Energies

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 (registering DOI) - 4 Jan 2026

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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24 pages, 2136 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 (registering DOI) - 4 Jan 2026

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)

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 4 Jan 2026

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, 1493 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 (registering DOI) - 4 Jan 2026

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 CO

_{2}

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 CO

_{2}

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 CO

_{2}

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 CO

_{2}

constraints. Full article

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

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 4 Jan 2026

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, 5125 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 (registering DOI) - 4 Jan 2026

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)

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 4 Jan 2026

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 (registering DOI) - 3 Jan 2026

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 (registering DOI) - 3 Jan 2026

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 (registering DOI) - 3 Jan 2026

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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29 pages, 5082 KB

Open AccessArticle

Technology Readiness of Biomass Waste-to-Energy in Indonesia: A Multistakeholder Assessment of Anaerobic Digestion of Palm Oil Mill Effluent and Municipal Organic Waste

by Nanda Asridinan Noor, Andante Hadi Pandyaswargo, Meita Rumbayan and Hiroshi Onoda

Energies 2026, 19(1), 255; https://doi.org/10.3390/en19010255 - 2 Jan 2026

Abstract

Indonesia faces growing pressure to strengthen waste management while expanding renewable energy generation, particularly from high-moisture biomass such as palm oil mill effluent (POME) and the organic fraction of municipal solid waste (OFMSW). Anaerobic digestion technology (ADT) is technically suitable for both feedstocks; [...] Read more.

Indonesia faces growing pressure to strengthen waste management while expanding renewable energy generation, particularly from high-moisture biomass such as palm oil mill effluent (POME) and the organic fraction of municipal solid waste (OFMSW). Anaerobic digestion technology (ADT) is technically suitable for both feedstocks; however, its deployment depends on broader operational, financial, social, and institutional conditions. This study evaluates ADT readiness for biomass waste-to-energy (BWTE) development in Indonesia using a multistakeholder Japanese Technology Readiness Assessment (J-TRA) framework. The results and discussion are supported by a literature review, secondary data analysis, and interviews with government agencies, industry actors, financiers, non-governmental organizations, and researchers. The results reveal a clear divergence in readiness outcomes. POME-based ADT reaches Technology Readiness Levels (TRLs) of 6–8, supported by a stable and homogeneous feedstock supply, established industrial operations, and corporate incentives to mitigate methane emissions. Key remaining constraints relate to high capital costs for smaller mills, low electricity purchase tariffs, and competing export incentives for untreated POME. In contrast, OFMSW-based ADT remains at TRL 2–4, constrained by inconsistent waste segregation, insufficient operation and maintenance capacity, limited municipal budgets, residential safety concerns, and fragmented governance across waste and energy institutions. Across both cases, readiness is shaped by five interacting forces. The first three are technical: feedstock characteristics, operations and maintenance (O&M) capability, and financial certainty. The remaining two are enabling conditions: social acceptance and institutional coordination. This study concludes that Indonesia’s BWTE transition requires integrated technological, behavioral, and policy interventions, supported by further research on hybrid valorization pathways and context-specific life-cycle and cost analyses. Full article

(This article belongs to the Special Issue From Waste to Energy: Towards Resource Recovery Optimization from Waste)

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16 pages, 3070 KB

Open AccessArticle

GSTAformer: Graph-Guided Spatio-Temporal Autoformer for Mid-Term Wind Power Forecasting

by Shi Yuan, Yulu Mao, Chenyu Tian, Fang Yu, Tengyue Guo and Min Xia

Energies 2026, 19(1), 254; https://doi.org/10.3390/en19010254 - 2 Jan 2026

Abstract

Accurate wind power forecasting is crucial for modern power systems, yet most deep learning models neglect spatial relationships between turbines. We propose GSTAformer, a graph-guided spatio-temporal model capturing both spatial and temporal dependencies through MIC- and PCC-built graphs; GraphSAGE for spatial feature extraction; [...] Read more.

Accurate wind power forecasting is crucial for modern power systems, yet most deep learning models neglect spatial relationships between turbines. We propose GSTAformer, a graph-guided spatio-temporal model capturing both spatial and temporal dependencies through MIC- and PCC-built graphs; GraphSAGE for spatial feature extraction; multi-scale convolution for trend detection; and an improved Autoformer for temporal modeling. Experiments on SDWPF and GEFCom2012 datasets demonstrate GSTAformer’s superior performance, achieving a 24 h mean squared error (MSE) of 0.7480 and mean absolute error (MAE) of 0.6362 on SDWPF. This work integrates graph-based spatial modeling with enhanced temporal forecasting for medium-term wind power prediction, providing a coherent framework suited to complex wind energy scenarios. Full article

(This article belongs to the Special Issue Development of Artificial Intelligence in Green Buildings and Renewable Energy)

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30 pages, 1113 KB

Open AccessArticle

Production of SNG from Biomass Using a Commercial-Scale Fluidized Bed Gasifier Integrated with Water Electrolysis

by Tomasz Marcin Chmielniak, Tadeusz Jan Chmielniak, Tomasz Iluk, Tomasz Billig and Leszek Stepien

Energies 2026, 19(1), 253; https://doi.org/10.3390/en19010253 - 2 Jan 2026

Abstract

Biomass gasification, as a thermochemical process, has attracted growing interest due to the increasing popularity of biofuel production based on syngas or pure hydrogen. Moreover, when integrated with CO₂ capture, this method of producing gaseous fuels can achieve negative CO₂ emissions, [...] Read more.

Biomass gasification, as a thermochemical process, has attracted growing interest due to the increasing popularity of biofuel production based on syngas or pure hydrogen. Moreover, when integrated with CO₂ capture, this method of producing gaseous fuels can achieve negative CO₂ emissions, making it competitive with other production systems based on either fossil or renewable sources. This paper presents the results of a process and economic analysis of synthetic natural gas (SNG) production systems integrated with a commercial fluidized-bed gasification reactor based on Synthesis Energy Systems (SES) technology. The study examines the potential integration of the system with a water electrolyzer at two levels of coupling: one providing oxygen for the gasification process, and the other eliminating the need for CO₂ separation before the SNG synthesis stage. Using a single gasification unit with a raw biomass feed rate of 60 t/h, the system produces 188 t/d of SNG. Integration with a water electrolyzer increases SNG production to 259 and 621 t/d. For cases without electrolyzer integration and under the assumption of zero emissions from biomass processing, the application of CO₂ separation enables the achievement of negative CO₂ emissions. This creates an opportunity for additional revenue from the sale of CO₂ emission allowances, which can significantly reduce SNG production costs. In this analysis, the break-even CO₂ price, above which the SNG production cost becomes negative, is USD 251/t CO₂. In systems integrated with water electrolysis, the cost and carbon footprint of the electricity consumed in the electrochemical water-splitting process have a decisive impact on both the overall SNG production cost and its carbon intensity. Full article

(This article belongs to the Topic Advanced Bioenergy and Biofuel Technologies)

33 pages, 7199 KB

Open AccessArticle

Predicting Defluidization in Fluidized Bed Conversion: From Plastics Pyrolysis to Biomass Combustion via Surface Coating Models

by Kaicheng Chen, Zhongyi Li, Evangelos Tsotsas and Andreas Bück

Energies 2026, 19(1), 252; https://doi.org/10.3390/en19010252 - 2 Jan 2026

Abstract

In fluidized bed conversion processes such as pyrolysis and combustion, defluidization mainly arises from particle agglomeration, which originates from the surface coating of primary bed materials (e.g., sand) by partially liquefied feedstock components, e.g., plastics or biomass. For reliable operation, the probability of [...] Read more.

In fluidized bed conversion processes such as pyrolysis and combustion, defluidization mainly arises from particle agglomeration, which originates from the surface coating of primary bed materials (e.g., sand) by partially liquefied feedstock components, e.g., plastics or biomass. For reliable operation, the probability of occurrence of defluidization must be quantifiable. However, existing models are either computationally expensive or difficult to transfer across feedstocks with different rheological behaviors. Furthermore, such transferability challenges are particularly pronounced in technically relevant systems involving liquefied components, such as molten polymers and ash-derived silicate melts. In this study, we propose two new coating approaches: (i) a simplified full coating model, where a fraction of bed particles is directly assumed to be fully covered upon feed introduction, and (ii) a partial coating model, where only local surface regions of particles are coated. The proposed models are implemented within a Monte Carlo framework and validated against experimental data reported in the literature for polyethylene and polypropylene pyrolysis as well as for wheat straw combustion. Across all cases, the model predictions capture the experimentally observed defluidization behavior reported in reference studies (e.g., with coefficients of determination of

R^{2} = 0.912

for the polymer series and

R^{2} = 0.917

for the wheat straw series). Beyond model validation, several model-based analyses and discussions are further conducted based on the characteristics of the proposed framework. Overall, the developed methodology provides a generalized basis for analyzing coating-driven defluidization across polymers and biomass, with potential extensions to co-pyrolysis, co-gasification, and other thermochemical conversion processes. Full article

(This article belongs to the Special Issue Fluidized Bed Technologies for Bio-Based Materials Conversion: Advancing Combustion, Pyrolysis, and Gasification)

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