Energies

40 pages, 5543 KB

Open AccessArticle

An Adaptive Decomposition–Ensemble Modeling Method for Multi-Category Power Materials Demand Forecasting with Uncertainty Quantification

by Nan Zhu, Xiao-Ning Ma, Shi-Yu Zhang, Qian-Qian Meng and Wei Lu

Energies 2026, 19(8), 2008; https://doi.org/10.3390/en19082008 - 21 Apr 2026

Viewed by 381

Abstract

Accurate demand forecasting with uncertainty quantification is critical for materials management in power grid enterprises, yet existing methods struggle to capture multi-scale temporal dynamics across heterogeneous material categories while providing reliable confidence estimates. This paper proposes an Adaptive Decomposition–Ensemble Modeling (ADEM) method that [...] Read more.

Accurate demand forecasting with uncertainty quantification is critical for materials management in power grid enterprises, yet existing methods struggle to capture multi-scale temporal dynamics across heterogeneous material categories while providing reliable confidence estimates. This paper proposes an Adaptive Decomposition–Ensemble Modeling (ADEM) method that integrates adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) with category-specific depth selection, a heterogeneous ensemble of a GBM (Gradient Boosting Machine), ELM (Extreme Learning Machine), and SVR (Support Vector Regression) with per-component optimized weights, and Bayesian uncertainty quantification with conformal calibration for distribution-free coverage guarantees. Experiments on real-world data spanning 18 material categories over 60 months demonstrate that ADEM consistently outperforms 14 baselines spanning statistical, machine learning, deep learning, and decomposition-based methods in both point prediction accuracy and prediction interval quality. Rolling-origin evaluation across six temporal windows further exhibits the robustness and statistical significance of these improvements. Full article

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29 pages, 3907 KB

Open AccessReview

From Algorithm to Operation: A Scoping Review of Realization Conditions for Deploying Data-Driven Thermally Activated Building Systems

by Zheng Grace Ma, Simon Soele Madsen, Benjamin Eichler Staugaard, Joy Dalmacio Billanes and Bo Nørregaard Jørgensen

Energies 2026, 19(8), 2007; https://doi.org/10.3390/en19082007 - 21 Apr 2026

Viewed by 473

Abstract

Thermally activated building systems offer significant potential for low-carbon building operation and energy flexibility by using building mass as distributed thermal storage. Recent advances in data-driven control, machine learning, and digital building infrastructure have expanded their technical capabilities. However, practical deployment remains limited. [...] Read more.

Thermally activated building systems offer significant potential for low-carbon building operation and energy flexibility by using building mass as distributed thermal storage. Recent advances in data-driven control, machine learning, and digital building infrastructure have expanded their technical capabilities. However, practical deployment remains limited. This paper addresses that gap through a scoping review of the literature on data-driven thermally activated building systems, with a focus on the conditions required for implementation, integration, and sustained operation in practice. The review examines publication patterns, realization stages, dominant realization pathways, and recurring enablers and barriers across the field. The results show that the literature is concentrated in conceptual, simulation, and pilot-stage studies, while evidence of long-term operation in occupied buildings remains scarce and evidence of scalable or transferable realization in the reviewed TABS literature remains limited. The paper proposes five realization conditions for deployment as an interpretive synthesis of the reviewed literature: operational observability, deployable model architecture, embedded digital integration, operational acceptability, and organizational handover capacity. The review reframes data-driven thermally activated building systems as a realization challenge rather than only a control problem and provides a structured analytical framework to support future research and deployment-oriented evaluation in energy informatics. Full article

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

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15 pages, 1727 KB

Open AccessArticle

Mathematical Model Establishment for the Multi-Scale Permeability of Coal Reservoirs and Its Engineering Significance

by Zhigang Du, Feilong Xiong, Yingying Li, Guiyang Ren, Jianggen He, Yongyan Yan, Qi Liu and Hongyang Bai

Energies 2026, 19(8), 2006; https://doi.org/10.3390/en19082006 - 21 Apr 2026

Viewed by 266

Abstract

Permeability is a critical parameter governing the gas flow behavior of the coalbed methane (CBM) reservoir during the exploration and exploitation of CBM, as well as the geological storage of CO₂ in the coalbeds. It is strongly associated with the multi-scale fractures [...] Read more.

Permeability is a critical parameter governing the gas flow behavior of the coalbed methane (CBM) reservoir during the exploration and exploitation of CBM, as well as the geological storage of CO₂ in the coalbeds. It is strongly associated with the multi-scale fractures developed in coal. Based on the distribution characteristics of micro-fractures, a multi-scale permeability model for coal reservoirs was established by introducing the permeability tensor, which comprehensively considers adsorption-induced coal swelling, pore pressure, effective stress, and micro-fractures. Further, the dynamic evolution law and mechanism of multi-scale permeability of coal reservoirs under different adsorption pressures were discussed. The results indicate that the increase in effective stress on the coal caused by adsorption-induced swelling essentially leads to a decrease in the equivalent multi-scale permeability of coal. Two key indicators, namely equilibrium pressure and rebound pressure, were defined to quantitatively characterize the evolution law of the equivalent multi-scale permeability during gas adsorption or desorption processes. The effective stress generated by the CO₂ adsorption-induced swelling effect in the low-rank coal is 1.47 times that in the middle-rank coal and 2.51 times that in the high-rank coal. Additionally, the effective stress generated by the CO₂ adsorption-induced swelling effect in the low-rank coal is 5.15 times that generated by N₂, while this level is 4.32 times higher than that in the middle-rank coal. Therefore, compared with the low- and middle-rank coal, the high-rank coal exhibits a smaller decrease in multi-scale permeability due to its weaker adsorption-induced swelling effect. During N₂ adsorption, the pore pressure effect dominates over the adsorption-induced swelling effect, resulting in a decrease in the effective stress on the coal with increasing gas pressure. Consequently, the equivalent multi-scale permeability of coal will increase much more significantly with an increase in injected N₂ pressure than with an increase in CO₂ pressure. By accounting for the differences between the effects of adsorption-induced swelling and pore compression on the equivalent multi-scale permeability of coal reservoir, the injectivity of CO₂ can be improved by mixing it with N₂. Full article

(This article belongs to the Topic Advances in Integrated Geological, Mineralogical, and Engineering Research on Unconventional Oil and Gas)

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21 pages, 8473 KB

Open AccessArticle

Vacuum Degree Monitoring of Distribution Class Vacuum Interrupter Using Non-Contact Coupling Capacitor Based on AC and DC Partial Discharge

by Seungmin Bang, Chanyeol Ryu and Bang-Wook Lee

Energies 2026, 19(8), 2005; https://doi.org/10.3390/en19082005 - 21 Apr 2026

Viewed by 234

Abstract

Vacuum degree inside vacuum interrupter (VI) deteriorates due to cracks from long-term operation of VI, gas emitted from internal arc heat, leakage through the joint, etc. Partial discharge occurs between the two contacts inside the VI or between the contact and floating shield, [...] Read more.

Vacuum degree inside vacuum interrupter (VI) deteriorates due to cracks from long-term operation of VI, gas emitted from internal arc heat, leakage through the joint, etc. Partial discharge occurs between the two contacts inside the VI or between the contact and floating shield, which leads to dielectric breakdown and electrical accidents of high voltage apparatus. In this paper, the study on the vacuum degree monitoring of distribution class vacuum interrupter according to non-contact method of coupling capacitor based on partial discharge was performed. In order to monitor the partial discharge between two contacts inside VI with high accuracy, a partial discharge sensing electrode (PDDE) was designed using the 3D finite element method (FEM). In addition, after calculating the internal capacitance according to the structure and size characteristics inside VI, the capacity of the coupling capacitor to detect the signal was calculated. The partial discharge characteristics according to the vacuum degree were analyzed by applying PDDE and a coupling capacitor. As results, it was found that the partial discharge characteristics inside VI differ depending on the voltage type. In addition, it was confirmed that even if VI has the same internal structure and size, the partial discharge characteristics appear differently. Based on the experimental results, we proposed maintenance criteria for VI for each voltage type. Full article

(This article belongs to the Section F: Electrical Engineering)

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33 pages, 14849 KB

Open AccessArticle

Simulation and Experimental Research on Arc-Induced Fires in Photovoltaic Systems

by Runan Song, Penghe Zhang, Yang Xue and Wei Wang

Energies 2026, 19(8), 2004; https://doi.org/10.3390/en19082004 - 21 Apr 2026

Viewed by 355

Abstract

DC fault arcs comprise one of the most serious safety hazards in photovoltaic systems, and their danger far exceeds that of AC arcs. DC arcs lack a natural zero-crossing point, and their burning time can last from several seconds to several minutes, which [...] Read more.

DC fault arcs comprise one of the most serious safety hazards in photovoltaic systems, and their danger far exceeds that of AC arcs. DC arcs lack a natural zero-crossing point, and their burning time can last from several seconds to several minutes, which is sufficient to ignite cable lines and surrounding combustibles, causing fires. To explore the characteristics and mechanism of the ignition of external combustibles by DC fault arcs, this paper, based on the theory of magnetohydrodynamics (MHD), constructed a three-dimensional numerical simulation model of a DC fault arc considering the coupling of electromagnetic, thermal, and flow fields. A DC fault arc experimental platform that can simulate the actual working conditions of photovoltaic systems was built to verify the accuracy of the model. Based on this, by integrating the complex pyrolysis model and the combustion reaction model, and selecting cotton fibers as the typical combustible indicator substances, as stipulated in the UL 1699 standard, a coupled simulation model for the ignition of solid combustibles by direct current fault arcs was established. The numerical simulation of the entire ignition process of the arc was realized, and the coupling mechanism of heat transfer, mass transfer, and chemical reactions during the ignition process was revealed. The research results of this paper fill a research gap in the numerical simulation of arc ignition caused by DC faults in photovoltaic systems, clarify the fire ignition risk patterns of DC fault arcs under different working conditions, and provide important theoretical support and technical references for the formulation of arc fire prevention strategies and the optimized design of fault arc protection devices for photovoltaic systems and other DC power systems. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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23 pages, 2822 KB

Open AccessArticle

A Simplified Temperature Field Calculation Model for Oil-Immersed Transformers Based on the FVM-POD Field–Circuit Coupling Method

by Yanan Yuan, Hao Yang, Shijun Wang and Linhong Yue

Energies 2026, 19(8), 2003; https://doi.org/10.3390/en19082003 - 21 Apr 2026

Viewed by 341

Abstract

In the context of new-type power system construction, digital twin has become the core technology for power transformers, supporting their full-life cycle intelligent operation and maintenance. The real-time, high-precision calculation of the internal temperature field serves as the core supporting element for realizing [...] Read more.

In the context of new-type power system construction, digital twin has become the core technology for power transformers, supporting their full-life cycle intelligent operation and maintenance. The real-time, high-precision calculation of the internal temperature field serves as the core supporting element for realizing the real-time mapping between the physical transformer entity and its virtual twin. Aiming at the inherent defects of traditional temperature rise calculation methods, such as insufficient accuracy and an excessively long computation time, this paper proposes a simplified calculation model for the transformer temperature field. In this model, the transformer oil tank is simplified into a two-dimensional axisymmetric thermal–fluid coupled field model solved by the finite volume method (FVM). The Proper Orthogonal Decomposition (POD) technique is adopted to perform order reduction on the matrices involved in the governing equations, so as to reduce the computational degrees of freedom. Meanwhile, the radiator is equivalent to a one-dimensional thermal circuit model, and the field–circuit coupled solution is achieved through bidirectional data mapping. Temperature field calculation is carried out for a 220 kV oil-immersed transformer based on the proposed model. The results show that the average relative error between the calculated results and the experimental data is around 0.86%, while the computation time is merely 0.04% of that of the traditional three-dimensional full-scale model. Furthermore, taking the real-time overload capacity evaluation of the transformer as a case, it is verified that the proposed model can successfully support the requirements of practical engineering applications. Full article

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72 pages, 3368 KB

Open AccessReview

The Use of Modern Hybrid Membranes for CO₂ Separation from Synthetic and Industrial Gas Mixtures in Light of the Energy Transition

by Aleksandra Rybak, Aurelia Rybak, Jarosław Joostberens and Spas D. Kolev

Energies 2026, 19(8), 2002; https://doi.org/10.3390/en19082002 - 21 Apr 2026

Viewed by 438

Abstract

The global energy transition and the implementation of carbon capture, utilization, and storage (CCUS) strategies require energy-efficient and scalable CO₂ separation technologies. Mixed-matrix membranes (MMMs), combining polymer matrices with functional inorganic or hybrid nanofillers, have emerged as advanced separation platforms capable of [...] Read more.

The global energy transition and the implementation of carbon capture, utilization, and storage (CCUS) strategies require energy-efficient and scalable CO₂ separation technologies. Mixed-matrix membranes (MMMs), combining polymer matrices with functional inorganic or hybrid nanofillers, have emerged as advanced separation platforms capable of surpassing the conventional permeability–selectivity trade-off observed in neat polymer membranes. This review critically evaluates recent developments in modern hybrid membranes for CO₂ separation from synthetic and industrial gas mixtures, including CO₂/N₂ (flue gas), CO₂/CH₄ (natural gas and biogas upgrading), and syngas systems. Particular emphasis is placed on MMMs incorporating covalent organic frameworks (COFs), metal–organic frameworks (MOFs), graphene oxide (GO), MXenes, transition metal dichalcogenides (TMDs), carbon nanotubes (CNTs), g-C₃N₄, layered double hydroxides (LDH), zeolites, metal oxides, and magnetic nanoparticles. Reported performance ranges include CO₂ permeability (P_CO2) typically between 100 and 800 Barrer, CO₂/N₂ selectivity up to 319, and CO₂/CH₄ selectivity up to 249, depending on filler chemistry, loading, and interfacial compatibility. The mechanisms governing gas transport—molecular sieving, selective adsorption, facilitated transport, and diffusion-pathway engineering—are systematically discussed. Key challenges addressed include filler dispersion, polymer–filler interfacial defects, physical aging, moisture sensitivity, oxidation (particularly in MXenes), and scalability toward industrial membrane modules. Future perspectives focus on sub-nanometer pore engineering, surface functionalization to enhance CO₂ affinity, controlled alignment of 2D nanosheets to promote directional transport, multifunctional core–shell and hollow structures, and the integration of computational modeling and machine learning for accelerated material design. Modern hybrid MMMs are identified as strategically important materials enabling high-efficiency CO₂ separation processes aligned with decarbonization and energy transition objectives. Full article

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

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27 pages, 5602 KB

Open AccessArticle

Low-Power Direct Hardware Implementation of Logic Controllers Using Standard Languages

by Adam Milik, Wojciech Kierat and Tomasz Rudnicki

Energies 2026, 19(8), 2001; https://doi.org/10.3390/en19082001 - 21 Apr 2026

Viewed by 347

Abstract

The paper shows the methodologies of implementing a high-performance low-power logic control system designed with the use of standard languages like LD and SFC (according to the IEC61131-3 standard) directly in hardware utilizing FPGA devices. The essential idea is to convert the sequential [...] Read more.

The paper shows the methodologies of implementing a high-performance low-power logic control system designed with the use of standard languages like LD and SFC (according to the IEC61131-3 standard) directly in hardware utilizing FPGA devices. The essential idea is to convert the sequential sentences of a language to parallel computations and then map them to a dedicated hardware structure. The flexible graph-based method of language mapping is shown. It enables extracting control and data flow from language sentences. The direct hardware mapping technique enables building not only a high-performance structures but also a low-power implementations. All implementations retain a very short response time consisting of several clock cycles (from 3 to 7). The proposed low-power mapping strategies enable power saving up to 10 times while retaining processing performance. The obtained results are compared with a standard implementation using a benchmark program set. The paper is concluded with a comparison of the performance and energy consumption for the proposed implementation strategies. Full article

(This article belongs to the Topic VLSI-Based Sequential Devices in Cyber-Physical Systems)

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18 pages, 2346 KB

Open AccessArticle

Research on Fault Reconfiguration Strategy of Shipboard Integrated Power System Considering Power Reduction Characteristics of Propulsion Loads

by Bingchen Pan, Jing Huang, Yonglin Peng, Haijun Liu and Han Xiao

Energies 2026, 19(8), 2000; https://doi.org/10.3390/en19082000 - 21 Apr 2026

Viewed by 307

Abstract

When a fault occurs in a shipboard integrated power system, traditional reconfiguration strategies only adopt simple switching operations to change the system topology so as to isolate the faulty area. However, such strategies have limitations. For instance, under some specific operating conditions, the [...] Read more.

When a fault occurs in a shipboard integrated power system, traditional reconfiguration strategies only adopt simple switching operations to change the system topology so as to isolate the faulty area. However, such strategies have limitations. For instance, under some specific operating conditions, the propulsion load is not allowed to lose power completely, and certain critical loads cannot tolerate power interruption. Considering that the propulsion load is a high-power load compared with other loads in the shipboard integrated power system, the navigation speed can be reduced according to the relationship between speed and power in the reconfiguration strategy to ensure the power supply of other loads. In addition, traditional optimization methods also suffer from drawbacks, such as being prone to local optima or failing to solve the fault reconfiguration problem of the shipboard integrated power system in real time. Therefore, this paper uses the DQN method to simulate and verify the proposed objective function under cruise operating conditions. The results demonstrate the effectiveness and real-time performance of the proposed method. Full article

(This article belongs to the Section F1: Electrical Power System)

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22 pages, 21973 KB

Open AccessArticle

Failure Modes and Degradation Mechanisms of Thyristors Under Combined Electric and Thermal Stress

by Yingfeng Zhu, Donglin Xu, Ming Li, Chenhao Li, Fei Chen, Andong Wang, Zhiwei Cao, Wenyu Mao and Lei Pang

Energies 2026, 19(8), 1999; https://doi.org/10.3390/en19081999 - 21 Apr 2026

Viewed by 398

Abstract

The reliability of the characteristics of high-voltage (HV) thyristors is related to the operational safety of the entire HVDC project. In order to investigate the degradation mode of thyristors in HVDC projects more realistically, aging experiments were conducted on HV thyristors under the [...] Read more.

The reliability of the characteristics of high-voltage (HV) thyristors is related to the operational safety of the entire HVDC project. In order to investigate the degradation mode of thyristors in HVDC projects more realistically, aging experiments were conducted on HV thyristors under the combined action of sinusoidal half-wave voltage and current in a simulated operating environment. Experimental results show that the on-state voltage, reverse recovery characteristics, and reverse leakage current of thyristors have all degraded to varying degrees during the aging process. The main failure mode of thyristors can be summarized as the failure of the reverse blocking characteristic. Microstructural characterization of failed HV thyristors is conducted to explain the degradation mechanisms, including device surface morphology and elemental composition analysis. Observations have shown that the failed thyristor silicon wafer has been burned and hollowed out, accompanied by copper impurities, and significant thermal breakdown has occurred at the edge of the anode surface of the chip. Defects in chip structure and the invasion of impurities can lead to a decrease in the minority carrier lifetime of materials, which is an important factor in the characteristics of semiconductor devices. On this basis, further simulation research is carried out to conclude that the shortening of the minority carrier lifetime of the thyristor will distort the carrier space distribution, resulting in the rise in the on-state voltage. Meanwhile, the carrier transport capability decreases, leading to a decrease in the reverse recovery speed. The energy released during the rapid generation and recombination of carriers is one of the main reasons for the failure of blocking characteristics. This work provides comprehensive insights into the failure modes and mechanisms of HV thyristors. Full article

(This article belongs to the Special Issue Digital and Intelligent Operations, Maintenance, and Inspection of Dielectrics and High-Voltage Equipment)

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44 pages, 3887 KB

Open AccessArticle

Machine Learning-Based Power Quality Prediction in a Microgrid for Community Energy Systems

by Ibrahim Jahan, Khoa Nguyen Dang Dinh, Vojtech Blazek, Vaclav Snasel, Stanislav Misak, Ivo Pergl, Faisal Mohamed and Abdesselam Mechali

Energies 2026, 19(8), 1998; https://doi.org/10.3390/en19081998 - 21 Apr 2026

Viewed by 697

Abstract

To mitigate environmental impact, specifically the CO₂ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the [...] Read more.

To mitigate environmental impact, specifically the CO₂ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include

L S T M

,

G R U

,

D N N

,

C N N 1 D - L S T M

,

B i L S T M

, attention mechanisms,

D T

,

S V M

, and

X G B o o s t

. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion (

T H D_{u}

), Current Total Harmonic Distortion (

T H D_{i}

), and short-term flicker severity (

P_{s t}

). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error (

R M S E

) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article

(This article belongs to the Special Issue Machine Learning Algorithms for Power Systems and Renewable Energy Applications)

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27 pages, 16244 KB

Open AccessArticle

Microfluidic Investigation on the Seepage Mechanism and Development Strategy Optimization of Water/Gas Flooding in Carbonate Reservoirs

by Yujie Gao, Qianhui Wu, Lun Zhao, Wenqi Zhao and Junjian Li

Energies 2026, 19(8), 1997; https://doi.org/10.3390/en19081997 - 21 Apr 2026

Viewed by 435

Abstract

Carbonate reservoirs exhibit complex combinations of pores, fractures, and vugs, and their strong heterogeneity makes pore-scale displacement mechanisms and recovery enhancement difficult to predict. In this study, six microfluidic glass-etched models representative of pore-type, vuggy, and fracture-pore carbonate reservoirs were designed from cast [...] Read more.

Carbonate reservoirs exhibit complex combinations of pores, fractures, and vugs, and their strong heterogeneity makes pore-scale displacement mechanisms and recovery enhancement difficult to predict. In this study, six microfluidic glass-etched models representative of pore-type, vuggy, and fracture-pore carbonate reservoirs were designed from cast thin sections of the S oilfield. Experiments were conducted to investigate the effects of different factors on microscopic displacement behavior and residual-oil distribution. The results show that microscopic residual oil in carbonate reservoirs mainly occurs as film flow, droplet flow, columnar flow, multi-pore flow, and cluster flow, with cluster flow dominating the late stage of development in all model types. Under waterflooding, pore-type reservoirs exhibit the most uniform sweep and the highest recovery factor (44.26%), whereas vuggy reservoirs readily develop preferential flow channels and show the lowest recovery factor (41.58%). For fracture-pore reservoirs, injection perpendicular to the fracture provides the best performance, and wider or denser fractures improve displacement efficiency. Compared with gas flooding, waterflooding increases recovery by 10.48% in pore-type reservoirs and by 16.44% in fracture-type reservoirs. High-rate waterflooding and mid-stage flow diversion further improve recovery by 9.05–10.87% and 17.12–19.63%, respectively. These results provide pore-scale evidence for optimizing development strategies for carbonate reservoirs. Full article

(This article belongs to the Section H1: Petroleum Engineering)

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18 pages, 5440 KB

Open AccessArticle

Analysis and Modeling of Physical Evolution Mechanism for High-Resistance to Low-Resistance Grounding Faults in 10 kV Cable Joints

by Yifeng Zhao, Yanqi Zeng, Ran Hu, Luliang Zhang, Gang Liu, Yihua Qian and Zhi Li

Energies 2026, 19(8), 1996; https://doi.org/10.3390/en19081996 - 21 Apr 2026

Viewed by 411

Abstract

Currently, the lack of analysis and applicable circuit models for the evolution of cable joint faults is responsible for explosions or fire accidents in the distribution network system. In this paper, the modeling of high-resistance to low-resistance grounding faults for 10 kV cable [...] Read more.

Currently, the lack of analysis and applicable circuit models for the evolution of cable joint faults is responsible for explosions or fire accidents in the distribution network system. In this paper, the modeling of high-resistance to low-resistance grounding faults for 10 kV cable joints is investigated. Firstly, the physical evolution from high-resistance to low-resistance grounding faults in 10 kV cable joints is analyzed. Secondly, the common discharge characteristics under different evolution stages are extracted by simulation experiments and fault-recording data. Thirdly, an interface breakdown circuit model and a radial breakdown circuit model are established to quantitatively describe the high-resistance to low-resistance grounding faults of cable joints. Fourthly, the corresponding arc resistance models are proposed, and the controlled parameter values of the models under different evolution stages are given. Finally, the fault identification control model is implemented for relay protection. This paper provides theoretical and modeling support for the fault identification of 10 kV cable joints, filling the knowledge gap of this critical fault type in relay protection. Full article

(This article belongs to the Section F6: High Voltage)

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24 pages, 1233 KB

Open AccessArticle

Imbalance-Aware Spatiotemporal Load Forecasting via Cluster-Weighted State Space Modeling

by Moses A. Acquah, Yuwei Jin, Vahid Disfani and Jan Kleissl

Energies 2026, 19(8), 1995; https://doi.org/10.3390/en19081995 - 21 Apr 2026

Viewed by 266

Abstract

Electrical load time series exhibit strong heterogeneity across daily patterns driven by calendar effects and behavioral variability, leading many forecasting models to favor dominant weekday profiles while degrading on weekends, holidays, and transition days. This paper proposes an imbalance-aware spatiotemporal forecasting framework via [...] Read more.

Electrical load time series exhibit strong heterogeneity across daily patterns driven by calendar effects and behavioral variability, leading many forecasting models to favor dominant weekday profiles while degrading on weekends, holidays, and transition days. This paper proposes an imbalance-aware spatiotemporal forecasting framework via a cluster-conditioned state space model. Daily load patterns are identified via time-series clustering and incorporated as conditioning covariates within a sequence-continuous selective state space models (Mamba), preserving temporal coherence without explicit sequence partitioning. A cluster-weighted training objective further mitigates pattern imbalance while avoiding future-information leakage. The resulting cluster-conditioned Time Series Mamba (TSMamba) consistently improves forecasting robustness across both frequent and infrequent profiles, achieving weighted absolute percentage error (WAPE) reductions of approximately 15% on weekdays, 42% on weekends, and 39% on holidays relative to the vanilla TSMamba, with similar gains in mean absolute error (MAE) and coefficient of variation of the root mean square error (CVRMSE). These results demonstrate that conditioning state dynamics on latent load patterns yields stable and computationally efficient short-term load forecasts under profile transitions. Full article

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16 pages, 2151 KB

Open AccessArticle

Energy Profiling of Solar-Powered Smart Hydroponic Systems in Kazakhstan

by Ali Serikov, Yerassyl Olzhabay, Alikhan Talipbayev, Damir Aidarkhanov and Annie Ng

Energies 2026, 19(8), 1994; https://doi.org/10.3390/en19081994 - 21 Apr 2026

Viewed by 492

Abstract

Kazakhstan, the largest landlocked country and the ninth-largest country by land area in the world, played a central role in the Soviet Virgin Lands campaign. However, decades of cultivation left the soil degraded and vulnerable to erosion. This legacy, along with worldwide water [...] Read more.

Kazakhstan, the largest landlocked country and the ninth-largest country by land area in the world, played a central role in the Soviet Virgin Lands campaign. However, decades of cultivation left the soil degraded and vulnerable to erosion. This legacy, along with worldwide water scarcity, drives the search for alternative farming methods such as hydroponics. This study investigates the feasibility of powering an indoor hydroponic system with photovoltaic (PV) technology in different regions of Kazakhstan. Three PV configurations, 16, 20, and 24 panels, were simulated in PVsyst (8.0.12) to meet the monthly energy demand of the system. The goal was to determine the minimum PV size and storage capacity for continuous year-round operation. Results showed that 16 panels were sufficient only from April to July, whereas 20- and 24-panel systems provided better reliability throughout the year. Optimal designs varied by region. For instance, those in the south, such as Turkistan, required smaller setups (6.8 kWp, 26 panels, 7 batteries), whereas those in the north, such as Akmola, needed larger ones (10.9 kWp, 42 panels, 10 batteries). Performance ratios ranged from 41% to 66% depending on the region. These results indicate that PV-powered hydroponic systems are feasible in Kazakhstan, although system configurations must be adapted to specific regional solar conditions. Full article

(This article belongs to the Special Issue Solar Energy Applications: Thermal and Photovoltaic Opportunities and Challenges)

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48 pages, 13773 KB

Open AccessReview

The Smart City from the Energy Perspective

by Florentin-Robert Drăgan, Lucian Toma and Irina-Ioana Picioroagă

Energies 2026, 19(8), 1993; https://doi.org/10.3390/en19081993 - 21 Apr 2026

Viewed by 732

Abstract

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence [...] Read more.

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence among energy systems, digitalization and cutting-edge communication technologies. Adopting a system-of-systems perspective, we examine how different urban subsystems, including energy grids, transportation networks and data management systems, interact to improve overall urban functionality and long-term viability. Through a structured analysis of recent literature, we highlight the transformative potential of renewable energy integration, intelligent energy management systems and the crucial transition from 5G to 6G communication infrastructures, which collectively promise significant enhancements in urban sustainability, efficiency and resilience. Additionally, we address key challenges such as cybersecurity vulnerabilities, fragmented standardization frameworks and the need for comprehensive data governance. Viewing smart cities as a complex system of systems, this article argues for a holistic and interdisciplinary approach, emphasizing enhanced interoperability, robust cybersecurity protocols and inclusive participatory governance frameworks. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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19 pages, 2816 KB

Open AccessArticle

Improved Piecewise Terminal Integral Sliding-Mode Adaptive Control for PMSM Speed Regulation in Rail Transit Traction

by Jiahui Wang, Zhongli Wang and Jingyu Zhang

Energies 2026, 19(8), 1992; https://doi.org/10.3390/en19081992 - 21 Apr 2026

Viewed by 385

Abstract

Aiming at solving the problems of severe chattering, irreconcilable convergence speed, and steady-state accuracy in traditional sliding-mode control (SMC) for the speed regulation system of permanent magnet synchronous motors (PMSMs) in rail transit traction, as well as its poor adaptability to complex disturbances [...] Read more.

Aiming at solving the problems of severe chattering, irreconcilable convergence speed, and steady-state accuracy in traditional sliding-mode control (SMC) for the speed regulation system of permanent magnet synchronous motors (PMSMs) in rail transit traction, as well as its poor adaptability to complex disturbances such as frequent acceleration/deceleration and sudden load changes under traction conditions, a sliding-mode control strategy integrating improved piecewise terminal integral sliding-mode control (IPTISMC) with an adaptive smooth exponential reaching law (ASERL) is proposed. Taking the surface-mounted PMSM for rail transit traction as the research object, the d-q axis mathematical model is established, and a terminal integral sliding surface with a piecewise nonlinear function is designed, which resolves the problems of complex solutions and steady-state errors of the traditional sliding surface through a piecewise cooperative mechanism for large and small error stages. The designed ASERL realizes adaptive gain adjustment based on the state variables of the sliding surface and replaces the sign function with the hyperbolic tangent function, thus alleviating the inherent contradiction between convergence and chattering in the fixed-gain reaching law. The global stability and finite-time convergence of the system are rigorously proved based on Lyapunov stability theory. Furthermore, comparative experiments involving no-load operation, acceleration and deceleration, sudden load application and removal, and parameter perturbation are carried out on a DSP experimental platform for SMC-ERL, ISMC-ERL, IPTISMC-ERL and the proposed IPTISMC-ASERL. Experimental results show that the proposed IPTISMC-ASERL strategy can significantly improve the dynamic response and steady-state control accuracy of the PMSM speed regulation system for rail transit traction, effectively suppress chattering to enhance riding comfort, and simultaneously strengthen the system’s anti-disturbance capability and parametric robustness. It can fully meet the engineering control requirements for high precision and high stability of PMSMs in rail transit traction applications. Full article

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26 pages, 3271 KB

Open AccessArticle

Comparative Evaluation of Deep-Learning and SARIMA Models for Short-Term Residential PV Power Forecasting

by Kalsoom Bano, Vishnu Suresh, Francesco Montana and Przemyslaw Janik

Energies 2026, 19(8), 1991; https://doi.org/10.3390/en19081991 - 20 Apr 2026

Viewed by 353

Abstract

Accurate photovoltaic (PV) power forecasting is essential for the efficient operation of residential energy systems and microgrids, as reliable short-term predictions enable improved energy scheduling, demand management, and operational planning in distributed energy environments. In this study, one-hour-ahead forecasting of residential PV power [...] Read more.

Accurate photovoltaic (PV) power forecasting is essential for the efficient operation of residential energy systems and microgrids, as reliable short-term predictions enable improved energy scheduling, demand management, and operational planning in distributed energy environments. In this study, one-hour-ahead forecasting of residential PV power generation is investigated using real-world data collected from multiple households within an Irish energy community. Several deep-learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), CNN–LSTM hybrid networks, and attention-based LSTM models, are evaluated and compared with a seasonal autoregressive integrated moving average (SARIMA) statistical model. A sliding-window approach is employed to transform the PV time series into a supervised learning problem. To ensure statistical robustness, deep-learning models are evaluated using a multi-run framework, and results are reported as mean ± standard deviation based on MAE, RMSE, MAPE, and R² metrics across multiple households. The results indicate that deep-learning models achieve consistently strong forecasting performance, with GRU frequently providing the most reliable predictions across several households. For instance, in House 5, GRU achieved an RMSE of 142.02 ± 1.87 W and an R² of 0.694 ± 0.008, while in Houses 11 and 13 it attained R² values of 0.837 ± 0.002 and 0.835 0.08, respectively. However, performance varied across households, reflecting the influence of data variability and generation patterns on model effectiveness. In comparison, the SARIMA model demonstrated competitive performance and, in certain cases, outperformed deep-learning models. For example, in House 4, it achieved the lowest RMSE of 90.68 W and the highest R² of 0.709. Overall, these findings highlight that while deep-learning models offer greater adaptability and stability, statistical models remain effective for more regular PV generation patterns. Consequently, the study emphasizes the importance of evaluating forecasting models under realistic household-level conditions and demonstrates that both deep-learning and statistical approaches can provide short-term PV forecasting. Full article

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20 pages, 5426 KB

Open AccessArticle

Ignition of Vegetation Induced by Discharge from Abraded Medium-Voltage Insulated Overhead Lines

by Tian Tan, Huajian Peng, Xin Yang, Jiaxi Liu, Mingzhe Li, Shuaiwei Fu and Yafei Huang

Energies 2026, 19(8), 1990; https://doi.org/10.3390/en19081990 - 20 Apr 2026

Viewed by 325

Abstract

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, [...] Read more.

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, which can subsequently trigger partial discharge and even ignition. This study systematically investigates the discharge-induced ignition mechanism for 10 kV overhead insulated conductors in tree contact scenarios by establishing an experimental platform integrated with high-speed imaging, ultraviolet detection, and simulation methods. Three types of typical defects were set up in the experiments: complete insulation abrasion, lightning puncture holes accompanied by localized abrasion, and lightning puncture holes without abrasion. The development process and characteristics of different discharge forms were observed and analyzed. The results indicate that the tree contact discharge ignition mechanism can be categorized into two types: thermal accumulation and direct arcing. The former occurs when insulation abrasion or composite defects exist, where sustained partial discharge or a high-resistance current leads to gradual heat accumulation, resulting in an ignition delay lasting tens of seconds. The latter occurs when only small defects such as lightning puncture holes exist in the insulation layer. A concentrated arc forms due to gap breakdown under high voltage, leading to a millisecond-level ignition process. The study found that different discharge forms produce significantly distinct ablation and carbonization patterns on both the insulation layer and the branch surface, reflecting differences in energy transfer pathways. Simulation analysis further indicated that the thickness of the insulation layer affects the electric field distribution in the tree contact gap, with the initial discharge field strength decreasing as the thickness increases. This study provides experimental evidence and classification guidance for tree contact fault monitoring, insulation condition assessment, and wildfire prevention and control in medium-voltage distribution networks. Full article

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19 pages, 5014 KB

Open AccessArticle

Investigation on the Design Space of the Primary Drying Stage of Spray-Freeze-Drying Technology

by Shen Weihua, Liu Bo, Luo Chun, Sun Dongze and Yin Wei

Energies 2026, 19(8), 1989; https://doi.org/10.3390/en19081989 - 20 Apr 2026

Viewed by 414

Abstract

Spray-freeze-drying technology has gained considerable interest worldwide. However, the high energy consumption and lengthy process duration have hindered its further development. The primary drying stage accounts for the largest proportion of both the total energy consumption and process duration. To improve the energy [...] Read more.

Spray-freeze-drying technology has gained considerable interest worldwide. However, the high energy consumption and lengthy process duration have hindered its further development. The primary drying stage accounts for the largest proportion of both the total energy consumption and process duration. To improve the energy utilization efficiency of the drying stage, a mathematical model describing the drying stage was established. The obtained drying time and maximum product temperature were selected to represent the drying efficiency and the risk of failure, respectively. The design space of the drying stage was then constructed. The results show that the mathematical model gives an accurate description of the drying stage, and increasing the shelf temperature and decreasing the chamber pressure would be beneficial for improving drying efficiency but unfavorable for reducing the risk of failure. In addition, the drying efficiency shows higher sensitivity to the change in the operating conditions compared with the risk of failure. Moreover, the packing porosity is found to affect the design space. A lower packing porosity is found to expand the design space, allowing for a wider range of operating conditions. This study provides insights into the drying process and supports the optimization of operating parameters. Full article

(This article belongs to the Section J1: Heat and Mass Transfer)

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44 pages, 10834 KB

Open AccessArticle

ANN-MILP Hybrid Techniques for the Integration Challenge, Power Management of the EV Charging Station with Solar-Based Grid System, and BESS

by Km Puja Bharti, Haroon Ashfaq, Rajeev Kumar and Rajveer Singh

Energies 2026, 19(8), 1988; https://doi.org/10.3390/en19081988 - 20 Apr 2026

Viewed by 366

Abstract

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose [...] Read more.

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

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20 pages, 11413 KB

Open AccessArticle

Improved LADRC-Based DC-Bus Voltage Control Strategy for Bidirectional Converters in AC/DC Hybrid Microgrids

by Jiamian Wang, Yi Zhang and Baojiang Wu

Energies 2026, 19(8), 1987; https://doi.org/10.3390/en19081987 - 20 Apr 2026

Viewed by 316

Abstract

Bidirectional AC/DC converters in hybrid microgrids are prone to DC-bus voltage instability caused by source-side, grid-side, and load-side disturbances. Conventional linear active disturbance rejection control (LADRC) suffers from a trade-off between transient overshoot suppression and disturbance rejection capability, which limits its practical application. [...] Read more.

Bidirectional AC/DC converters in hybrid microgrids are prone to DC-bus voltage instability caused by source-side, grid-side, and load-side disturbances. Conventional linear active disturbance rejection control (LADRC) suffers from a trade-off between transient overshoot suppression and disturbance rejection capability, which limits its practical application. To address this issue, an improved LADRC strategy for bidirectional AC/DC converters is proposed in this paper. First, a linear tracking differentiator (LTD) is introduced to smooth the DC-bus voltage reference and suppress overshoot caused by abrupt command changes. Second, a proportional-derivative (PD) term is embedded into the linear extended state observer (LESO) to introduce phase lead compensation, thereby improving the observer phase characteristics without excessively increasing the observation bandwidth or amplifying high-frequency noise. Frequency domain analysis, MATLAB/Simulink simulations, and full-hardware prototype experiments are carried out to validate the proposed method. The simulation study covers grid voltage sag, photovoltaic-side source fluctuation, and DC-side load disturbance conditions. To further strengthen the experimental verification, hardware tests are conducted under grid voltage dip, PV-side voltage reduction, and DC-side load-switching conditions. The results consistently show that the proposed strategy can effectively reduce DC-bus voltage fluctuation and improve transient recovery performance compared with conventional LADRC. Therefore, the improved LADRC provides a practical and robust control solution for stabilizing bidirectional converters in AC/DC hybrid microgrids. Full article

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26 pages, 2023 KB

Open AccessReview

Integration and Interaction Between Electric Vehicles and the Power Grid: Research Progress and Practice in China

by Feng Wang and Hongzhe Cao

Energies 2026, 19(8), 1986; https://doi.org/10.3390/en19081986 - 20 Apr 2026

Viewed by 669

Abstract

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged [...] Read more.

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

(This article belongs to the Section E: Electric Vehicles)

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25 pages, 2246 KB

Open AccessArticle

Optimal Sizing and Hourly Scheduling of Wind-PV-Battery Systems for Islanded Expressway Service Area Microgrids Under Tiered Electricity Pricing

by Yaguang Shi, Zhangjie Liu and Mandi He

Energies 2026, 19(8), 1985; https://doi.org/10.3390/en19081985 - 20 Apr 2026

Viewed by 280

Abstract

External electricity supplementation for islanded microgrids at expressway service areas is often settled under tiered electricity pricing based on cumulative energy consumption, where marginal prices increase discontinuously once tier thresholds are exceeded. This mechanism reshapes battery dispatch behavior and may alter economically optimal [...] Read more.

External electricity supplementation for islanded microgrids at expressway service areas is often settled under tiered electricity pricing based on cumulative energy consumption, where marginal prices increase discontinuously once tier thresholds are exceeded. This mechanism reshapes battery dispatch behavior and may alter economically optimal storage sizing. This paper proposes a unified planning—operation optimization framework for wind–PV–battery microgrids that jointly determines the storage capacity and hourly scheduling while enforcing power balance, battery state-of-charge dynamics, and tiered settlement costs. By introducing tier-wise energy allocation variables and tier cap constraints, the nonlinear settlement rule is reformulated into an equivalent piecewise-linear structure, leading to a mixed-integer linear programming (MILP) model that can be solved using standard optimization solvers. A season-weighted annualized case study using four typical seasonal days reveals critical cross-tier dispatch behaviors, where charging–discharging schedules shift near tier boundaries and external electricity purchases are actively suppressed from entering higher-priced tiers. The proposed framework quantifies the premium-avoidance value of storage and provides a practical decision support tool for premium risk-aware sizing and operation of islanded expressway service-area microgrids. Full article

(This article belongs to the Special Issue Optimizing Power-to-X Pathways for Renewable Energy Utilization and Carbon-Neutral Transitions)

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27 pages, 6002 KB

Open AccessArticle

Heliostat Field Layout Optimization Considering Power Generation and Layout Parameters

by Xiao Zhou, Zekang Dou, Jialin Sun, Chunyan Ma, Cheng Cui, Jingxue Guo and Yuchen Wang

Energies 2026, 19(8), 1984; https://doi.org/10.3390/en19081984 - 20 Apr 2026

Viewed by 335

Abstract

To explicitly illustrate the relationship between heliostat field optimization and power generation, a coupled model was established in Simulink. By optimizing the geometric layout of the heliostat field, the solar heat collection efficiency can be significantly improved, thereby increasing the thermal input to [...] Read more.

To explicitly illustrate the relationship between heliostat field optimization and power generation, a coupled model was established in Simulink. By optimizing the geometric layout of the heliostat field, the solar heat collection efficiency can be significantly improved, thereby increasing the thermal input to the system. The optimized heliostat field design can convert solar energy into thermal energy more efficiently and transfer it to the steam generator through the molten salt loop, thereby driving power generation in the Rankine cycle. In this process, the Rankine cycle is responsible for converting the thermal energy supplied by the molten salt loop into mechanical work and ultimately into electrical power output. At the same time, real meteorological data from a commercial heliostat field were introduced, and annual power generation simulations demonstrated that the integrated modeling of the heliostat field, thermal storage, and power block based on actual meteorological boundary conditions and system parameters can effectively reflect the power generation performance of a commercial tower solar thermal power plant. Meanwhile, research on heliostat field optimization should further evolve from identifying general patterns toward parameter design and overall system performance improvement. For molten-salt tower solar thermal power plants, key design variables such as receiver tower height, receiver dimensions, heliostat dimensions, and heliostat field spacing parameters affect not only the annual average optical efficiency of the heliostat field and the thermal power output of the receiver, but also the annual power generation of the entire plant. By integrating SOLARPILOT 1.5.2 and SAM 2025.4.16, the design variables were systematically analyzed to investigate their effects on the annual average optical efficiency of the heliostat field, the number of heliostats, the receiver output power, and the annual power generation, and the reasonable value ranges of the heliostat field parameters were determined accordingly. The established Rankine cycle power block model was then coupled with the parameter optimization results to carry out a secondary optimization of the initial heliostat field. Through the above study, the aim is to realize a shift from single-objective geometric optimization of the heliostat field to comprehensive optimization oriented toward annual plant power generation performance and scenario adaptability, thereby providing a basis for scheme design and parameter selection of molten-salt tower solar thermal power plants. For external validation, the annual generation predicted for the Delingha 50 MW commercial plant was 142.15 GWh, corresponding to a relative deviation of 2.64% from the published design value of 146 GWh. This indicates that the coupled framework can reasonably capture the integrated response of the heliostat field, thermal storage system, and power block at the plant level. The model is therefore suitable for generation-oriented parameter screening and preliminary design of tower molten-salt CSP plants, while detailed component-level transient design still requires higher-fidelity engineering models. Full article

(This article belongs to the Topic Advances in Solar Technologies, 2nd Edition)

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21 pages, 1453 KB

Open AccessArticle

Life-Cycle Cost–Optimal Right-Sizing and Replacement Assessment of Distribution Transformers Under Demand Uncertainty

by Jorge Muñoz-Pilco, Milton Ruiz, Cristian Cuji and Edwin García

Energies 2026, 19(8), 1983; https://doi.org/10.3390/en19081983 - 20 Apr 2026

Viewed by 620

Abstract

This paper presents a scenario-based optimization framework for evaluating the life-cycle cost of right-sizing and replacement timing for distribution transformers under demand–growth uncertainty. The proposed formulation jointly considers the discrete commercial transformer ratings, the discounted investment cost, and the monetized iron and copper [...] Read more.

This paper presents a scenario-based optimization framework for evaluating the life-cycle cost of right-sizing and replacement timing for distribution transformers under demand–growth uncertainty. The proposed formulation jointly considers the discrete commercial transformer ratings, the discounted investment cost, and the monetized iron and copper losses over a 15-year planning horizon. Demand uncertainty is represented by nine scenarios defined by combinations of initial apparent power demand and annual growth rate, with

D_{1} \in {45, 50, 55}

kVA and

g \in {3 %, 4 %, 5 %}

. Under these assumptions, the demand envelope evolves from an initial range of 45–55 kVA to approximately 68.1–108.9 kVA in Year 15, while expected demand increases from 50 kVA to about 87 kVA. The optimization results show that the economically optimal policy is to install a 112.5 kVA transformer in Year 1 and maintain that rating throughout the horizon, without triggering any replacement events. The selected transformer maintains expected loading between approximately 0.44 p.u. and 0.77 p.u., while the upper-demand scenario remains below 1.0 p.u. over the entire horizon. These results indicate that, for the demand–growth conditions analyzed, the preferred outcome is a single initial sizing decision rather than a phased replacement strategy. Therefore, the proposed framework provides a consistent scenario-based alternative to deterministic margin-based planning for distribution transformer asset management. Full article

(This article belongs to the Special Issue Advancements in Power Transformers)

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16 pages, 1926 KB

Open AccessArticle

Performance Evaluation of a Cloud-Native Open-Source Power System Digital Twin for Real-Time Simulation

by Juan-Pablo Noreña and Ernesto Perez

Energies 2026, 19(8), 1982; https://doi.org/10.3390/en19081982 - 20 Apr 2026

Viewed by 497

Abstract

The increasing complexity of Cyber-Physical Energy Systems, driven by the high penetration of power electronics, advanced control, and digitalization, demands scalable, flexible real-time simulation platforms beyond traditional laboratory-based solutions. This paper investigates the feasibility of deploying open-source real-time power system simulation frameworks on [...] Read more.

The increasing complexity of Cyber-Physical Energy Systems, driven by the high penetration of power electronics, advanced control, and digitalization, demands scalable, flexible real-time simulation platforms beyond traditional laboratory-based solutions. This paper investigates the feasibility of deploying open-source real-time power system simulation frameworks on cloud-based infrastructures while meeting real-time computational constraints. An open-source architecture based on DPsim and the VILLAS framework is implemented and evaluated across five computing environments using open-source tools: bare-metal, non-cloud virtual machines, private cloud Kubernetes clusters, public cloud virtual machines, and public cloud Kubernetes clusters. Each environment is carefully configured and tuned using real-time operating systems, CPU isolation, and affinity mechanisms to improve deterministic behavior. Performance and scalability are assessed through a benchmark based on replicated IEEE 9-bus systems, progressively increasing system size, and measuring simulation-timestep execution time. The results show that cloud and cloud-like infrastructures can support soft and, under controlled conditions, firm real-time simulation tasks, although achievable system scale decreases as additional abstraction layers are introduced. The study identifies practical performance limits for each infrastructure and discusses their suitability for different real-time simulation and co-simulation applications. These findings demonstrate that cloud-based real-time simulation can complement traditional digital real-time simulators, enabling scalable and cost-effective CPES experimentation. Full article

(This article belongs to the Section F1: Electrical Power System)

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28 pages, 2994 KB

Open AccessArticle

Graph Neural Networks and Bi-Level Optimization for Equitable Electric Vehicle Charging Infrastructure Planning

by Javier Alexander Guerrero Silva, Jorge Ivan Romero Gelvez and Sebastian Zapata

Energies 2026, 19(8), 1981; https://doi.org/10.3390/en19081981 - 20 Apr 2026

Viewed by 609

Abstract

Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). [...] Read more.

Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). Household travel survey data (12,500 households across 142 zones) were used to estimate zone-level priority scores and venue-specific temporal weights. EVI-Pro Lite simulations projected a 2025 requirement of 10,870 charging ports (7352 residential, 2739 workplace, and 779 public). In the allocation stage, Level 1 preserved priority-proportional targets, while Level 2 minimized inter-zonal inequality in Hansen accessibility subject to near-optimal Level-1 compliance. The final allocation retained strong priority alignment in installed ports (Spearman

ρ = 0.799

,

p < 10^{- 31}

), while the priority–accessibility association was lower (Spearman

ρ = 0.320

,

p = 1.04 \times 10^{- 4}

), consistent with second-stage equity redistribution. Equity outcomes also improved (Hansen Gini = 0.433; bottom-50% Lorenz share = 0.204). The mean Hansen accessibility reached 296.630 (standard deviation 248.099; minimum 1.126). These findings indicate that reproducible, equity-oriented EV infrastructure plans can be produced in cities where revealed charging microdata are limited. Full article

(This article belongs to the Special Issue Vehicle Engines and Powertrains: Performance, Combustion and Emission—2nd Edition)

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23 pages, 329 KB

Open AccessArticle

Primary Energy Demand in Korea: Substitution and Structural Change

by Ji-Whan Kim and Yoon-Kyung Kim

Energies 2026, 19(8), 1980; https://doi.org/10.3390/en19081980 - 20 Apr 2026

Viewed by 341

Abstract

International energy price changes can lead energy-importing economies to adjust their input factor choices, and Korea provides a useful case given its very high dependence on imported primary energies. This study estimates a primary energy input-demand system for Korea using quarterly data from [...] Read more.

International energy price changes can lead energy-importing economies to adjust their input factor choices, and Korea provides a useful case given its very high dependence on imported primary energies. This study estimates a primary energy input-demand system for Korea using quarterly data from 2000 to 2021, covering coal; crude oil; natural gas; labor; and others, including non-primary energy inputs. Our analysis uses LA-AIDS specifications. Breakpoint unit-root and cointegration tests support structural change around the global financial crisis, and this shift is incorporated through period-specific parameters within a unified demand system. The compensated elasticities indicate that crude oil becomes more price sensitive after the break, while coal and natural gas become less responsive to their own prices. Cross-price relationships also change, with weaker substitution among the primary energies and greater substitution between crude oil and others. These findings suggest that the ability to adjust inputs and the economic effects of international price changes can vary over time, which should be taken into account in energy policy evaluation. Full article

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

23 pages, 7320 KB

Open AccessArticle

Intelligent Data-Driven Fuzzy Logic Control for Demand-Responsive Operation of Hybrid Geothermal Heat Pump Systems

by Kanet Katchasuwanmanee, Sappasiri Pipatnawakit, Kai Cheng and Thongchart Kerdphol

Energies 2026, 19(8), 1979; https://doi.org/10.3390/en19081979 - 20 Apr 2026

Viewed by 488

Abstract

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption [...] Read more.

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

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