Energies

23 pages, 1067 KB

Open AccessReview

Reliability Assessment Methods for Power Supply Systems Considering the Technical Condition of Electrical Equipment: A Critical Review

by Iliya Iliev, Alexander Nazarychev, Sergei Solovev, Ivan Beloev, Konstantin Suslov and Hristo Beloev

Energies 2026, 19(10), 2440; https://doi.org/10.3390/en19102440 (registering DOI) - 19 May 2026

Abstract

The reliability assessment of power supply systems is complicated by the branched network topology, redundancy, switching operations, post-failure restoration, operating constraints, and uneven equipment degradation. Additional difficulties arise because calculations based on averaged normative parameters often fail to reflect the actual condition of [...] Read more.

The reliability assessment of power supply systems is complicated by the branched network topology, redundancy, switching operations, post-failure restoration, operating constraints, and uneven equipment degradation. Additional difficulties arise because calculations based on averaged normative parameters often fail to reflect the actual condition of specific components. This review critically compares reliability assessment methods for power supply systems, with particular attention to the technical condition of electrical equipment. Fault tree analysis and the logic–probabilistic method are discussed as structural approaches. Markov models are examined as tools for describing transitions between equipment states. Monte Carlo-based simulation is considered for calculating adequacy deficits and the operational consequences of supply interruptions. Reliability parameterization based on health indices, correction factors, and consumed life is also analyzed. The analysis indicates that no single methodology is equally effective at all problem levels. The main contribution is to separate technical condition parameterization, structural operability modeling, and adequacy assessment into interacting layers. This structure links condition indicators, such as health indices or consumed life, with system-level reliability and deficit indices through element-state probabilities. The review therefore supports a multi-level framework in which technical condition is considered at the element level, structural reliability is evaluated by the logic–probabilistic method, and failure consequences are assessed by simulation. Full article

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30 pages, 4058 KB

Open AccessArticle

Dimethyl Ether as a Compression Ignition Engine Fuel for Simultaneous NO_x and PM Reduction

by Matthias Rollins, Juan Felipe Rodriguez, Bret C. Windom and Daniel B. Olsen

Energies 2026, 19(10), 2439; https://doi.org/10.3390/en19102439 (registering DOI) - 19 May 2026

Abstract

Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NO_x) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and [...] Read more.

Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NO_x) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and exhaust gas recirculation (EGR) conditions remains insufficiently characterized for practical calibration. This study investigates the combustion, emissions, and performance of DME relative to diesel using a fully instrumented John Deere 6068CI550 single-cylinder research engine modified for high-pressure common-rail DME operation. Baseline tests were conducted at three ISO 8178 C1 steady-state modes with matched combustion phasing, load, and EGR to isolate fuel property effects. Injection timing and EGR sweeps were then performed at 1600 rpm and 50% load. Results show that DME produces 10–35% lower NO_x and orders-of-magnitude lower PM than diesel while maintaining comparable thermal efficiency. DME exhibits a single-stage premixed heat release structure with reduced peak apparent heat release rates and 4–5° shorter combustion durations than diesel. Stable combustion was sustained up to 55% EGR, beyond which incomplete combustion increased carbon monoxide (CO), total hydrocarbons (THC), and fuel consumption. Optimal low-emission operation occurred near CA50 ≈ 16° ATDC and EGR levels of 30–40%. These findings demonstrate DME’s ability to mitigate the traditional diesel NO_x–PM tradeoff and support its viability as a low-emission CI fuel. Full article

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

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29 pages, 28901 KB

Open AccessArticle

A Novel Probabilistic Electricity Price Forecasting Framework with Multi-Factorial Cross-Correlation Embedding and Patch Processing

by Ying Lu, Hang Fan, Weican Liu, Zhenshang Wang, Yuming Zhao and Dunnan Liu

Energies 2026, 19(10), 2438; https://doi.org/10.3390/en19102438 (registering DOI) - 19 May 2026

Abstract

Accurate probabilistic electricity price forecasting can characterize the uncertainty of electricity price information and provide more comprehensive forecasting information for market traders. However, existing methods struggle to effectively identify and extract factors related to electricity prices, resulting in limited prediction accuracy. To this [...] Read more.

Accurate probabilistic electricity price forecasting can characterize the uncertainty of electricity price information and provide more comprehensive forecasting information for market traders. However, existing methods struggle to effectively identify and extract factors related to electricity prices, resulting in limited prediction accuracy. To this end, we propose a novel probabilistic electricity price forecasting framework. Specifically, the framework can be divided into two parts. In the two-stage data preprocessing module, we utilize the Isolation Forest algorithm for outlier correction and ReliefF for feature selection to reduce feature redundancy. In the probabilistic electricity price forecasting module, we utilize the Multilayer Perceptron (MLP) to learn trend features from historical price data, while the customized feature convolutional network (FConv) extracts the feature patterns of factors related to electricity prices. Then, the cross-correlation embedding technique fuses these extracted features for a more comprehensive representation. In addition, the patch processing technique partitions the fused data into multiple patches to capture local features. Moreover, the incorporation of positional encoding helps the model to better capture temporal dependencies in the data. Then, we utilize the MLP to generate the final forecasts. Additionally, the proposed framework is trained using an improved loss function with penalty weights, aiming to enhance the model’s capability to predict outliers. Finally, we validated the effectiveness of our proposed model using two electricity price datasets from China. Full article

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14 pages, 9048 KB

Open AccessArticle

Evolution and Failure Mechanism of Moisture Absorption, Mechanical, and Electrical Insulation Properties of Glass Fiber/Epoxy Resin (GF/EP) Composites Under Hygrothermal Aging

by Bowen Xu, Chenglu Wang, Jinghan Wang and Chen Cao

Energies 2026, 19(10), 2436; https://doi.org/10.3390/en19102436 (registering DOI) - 19 May 2026

Abstract

Glass fiber/epoxy (GF/EP) composites are widely used in high-voltage electrical equipment due to their excellent specific strength, durability and dielectric properties. However, long-term exposure to hygrothermal environments will lead to performance degradation of the material, which seriously threatens its service reliability. To solve [...] Read more.

Glass fiber/epoxy (GF/EP) composites are widely used in high-voltage electrical equipment due to their excellent specific strength, durability and dielectric properties. However, long-term exposure to hygrothermal environments will lead to performance degradation of the material, which seriously threatens its service reliability. To solve this problem, accelerated aging tests were systematically carried out in this study by immersing GF/EP specimens in deionized water at room temperature and 80 °C. The performance evolution laws and failure mechanisms of the material were investigated through moisture absorption kinetic analysis, tensile property testing, scanning electron microscope (SEM) fracture observation and breakdown voltage testing. The results show that the initial moisture absorption behavior of the material follows the Fickian diffusion mechanism, and the water diffusion rate at 80 °C is 31.8 times that at room temperature. After 35 days of aging, the retention rate of the maximum tensile force is 86.6% for the room temperature group, while it decreases to 38.2% for the 80 °C group. SEM observations show that the failure mode of the material changes from ductile fracture to brittle fracture after aging at 80 °C, accompanied by serious interfacial debonding. Temperature is the dominant factor for insulation performance degradation: the breakdown voltage retention rate is above 91% at room temperature, while it decreases to about 37% at 80 °C, and the influence of 60% maximum tensile force (

F_{m a x}

) preloading is relatively small. This study provides experimental data and theoretical support for the performance evaluation and life prediction of GF/EP composites in harsh hygrothermal service environments of high-voltage electrical equipment. Full article

(This article belongs to the Special Issue Advanced Control and Monitoring of High Voltage Power Systems)

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21 pages, 11348 KB

Open AccessArticle

Robust State of Health Estimation for On-Road Electric Vehicles Using an LSTM-Improved iTransformer Hybrid Network

by Jianyao Hu, Guangdi Hu and Hongli Gao

Energies 2026, 19(10), 2435; https://doi.org/10.3390/en19102435 (registering DOI) - 19 May 2026

Abstract

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring the safety, efficiency, and lifecycle management of electric vehicles (EVs). Although data-driven approaches have become the mainstream solution for SOH estimation, most existing studies rely heavily on laboratory [...] Read more.

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring the safety, efficiency, and lifecycle management of electric vehicles (EVs). Although data-driven approaches have become the mainstream solution for SOH estimation, most existing studies rely heavily on laboratory datasets collected under controlled and idealized conditions. Such datasets fail to capture the stochastic characteristics of real-world vehicle operation, including fragmented charging behaviors, varying environmental conditions, and significant sensor noise. Moreover, single deep learning architectures often struggle to simultaneously model the long-term temporal evolution of battery degradation and the complex multivariate correlations among operational variables. To address these challenges, this study proposes a hybrid neural network framework termed Long Short-Term Memory (LSTM)-improved iTransformer, which integrates the temporal modeling capability of LSTM networks with the multivariate feature interaction ability of an improved inverted Transformer architecture. In addition, a high-fidelity dataset was constructed using operational data collected from ten real-world electric vehicles. To simulate a realistic cloud-deployment scenario, a strict cross-vehicle validation strategy was adopted, where data from seven vehicles were used for model training and data from three entirely unseen vehicles were reserved for testing. The experimental results demonstrate that the proposed framework significantly outperforms conventional baseline models. In the multi-vehicle experiment, the model achieved a root mean square error (RMSE) of 1.18% and a coefficient of determination (R²) of 0.97, indicating promising cross-vehicle prediction performance within the available fleet. Furthermore, in the single-vehicle robustness experiment with limited training data, the proposed model achieved the lowest prediction error with an RMSE of 0.0045% and an MAE of 0.0034%, demonstrating superior accuracy and robustness compared with baseline models. These results suggest that the proposed method is a promising solution for battery health monitoring based on real-world operational data. Full article

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

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16 pages, 1660 KB

Open AccessArticle

Application and Verification of Formation Pressure Estimation for Geo-Energy Engineering Based on Flow Regime Identification Analysis of Different Injection/Shut-In Tests

by Qiuyang Xu, Yuehui Yang, Awei Li, Bangchen Wu, Hao Zhang, Ran Li, Shiyuan Li, Chongyuan Zhang, Qunce Chen and Dongsheng Sun

Energies 2026, 19(10), 2434; https://doi.org/10.3390/en19102434 - 19 May 2026

Abstract

Conventional Diagnostic Fracture Injection Tests (DFITs) are widely used for formation pressure estimation, but in practice, they frequently require days, weeks, or even months of extended shut-in periods, a challenge particularly pronounced when large injection volumes are coupled with ultra-low formation permeability. While [...] Read more.

Conventional Diagnostic Fracture Injection Tests (DFITs) are widely used for formation pressure estimation, but in practice, they frequently require days, weeks, or even months of extended shut-in periods, a challenge particularly pronounced when large injection volumes are coupled with ultra-low formation permeability. While recent studies have proposed various modified DFIT approaches to reduce testing time, direct physical validation confirming the reliability of the derived formation pressure estimates remains scarce in the literature. This study applies a low-rate/volume injection mini-frac approach that integrates flow regime identification and Horner analysis. Two complementary field cases are presented: a standard DFIT in a shale reservoir to validate the baseline methodology, and a low-volume mini-frac in a tight granite formation to demonstrate rapid estimation. Results show that low-volume injections exhibit a flow regime evolution identical to standard DFITs, yet this approach is expected to accelerate the transition to the pseudo-radial flow regime. To verify the reliability of formation pressure estimates derived from such methods, the formation pressure estimated in the low-rate/volume injection mini-frac case was benchmarked against a decade of continuous downhole fluid pressure monitoring data from the same well, yielding a relative error of less than 5%. The findings suggest that employing a lower injection rate and volume can improve formation pressure testing efficiency, with potential applications in unconventional hydrocarbon development and deep geo-energy engineering. Full article

(This article belongs to the Special Issue Advanced Monitoring and Characterization Techniques for Energy Reservoirs)

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27 pages, 3141 KB

Open AccessArticle

Driving Decarbonization: A Life Cycle Assessment of Road Freight Transport Using Locally Produced Green Hydrogen in The Netherlands

by Ruben van den Berg, Daniël Bakker, Coen van der Giesen, Ron Bol and Tessa van den Brand

Energies 2026, 19(10), 2433; https://doi.org/10.3390/en19102433 (registering DOI) - 19 May 2026

Abstract

Road freight transport is an important driver of global greenhouse gas (GHG) emissions. Decarbonizing this sector demands a comprehensive assessment of emerging powertrain technologies, which are currently lacking in the literature. To fill this knowledge gap, we performed a life cycle assessment (LCA) [...] Read more.

Road freight transport is an important driver of global greenhouse gas (GHG) emissions. Decarbonizing this sector demands a comprehensive assessment of emerging powertrain technologies, which are currently lacking in the literature. To fill this knowledge gap, we performed a life cycle assessment (LCA) on 10 impact categories to evaluate road freight transport in the Netherlands of four truck alternatives, assuming similar performance: fuel-cell electric (FCEV), hydrogen internal combustion engine (HICEV), battery electric (BEV), and diesel internal combustion engine (DICEV). We compared locally produced green hydrogen, according to EU regulations, with electricity and diesel as alternative fuel chains, while also considering the environmental impact of road infrastructure. We found that FCEV and HICEV trucks achieve the lowest global warming impact when green hydrogen is used. We identified discrepancies between the transport alternatives, highlighting key factors influencing NO_x and particulate matter emissions. Our research also showed that water consumption (WC) for green hydrogen is strongly influenced by upstream processes, with solar-powered electricity emerging as a crucial contributor. Our results highlight the need for more exploration on the environmental impact of green hydrogen and can be used by researchers and practitioners to further understand the complexity of reducing emissions in road freight transport. Full article

(This article belongs to the Special Issue 11th International Conference on Smart Energy Systems (SESAAU2025))

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15 pages, 1914 KB

Open AccessArticle

Simulation Study on SF₆ Circuit Breaker Arc-Extinguishing Chamber Based on Lattice Boltzmann Method (LBM)

by Ran Zang, Bowen Xu, Chen Cao, Huancheng Zou and Yihua Zhang

Energies 2026, 19(10), 2432; https://doi.org/10.3390/en19102432 - 19 May 2026

Abstract

The SF₆ circuit breaker is an essential piece of high-voltage equipment in ensuring the safe operation of the power grid. Regarding the arc-extinguishing chamber, as the most essential component, its performance is directly related to the breaking capacity of the circuit breaker. [...] Read more.

The SF₆ circuit breaker is an essential piece of high-voltage equipment in ensuring the safe operation of the power grid. Regarding the arc-extinguishing chamber, as the most essential component, its performance is directly related to the breaking capacity of the circuit breaker. This study applies the Double Distribution Function Lattice Boltzmann Method (DDF-LBM), combined with the Smagorinsky sub-grid scale (SGS) model, to systematically simulate the dynamic breaking process of a 252 kV SF₆ arc-extinguishing chamber under 50 kA breaking current conditions. Two independent distribution functions are employed to describe the fluid field and the temperature field, respectively, thereby simulating the physical flow–heat coupling process. A dynamic simulation framework is constructed using the D2Q9 model to describe the mechanical motion of the contacts and the fluid flow. The description of contact movement is achieved by dynamically updating the geometric mesh, thereby realizing fluid–solid transformation. The research results indicate that the proposed method can simulate the pressure variation of the fluid field during the breaking process. The value of the Smagorinsky constant (C_s) exhibits a non-negligible influence on the pressure field predictions. The optimal value of C_s = 0.10 is determined through analysis, and the peak pressures at the upstream and throat measurement points reach 1.11 MPa and 1.37 MPa, respectively. Numerical simulations are conducted on the dynamic breaking process of the arc-extinguishing chamber, revealing the evolution of the pressure field upstream of the nozzle and at the throat regions. This study provides new numerical simulation methods for the investigation of SF₆ arc-extinguishing chambers and establishes a foundation for the application of the Lattice Boltzmann Method in the field of high-voltage electrical appliances. Full article

(This article belongs to the Special Issue Artificial Intelligence and Its Application in Condition Monitoring and Evaluation of Power Equipment)

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2 pages, 136 KB

Open AccessCorrection

Correction: Kamin et al. Barriers to the Diffusion of Clean Energy Communities: Comparing Early Adopters and the General Public. Energies 2025, 18, 2248

by Tanja Kamin, Urša Golob and Tina Kogovšek

Energies 2026, 19(10), 2431; https://doi.org/10.3390/en19102431 - 19 May 2026

Abstract

In the original publication [...] Full article

21 pages, 7887 KB

Open AccessArticle

A Deep Multi-Task Warning Network for Grid Harmonics: Multi-Step Regression and Multi-Dimensional Tracing

by Xin Zhou, Li Zhang, Qiaoling Chen, Qianggang Wang, Niancheng Zhou, Junzhen Peng and Yongshuai Zhao

Energies 2026, 19(10), 2430; https://doi.org/10.3390/en19102430 - 18 May 2026

Abstract

With the large-scale integration of offshore wind farms (OWFs), harmonic issues caused by the interaction between high-frequency switching of converters and complex network impedances pose severe challenges to power quality. Traditional harmonic monitoring heavily relies on post-event fixed-threshold alarm mechanisms, which struggle to [...] Read more.

With the large-scale integration of offshore wind farms (OWFs), harmonic issues caused by the interaction between high-frequency switching of converters and complex network impedances pose severe challenges to power quality. Traditional harmonic monitoring heavily relies on post-event fixed-threshold alarm mechanisms, which struggle to achieve early warning during the low-distortion sub-health operation stage and lack the capability for multi-dimensional tracing of harmonic degradation sources. To address these limitations, this paper proposes a deep warning network for grid harmonics combining multi-step regression and multi-dimensional tracing within a unified multi-task learning (MTL) architecture. First, a deep shared feature encoder, integrating a bi-directional long short-term memory (Bi-LSTM) network with a multi-head self-attention (MHSA) mechanism, is utilized to extract high-order temporal coupling features between meteorological evolution and multi-node electrical states. Subsequently, the main task branch executes a k-step-ahead multivariate time-series regression to accurately predict the evolution trend of total harmonic distortion (THD) at both the point of common coupling (PCC) and the turbine terminal. Simultaneously, the auxiliary task branch performs multi-label micro-state classification based on relative degradation thresholds, achieving fine-grained multi-dimensional tracing covering spatial nodes, electrical attributes, and their joint micro-states. Experimental results on real-world OWF operational data demonstrate that through the joint optimization of regression and tracing tasks, the proposed MultiDimKStepMTL model significantly improves time-series prediction accuracy, achieving a

10.3 %

relative improvement over single-task baselines, while substantially reducing computational overhead. This research successfully advances grid harmonic monitoring from passive response to proactive micro-state early warning, providing a solid, highly interpretable data-driven foundation for active filter control of offshore wind clusters. Full article

(This article belongs to the Special Issue Technology for Analysis and Control of Power Quality)

35 pages, 9316 KB

Open AccessArticle

Hybrid Game-Based Optimal Operation of Multi-Energy Prosumers Under Coupled Carbon and Green Certificate Markets

by Yuzhe Li, Gaiping Sun, Deting Shen and Bin Wu

Energies 2026, 19(10), 2429; https://doi.org/10.3390/en19102429 - 18 May 2026

Abstract

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed [...] Read more.

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

19 pages, 2383 KB

Open AccessArticle

Research on Application Performance of Controllable Line-Commutated Converters with Supporting Reactive Power Capability Dynamically

by Tingting Deng, Zhaoxin Du, Wenbin Zhao, Jing Zhang and Guangqing Zhang

Energies 2026, 19(10), 2428; https://doi.org/10.3390/en19102428 - 18 May 2026

Abstract

Conventional high-voltage direct current (HVDC) systems based on line-commutated converters (LCC) are prone to commutation failures and consume excessive reactive power during AC grid faults. The controllable line-commutated converter (CLCC) was developed to solve these problems. To further investigate CLCC’s practical application in [...] Read more.

Conventional high-voltage direct current (HVDC) systems based on line-commutated converters (LCC) are prone to commutation failures and consume excessive reactive power during AC grid faults. The controllable line-commutated converter (CLCC) was developed to solve these problems. To further investigate CLCC’s practical application in the AC system, this paper proposes a fixed AC voltage control strategy for the inverter-side CLCC. A hybrid LCC-CLCC HVDC transmission system model is built in PSCAD. Simulations are performed under three-phase short-circuit faults and wind power fluctuation scenarios. The results show that, unlike traditional LCC, the CLCC under the proposed control can actively increase its firing angle over 160 degrees during disturbances. This action injects dynamic reactive power into the grid and significantly reduces the AC bus voltage drop. Especially in weak grid conditions, CLCC can greatly reduce reactive power consumption through wide-range active adjustment of the firing angle, thereby improving voltage stability. Full article

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

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26 pages, 2005 KB

Open AccessArticle

Dependence and Spillover Dynamics Between Clean Energy, Non-Ferrous Metals, and Technological Innovation: Insights from a Global Stress Event

by Noureddine Benlagha and Slim Mseddi

Energies 2026, 19(10), 2427; https://doi.org/10.3390/en19102427 - 18 May 2026

Abstract

The rapid expansion of clean energy markets, coupled with the growing importance of non-ferrous metals and technological innovation, has created a highly interconnected financial and economic system. Understanding the dynamics of these interdependencies is essential for assessing market resilience, investment diversification, and the [...] Read more.

The rapid expansion of clean energy markets, coupled with the growing importance of non-ferrous metals and technological innovation, has created a highly interconnected financial and economic system. Understanding the dynamics of these interdependencies is essential for assessing market resilience, investment diversification, and the sustainability of the global energy transition. This paper investigates the dynamic dependence and connectedness between clean energy, non-ferrous metals, and technological innovation indices, with particular attention to the impact of the COVID-19 pandemic as a global stress event. Using daily data from December 2004 to July 2020, we employ a comprehensive empirical framework that combines copula-based dependence modeling with a dynamic connectedness approach. This methodology allows us to capture nonlinear relationships, tail dependencies, and volatility spillovers across markets. The results reveal that the dependence structure between clean energy and the other sectors is symmetric and time-varying, with stronger linkages observed between clean energy and technological innovation than with non-ferrous metals. The connectedness analysis indicates a moderate level of total spillovers, with clean energy acting as the main transmitter of shocks and technological innovation as the primary receiver. Focusing on the COVID-19 period, we find a significant increase in both dependence and connectedness, suggesting that these markets become more severely integrated during periods of extreme uncertainty. These findings support the presence of contagion effects and highlight the reduced effectiveness of diversification strategies during crisis episodes. The results offer forward-looking implications for investors and policymakers regarding risk transmission, portfolio management, and the resilience of markets supporting the global transition toward sustainable energy. Full article

(This article belongs to the Special Issue Economic and Financial Transformations in the Global Energy Transition: Market Mechanisms, Systemic Risks, and Adaptive Policy Frameworks)

38 pages, 5628 KB

Open AccessReview

Recent Advances in Biopolymer-Based Membranes for Proton Exchange Membrane Fuel Cells

by Bruno Ševo, Anita Bašić, Nadav Amdursky and Željko Penga

Energies 2026, 19(10), 2426; https://doi.org/10.3390/en19102426 - 18 May 2026

Abstract

Proton exchange membrane fuel cells (PEMFCs) are among the most promising clean energy conversion technologies, offering high efficiency and zero emissions. However, their large-scale commercialisation is limited by the high cost and environmental impact of conventional perfluorosulfonic acid membranes such as Nafion. In [...] Read more.

Proton exchange membrane fuel cells (PEMFCs) are among the most promising clean energy conversion technologies, offering high efficiency and zero emissions. However, their large-scale commercialisation is limited by the high cost and environmental impact of conventional perfluorosulfonic acid membranes such as Nafion. In recent years, increasing attention has been directed toward biopolymer-based membranes as sustainable, low-cost, and biodegradable alternatives. This review provides a comprehensive overview of recent advances in the development and modification of biopolymer membranes, including polysaccharide-based materials such as chitosan, cellulose, gellan gum, sodium alginate, and starch, as well as protein-based materials such as keratin and collagen. Various modification strategies, including sulfonation, phosphorylation, cross-linking, and incorporation of inorganic or hybrid fillers, are analysed for their impact on key parameters, including proton conductivity, methanol permeability, and power density. Comparative data indicate that several modified biopolymer membranes achieve proton conductivities of 50 mS/cm or higher. However, higher conductivity values are generally reported for membranes primarily composed of synthetic polymers, where the biopolymer is incorporated only as an additive. In addition, some biopolymer-based membranes exhibit significantly lower methanol permeability than Nafion. The lowest reported value among the membranes discussed in this article is 0.98 × 10⁻¹⁶, representing the best-performing biopolymer membrane in terms of methanol permeability alone. Although many biopolymer membranes demonstrate relatively poor performance in single PEMFC tests, several have achieved power densities comparable to Nafion, while simultaneously offering improved environmental compatibility and sustainability. Finally, current challenges and future directions are discussed, emphasising the potential of these renewable materials to advance PEMFC technology toward more sustainable and economically viable energy systems. Full article

(This article belongs to the Special Issue Digitalization and Automation in the Transportation: Pathways Toward Decarbonization, Enhanced Energy Efficiency and Energy Conservation)

46 pages, 7310 KB

Open AccessArticle

Battery-Based Dynamic Voltage Compensator for Thermoelectric Generation System with Adaptive Hierarchical Actor–Critic Algorithm: Modeling, Design and Hardware-in-the-Loop Experiment Validation

by Wenli Huang, Long Wang, Yinyuan Guo, Zhuo Chen and Bo Yang

Energies 2026, 19(10), 2425; https://doi.org/10.3390/en19102425 - 18 May 2026

Abstract

Thermoelectric generation (TEG) systems suffer from severe power losses under heterogeneous temperature distributions (HTDs). This paper proposes a battery-based dynamic voltage compensation scheme optimized by an adaptive hierarchical actor–critic (AHAC) reinforcement learning algorithm. Unlike conventional methods, the AHAC controller is rigorously mapped to [...] Read more.

Thermoelectric generation (TEG) systems suffer from severe power losses under heterogeneous temperature distributions (HTDs). This paper proposes a battery-based dynamic voltage compensation scheme optimized by an adaptive hierarchical actor–critic (AHAC) reinforcement learning algorithm. Unlike conventional methods, the AHAC controller is rigorously mapped to the physical TEG model, where the state vector explicitly incorporates temperature-dependent Seebeck coefficients, internal resistances, column voltages, currents, thermal profiles, and battery states. The action corresponds directly to battery voltage injection, and the reward function is strictly derived from the net power maximization objective defined by the system’s power balance equations. By complying with thermoelectric material characteristics and thermal–electrical coupling dynamics, the proposed method ensures physical interpretability and reproducibility. The simulation and hardware-in-the-loop (HIL) results confirm the real-time feasibility of online inference and control execution, with power enhancement rates from 3.14% to 13.91% (9 × 9) and 0.44% to 13.23% (9 × 6), outperforming Dyna-Q, GA, and PG methods. The revised framework guarantees methodological coherence between the control algorithm and the TEG’s physical and optimization models. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 3rd Edition)

44 pages, 1274 KB

Open AccessArticle

Deployment Feasibility as a Layered Construct: A Sequential Gate Framework for Evaluating Battery Dispatch Strategies in Distribution Grids

by Zheng Grace Ma, Lu Cong and Bo Nørregaard Jørgensen

Energies 2026, 19(10), 2424; https://doi.org/10.3390/en19102424 - 18 May 2026

Abstract

Conventional multi-criteria decision-making approaches for battery energy storage system (BESS) dispatch evaluation treat regulatory and policy conditions as compensable criteria within a single aggregate score. This becomes problematic when institutional admissibility functions as a prerequisite for deployment rather than a tradeable attribute. This [...] Read more.

Conventional multi-criteria decision-making approaches for battery energy storage system (BESS) dispatch evaluation treat regulatory and policy conditions as compensable criteria within a single aggregate score. This becomes problematic when institutional admissibility functions as a prerequisite for deployment rather than a tradeable attribute. This study aims to develop and test a sequential gate framework. The methodological contribution lies in the evaluation architecture itself: the framework distinguishes sequential admissibility gating from conventional compensatory Multi-Criteria Decision-Making (MCDM). Deployment feasibility is conceptualized as a layered construct in which regulatory admissibility defines the feasible solution space and technical performance differentiates among admissible options. The framework integrates systematic literature screening, quantitative policy and regulatory assessment, and technical ranking using a hybrid Best-Worst Method, Entropy weighting, and TOPSIS approach. A Danish case study covering twelve dispatch strategies compares the proposed sequential design with two flat alternatives. The results show that the evaluation architecture materially affects outcomes: sequential gating excludes an institutionally incomplete strategy and reorders the upper tier by removing compensatory policy effects. Coordinated multi-BESS control at Electric Vehicle charging parks achieves the highest combined feasibility (closeness coefficient 0.891, ranked 1st), while mobile BESS is excluded by the admissibility gate. The sequential design reorders the upper tier relative to flat MCDM, with S4 and S6 rising and S2 and S10 falling once policy compensation is neutralized after the gate. The top-ranked strategy remains robust across sensitivity analysis, Monte Carlo simulation, score perturbation, and VIKOR cross-validation. The framework is presented as an analytical pre-simulation screening tool rather than a validated implementation instrument; external validation against real deployment outcomes is identified as a priority for future research. The framework provides a structured, decision-consistent approach for evaluating deployment feasibility in regulated energy systems. Full article

(This article belongs to the Section D: Energy Storage and Application)

21 pages, 1719 KB

Open AccessArticle

Preliminary Physical and Thermal Design of a Small Chloride Salt Fast Reactor Based on Transmutation

by Minyu Peng, Zhiquan Song, Yuhan Fan, Yang Zou, Yafen Liu and Rui Yan

Energies 2026, 19(10), 2423; https://doi.org/10.3390/en19102423 - 18 May 2026

Abstract

A design for a small chloride salt fast reactor (sm-MCFR) is presented through the integration of molten salt reactor and small reactor technologies, targeting efficient transmutation of transuranic (TRU) elements in spent nuclear fuel and rapid reactor deployment. The feasibility exploration and research [...] Read more.

A design for a small chloride salt fast reactor (sm-MCFR) is presented through the integration of molten salt reactor and small reactor technologies, targeting efficient transmutation of transuranic (TRU) elements in spent nuclear fuel and rapid reactor deployment. The feasibility exploration and research on the design boundaries of sm-MCFR will be conducted in this article. The core adopts a dual-fluid configuration, in which the fuel salt and coolant circulate independently. Chloride salt is selected as the fuel carrier due to its high solubility for heavy metal nuclides and the low neutron absorption cross-section of chlorine, which help to form a hard fast-neutron spectrum and thereby enhance transmutation efficiency. The cooling system employs a direct supercritical carbon dioxide (s-CO₂) cycle, simplifying the overall layout. For the neutronics design, simulations were carried out using the TMCBurnup (TRITON MODEC Coupled Burnup Code). By adjusting the core geometry, fuel salt composition, and reprocessing strategy, the sm-MCFR achieves a hard fast-neutron spectrum but also demonstrates good potential for fuel utilization. In terms of thermal–hydraulic design, the heat exchange effect of the reactor core can be improved by adjusting the proportion of the coolant and the flow direction. The sm-MCFR is expected to become a promising candidate for advanced small reactors that have potential applications in nuclear waste transmutation and distributed energy generation. Full article

(This article belongs to the Section B4: Nuclear Energy)

29 pages, 3774 KB

Open AccessArticle

A Physics-Informed Parameter Transfer Framework Between DFN and NTGK Models for Lithium-Ion Cells

by Biswajit Haridasan, Prabhu Selvaraj and Ratna Kishore Velamati

Energies 2026, 19(10), 2422; https://doi.org/10.3390/en19102422 - 18 May 2026

Abstract

Physics-based electrochemical models such as the Doyle–Fuller–Newman (DFN) framework provide high predictive accuracy for lithium-ion batteries but are computationally intensive, limiting their applicability in large-scale and real-time simulations. Reduced-order models such as the Newman–Tiedemann–Gu–Kim (NTGK) model offer improved computational efficiency but typically require [...] Read more.

Physics-based electrochemical models such as the Doyle–Fuller–Newman (DFN) framework provide high predictive accuracy for lithium-ion batteries but are computationally intensive, limiting their applicability in large-scale and real-time simulations. Reduced-order models such as the Newman–Tiedemann–Gu–Kim (NTGK) model offer improved computational efficiency but typically require experimentally fitted parameters, restricting their scalability across chemistries and operating conditions. This work proposes a physics-informed parameter transfer framework in which NTGK model parameters are derived directly from experimentally validated DFN simulation outputs using a regression-based formulation, thereby reducing dependence on direct experimental parameterization. The approach is applied to LCO–graphite and NMC–graphite cells across multiple discharge rates. The DFN model shows good agreement with experimental data at low to moderate C-rates, with mean absolute errors (MAE) in the range of 20–35 mV at 0.5C. The NTGK model parameterized using DFN-generated synthetic data accurately reproduces the DFN voltage response, with model reduction MAE values as low as 4.5 mV for LCO and 7.17 mV for NMC cells under low-rate operating conditions. Validation against experimental data yields MAE values up to 74 mV for LCO cells and 98 mV for NMC cells at higher C-rates. The proposed framework establishes a direct and physically consistent mapping between high-fidelity electrochemical models and reduced-order representations, enabling scalable and computationally efficient battery simulations while minimizing reliance on extensive experimental parameterization. This approach provides a practical pathway for integrating electrochemical fidelity into system-level and multi-physics battery simulations. Full article

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23 pages, 2569 KB

Open AccessArticle

Model-Free Predictive Synthesis Performance Optimization of DAB Converters Based on an Ultra-Local Model

by Luan Wang, Guoqiang Qiu, Bowen Chi, Dejun Liu and Yanming Cheng

Energies 2026, 19(10), 2421; https://doi.org/10.3390/en19102421 - 18 May 2026

Abstract

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but [...] Read more.

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but its strong parameter dependence is a key factor limiting the development of MPC. Therefore, a model-free predictive control (MFPC) method combining an ultra-local model with model predictive control is proposed to solve the problem of strong dependence of traditional MPC on system model parameters. Firstly, establish the ultra-local mathematical model of the DAB converter. The system’s lumped disturbances are identified using the residual prediction method and substituted into the discrete model of the system at the next time step to achieve model-free prediction. Secondly, a minimum back-flow power constraint is added to the cost function to improve the steady-state performance of the converter. Thirdly, in the extended phase shift modulation, the Lagrange multiplier method (LMM) is proposed to reduce the current stress, ultimately achieving the collaborative optimization of the comprehensive performance of the DAB. Finally, a simulation model is built using MATLAB/Simulink, and compared with traditional control methods, the voltage ripple has been reduced by 51.3%, 89.1%, and 85.1%, respectively; the current stress significantly decreases both when the output voltage reference value changes and when the load resistance changes abruptly, and both can basically achieve zero back-flow power operation. The validity and superiority of the proposed strategy have been verified. Full article

(This article belongs to the Special Issue Advances in Power Converters and Inverters)

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