular, grid voltage harmonics, voltage imbalance, and frequency variations can also impair current control performance and system stability. To address these concerns, a frequency-adaptive current controller integrated with a Kalman filter (KF)-based observer is developed to ensure a stable operation of multiple GCIs. Moreover, a stability evaluation is presented for multi-inverter systems by using admittance-based stability analysis. A Kalman filter-based state observer is applied to improve the estimation accuracy under noisy measurement conditions. In addition, a moving average filter-based phase-locked loop (MAF-PLL) is applied to improve the detection accuracy and reliability of the grid frequency and phase angle under harsh grid conditions to ensure an effective frequency-adaptive control design. The effectiveness and performance of the proposed current controller are assessed through the PSIM simulations. The simulation results show that the MAF-PLL reduces the maximum frequency fluctuation from ±7 Hz to ±1.1 Hz. In addition, the KF-based observer reduces the RMS estimation error to 0.0001 A. On the other hand, those values are 1.3 A with the conventional observer and 0.0003 A with the LQR-based observer, respectively. The practicality of the proposed scheme is also confirmed experimentally using 2 kW parallel multiple GCI prototype systems under harsh grid conditions. Full article
(This article belongs to the Special Issue Advancements in Digital Signal Processing and Power Electronics: Innovations and Applications)
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29 pages, 5091 KB  
Article
Two-Phase Flow Distribution in Plate Heat Exchangers Using a Coupled CFD–Distributed Parameter Model
by Lin He, Zhipeng Ye, Shunan Zhao, Qing Luo, Bin Li and Zhichun Liu
Energies 2026, 19(13), 3215; https://doi.org/10.3390/en19133215 - 7 Jul 2026
Viewed by 387
Abstract
Plate heat exchangers (PHEs) play a critical role in the energy efficiency of heat pump systems. However, non-uniform two-phase flow distribution across parallel channels remains a key limitation, as it may cause local dryout and degrade heat transfer performance. To address the limitations [...] Read more.
Plate heat exchangers (PHEs) play a critical role in the energy efficiency of heat pump systems. However, non-uniform two-phase flow distribution across parallel channels remains a key limitation, as it may cause local dryout and degrade heat transfer performance. To address the limitations of existing prediction approaches, a hybrid modeling framework coupling computational fluid dynamics (CFD) simulations with a distributed parameter model is developed. The model is validated against experimental data under 12 representative operating conditions. The results show that the average prediction errors for the total mass flow rate, pressure drop, and heat transfer rate are within 3%, ±10%, and ±5%, respectively. The influences of refrigerant outlet conditions and inlet distributor geometry on flow distribution uniformity are systematically investigated, identifying the dominant factors governing pressure drop and the mechanism by which distributor orientation improves uniformity. Quantitative optimization shows that an orifice orientation of 225° reduces flow non-uniformity by 67.8% and enhances the heat transfer rate by 4.33% compared with the distributor-free design. The proposed method is robust across various operating scenarios and provides a reliable, quantitative tool for optimizing PHE inlet distributor designs. Full article
(This article belongs to the Section J: Thermal Management)
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30 pages, 3446 KB  
Article
Effects of Hydrogen Enrichment on Combustion Stability, Pressure Behavior, Harmonic Response, and Emissions in a Marine Auxiliary Diesel Engine
by Petros G. Savva
Energies 2026, 19(13), 3214; https://doi.org/10.3390/en19133214 - 7 Jul 2026
Viewed by 294
Abstract
Hydrogen supplementation in compression-ignition diesel engines is increasingly investigated as a practical retrofit approach for reducing the environmental impact of existing marine and stationary diesel power systems. This study examines the effects of hydrogen enrichment on combustion stability, pressure behavior, harmonic response, fuel [...] Read more.
Hydrogen supplementation in compression-ignition diesel engines is increasingly investigated as a practical retrofit approach for reducing the environmental impact of existing marine and stationary diesel power systems. This study examines the effects of hydrogen enrichment on combustion stability, pressure behavior, harmonic response, fuel consumption, and exhaust emissions in a 1966 Deutz A12L 714 marine auxiliary generator-set. The engine was operated at 900, 1200, and 1500 rpm with hydrogen supplied through the intake-air stream at flow rates up to 130.15 L/min. Results indicate that hydrogen enrichment improved fuel consumption and combustion-related dynamic behavior without increasing peak cylinder pressure or exhaust-gas temperature. Low-order vibration harmonics, particularly the 1X and 3X components associated with torque ripple and cyclic combustion variability, decreased with hydrogen addition. COV(Pmax) remained below 0.27% across all operating conditions, indicating preserved combustion stability, while hydrocarbon emissions and fuel consumption decreased by approximately 30% and 13%, respectively, at the highest hydrogen enrichment conditions. Phase-averaged pressure traces obtained showed virtually unchanged combustion-cycle structure and periodicity under maximum hydrogen enrichment. The findings indicate that hydrogen enrichment improves combustion stability and overall engine performance without increasing combustion severity, supporting its potential application as a retrofit solution for marine auxiliary engines, distributed generators, and other legacy diesel-engine systems. Full article
(This article belongs to the Special Issue Sustainable Development of Fuel Cells and Hydrogen Technologies: 2nd Edition)
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19 pages, 1259 KB  
Article
The Importance of Feature Descriptors in Identifying Fuel Types Using Machine Learning Models: An Ablation Study
by Hemachandiran Shanmugam and Aghila Gnanasekaran
Energies 2026, 19(13), 3213; https://doi.org/10.3390/en19133213 - 7 Jul 2026
Viewed by 300
Abstract
Oil and gas industry operations are critical and laborious. Recent advances in artificial intelligence and machine learning (ML) have opened up a variety of Industry 4.0 applications in the oil and gas sector. This paper introduces the architecture of an automation system for [...] Read more.
Oil and gas industry operations are critical and laborious. Recent advances in artificial intelligence and machine learning (ML) have opened up a variety of Industry 4.0 applications in the oil and gas sector. This paper introduces the architecture of an automation system for identifying the fuel types in the downstream sectors. In this research, a real-time image dataset of fuel samples is collected and annotated with the corresponding class labels, i.e., petrol and diesel. The three core modules of the architecture are pre-processing, feature extraction and classification. In pre-processing, the input images are rescaled for spatial normalization. Then, discrete wavelet transform (DWT) is applied to extract approximate, vertical, horizontal and diagonal subimages. In the feature extraction module, the features from the DWT subimages are extracted to exploit the textural properties of the input images. This work is an ablation study of various individual image features and their combinations. During classification, the extracted features are modeled using six different ML models to provide a detailed study of the image features for identifying fuel types. To fine tune the ML models, the hyperparameters are adjusted using grid search and randomized search approaches. The results show that the extreme gradient boosting (XGB) model fine tuned with randomized search is the most effective classification model with an accuracy of 97.6% on the fuel classification task. Full article
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30 pages, 3900 KB  
Article
Detection and Localization of False Data Injection Attacks in Smart Grids: A Spatiotemporal Feature-Fusion Deep-Learning Method Based on Gray Wolf Optimizer
by Jinyan Pan, Yuan Li and Xinyu Wang
Energies 2026, 19(13), 3212; https://doi.org/10.3390/en19133212 - 7 Jul 2026
Viewed by 379
Abstract
False Data Injection Attacks (FDIA) pose a serious threat to smart grid security due to their high concealment. In view of the defect that existing detection methods for FDIA in smart grids can only judge whether an attack occurs but fail to accurately [...] Read more.
False Data Injection Attacks (FDIA) pose a serious threat to smart grid security due to their high concealment. In view of the defect that existing detection methods for FDIA in smart grids can only judge whether an attack occurs but fail to accurately locate attacked grid nodes, this study proposes a specialized attack localization and detection framework. To address the node-level class imbalance inherent in attack localization, we design a node-adaptive weighting strategy tailored for multi-label classification. Furthermore, we employ a Hadamard-product-based deep fusion mechanism to integrate spatial and temporal features, which, unlike simple concatenation, enables a more profound feature interaction. The framework is optimized using the gray wolf optimizer (GWO) to enhance convergence and stability. In this method, a graph convolutional network (GCN) is used to extract spatial topological correlation features of power grid measurement data, and a bidirectional long short-term memory (BiLSTM) is adopted to mine temporal dependency features of time-series data. The deep fusion of spatial and temporal features is realized through the Hadamard product. Meanwhile, the GWO is introduced for global optimization of model hyperparameters to optimize network performance and further improve detection and localization accuracy. Simulation results on the IEEE 14-bus and IEEE 118-bus power systems show that the GWO-STFFN model surpasses existing comparative models in key indicators, including detection accuracy, F1-Score, and AUC value, delivering higher node localization precision and lower Hamming Loss. In addition, it maintains favorable robustness under different noise intensities. Full article
(This article belongs to the Special Issue Emerging Trends in Modern Power Systems: Renewable Integration, Microgrids, and Smart Technologies)
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17 pages, 2073 KB  
Article
Short-Term Electrical Load Forecasting at a 15-Minute Resolution: A Benchmarking Study Using a Rolling-Window Training Approach Against Official TSO Forecasts
by Kamil Misiurek, Tadeusz Olkuski and Janusz Zyśk
Energies 2026, 19(13), 3211; https://doi.org/10.3390/en19133211 - 7 Jul 2026
Viewed by 397
Abstract
Modern power systems require increasingly precise forecasts of electricity consumption, which are crucial for effective planning, reducing the risk of power shortages, and integrating renewable energy sources. This article presents the results of comparative benchmarking studies on short-term load forecasting (STLF) at a [...] Read more.
Modern power systems require increasingly precise forecasts of electricity consumption, which are crucial for effective planning, reducing the risk of power shortages, and integrating renewable energy sources. This article presents the results of comparative benchmarking studies on short-term load forecasting (STLF) at a 15 min resolution. The study uses the rolling-window training method, and the results were compared with the official forecasts of the Transmission System Operator (TSO). The analysis is based on time series and machine learning methods, with the aim of improving the operational accuracy necessary for stable and secure management of the power management system. Full article
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29 pages, 3662 KB  
Article
AMI-Informed Hierarchical Deep Reinforcement Learning–Model Predictive Control for Coordinated EV, PV, and Battery Energy Management in Campus Microgrids
by Mousa A. Aljabri, Mohammed O. Bahabri, Nasser A. Alakhrash, Fahd A. Hariri and Mohammad N. Ajour
Energies 2026, 19(13), 3210; https://doi.org/10.3390/en19133210 - 7 Jul 2026
Viewed by 497
Abstract
This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning [...] Read more.
This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article
(This article belongs to the Special Issue Modeling and Intelligent Control for Microgrids and Smart Grids)
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18 pages, 27162 KB  
Article
Biomass-Derived Carbon Quantum Dots as Multifunctional Electrolyte Additives for Mitigating Hydrogen Evolution and Zinc Corrosion in Rechargeable Zinc–Air Batteries
by Mustapha Balarabe Idris, Indiphile Nompetsheni, Bhekie B. Mamba and Xolile Fuku
Energies 2026, 19(13), 3209; https://doi.org/10.3390/en19133209 - 7 Jul 2026
Viewed by 541
Abstract
Rechargeable zinc–air batteries (ZABs) are attractive energy storage systems owing to their high theoretical energy density, intrinsic safety, and low cost. Yet, their practical deployment is hindered by parasitic hydrogen evolution reaction (HER), zinc corrosion, and poor interfacial stability in alkaline electrolytes. Herein, [...] Read more.
Rechargeable zinc–air batteries (ZABs) are attractive energy storage systems owing to their high theoretical energy density, intrinsic safety, and low cost. Yet, their practical deployment is hindered by parasitic hydrogen evolution reaction (HER), zinc corrosion, and poor interfacial stability in alkaline electrolytes. Herein, biomass-derived carbon quantum dots (CQDs) synthesised from lemon peel waste via a hydrothermal route were employed as multifunctional electrolyte additives to regulate the zinc/electrolyte interface and mitigate these challenges. The CQDs exhibited oxygen-rich surface functionalities and quasi-spherical nanoscale morphology, enabling stable dispersion in 6 M KOH. Electrolyte modification with CQDs significantly altered the physicochemical properties of the electrolyte, increasing the zeta potential from −28.2 to +48.5 mV while maintaining high ionic conductivity. Electrochemical studies demonstrated progressive suppression of HER, evidenced by a shift in HER onset potential from 146 to 291 mV, an increase in overpotential at 10 mA cm−2 from 398 to 477 mV, and an increase in Tafel slope from 82 to 130 mV dec−1. Corrosion studies revealed enhanced zinc stability, with the charge transfer resistance increasing from 1.35 to 3.80 Ω and a maximum corrosion inhibition efficiency of 64.47% achieved at an optimal CQD loading of 1.0 mg. Furthermore, the CQD-modified electrolyte improved the average operating power density of the ZAB from approximately 4.5 to 5.5 mW cm−2 and reduced charge–discharge polarisation during cycling. The enhanced performance is attributed to a combination of surface-controlled and transport-related processes, whereby oxygen-functionalized CQDs modify the electrical double layer, retard HER kinetics, and inhibit zinc corrosion. This work demonstrates a sustainable electrolyte engineering strategy for improving the durability and electrochemical performance of ZABs using biomass-derived carbon quantum dots. Full article
(This article belongs to the Special Issue Electrochemical Technologies for Energy Conversion and Storage)
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19 pages, 18850 KB  
Article
Harnessing Direct Geothermal Uses for a Just Energy Transition in Sonora (Northwestern México)
by Orlando Miguel Espinoza-Ojeda, Hector Miguel Aviña-Jiménez, Eduardo Pérez-González, Rodrigo Alarcón-Flores, Jesus Arturo Muñiz-Jauregui, Carlos Alberto García-Bustamante, Orlando Hernández-Cristóbal, Rafael Trueba-Regalado, Erna Martha López-Granados, Ana Teresa Mendoza-Rosas and Ruth Alfaro-Cuevas-Villanueva
Energies 2026, 19(13), 3208; https://doi.org/10.3390/en19133208 - 7 Jul 2026
Viewed by 521
Abstract
This study poses the following research question: Where and how can low- to medium-enthalpy geothermal resources in Northern México be harnessed to promote a territorially anchored, socially inclusive energy transition? Hence, the potential contribution of geothermal direct uses to sustainable local development in [...] Read more.
This study poses the following research question: Where and how can low- to medium-enthalpy geothermal resources in Northern México be harnessed to promote a territorially anchored, socially inclusive energy transition? Hence, the potential contribution of geothermal direct uses to sustainable local development in Sonora—one of México’s largest and most economically diverse states—is examined in this article. In Sonora, a semi-arid region with dispersed populations and underutilized geothermal resources, the research integrates spatial analysis and socio-territorial indicators to identify areas where geothermal direct uses can deliver inclusive development benefits. Thermal data of 88 thermal springs and 36 wellbores were examined, in which in situ temperatures and geothermal gradients were found from 30 to 80 °C and 20–200 °C/km, respectively. This resulted in a catalog of 28 direct uses based on the energy needs and demands of the population near the sites. Then, a composite methodological framework was developed that combined the Geothermal Suitability Index (GSI), the Socio-Productive Energy Demand Index (SPEDI), and the Territorial Vulnerability Index (TVI). These indices and the catalog were overlaid to detect municipalities where high geothermal potential, energy needs, and social vulnerability intersect. Results show that sites such as Bacadehuachi, Cajeme, and Fronteras offer high-priority opportunities for agri-food processing, aquaculture, and heating/cooling applications. The findings contribute to broader debates on rural energy access, energy justice, and decentralized planning, providing evidence-based guidance for policy design that aligns renewable energy deployment with regional equity and resilience goals. Full article
(This article belongs to the Special Issue Deep Geothermal Energy Development and Utilization)
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17 pages, 3322 KB  
Article
Low-Carbon Robust Planning for PIESs with Multi-Time-Scale Uncertainties and Elastic DR Regulation
by Xin Huang, Shucan Zhou, Jian Xiong, Keteng Jiang, Hao Yu and Haibo Li
Energies 2026, 19(13), 3207; https://doi.org/10.3390/en19133207 - 7 Jul 2026
Viewed by 347
Abstract
With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth [...] Read more.
With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth uncertainties and fails to fully utilize the flexibility of demand-side resources during the planning phase. This paper proposes a robust planning method for PIESs considering dynamic demand response and multi-timescale uncertainties. First, an energy flow framework encompassing cooling, heating, electricity, gas, and hydrogen is constructed. To overcome the limitations of traditional fixed-boundary DR, a dynamic elastic DR mechanism featuring transferable, substitutable, and curtailable types is established. Transferable demand boundaries are defined by a price–demand elasticity matrix, and actual responses are dynamically adjusted in synergy with system power balance conditions for optimal configuration. Second, multivariate dynamic time warping and hierarchical clustering algorithms derive typical daily scenarios accounting for short-term uncertainties. Finally, information gap decision theory characterizes long-term load growth uncertainty, constructing a robust planning model addressing both timescales. Case studies show that flexible resources and demand response reduce lifecycle cost by 55.24% and carbon emissions by 47.75%. The proposed demand response method further cuts costs by 153,800 yuan and emissions by 11.36%. The robust planning method synergistically addresses multi-timescale uncertainties, ensuring economy while maximizing resilience to uncertain fluctuations. Full article
(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions: 2nd Edition)
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29 pages, 2010 KB  
Article
Improved Dung Beetle Algorithm for Multi-Objective Environmental Economic Dispatch of Microgrid
by Jinming Luo, Lingshang Kong, Fujia Chen and Huijie Liu
Energies 2026, 19(13), 3206; https://doi.org/10.3390/en19133206 - 6 Jul 2026
Viewed by 353
Abstract
With the widespread integration of renewable energy, microgrid environmental economic dispatch (EED) faces challenges such as uncertainties in wind and solar power outputs and multi-objective conflicts. This paper proposes a stochastic expected dispatch framework based on an improved multi-objective dung beetle optimization algorithm [...] Read more.
With the widespread integration of renewable energy, microgrid environmental economic dispatch (EED) faces challenges such as uncertainties in wind and solar power outputs and multi-objective conflicts. This paper proposes a stochastic expected dispatch framework based on an improved multi-objective dung beetle optimization algorithm (MO-CLDBO). First, considering both wind–solar uncertainties and demand response, a Gaussian Copula function is employed to characterize the 24-h temporal correlations among wind speed, solar irradiance, and load, and typical scenarios are generated via Monte Carlo sampling and simultaneous backward reduction; a time-of-use demand response model is also introduced. Second, taking expected operational cost and environmental emission as dual objectives, three improvements are proposed to address the issues of uneven initial population, easy local convergence, and Pareto front collapse in the standard dung beetle algorithm: a Folded Two-Dimensional Modified Coupled Logistic-Sine Map (Folded 2D-MCLSM) is used to initialize a high-quality population, a non-dominated sorting mechanism is introduced, and a dynamic lens imaging backward learning strategy is designed. Finally, the proposed algorithm is compared with several classical algorithms in the mathematical model of microgrid optimal dispatch through 50 independent runs. Experimental results show that the improved dung beetle optimization algorithm achieves not only the lowest average operating cost, but also the best hypervolume (HV) indicator, demonstrating excellent comprehensive performance in multi-objective search convergence and solution set diversity. Full article
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37 pages, 15819 KB  
Article
Multi-Source Coordinated Supply-Guarantee Dispatch Strategy Under Consecutive-Day Renewable Energy Drought
by Xiaojie Pan, Bo Yang, Dejun Shao, Mujie Zhang, Mengxuan Shi, Yajun Wu and Dongsheng Li
Energies 2026, 19(13), 3205; https://doi.org/10.3390/en19133205 - 6 Jul 2026
Viewed by 431
Abstract
The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to [...] Read more.
The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article
(This article belongs to the Special Issue Advances in Power and Electrical Engineering)
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24 pages, 3663 KB  
Article
Deviation-Based Operating Reserve Sizing and Market Co-Optimization for Data-Constrained Island Power Systems
by Máximo A. Domínguez-Garabitos, René Báez-Santana, Víctor S. Ocaña-Guevara, Yeulis V. Rivas-Peña, Rafael O. Uceta-Acosta and Miguel E. Aybar-Mejía
Energies 2026, 19(13), 3204; https://doi.org/10.3390/en19133204 - 6 Jul 2026
Viewed by 379
Abstract
Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small [...] Read more.
Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small Island Developing States (SIDS). This paper develops a market-based framework for the co-optimization of energy and operating reserves in low-inertia island power systems, in which reserve requirements are established using historically observed extreme generation or load deviations that represent operationally validated high-risk system conditions, while reserve allocation and pricing emerge from the co-optimization process. By relying on observed operational variability, the proposed approach avoids explicit probabilistic uncertainty modeling while retaining sensitivity to system stress conditions. The approach is evaluated using a stylized island power system representative of Caribbean SIDS. Results show that reserve requirements are highly sensitive to operating conditions, reaching up to 26.7% of demand under high variability and significantly exceeding conventional fixed reserve criteria. The framework reduces non-served energy, improves reserve allocation efficiency, and generates scarcity-consistent reserve prices under stressed conditions. These findings demonstrate that the proposed methodology provides a practical intermediate solution between deterministic and probabilistic reserve sizing approaches while remaining suitable for data-constrained island power systems. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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22 pages, 571 KB  
Article
An Invertible Extended Sequence Transform for Untransposed Three-Phase Overhead Lines
by Jozef Bendík and Matej Cenký
Energies 2026, 19(13), 3203; https://doi.org/10.3390/en19133203 - 6 Jul 2026
Viewed by 255
Abstract
The classical Fortescue symmetrical-component transform remains fully invertible as a similarity transform when the complete sequence-domain matrix is retained. In practice, however, untransposed three-phase overhead lines are often summarized by the diagonal sequence quantities Z0 and Z1, while the asymmetry [...] Read more.
The classical Fortescue symmetrical-component transform remains fully invertible as a similarity transform when the complete sequence-domain matrix is retained. In practice, however, untransposed three-phase overhead lines are often summarized by the diagonal sequence quantities Z0 and Z1, while the asymmetry appears through coupled off-diagonal terms that are less convenient for compact parameterization and measurement-based interpretation. This paper presents an Extended Sequence Transform that reorganizes the six independent entries of the phase-domain impedance matrix into six structured parameters by means of a lossless, invertible 6×6 linear mapping. The first two parameters are identical to the classical zero- and positive-sequence impedances, which preserves backward compatibility. The remaining four parameters isolate asymmetry information in a form from which all entries of the classical sequence matrix can be recovered exactly, and from which the full phase-domain matrix is reconstructed to machine precision. The proposed representation does not diagonalize an untransposed line; rather, it provides a compact and explicit six-scalar parameterization that separates the classical sequence pair from four asymmetry descriptors. Numerical validation on a 50 km untransposed overhead line confirms exact round-trip reconstruction and exact agreement of the unbalance factors obtained from the classical and extended representations. A stochastic perturbation study further shows that round-trip reconstruction remains at numerical precision and that, within the tested perturbation grid, the M2 factor is more sensitive than M0. Line-constant calculations performed in OpenDSS for two typical 400 kV tower geometries link the four asymmetry parameters to specific geometric features, a worked offline measurement example recovers them from simulated three-phase terminal tests, and a distributed-model study confirms that the lumped-parameter description of the asymmetry remains accurate to within about 0.6% up to 150 km. Full article
(This article belongs to the Special Issue Advanced Electric Power Systems, 2nd Edition)
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23 pages, 4157 KB  
Article
Experimental Study on the Thermal, Electrical, and Visual Performance of a Transparent Vacuum Insulation Panel with Attached Film-Based Semi-Transparent Photovoltaic Panel
by Erkki Hirvonen and Takao Katsura
Energies 2026, 19(13), 3202; https://doi.org/10.3390/en19133202 - 6 Jul 2026
Viewed by 319
Abstract
This proof-of-concept study proposes a photovoltaic transparent vacuum insulation panel (PV-TVIP) and evaluates its heat transfer and power generation characteristics with increased temperatures, and light transmission characteristics for visible light and ultraviolet wavelengths. The study was conducted with a climate-controlled chamber mimicking the [...] Read more.
This proof-of-concept study proposes a photovoltaic transparent vacuum insulation panel (PV-TVIP) and evaluates its heat transfer and power generation characteristics with increased temperatures, and light transmission characteristics for visible light and ultraviolet wavelengths. The study was conducted with a climate-controlled chamber mimicking the common temperature range of Sapporo, Japan. The average TVIP heat flux was measured to be 65–75 W/m2 with a U-value of 1.95–2.3 W/(m2∙K). Compared to earlier measurements to see the effect of seasonal atmospheric conditions to the quality of the TVIP, it was determined that the TVIP manufactured during winter conducted less heat, assumed to be caused by decreased humidity. Placing the PV between the TVIP and a glass pane increased the operating temperature by 26.06 °C and decreased power generation by 13%. Afterwards, the transparency of the TVIP and PV-TVIP were measured under a bright light therapy lamp, showing that TVIP reduced the amount of most visible light wavelengths by 50% and the PV-TVIP by 90%. UV radiation was respectively reduced by approximately 78% and 100%. The results show that while PV-TVIP shows potential as a BAPV window retrofit solution, its manufacturing requires optimized, low-humidity conditions during all phases of the manufacturing process. Full article
(This article belongs to the Special Issue New Challenges in Renewable Energy Integration and Net Zero Energy Future)
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34 pages, 19395 KB  
Article
China’s Terrestrial Hydro-, Wind-, and Photovoltaic-Power Potentials and CO2 Emission Reductions Under Different Development Scenarios
by Bing Li, Mingwei Ma, Chongxu Zhao, Caihong Hu and Liangyan Zhang
Energies 2026, 19(13), 3201; https://doi.org/10.3390/en19133201 - 6 Jul 2026
Viewed by 415
Abstract
This study evaluates the resource, technical, economic, and CO2 mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, [...] Read more.
This study evaluates the resource, technical, economic, and CO2 mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, and CMIP6 climate outputs, we estimate renewable-energy potentials through a consistent national-scale screening framework and cost–supply curve analysis. The results show clear spatial heterogeneity among the three energy sources. Hydropower potential is concentrated mainly in the Yangtze River basin, Pearl River basin, and Southwestern International Rivers. Wind-power potential is relatively high in northwestern, northeastern, and plateau regions, while PV potential is particularly large in northwestern, northern, northeastern, and selected southeastern regions. Under the adopted assumptions, PV shows the largest resource and technical potential, followed by wind power and hydropower; however, this ranking reflects resource potential rather than comprehensive deployment superiority. Practical development is also constrained by ecological flow requirements, land-use competition, grid integration, storage demand, transmission capacity, curtailment risk, and regional demand matching. The findings provide a national-scale comparative reference for renewable-energy planning and CO2 mitigation, while highlighting the need for future work that incorporates dynamic land use, system-level integration costs, detailed turbine or power-curve modeling, and dynamic grid-emission factors. Full article
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24 pages, 8971 KB  
Article
Study on the Sensitivity of Gas Extraction Parameters and the Dynamic Evolution of the Effective Extraction Radius Under Multiphysics Coupling
by Huogen Luo and Qianting Hu
Energies 2026, 19(13), 3200; https://doi.org/10.3390/en19133200 - 6 Jul 2026
Viewed by 297
Abstract
To investigate the sensitivity and underlying mechanisms of key parameters for gas extraction, a three-dimensional numerical model was established. Using the control variable method, the effects of critical extraction parameters on gas pressure evolution, the effective extraction radius of drilling, and cumulative gas [...] Read more.
To investigate the sensitivity and underlying mechanisms of key parameters for gas extraction, a three-dimensional numerical model was established. Using the control variable method, the effects of critical extraction parameters on gas pressure evolution, the effective extraction radius of drilling, and cumulative gas production were systematically analyzed. The results indicate the following: Extraction time is the primary factor controlling the expansion of the pressure disturbance zone, and gas extraction exhibits a significant characteristic of diminishing marginal returns. Increasing the extraction negative pressure and drilling diameter mainly improves near-drilling flow conditions and contributes only marginally to the overall extraction effectiveness. Drilling length determines the gas resource volume controlled by a single drilling operation and its sustained extraction capacity while exerting only a limited influence on the effective extraction radius. The initial porosity of the coal seam is the dominant factor controlling both the effective extraction radius and extraction efficiency. Field extraction data verified the model’s reliable representation of extraction patterns and parameter influence characteristics. A synergistic gas control strategy integrating long drilling coverage, enhanced permeability, reasonable negative pressure, and continuous extraction was proposed. These research results can provide a theoretical basis and technical support for the optimization of gas extraction parameters. Full article
(This article belongs to the Special Issue Advances in Extraction and Utilization of Coal and Shale Gas)
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27 pages, 8716 KB  
Article
Integrated Traffic–Weather-Aware Forecasting of Urban EV Charging Demand for Infrastructure Planning
by Christoph Sommer, Jahangir Hossain and Abbas Tabandeh
Energies 2026, 19(13), 3199; https://doi.org/10.3390/en19133199 - 6 Jul 2026
Viewed by 299
Abstract
The accelerating adoption of electric vehicles (EVs) presents significant challenges for maintaining grid stability and optimizing charging infrastructure. Accurate short-term forecasting of EV charging demand is therefore critical to support reliable grid operation and effective energy management in urban environments. However, existing forecasting [...] Read more.
The accelerating adoption of electric vehicles (EVs) presents significant challenges for maintaining grid stability and optimizing charging infrastructure. Accurate short-term forecasting of EV charging demand is therefore critical to support reliable grid operation and effective energy management in urban environments. However, existing forecasting models often fail to capture the intricate interdependencies among mobility patterns, weather variations, and real-world charging behaviors, which constrains their generalizability and robustness. This study develops a multi-model forecasting framework that leverages Transformer-based deep learning architectures to integrate real-world charging data with traffic flow and meteorological variables for predicting short-term EV charging demand across metropolitan areas. To benchmark performance, two additional machine learning models—CatBoost and convolutional neural networks (CNNs)—are systematically evaluated using datasets from urban EV supply equipment (EVSE) and electric bus systems. The results indicate that Transformer-based models deliver superior predictive accuracy, temporal consistency, and adaptability compared with CNNs and CatBoost. Furthermore, sensitivity analysis reveals that traffic dynamics and user charging behavior exert the strongest influence on forecast performance. The proposed framework offers actionable insights for utilities and urban planners, facilitating resilient grid operation, optimized charging infrastructure deployment, and accelerated integration of EVs into the power system. Full article
(This article belongs to the Special Issue Advancements in Vehicle-to-Grid Technology for Smart Energy Systems)
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25 pages, 15716 KB  
Article
Electricity Consumption Databases and Contribution of a New Equatorial Dataset from Ecuador for Load Forecasting Applications
by Erik Fernando Mendez-Garces, David Buldain and María Paz Comech
Energies 2026, 19(13), 3198; https://doi.org/10.3390/en19133198 - 6 Jul 2026
Cited by 1 | Viewed by 338
Abstract
Accurate electricity consumption forecasting is essential for the efficient planning and operation of modern power systems. The development of predictive models based on machine learning and deep learning strongly depends on the availability of well-documented and publicly accessible electricity consumption datasets. However, most [...] Read more.
Accurate electricity consumption forecasting is essential for the efficient planning and operation of modern power systems. The development of predictive models based on machine learning and deep learning strongly depends on the availability of well-documented and publicly accessible electricity consumption datasets. However, most existing databases are concentrated in Europe and North America and are typically focused on residential measurements obtained from smart meters, resulting in limited representation of equatorial regions. This work presents a structured review of public electricity consumption repositories, analyzing characteristics such as geographical coverage, temporal resolution, user type, and accessibility. Based on the limitations identified in the literature, a new electricity consumption dataset obtained from real measurements collected at distribution substations located in an equatorial region is presented. The dataset was organized through a systematic preprocessing workflow that included temporal standardization, construction of 48-h sliding windows, normalization, and stratified partitioning into training, validation, and test subsets. The descriptive statistical analysis confirms the consistency of the generated subsets and reveals differences between working-day and non-working-day consumption patterns. The proposed dataset provides a reproducible resource for the development and evaluation of multi-horizon electricity demand forecasting models, as well as for load analysis and energy management studies in equatorial regions. Full article
(This article belongs to the Section F1: Electrical Power System)
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20 pages, 5658 KB  
Article
Power Transformer Component Reliability Using CIGRE Large-Scale Data Surveys
by Daniel Martin and Stefan Tenbohlen
Energies 2026, 19(13), 3197; https://doi.org/10.3390/en19133197 - 6 Jul 2026
Viewed by 869
Abstract
The probability of failure of a power transformer is difficult to quantify within a single utility because major failures are rare and operating histories are often incomplete. This paper uses large-scale CIGRE surveys (50 utilities, 26,533 transformers, 331,379 operating years) to estimate age-dependent [...] Read more.
The probability of failure of a power transformer is difficult to quantify within a single utility because major failures are rare and operating histories are often incomplete. This paper uses large-scale CIGRE surveys (50 utilities, 26,533 transformers, 331,379 operating years) to estimate age-dependent component reliability by voltage class. In total, 1358 major failures and 991 retirements were reported for a reference period of up to 34 years. The data were treated as left-truncated and right-censored. Hazard rates were calculated for active parts, bushings, and on-load tap changers, retirements were assessed using the Kaplan–Meier estimator, and Weibull distribution models were fitted to 100–199 kV and 200–700 kV populations. The overall major failure rate was 0.41% per year. For the 100–199 kV transformers, the component hazard rates were close to constant with age (β ≈ 1), and the late-life pattern was influenced by increasing retirements. For the 200–700 kV transformers, bushing hazard showed a stronger age dependency and exceeded active-part hazard at around 50 years. The results highlight the value of component-focused risk management and show that fleet reliability should be interpreted alongside retirement and condition-management practices. Key limitations include data truncation, censoring, and the lack of categorisation of failures by technology type. Full article
(This article belongs to the Special Issue Emerging Trends in Enhancing Power Grid Performance)
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31 pages, 8411 KB  
Article
Experimental Comparison of Sensible and Latent Heat Storage in a Packed-Bed Thermal Energy Storage System
by Tomasz Spietz, Szymon Dobras, Kinga Kulik, Rafał Fryza and Agata Czardybon
Energies 2026, 19(13), 3196; https://doi.org/10.3390/en19133196 - 6 Jul 2026
Viewed by 373
Abstract
Thermal energy storage (TES) is essential for improving the flexibility and efficiency of renewable and industrial energy systems. This study experimentally compares sensible and latent heat storage using basalt aggregate and an encapsulated phase change material (PCM), specifically 60 wt.% NaNO3–40 [...] Read more.
Thermal energy storage (TES) is essential for improving the flexibility and efficiency of renewable and industrial energy systems. This study experimentally compares sensible and latent heat storage using basalt aggregate and an encapsulated phase change material (PCM), specifically 60 wt.% NaNO3–40 wt.% KNO3, as packed-bed materials under elevated-temperature operating conditions. Tests were conducted in an air-based TES rig at air flow rates of 60–120 kg/h, with packed bed temperatures exceeding 400 °C. Key parameters included temperature profiles, thermal power, energy storage, and recovery during charging and discharging phases. The results indicate that increasing the air flow rate accelerated thermal front propagation and improved charging and discharging power, but did not proportionally increase stored or recovered energy. The basalt bed achieved recovered volumetric energy densities of 108–160 MJ/m3 at about 150 °C and 351–405 MJ/m3 above 320 °C. The encapsulated solar salt bed reached higher values, from 412–508 MJ/m3 near 290 °C to 523–626 MJ/m3 at higher temperatures. Both materials showed high TES efficiencies, in the range of 80–94%. Encapsulated PCM significantly increased energy storage density in packed-bed TES systems, while basalt aggregate provides higher short-term thermal power output. Full article
(This article belongs to the Section D: Energy Storage and Application)
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20 pages, 2747 KB  
Article
ML-Based Feasibility-Prediction for NB-IoT Smart Metre Deployment in Thailand: A Cross-Environment Multi-Site Study
by Kittiwat Srivilas and Chaiyod Pirak
Energies 2026, 19(13), 3195; https://doi.org/10.3390/en19133195 - 6 Jul 2026
Viewed by 318
Abstract
Thailand’s Provincial Electricity Authority (PEA) is rolling out Advanced Metering Infrastructure (AMI) under its smart-grid initiative, requiring a reliable last-mile wireless network across heterogeneous propagation environments. Narrowband IoT (NB-IoT) is a leading candidate, but per-area deployment decisions have lacked a data-driven framework anchored [...] Read more.
Thailand’s Provincial Electricity Authority (PEA) is rolling out Advanced Metering Infrastructure (AMI) under its smart-grid initiative, requiring a reliable last-mile wireless network across heterogeneous propagation environments. Narrowband IoT (NB-IoT) is a leading candidate, but per-area deployment decisions have lacked a data-driven framework anchored to measured Thai propagation. Building on our sixteen-site composite-channel characterisation, this study presents a machine-learning feasibility-prediction framework integrating measured channel parameters (n, σsh, m^), an OpenStreetMap-derived synthetic meter-density layer, and a benchmark of Random Forest, Gradient Boosting (GB), and Multi-Layer Perceptron classifiers trained on Monte-Carlo coverage labels to predict 95% RSRP-coverage feasibility per spatial cell. Across 411 cells from four Thai sites spanning Urban Dense, Urban Outdoor, Suburban, and Rural environments, GB achieves accuracy 0.971 and F1 0.969 at 1.7 ms inference latency—four orders of magnitude faster than direct Monte-Carlo simulation. The ML predictor approximates the Monte-Carlo engine under the assumed composite-channel model. A theoretical LPWAN comparison places NB-IoT as recommended for Suburban and Rural AMI; Suphan Buri (Rural) is the only RECOMMENDED case (88.5% cells feasible), with hybrid PLC backhaul suggested for dense urban areas. Full article
(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)
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