Energies

18 pages, 2151 KB

Open AccessArticle

A Communication-Free Cooperative Fault Recovery Control Method for DNs Based on Staged Active Power Injection of ES

by Bin Yang, Ning Wei, Yuhang Guo, Jince Ge and Liyuan Zhao

Energies 2026, 19(1), 285; https://doi.org/10.3390/en19010285 - 5 Jan 2026

Viewed by 487

To address the reclosing failures in the distribution networks (DNs) with high penetration of distributed energy resources (DERs), this paper proposes a communication-free cooperative fault recovery control method based on staged active power injection of an energy storage (ES) system. First, during the [...] Read more.

To address the reclosing failures in the distribution networks (DNs) with high penetration of distributed energy resources (DERs), this paper proposes a communication-free cooperative fault recovery control method based on staged active power injection of an energy storage (ES) system. First, during the initial phase of a fault, a back-electromotive force (b-EMF) suppression arc extinction control strategy was designed for the ES converter, promoting fault arc extinction. Subsequently, the ES switches to grid-forming (GFM) control, providing active power injection to the network following the circuit breaker (CB) tripping. A time-limited variable power control of ES converter is also designed to establish voltage characteristics for fault state detection. And a fault state criterion based on voltage relative entropy is designed, helping reliable reclosing. Simulation results demonstrate that the proposed method achieves coordination solely through local measurements without the need for real-time communication between ES and CB, and can shorten the recovery time of transient faults to hundreds of milliseconds. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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28 pages, 4479 KB

Open AccessArticle

Patch Time Series Transformer−Based Short−Term Photovoltaic Power Prediction Enhanced by Artificial Fish

by Xin Lv, Shuhui Cui, Yue Wang, Jinye Lu, Puming Yu and Kai Wang

Energies 2026, 19(1), 284; https://doi.org/10.3390/en19010284 - 5 Jan 2026

Cited by 5 | Viewed by 807

Abstract

The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short−term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish [...] Read more.

The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short−term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish Swarm Algorithm–Isolation Forest (AFSA–IF) anomaly detection, Generative Adversarial Network−based feature extraction, multimodal data fusion, and a Patch Time Series Transformer (PatchTST) model. The framework includes advanced preprocessing, fusion of meteorological and historical power data, and weather classification via one−hot encoding. Experiments on datasets from six PV plants show significant improvements in mean absolute error, root mean square error, and coefficient of determination compared with Transformer, Reformer, and Informer models. The results confirm the robustness and efficiency of the proposed model, especially under challenging conditions such as rainy weather. Full article

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

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22 pages, 3850 KB

Open AccessArticle

Income, Heating Technologies and Behavioral Patterns as Drivers of Particulate Matter Emissions in the Kraków Metropolitan Area

by Elżbieta Węglińska, Maciej Sabal, Mateusz Zareba and Tomasz Danek

Energies 2026, 19(1), 283; https://doi.org/10.3390/en19010283 - 5 Jan 2026

Cited by 2 | Viewed by 618

Abstract

Air pollution episodes caused by particulate matter (PM) persist in and around Kraków even after the city’s ban on solid fuels. We examine how household wealth and the ongoing replacement of old heat sources with modern, energy-efficient units affect these emissions. Years of [...] Read more.

Air pollution episodes caused by particulate matter (PM) persist in and around Kraków even after the city’s ban on solid fuels. We examine how household wealth and the ongoing replacement of old heat sources with modern, energy-efficient units affect these emissions. Years of hourly data from a network of low-cost sensors for neighboring municipalities are combined with the Poland building emissions register specifying the number and type of heating devices and municipal personal income tax records. Two distinct emission patterns emerge. Episodes of elevated concentrations near houses with old hand-loaded stoves follow pronounced behavioral cycles tied to residents return home hours and the nightly sleep cycle, whereas elsewhere the pattern is smoother—consistent with modern heating sources or with advection from dispersed upwind sources. Municipalities that recorded per capita income growth also showed declines in average PM concentrations, suggesting that rising incomes accelerate the transition to cleaner, more efficient heating. Our findings suggest that economic development is linked to the shift towards cleaner and more efficient energy, and that providing targeted support for low-income households should not be overlooked in completing the transition. Full article

(This article belongs to the Special Issue Towards Sustainable Energy Development: Energy Efficiency and Economic Growth)

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18 pages, 3311 KB

Open AccessArticle

A Parametric Study on the Aerodynamic Parameters of Desert Photovoltaic Arrays: The Effect of Spacing on Friction Velocity and Roughness Length

by Xiang Dou, Zhuoqun Li and Xin Wen

Energies 2026, 19(1), 282; https://doi.org/10.3390/en19010282 - 5 Jan 2026

Cited by 1 | Viewed by 514

Abstract

Desert photovoltaic (PV) plants suffer significant efficiency loss due to dust deposition, which is closely linked to near-surface aerodynamic conditions. This study investigates how PV array row spacing influences key aerodynamic parameters. Numerical simulations using the Realizable k-ε turbulence model were performed for [...] Read more.

Desert photovoltaic (PV) plants suffer significant efficiency loss due to dust deposition, which is closely linked to near-surface aerodynamic conditions. This study investigates how PV array row spacing influences key aerodynamic parameters. Numerical simulations using the Realizable k-ε turbulence model were performed for multi-row arrays with varying normalized spacings (D/L = 0, 0.5, 1, 1.5, 2). Results show that the friction velocity and aerodynamic roughness length initially increase, then decrease with row number before stabilizing. Their stabilized values exhibit a positive linear correlation with D/L. Empirical formulas were fitted. These findings provide a theoretical basis for optimizing the layout of desert PV plants to mitigate dust-related efficiency losses. Full article

(This article belongs to the Special Issue Progress and Challenges in Solar Photovoltaic Materials and Intelligent Control)

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17 pages, 22627 KB

Open AccessArticle

RMS-Based PLL Stability Limit Estimation Using Maximum Phase Error for Power System Planning in Weak Grids

by Beomju Kim, Jeonghoo Park, Seungchan Oh, Hwanhee Cho and Byongjun Lee

Energies 2026, 19(1), 281; https://doi.org/10.3390/en19010281 - 5 Jan 2026

Viewed by 534

Abstract

The increasing interconnection of inverter-based resources (IBRs) with low short-circuit current has weakened grid strength, making phase-locked loops (PLLs) susceptible to instability due to accumulated phase-angle error under current limiting. This study defines such instability as IBR instability induced by reduced grid robustness [...] Read more.

The increasing interconnection of inverter-based resources (IBRs) with low short-circuit current has weakened grid strength, making phase-locked loops (PLLs) susceptible to instability due to accumulated phase-angle error under current limiting. This study defines such instability as IBR instability induced by reduced grid robustness and proposes a root-mean-square (RMS) model-based screening method. After fault clearance, the residual q-axis voltage observed by the PLL is treated as a disturbance signal and, using the PLL synchronization equations, is analyzed with a standard second-order formulation. The maximum phase angle at which synchronization fails is defined as

θ_{p e a k}

, and the corresponding q-axis voltage is defined as

V_{q, c r i t}

. This value is then mapped to a screening metric

P_{p e a k}

suitable for RMS-domain assessment. The proposed methodology is applied to the IEEE 39-bus test system: the stability boundary and

P_{p e a k}

are obtained in Power System Simulator for Engineering (PSSE), and the results are validated through electromagnetic transient (EMT) simulations in PSCAD. The findings demonstrate that the RMS-based screening can effectively identify operating conditions that are prone to PLL instability in weak grids, providing a practical tool for planning and operation with high IBR penetration. This screening method supports power system planning for high-penetration inverter-based resources by identifying weak-grid locations that require EMT studies to ensure secure operation after grid faults. Full article

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

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30 pages, 4766 KB

Open AccessArticle

Enhancing Energy Market Forecasting with Graph Convolutional Networks: A Multi-Node Time-Series Analysis Framework

by Josue Ngondo Otshwe, Bin Li, Jaime Chabrol Ngouokoua, Bing Qi, Christian Mugisho Tabaro, Qi Guo and Yi Kang

Energies 2026, 19(1), 280; https://doi.org/10.3390/en19010280 - 5 Jan 2026

Cited by 1 | Viewed by 544

Abstract

Accurate multi-node energy market forecasting is critical for secure and economic grid operation under increasing penetration of renewable energy and electric vehicles. This paper proposes a physics-aware spatiotemporal forecasting framework that integrates Graph Convolutional Networks (GCNs) for modeling network-level spatial dependencies with a [...] Read more.

Accurate multi-node energy market forecasting is critical for secure and economic grid operation under increasing penetration of renewable energy and electric vehicles. This paper proposes a physics-aware spatiotemporal forecasting framework that integrates Graph Convolutional Networks (GCNs) for modeling network-level spatial dependencies with a self-attention mechanism for capturing long-range temporal correlations. Unlike existing GCN + RNN or attention-based forecasting approaches, physical feasibility is enforced during learning through structured penalty terms reflecting power balance, generation limits, EV state-of-charge dynamics, and AC load flow constraints, rather than via post-processing optimization. The model is evaluated on a synthetic IEEE 24-bus benchmark with realistic load scaling, renewable variability, and EV charging profiles. Results show a mean squared error of 1.84 MW² and a 7–10% reduction in forecasting error relative to baseline ARIMA and LSTM models, while maintaining constraint violation rates below 5%. Multi-step forecasting experiments demonstrate stable error growth under high volatility conditions. The proposed framework establishes a bridge between purely data-driven forecasting and physically consistent grid-aware prediction, offering a scalable foundation for operationally feasible energy market forecasting. Full article

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

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19 pages, 5014 KB

Open AccessArticle

In Situ Electrochemical Detection of Silicon Anode Crystallization for Full-Cell Health Management

by Hyeon-Woo Jung, Ga-Eun Lee and Heon-Cheol Shin

Energies 2026, 19(1), 279; https://doi.org/10.3390/en19010279 - 5 Jan 2026

Viewed by 542

Abstract

In this study, we investigate the relationship between the progressive lowering of the silicon (Si) anode potential during lithiation and the accompanying crystallization reaction to enable in situ electrochemical detection in Si-based full cells. Si–Li half cells were first analyzed by differential capacity [...] Read more.

In this study, we investigate the relationship between the progressive lowering of the silicon (Si) anode potential during lithiation and the accompanying crystallization reaction to enable in situ electrochemical detection in Si-based full cells. Si–Li half cells were first analyzed by differential capacity (dQ/dV), revealing a crystallization feature near 0.05 V vs. Li⁺/Li, commonly associated with crystallization to Li₁₅Si₄. In the initial cycle, this signal was obscured by a dominant amorphization peak near 0.1 V; however, once amorphization was completed and the end-of-lithiation potential dropped below ~0.05 V in later cycles, a distinct crystallization peak became clearly resolvable. Under capacity-limited cycling that mimics full-cell operation, degradation-induced lowering of the Si-anode potential led to the appearance of the crystallization signal when the anode potential crossed this threshold. Based on these results, we extended the analysis to LiFePO₄–Si three-electrode full cells and, by reparameterizing dQ/dV as a function of charge time, separated electrode-specific contributions and identified the Si crystallization feature within the full-cell response when N/P ≈ 1. A simple degradation-modeling scenario further showed that in cells initially designed with N/P > 1, loss of anode active material can reduce the effective N/P, drive the Si potential into the crystallization window, and introduce a new peak in the full-cell dQ/dV curve associated with Si crystallization. These combined experimental and modeling results indicate that degradation-driven lowering of the Si-anode potential triggers crystallization and that this process can be detected in full cells via dQ/dV analysis. Practically, the emergence of the Si-crystallization feature provides an in situ marker that the effective N/P has drifted toward unity due to anode-dominated aging and may inform charge cut-off strategies to mitigate further Si-anode degradation. Full article

(This article belongs to the Special Issue Advanced Electrochemical Energy Storage Materials)

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20 pages, 1793 KB

Open AccessArticle

Multi-Time Scale Optimal Scheduling of Aluminum Electrolysis Parks Considering Production Economy and Operational Safety Under High Wind Power Integration

by Chiyin Xiao, Hao Zhong, Xun Li, Zhenhui Ouyang and Yongjia Wang

Energies 2026, 19(1), 278; https://doi.org/10.3390/en19010278 - 5 Jan 2026

Viewed by 388

Abstract

To address the power fluctuation challenges associated with high-proportion wind power integration and enhance the source–load coordination capability of aluminum electrolysis parks, this paper proposes a multi-time scale collaborative regulation strategy. Based on the production characteristics and regulation principles of aluminum electrolysis loads, [...] Read more.

To address the power fluctuation challenges associated with high-proportion wind power integration and enhance the source–load coordination capability of aluminum electrolysis parks, this paper proposes a multi-time scale collaborative regulation strategy. Based on the production characteristics and regulation principles of aluminum electrolysis loads, a multi-objective optimization model for regulating loads with multiple potline series is established, considering both production revenue and temperature penalties. On this basis, a multi-time scale optimal scheduling model is developed for the park, involving day-ahead commitment optimization, intraday rolling adjustment, and real-time dynamic responses. Case studies based on actual data demonstrate that the proposed strategy effectively alleviates wind power fluctuations and enhances local consumption capacity. Compared to the baseline scenario without load regulation, the integration of electrolytic aluminum load across day-ahead, intra-day, and real-time stages reduces wind curtailment by approximately 40.1%, 52.5%, and 74.6% in successive scenarios, respectively, while the total operating cost shows a decreasing trend with reductions of about 1.15%, 0.63%. This facilitates economical and high-quality operation while maintaining temperature stability for the aluminum electrolysis production process. Full article

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40 pages, 3262 KB

Open AccessArticle

Towards Green Transition: Sustainable Energy Future and Its Effects on Companies’ Financial Strategies

by Alexandra-Mădălina Țăran, Grațiela-Georgiana Noja, Alina Ionașcu, Mihaela Diaconu and Oana-Ramona Lobonț

Energies 2026, 19(1), 277; https://doi.org/10.3390/en19010277 - 5 Jan 2026

Cited by 1 | Viewed by 744

Abstract

Socio-economic resilience and sustainable development have become central themes in contemporary public debate, with the transition to sustainable, low-carbon energy systems emerging as a strategic priority. Within this context, our research specifically examines how CSR engagement, renewable energy deployment, and sustainable finance jointly [...] Read more.

Socio-economic resilience and sustainable development have become central themes in contemporary public debate, with the transition to sustainable, low-carbon energy systems emerging as a strategic priority. Within this context, our research specifically examines how CSR engagement, renewable energy deployment, and sustainable finance jointly influence firms’ exposure to climate-related financial risks, addressing a gap in the literature regarding corporate-level resilience. The empirical analysis employs a structured two-fold methodological framework comprising robust regression with Huber and biweight iterations, and quantile-on-quantile (Q–Q) regression. The dataset includes 300 European energy companies for 2024, extracted from the LSEG Data & Analytics platform. Our findings reveal that companies in the European energy sector must accelerate their transition to low-carbon operating models. Specifically, firms with stronger sustainability commitments exhibit reduced exposure to climate-induced financial instability and improved long-term performance indicators. These findings underscore the moderating role of CSR and renewable energy investments in enhancing corporate resilience. Sustainability-oriented firms are better positioned to absorb, mitigate, and adapt to climate-related shocks, supporting both environmental objectives and financial stability. Policy recommendations should focus on balancing ESG objectives with financial performance requirements, ensuring that energy companies receive adequate support for the green transition. Such alignment is essential to strengthen corporate resilience and improve the effectiveness of sustainable energy policies amid escalating climate challenges. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy: 2nd Edition)

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21 pages, 3047 KB

Open AccessArticle

Chemical Looping Gasification with Microalgae: Intrinsic Gasification Kinetics of Char Derived from Fast Pyrolysis

by Daofeng Mei, Francisco García-Labiano, Alberto Abad and Tobias Mattisson

Energies 2026, 19(1), 276; https://doi.org/10.3390/en19010276 - 5 Jan 2026

Viewed by 678

Abstract

Chemical looping gasification (CLG) based on interconnected fluidized beds is a viable technology to produce a syngas stream for chemical and fuel production. In this work, microalgae are studied for use in the CLG process; more specifically, the intrinsic kinetics of char gasification [...] Read more.

Chemical looping gasification (CLG) based on interconnected fluidized beds is a viable technology to produce a syngas stream for chemical and fuel production. In this work, microalgae are studied for use in the CLG process; more specifically, the intrinsic kinetics of char gasification have been analyzed, as it is important for the fuel conversion and design of reactor systems. Char produced from fast pyrolysis was used in a thermogravimetric analyzer (TGA) for intrinsic kinetics analysis, and measures were made to eliminate the interparticle and external particle gas diffusion. The effect of typical operational variables, such as temperature, concentration of gasification agents (H₂O and CO₂), and concentration of gasification products (H₂ and CO), were investigated. The TGA data is used to derive a suitable gasification model that can best fit the experimental data. The fitting with experiments then generates values of the model’s kinetics parameters. Based on the model and the kinetics values, the activation energies in the gasification with steam and CO₂ were calculated to be 43.3 and 91.6 kJ/mol, respectively. The model has a good capability in the prediction of the gasification profile with H₂O and CO₂ under a complex reacting atmosphere. Full article

(This article belongs to the Special Issue Advances in Carbon Capture Technologies in Thermochemical Conversion of Biomass)

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21 pages, 10371 KB

Open AccessArticle

Numerical Simulation of Gas-Liquid Two-Phase Flow in a Downhole Multistage Axial Compressor Under Different Inlet Conditions

by Mingchen Cao, Wei Pang, Huanle Liu, Shifan Su, Yufan Wang and Weihao Zhang

Energies 2026, 19(1), 275; https://doi.org/10.3390/en19010275 - 5 Jan 2026

Cited by 1 | Viewed by 621

Abstract

During natural gas field extraction, downhole compressors frequently encounter gas-liquid two-phase flow conditions, yet the internal flow characteristics and performance evolution mechanisms remain insufficiently understood. This paper investigates a small-scale, low-pressure-ratio five-stage axial compressor using a multiphase numerical simulation method based on the [...] Read more.

During natural gas field extraction, downhole compressors frequently encounter gas-liquid two-phase flow conditions, yet the internal flow characteristics and performance evolution mechanisms remain insufficiently understood. This paper investigates a small-scale, low-pressure-ratio five-stage axial compressor using a multiphase numerical simulation method based on the Euler-Lagrange framework. The study systematically examines the effects of different inlet pressures (0.1 MPa, 1 MPa, 8 MPa) and liquid mass fraction (0%, 5%, 10%) on its overall and stage-by-stage performance, droplet evolution, and flow field structure. The results indicate that the inlet pressure exerts a decisive influence on the overall efficiency trend of wet compression. The stage efficiency response displays a trend of an initial decrease in the front stages followed by an increase in the rear stages, showing significant variation under different inlet pressures. Flow field analysis reveals that increased inlet pressure intensifies droplet aerodynamic breakup, leading to higher flow losses in the compressor. Simultaneously, under high-pressure conditions, the cumulative cooling effect resulting from droplet heat transfer and evaporation effectively enhances the flow stability in the rear stages. This research elucidates the interstage interaction mechanisms of gas-liquid two-phase flow in low-pressure-ratio multistage compressors and highlights the competing influences of droplet breakup and evaporation effects on performance under different pressure conditions, providing a theoretical basis for the optimal design of downhole wet gas compression technology. Full article

(This article belongs to the Special Issue Advancing Oil and Gas Resource Exploration, Extraction, and Utilization for Sustainability)

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20 pages, 5104 KB

Open AccessArticle

A Novel Ultra-Short-Term PV Power Forecasting Method Based on a Temporal Attention-Variable Parallel Fusion Encoder Network

by Jinman Zhang, Zengbao Zhao, Rongmei Guo, Xue Hu, Tonghui Qu, Chang Ge and Jie Yan

Energies 2026, 19(1), 274; https://doi.org/10.3390/en19010274 - 5 Jan 2026

Cited by 1 | Viewed by 580

Abstract

Accurate photovoltaic (PV) power forecasting is critical for the stable operation of power systems. Existing methods rely solely on historical data, which significantly decline in forecasting accuracy at 3–4 h ahead. To address this problem, a novel ultra-short-term PV power forecasting method based [...] Read more.

Accurate photovoltaic (PV) power forecasting is critical for the stable operation of power systems. Existing methods rely solely on historical data, which significantly decline in forecasting accuracy at 3–4 h ahead. To address this problem, a novel ultra-short-term PV power forecasting method based on temporal attention-variable parallel fusion encoder network is proposed to enhance the stability of forecasting results by incorporating Numerical Weather Prediction data to correct temporal predictions. Specifically, independent encoding modules are constructed for both historical power sequences and future NWP sequences, enabling deep feature extraction of their respective temporal characteristics. During the decoding phase, a two-stage coupled decoding strategy is employed: for 1–8 steps predictions, the model relies solely on temporal features, while for 9–16 steps horizons, it dynamically fuses encoded information from historical power data and future NWP inputs. This approach allows for accurate characterization of future trend dynamics. Experimental results demonstrate that, compared with conventional methods, the proposed model reduces the average normalized root mean square error (NRMSE) at 4th ultra-short-term forecasting by 0.50–5.20%, while it improves the

R^{2}

by 0.047–0.362, validating the effectiveness of the proposed approach. Full article

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

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17 pages, 5644 KB

Open AccessArticle

Investigation of CH₄ Hydrate Formation Under the Synergistic Effects of Graphite Nanofluids and Cyclopentane and Its Storage Stability at Subzero Temperatures

by Zhansheng Tao, Jianwen Zhang, Ruogu Kuang, Wanming Bao, Dongliang Zhong and Liangmeng Wu

Energies 2026, 19(1), 273; https://doi.org/10.3390/en19010273 - 5 Jan 2026

Viewed by 411

Abstract

The solidified natural gas (SNG) technology presents a prospective strategy for CH₄ storage and transportation. Low gas storage capacity and slow formation rate remain the key challenges for its field applications. This study suggested a compound system of cyclopentane (CP) + graphite [...] Read more.

The solidified natural gas (SNG) technology presents a prospective strategy for CH₄ storage and transportation. Low gas storage capacity and slow formation rate remain the key challenges for its field applications. This study suggested a compound system of cyclopentane (CP) + graphite nanoparticle (GNP) nanofluid to enhance the formation kinetics of CH₄ hydrate. Results indicated that both gas consumption and hydrate formation rate were higher at a higher CP concentration, peaking at 14 wt%, where t₉₀ (the time to reach 90% of the final gas uptake) was 65.7 min, and the gas uptake reached 0.1346 mol/mol. However, an excessive CP (21 wt%) negatively affected CH₄ hydrate generation kinetics due to the excessive cage occupancy of CP in 5¹²6⁴ cavities. A lower temperature was determined to be more favorable for CH₄ hydrate formation within nanofluids, which was visually demonstrated by the denser hydrate crystals formed at 275.15 K. Moreover, storage stability analysis revealed that CH₄ hydrate formed in CP + GNP nanofluids can be preserved at atmospheric pressure and 268.15 K without significant decomposition. This work provides a superior scheme for hydrate-based CH₄ storage, offering great contributions to SNG technology advancement. Full article

(This article belongs to the Special Issue Oil and Gas Reservoirs: Phase Behavior, Seepage Mechanisms, Productivity Prediction, and Novel Modelling Methods—3rd Edition)

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21 pages, 2591 KB

Open AccessArticle

Fast Fault Identification Scheme for MMC-HVDC Grids Based on a Novel Current-Limiting DC Circuit Breaker

by Qiuyu Cao, Zhiyan Li, Xinsong Zhang, Chenghong Gu and Xiuyong Yu

Energies 2026, 19(1), 272; https://doi.org/10.3390/en19010272 - 5 Jan 2026

Cited by 1 | Viewed by 613

Abstract

The development of high-performance DC circuit breakers (DCCBs) and rapid fault detection schemes is a crucial and challenging part of advancing Modular Multilevel Converter (MMC) HVDC grids. This paper introduces a new current-limiting DCCB that uses the differential discharge times of shunt capacitors [...] Read more.

The development of high-performance DC circuit breakers (DCCBs) and rapid fault detection schemes is a crucial and challenging part of advancing Modular Multilevel Converter (MMC) HVDC grids. This paper introduces a new current-limiting DCCB that uses the differential discharge times of shunt capacitors to generate artificial current zero-crossings, thus facilitating arc quenching. This mechanism significantly reduces the effect of fault currents on the MMC. The shunt capacitors and arresters in the proposed breaker also offer voltage support during faults, effectively stopping transient traveling waves from spreading to nearby non-fault lines. This feature creates an effective line protection boundary in multi-terminal HVDC systems. Additionally, a fast fault detection scheme with primary and backup protection is proposed. A four-terminal MMC-HVDC (±500 kV) simulation model is built in PSCAD/EMTDC to validate the scheme. The results demonstrate the excellent fault detection performance of the proposed method. The voltage and current behavior during the interruption process of the new DCCB is also analyzed and compared with that of a hybrid DCCB. Full article

(This article belongs to the Topic Power System Protection)

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26 pages, 2761 KB

Open AccessArticle

Design and Research on an Active Contract Signing Mechanism for Demand Response in Community Electric Vehicle Orderly Charging Considering User Satisfaction

by Shuang Hao, Minghui Jia, Jian Zhang, Zhijie Zhang and Guoqiang Zu

Energies 2026, 19(1), 271; https://doi.org/10.3390/en19010271 - 4 Jan 2026

Viewed by 417

Abstract

To address grid security issues such as load fluctuation and transformer overloading caused by increasing community EV charging demand, this study proposes two active demand response mechanisms to encourage users to voluntarily participate in orderly charging: a single-signup mechanism and a hybrid mechanism [...] Read more.

To address grid security issues such as load fluctuation and transformer overloading caused by increasing community EV charging demand, this study proposes two active demand response mechanisms to encourage users to voluntarily participate in orderly charging: a single-signup mechanism and a hybrid mechanism integrating signing willingness with user satisfaction. A hierarchical user satisfaction model is developed, integrating incentive perception and dispatch satisfaction, to characterize nonlinear user responses under varying incentive and dispatch levels. A genetic algorithm is then applied to determine the optimal contract portfolio that maximizes community-wide satisfaction. Simulation results show that the hybrid mechanism achieves the highest average satisfaction (0.8788), significantly outperforming both the single-signup and traditional passive schemes, effectively enhancing user participation and grid flexibility. This study provides a new theoretical framework and optimization pathway for mechanism innovation in orderly electric vehicle charging under centralized construction and unified operation scenarios in residential communities and offers valuable insights for the coordinated development of vehicle–grid interaction and demand-side management models in community-based new power systems. Full article

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

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33 pages, 1482 KB

Open AccessReview

A New Paradigm for Physics-Informed AI-Driven Reservoir Research: From Multiscale Characterization to Intelligent Seepage Simulation

by Jianxun Liang, Lipeng He, Weichao Chai, Ninghong Jia and Ruixiao Liu

Energies 2026, 19(1), 270; https://doi.org/10.3390/en19010270 - 4 Jan 2026

Viewed by 1476

Abstract

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this [...] Read more.

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)

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22 pages, 3874 KB

Open AccessArticle

Cloud-Edge Collaboration-Based Data Processing Method for Distribution Terminal Unit Edge Clusters

by Ruijiang Zeng, Zhiyong Li, Sifeng Li, Jiahao Zhang and Xiaomei Chen

Energies 2026, 19(1), 269; https://doi.org/10.3390/en19010269 - 4 Jan 2026

Viewed by 393

Abstract

Distribution terminal units (DTUs) play critical roles in smart grid for supporting data acquisition, remote monitoring, and fault management. A single DTU generates continuous data streams, imposing new challenges on data processing. To tackle these issues, a cloud-edge collaboration-based data processing method is [...] Read more.

Distribution terminal units (DTUs) play critical roles in smart grid for supporting data acquisition, remote monitoring, and fault management. A single DTU generates continuous data streams, imposing new challenges on data processing. To tackle these issues, a cloud-edge collaboration-based data processing method is introduced for DTU edge clusters. First, considering the load imbalance degree of DTU data queues, a cloud-edge integrated data processing architecture is designed. It optimizes edge server selection, the offloading splitting ratio, and edge-cloud computing resource allocation in a collaboration mechanism. Second, an optimization problem is formulated to maximize the weighted difference between the total data processing volume and the load imbalance degree. Next, a cloud-edge collaboration-based data processing method is proposed. In the first stage, cloud-edge collaborative data offloading based on the load imbalance degree, and a data volume-aware deep Q-network (DQN) is developed. A penalty function based on load fluctuations and the data volume deficit is incorporated. It drives the DQN to evolve toward suppressing the fluctuation of load imbalance degree while ensuring differentiated long-term data volume constraints. In the second stage, cloud-edge computing resource allocation based on adaptive differential evolution is designed. An adaptive mutation scaling factor is introduced to overcome the gene overlapping issues of traditional heuristic approaches, enabling deeper exploration of the solution space and accelerating global optimum identification. Finally, the simulation results demonstrate that the proposed method effectively improves the data processing efficiency of DTUs while reducing the load imbalance degree. Full article

(This article belongs to the Special Issue Operation and Planning of Distribution Systems Under High Renewable Penetration)

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19 pages, 8178 KB

Open AccessArticle

SpectralNet-Enabled Root Cause Analysis of Frequency Anomalies in Solar Grids Using μPMU

by Arnabi Modak, Maitreyee Dey, Preeti Patel and Soumya Prakash Rana

Energies 2026, 19(1), 268; https://doi.org/10.3390/en19010268 - 4 Jan 2026

Viewed by 514

Abstract

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid [...] Read more.

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit (

μ

PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in

μ

PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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30 pages, 1305 KB

Open AccessArticle

Industrial Energy Efficiency Versus Energy Poverty in the European Union: Macroeconomic and Social Relationships

by Bożena Gajdzik, Rafał Nagaj, Brigita Žuromskaitė-Nagaj and Radosław Wolniak

Energies 2026, 19(1), 267; https://doi.org/10.3390/en19010267 - 4 Jan 2026

Cited by 2 | Viewed by 665

Abstract

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its [...] Read more.

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its direct link to energy poverty at the macro level has rarely been analysed, let alone with respect to structural changes in industry. Filling this gap, this paper evaluates whether reductions in industrial energy intensity result in reduced energy poverty, understood as the share of households unable to maintain adequate indoor thermal comfort. Empirical analysis relies on a balanced panel dataset and uses fixed-effects regression models to take into account unobserved country-specific and time-specific heterogeneity. In addition, potential endogeneity between industrial energy intensity and labour productivity is addressed by the instrumental variable approach using two-stage least squares. The main models also include key macroeconomic and social control variables: real GDP per capita, social benefit expenditure, electricity prices for households, and unit labour costs. The results yield a robust and statistically significant positive link between industrial energy intensity and energy poverty, suggesting that efficiency improvements in industry make a quantifiable difference in household energy deprivation. This effect even increases in strength after the correction for endogeneity, thereby corroborating the causal relevance of productivity-driven efficiency gains. The findings also show substantial heterogeneity between EU Member States, indicating that national structural features will determine baseline levels of energy poverty. However, no strong evidence is found for an indirect price-mediated transmission mechanism or for moderation effects bound to income levels or social expenditure. This study provides sound empirical evidence that industrial energy efficiency is an important but structurally conditioned lever to alleviate energy poverty in the European Union. The results emphasise the integration of industrial efficiency policies with social and institutional frameworks while designing strategies for a just and inclusive energy transition. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy: 2nd Edition)

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26 pages, 334 KB

Open AccessReview

Enhancing Energy Efficiency in Road Transport Systems: A Comparative Study of Australia, Hong Kong and the UK

by Philip Y. L. Wong, Tze Ming Leung, Wenwen Zhang, Kinson C. C. Lo, Xiongyi Guo and Tracy Hu

Energies 2026, 19(1), 266; https://doi.org/10.3390/en19010266 - 4 Jan 2026

Viewed by 742

Abstract

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving [...] Read more.

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems. Full article

(This article belongs to the Special Issue Towards Sustainable Energy Development: Energy Efficiency and Economic Growth)

25 pages, 1497 KB

Open AccessArticle

Optimization Models for Distributed Energy Systems Under CO₂ Constraints: Sizing, Operating, and Regulating Power Provision

by Azusa Miyazaki, Miku Muraoka and Takashi Ikegami

Energies 2026, 19(1), 265; https://doi.org/10.3390/en19010265 - 4 Jan 2026

Viewed by 376

Abstract

The increasing penetration of variable renewable energy sources has intensified the need for ancillary services to maintain grid stability, and demand-side flexibility, particularly through distributed energy systems (DESs), is expected to play an important role. This study proposes a two-stage optimization framework for [...] Read more.

The increasing penetration of variable renewable energy sources has intensified the need for ancillary services to maintain grid stability, and demand-side flexibility, particularly through distributed energy systems (DESs), is expected to play an important role. This study proposes a two-stage optimization framework for DESs under CO₂ constraints that enables gas engines and battery energy storage systems (BESS) to provide regulating power equivalent to Load Frequency Control (LFC). The framework consists of an Equipment Sizing Optimization Model (ESM) and an Equipment Operation Optimization Model (EOM), both formulated as mixed-integer linear programming (MILP) models. The ESM determines equipment capacities using simplified operational representations, where partial-load efficiencies are approximated through linear programming (LP)-based constraints. The EOM incorporates detailed operational characteristics, including start-up/shutdown states and partial-load efficiencies, to perform daily scheduling. Information obtained from the ESM, such as the CO₂ emissions, the equipment capacities, and the BESS state of charge, is passed to the EOM to maintain consistency. A case study shows that providing regulating power reduces total system cost and that CO₂ reduction constraints alter the equipment mix. These findings demonstrate that the proposed framework offers a practical and computationally efficient approach for designing and operating DESs under CO₂ constraints. Full article

(This article belongs to the Special Issue Distributed Energy Systems: Progress, Challenges, and Prospects)

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22 pages, 3251 KB

Open AccessArticle

Fuzzy Comprehensive State Evaluation Method of Filter Circuit Breaker Based on Field Operation Data

by Longcheng Dai, Jiaying Yu, Yousu Qin, Fenglong Ma, Hui Ni, Yongxing Wang and Zhihui Huang

Energies 2026, 19(1), 264; https://doi.org/10.3390/en19010264 - 4 Jan 2026

Viewed by 334

Abstract

This study focuses on an 800 kV Filter Circuit Breaker (FCB) equipped with phase-controlled closing. The real-time health status of the circuit breaker was evaluated by analyzing the factors influencing its condition based on field-operation data. Key performance parameters, including mechanical characteristics, switching [...] Read more.

This study focuses on an 800 kV Filter Circuit Breaker (FCB) equipped with phase-controlled closing. The real-time health status of the circuit breaker was evaluated by analyzing the factors influencing its condition based on field-operation data. Key performance parameters, including mechanical characteristics, switching performance, and insulation properties, were employed for the assessment. Furthermore, field operation data are preprocessed to establish a comprehensive database that integrates data from C2-class margin tests, phase-controlled switching tests, and phase-controlled closing factory tests. A combined subjective–objective weighting method is applied to assign weights to health impact factors, and an improved fuzzy comprehensive evaluation method incorporating field operation data is proposed. Based on this evaluation, different operation and maintenance strategies were formulated. The results demonstrate that the proposed method enables a comprehensive and accurate assessment of the health status of FCBs, offering a reliable framework for optimizing condition-based maintenance in practical engineering applications. Full article

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

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14 pages, 493 KB

Open AccessArticle

FinTech and Financial Stability in BRICS Economies

by Salim Bourchid Abdelkader, Kamal Si Mohammed and Syed Ale Raza Shah

Energies 2026, 19(1), 263; https://doi.org/10.3390/en19010263 - 4 Jan 2026

Viewed by 563

Abstract

This study examines the dynamic and distributional effects of financial technology (FinTech) and renewable energy (RE) on financial stability (FST) in BRICS economies from 2012 to 2022. Using a combination of Panel Autoregressive Distributed Lag (Panel ARDL) and Panel Quantile Regression (PQR) models, [...] Read more.

This study examines the dynamic and distributional effects of financial technology (FinTech) and renewable energy (RE) on financial stability (FST) in BRICS economies from 2012 to 2022. Using a combination of Panel Autoregressive Distributed Lag (Panel ARDL) and Panel Quantile Regression (PQR) models, the analysis captures both short-run versus long-run adjustment mechanisms and heterogeneous effects across different levels of financial stability. The ARDL results reveal a dual effect of FinTech: while FinTech expansion initially increases short-run financial volatility, it enhances long-run stability as regulatory and institutional frameworks mature. Renewable energy consistently strengthens financial stability, with its impact intensifying in higher quantiles of the stability distribution. The quantile results further show that the stabilizing effects of FinTech, RE, institutional quality, and industrial development become stronger in more resilient financial systems. These findings highlight the need for BRICS policymakers to coordinate digital financial innovation with clean energy strategies under robust governance frameworks to promote a more stable, inclusive, and sustainable macro-financial environment. Full article

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

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33 pages, 6011 KB

Open AccessArticle

Anticipatory Pitch Control for Small Wind Turbines Using Short-Term Rotor-Speed Prediction with Machine Learning

by Ernesto Chavero-Navarrete, Juan Carlos Jáuregui-Correa, Mario Trejo-Perea, José Gabriel Ríos-Moreno and Roberto Valentín Carrillo-Serrano

Energies 2026, 19(1), 262; https://doi.org/10.3390/en19010262 - 4 Jan 2026

Viewed by 487

Abstract

Small wind turbines operating at low heights frequently experience rapidly fluctuating and highly turbulent wind conditions that challenge conventional reactive pitch-control strategies. Under these non-stationary regimes, sudden gusts produce overspeed events that increase mechanical stress, reduce energy capture, and compromise operational safety. Addressing [...] Read more.

Small wind turbines operating at low heights frequently experience rapidly fluctuating and highly turbulent wind conditions that challenge conventional reactive pitch-control strategies. Under these non-stationary regimes, sudden gusts produce overspeed events that increase mechanical stress, reduce energy capture, and compromise operational safety. Addressing this limitation requires a control scheme capable of anticipating aerodynamic disturbances rather than responding after they occur. This work proposes a hybrid anticipatory pitch-control approach that integrates a conventional PI regulator with a data-driven rotor-speed prediction model. The main novelty is that short-term rotor-speed forecasting is embedded into a standard PI loop to provide anticipatory action without requiring additional sensing infrastructure or changing the baseline control structure. Using six years of real wind and turbine-operation data, an optimized Random Forest model is trained to forecast rotor speed 20 s ahead based on a 60 s historical window, achieving a prediction accuracy of RMSE = 0.34 rpm and R² = 0.73 on unseen test data. The predicted uses a sliding-window representation of recent wind–rotor dynamics to estimate the rotor speed at a fixed horizon (t + Δt), and the predicted signal is used as the feedback variable in the PI loop. The method is validated through a high-fidelity MATLAB/Simulink model of 14 kW small horizontal-axis wind turbine, evaluated under four wind scenarios, including two previously unseen conditions characterized by steep gust gradients and quasi-stationary high winds. The simulation results show a reduction in overspeed peaks by up to 35–45%, a decrease in the integral absolute error (IAE) of rotor speed by approximately 30%, and a reduction in pitch-actuator RMS activity of about 25% compared with the conventional PI controller. These findings demonstrate that short-term AI-based rotor-speed prediction can significantly enhance safety, dynamic stability, and control performance in small wind turbines exposed to highly variable atmospheric conditions. Full article

(This article belongs to the Special Issue Advanced Control Systems in Wind Energy: Optimizing Performance from Component to Grid Integration)

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27 pages, 3060 KB

Open AccessArticle

Near-Field Shock Wave Propagation Modeling and Energy Efficiency Assessment in Underwater Electrical Explosions

by Shihao Xin, Xiaobing Zhang, Lei Ni and Xipeng Zhou

Energies 2026, 19(1), 261; https://doi.org/10.3390/en19010261 - 4 Jan 2026

Viewed by 484

Abstract

This study systematically investigates the influence of capacitor energy storage parameters on the energy utilization efficiency of the underwater electrochemical explosion process. By integrating spherical and cylindrical shock wave propagation models, the pulse shock wave energy under different capacitor energy storage levels was [...] Read more.

This study systematically investigates the influence of capacitor energy storage parameters on the energy utilization efficiency of the underwater electrochemical explosion process. By integrating spherical and cylindrical shock wave propagation models, the pulse shock wave energy under different capacitor energy storage levels was theoretically calculated and experimentally validated. The results indicate that the applicability of the shock wave propagation model depends on the distance and aquatic environment: the spherical model is more suitable for short-distance, deep-water conditions, whereas the cylindrical model performs better for long-distance or shallow-water conditions. Within the energy storage range of up to 100 J, increasing the capacitance significantly enhances both the pulse energy output and energy utilization efficiency. Specifically, as the stored energy increased from 13 J to 100 J, the shock wave energy rose from 0.051 J to 2.45 J, and the energy utilization rate improved from 0.39% to 2.45%. Nevertheless, the overall energy utilization efficiency remains below 10%. This study confirms that rationally configuring capacitor parameters can effectively regulate the discharge process, providing important experimental and theoretical support for optimizing energy utilization efficiency. Full article

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17 pages, 849 KB

Open AccessArticle

Economic and Ecological Benefits of Thermal Modernization of Buildings Related to Financing from Aid Programs in Poland

by Janusz Adamczyk and Robert Dylewski

Energies 2026, 19(1), 260; https://doi.org/10.3390/en19010260 - 4 Jan 2026

Viewed by 606

Abstract

Improving the energy efficiency of buildings is a highly desirable investment in the context of implementing the sustainable development paradigm, as it reduces the building’s energy demand. Consequently, the economic costs of heating the building are diminished. Reducing the building’s negative environmental impact [...] Read more.

Improving the energy efficiency of buildings is a highly desirable investment in the context of implementing the sustainable development paradigm, as it reduces the building’s energy demand. Consequently, the economic costs of heating the building are diminished. Reducing the building’s negative environmental impact is also crucial. This article presents programs that subsidize thermal modernization investments for single-family buildings in Poland. Particular attention was paid to the Clean Air program. A methodology for the economic and ecological assessment of thermal modernization investments eligible for funding under this program was proposed. The methodology is based on the Net Present Value indicator, whereas the ecological analysis utilized the Life Cycle Assessment method. A case study was conducted for a model single-family building using the introduced methodology. The scope of the thermal modernization investment included replacing windows and doors, replacing the heat source, and thermal insulation of the vertical external walls. The analyzed thermal modernization investment brings substantial ecological benefits, significantly reducing the building’s negative environmental impact. Unfortunately, the economic viability for the investor is not so obvious and depends primarily on the level of subsidy. Full article

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25 pages, 747 KB

Open AccessArticle

Challenges of Market Maturity in Small-Scale Power Markets: The Cyprus Case

by Andreas Poullikkas

Energies 2026, 19(1), 259; https://doi.org/10.3390/en19010259 - 4 Jan 2026

Viewed by 841

Abstract

Cyprus launched its Competitive Electricity Market on 1 October 2025, marking a historic transition from monopolistic to liberalized electricity trading. This paper presents a comprehensive analysis of the market’s first month of operation, evaluating technical performance, price dynamics, market structure, and identifying critical [...] Read more.

Cyprus launched its Competitive Electricity Market on 1 October 2025, marking a historic transition from monopolistic to liberalized electricity trading. This paper presents a comprehensive analysis of the market’s first month of operation, evaluating technical performance, price dynamics, market structure, and identifying critical barriers to achieving competitive benefits. Analysis reveals technically successful operation of clearing mechanisms and settlement processes, but economically constrained performance driven by persistent structural limitations. The market exhibits extreme price volatility characteristic of isolated systems, ranging from zero to 500 EUR/MWh, with pronounced diurnal patterns reflecting solar generation dynamics. The monthly wholesale price averaged at 167.78 EUR/MWh. The market remains highly concentrated with only 17 participants, shallow liquidity, and heavy reliance on conventional generation (86%) despite installed renewable capacity exceeding 1000 MW. Critical infrastructure deficits including absent natural gas infrastructure, lack of utility-scale storage, electrical isolation, and incomplete smart metering deployment represent fundamental barriers to achieving EU Target Model objectives. Based on infrastructure deployment scenarios and international benchmarking, we suggest potential reductions in the wholesale price of 12.5% (base scenario) to 15% (optimistic scenario) by the end of 2027, dependent on timely natural gas commissioning, storage deployment, and regulatory reform. Policy recommendations address immediate regulatory actions, medium-term market development priorities, and critical infrastructure investments essential for transitioning from technically operational to economically beneficial market operation. This analysis contributes to understanding the challenges that small, isolated electricity markets face when implementing EU liberalization frameworks while highlighting policy interventions required for successful market maturation. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy: 2nd Edition)

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15 pages, 2605 KB

Open AccessArticle

A Two-Stage Voltage Sag Source Localization Method in Microgrids

by Ruotian Yao, Hao Bai, Shiqi Jiang, Tong Liu, Yiyong Lei and Yawen Zheng

Energies 2026, 19(1), 258; https://doi.org/10.3390/en19010258 - 3 Jan 2026

Viewed by 470

Abstract

Accurate localization of voltage sag sources is crucial for maintaining reliable and stable operation in microgrids with high penetration of distributed generation (DG). However, the complex topology, bidirectional and time-varying power flows, and measurement uncertainty make it difficult for these conventional model-based approaches [...] Read more.

Accurate localization of voltage sag sources is crucial for maintaining reliable and stable operation in microgrids with high penetration of distributed generation (DG). However, the complex topology, bidirectional and time-varying power flows, and measurement uncertainty make it difficult for these conventional model-based approaches to achieve high accuracy. To address these challenges, this paper proposes a two-stage voltage sag source localization method that integrates a data-driven spatio-temporal learning model with a model-based binary search refinement. In the first stage, an improved spatial-temporal graph convolutional network (STGCN) is developed to extract temporal and spatial correlations among voltage and current measurements, enabling section-level localization of sag sources. In the second stage, a binary search–based refinement strategy is applied within the candidate section to iteratively converge on the exact fault location with high precision and robustness. Simulations are conducted on a modified IEEE 33-node system with diverse PV output scenarios, covering combinations of fault types and locations. The results demonstrate that the proposed method maintains stable localization performance under high DG penetration and achieves high accuracy despite multiple fault types and noise interference. Full article

(This article belongs to the Special Issue Modeling, Stability Analysis and Control of Microgrids)

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18 pages, 4870 KB

Open AccessArticle

Characterization of Proton Exchange Membrane Fuel Cell Operating in Electrochemical Hydrogen Compression Mode

by Anamarija Stoilova Pavasović, Senka Gudić, Ivan Pivac and Frano Barbir

Energies 2026, 19(1), 257; https://doi.org/10.3390/en19010257 - 3 Jan 2026

Viewed by 598

Abstract

This study examines the performance of a proton exchange membrane fuel cell operated in electrochemical hydrogen compression (EHC) mode, focusing on the effects of temperature, relative humidity (RH), and pressure on water management and efficiency. Two humidification strategies were investigated: (i) a dry [...] Read more.

This study examines the performance of a proton exchange membrane fuel cell operated in electrochemical hydrogen compression (EHC) mode, focusing on the effects of temperature, relative humidity (RH), and pressure on water management and efficiency. Two humidification strategies were investigated: (i) a dry cathode with humidified anode hydrogen and (ii) a flooded cathode with controlled anode humidification. Experiments were conducted at different temperatures (from 35 to 70 °C), RH levels (from 0 to 100%), and compression ratios of 1 and 2, using polarization curves, electrochemical impedance spectroscopy, and linear sweep voltammetry (LSV). In the dry cathode configuration, optimal performance occurred at 70 °C with fully humidified anode gas, achieving current densities above 2 A cm⁻² at voltages below 0.3 V. Partial humidification caused instability due to membrane dehydration. In the flooded cathode, high cathode pressure increased mass transport resistance, while excessive inlet humidification promoted flooding and consequently reduced the efficiency. LSV results highlighted the trade-off between proton conductivity and hydrogen back diffusion, particularly for thin membranes used in this study. The findings demonstrate that precise water balance is essential for stable and efficient EHC operation and provide guidelines for optimizing compression performance, supporting the development of high-efficiency and low-maintenance hydrogen compression systems for stationary and mobile applications. Full article

(This article belongs to the Special Issue Research on Integration and Storage Technology of Hydrogen Energy: 2nd Edition)

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21 pages, 3813 KB

Open AccessArticle

Three-Electrode Dynamic Electrochemical Impedance Spectroscopy as an Innovative Diagnostic Tool for Advancing Redox Flow Battery Technology

by Eliza Hałas, Wojciech Bącalski, Łukasz Gaweł, Paweł Ślepski and Joanna Krakowiak

Energies 2026, 19(1), 256; https://doi.org/10.3390/en19010256 - 3 Jan 2026

Viewed by 827

Abstract

Vanadium redox flow batteries (VRFBs) experience performance losses driven by electrode ageing, yet the underlying mechanisms remain poorly resolved under operational conditions. This work presents a novel application of dynamic electrochemical impedance spectroscopy (DEIS) in both full-cell and three-electrode configurations to monitor kinetic [...] Read more.

Vanadium redox flow batteries (VRFBs) experience performance losses driven by electrode ageing, yet the underlying mechanisms remain poorly resolved under operational conditions. This work presents a novel application of dynamic electrochemical impedance spectroscopy (DEIS) in both full-cell and three-electrode configurations to monitor kinetic and transport processes throughout complete charge–discharge cycles. Carbon felt electrodes subjected to thermal activation, chemical degradation, and electrochemical ageing were systematically examined to capture a broad range of ageing-induced modifications. Complementary electrochemical impedance spectroscopy (EIS) measurements at selected states of charge were performed to highlight the substantial differences between spectra recorded under load and at open-circuit conditions. The results reveal that the impedance response of the full cell is dominated by processes occurring at the negative electrode, and that ageing leads to increased charge-transfer resistance and enhanced state of charge-dependent variation. X-ray photoelectron spectroscopy (XPS) analysis confirms significant modifications in surface chemistry, including variations in the sp²/sp³ carbon distribution and the enrichment of oxygen-containing functional groups, which correlate with the observed electrochemical behavior. Overall, this study demonstrates—for the first time under realistic VRFB cycling conditions—that DEIS provides unique diagnostic capabilities, enabling mechanistic insights into electrode ageing that are inaccessible to conventional impedance approaches. Full article

(This article belongs to the Special Issue Innovations and Challenges in New Battery Generations)

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