Energies

26 pages, 12104 KB

Open AccessArticle

A Dataset Establishment Method for Wind Turbine Wake and a Data-Driven Model of Wake Prediction

by Qinghong Tang, Yuxin Wu, Changhua Li, Peiyao Duan, Jiahao Wu and Junfu Lyu

Energies 2026, 19(5), 1385; https://doi.org/10.3390/en19051385 - 9 Mar 2026

Viewed by 397

A cross-construction method is proposed to establish a wind turbine wake dataset with significantly reduced computational fluid dynamics (CFD) costs. This method involves adjusting one operating parameter, such as the tip speed ratio (TSR), while maintaining the others at their optimal values. This [...] Read more.

A cross-construction method is proposed to establish a wind turbine wake dataset with significantly reduced computational fluid dynamics (CFD) costs. This method involves adjusting one operating parameter, such as the tip speed ratio (TSR), while maintaining the others at their optimal values. This procedure is repeated across another parameter (inflow velocity) to generate a sparse but informative dataset. CFD simulations were performed using large eddy simulation (LES) coupled with an actuator line model (ALM) to generate data. A pre-training and fine-tuning network based on error classification (PFNEC) was developed, achieving high prediction accuracy with coefficients of determination of 0.9750 and 0.9851 for two validation conditions. Two models based on a softmax function and a residual block were designed, and they achieved the best performance, with coefficients of determination of 0.9921 and 0.9891 under different conditions. The Fourier embedding was applied to enhance input features of neural networks. Four samples added to the original dataset improved the prediction accuracy for extreme operating conditions, from coefficient of determination values of 0.7143 and 0.7034 to 0.9939 and 0.9886 with Fourier embedding. This cross-construction method can significantly reduce the cost of dataset establishment. The models exhibited reliable generalization and prediction accuracy. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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21 pages, 607 KB

Open AccessArticle

The Interplay Between Economic (In)Security and Energy Dependency: An Analysis of EU Countries

by Laura Diaconu (Maxim), Cristian C. Popescu and Andrei-Ionut Pricop

Energies 2026, 19(5), 1384; https://doi.org/10.3390/en19051384 - 9 Mar 2026

Viewed by 345

Abstract

This article aims to analyze the impact of the energy sector on the economic security of European households over the period 2010–2023, addressing an important gap in the literature since there is no EU cross-country evidence linking energy dependence on non-EU countries to [...] Read more.

This article aims to analyze the impact of the energy sector on the economic security of European households over the period 2010–2023, addressing an important gap in the literature since there is no EU cross-country evidence linking energy dependence on non-EU countries to a multidimensional measure of household economic insecurity over a long-time span. To achieve our goal, the dependent variable considered was an aggregate index of economic insecurity developed in previous research and constructed based on three fundamental dimensions: basic needs of the household, household fragility, and the burden of unemployment. Subsequently, panel data regression analysis with fixed effects was performed (considering 23 EU countries and the time period 2010–2023). The results highlight how more energy dependency on third countries could lead to more economic insecurity for European households. The robustness tests confirmed the initial results and underlined structural differences between countries. The research demonstrates how the energy dependence of EU states on countries outside the EU could have serious repercussions on the long-term economic security of Europeans, but, at the same time, this can be counterbalanced by a possible shift towards domestic renewable energy sources. Based on these results, both our hypotheses were confirmed. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy—3rd Edition)

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16 pages, 1034 KB

Open AccessArticle

Evaluation of a Home Energy Management System Using One-Year Data Under Dynamic Tariff Conditions

by Emilia Kazanecka, Dominika Matuszewska, Lina Montuori, Mohsen Assadi and Piotr Olczak

Energies 2026, 19(5), 1383; https://doi.org/10.3390/en19051383 - 9 Mar 2026

Viewed by 359

Abstract

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real [...] Read more.

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real operational data and focuses on system performance under varying solar generation conditions. The results show that during sunny days, the battery storage absorbs the entire surplus PV generation until reaching full capacity, i.e., 10 kWh, effectively preventing curtailment and maximizing self-consumption. On days with limited solar production, the system actively utilizes the available storage capacity by shifting energy use in time and, when economically justified, temporarily charging the battery from the grid during low-price periods. This strategy reduces electricity purchases during peak-price hours and stabilizes household energy costs. For the analyzed case, daily PV generation self-consumption exceeded 70% on high-generation days, while the application of storage-based load shifting under dynamic tariffs reduced daily electricity costs by up to 30% compared to a fixed-rate tariff. The study confirms that the economic and operational performance of residential energy systems under dynamic pricing depends primarily on adaptive storage control rather than on PV capacity alone, highlighting the central role of battery energy storage in year-round energy optimization. Full article

(This article belongs to the Special Issue Transitioning to Green Energy: The Role of Hydrogen)

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25 pages, 1428 KB

Open AccessReview

Decarbonization of EU Road Freight Transport in the Short and Medium Term Through Renewable Liquid Fuels—A Review

by Ricardo Almeida, Luis Serrano, Diogo Silva, Helder Santos, João Pereira and Manuel Gameiro da Silva

Energies 2026, 19(5), 1382; https://doi.org/10.3390/en19051382 - 9 Mar 2026

Viewed by 490

Abstract

Road transport decarbonization remains a strategic priority in the context of the global climate emergency. Between 2013 and 2024, most economic sectors in the European Union reduced emissions, whereas the transport and storage sector increased them by 14%, largely driven by road freight [...] Read more.

Road transport decarbonization remains a strategic priority in the context of the global climate emergency. Between 2013 and 2024, most economic sectors in the European Union reduced emissions, whereas the transport and storage sector increased them by 14%, largely driven by road freight demand. This review provides an updated overview of the decarbonization status of the road transport fleet across all segments, with particular focus on heavy-duty freight, which remains 97.9% fossil-fuel dependent. It examines short- and medium-term decarbonization pathways for the existing fleet, highlighting liquid biofuels as an immediately deployable option where full electrification is constrained by technological and economic barriers. Among these options, fatty acid methyl ester (FAME) and hydrotreated vegetable oil (HVO) stand out due to their compatibility with current engines and fuel distribution infrastructure, but each presents specific limitations. Biodiesel raises concerns over long-term engine durability, while HVO requires further evidence on its impact on NO_x emissions and fuel lubricity. When these sustainable fuels are used with or without fossil diesel, there are still several unanswered questions. The emerging use of HVO/FAME blends is therefore discussed as a promising route to mitigate the drawbacks of each fuel, and a research agenda is proposed to support accelerated decarbonization of heavy-duty road freight in the EU. Full article

(This article belongs to the Section B: Energy and Environment)

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24 pages, 1730 KB

Open AccessArticle

Effective Planning and Management of Hybrid Renewable Energy Systems Through Graph Theory

by Aikaterini Kolioukou, Athanasios Zisos and Andreas Efstratiadis

Energies 2026, 19(5), 1381; https://doi.org/10.3390/en19051381 - 9 Mar 2026

Viewed by 430

Abstract

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on [...] Read more.

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece. Full article

(This article belongs to the Special Issue New Insights into Decision Making Methods for Renewable Energy Investments)

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38 pages, 3650 KB

Open AccessReview

Torrefaction of Biowastes for High-Performance Solid Biofuel Production: A Review

by Corinna Schloderer, Sonil Nanda and Janusz A. Kozinski

Energies 2026, 19(5), 1380; https://doi.org/10.3390/en19051380 - 9 Mar 2026

Viewed by 386

Abstract

To compete with fossil fuels, biofuels produced from renewable waste biomass must be cost-effective, adaptable to existing heat and power infrastructure, and possess desirable fuel properties and performance metrics matching those of fossil fuels, while having a much lower carbon footprint. However, handling [...] Read more.

To compete with fossil fuels, biofuels produced from renewable waste biomass must be cost-effective, adaptable to existing heat and power infrastructure, and possess desirable fuel properties and performance metrics matching those of fossil fuels, while having a much lower carbon footprint. However, handling and processing biowastes in thermochemical biorefineries is challenging owing to their high moisture content, low bulk density, poor grindability, low calorific value, and heterogeneous physicochemical properties. Torrefaction has emerged as an effective thermochemical technology for upgrading biowastes into torrefied biomass, which exhibits improved, homogeneous physicochemical properties, including higher calorific value, higher bulk density, better grindability, and hydrophobicity. This review synthesizes the current state of research on torrefaction, with particular emphasis on process parameters, reactor designs, commercial-scale implementations, and an analysis of its strengths, weaknesses, opportunities, and threats. The comparative advantages and limitations of different torrefaction reactors are highlighted, emphasizing how each reactor’s characteristics determine its suitability for specific circumstances and operating conditions. This article also considers the technical and economic challenges associated with scaling up torrefaction. The discussion on specific case studies on techno-economic analysis of torrefaction outlines the key barriers and provides incentives for researchers to consider when upscaling the technology. The strengths, weaknesses, opportunities, and threat analysis offers strategic insights for policymakers and industry stakeholders into possible actions to support torrefaction and its upscaling. Full article

(This article belongs to the Special Issue Waste-to-Energy Biorefinery Technologies)

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32 pages, 7360 KB

Open AccessArticle

Short-Term Load Forecasting for a Renewable-Rich Power System Using an IMVMD-XLSTM

by Qiujing Lin, Hongquan Zhu, Xiaolong Wang and Xiangang Peng

Energies 2026, 19(5), 1379; https://doi.org/10.3390/en19051379 - 9 Mar 2026

Viewed by 313

Abstract

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate [...] Read more.

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

(This article belongs to the Topic Game Theory and Artificial Intelligence Methods in Sustainable and Renewable Energy Power Systems)

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32 pages, 1539 KB

Open AccessArticle

Mechanisms Shaping Greenhouse Gas Emission Intensity Through the Integration of Power Generation Availability Indicators and Energy Intensity Measures: Case Study of Poland

by Bożena Gajdzik, Rafał Nagaj, Radosław Wolniak and Wiesław-Wes Grebski

Energies 2026, 19(5), 1378; https://doi.org/10.3390/en19051378 - 9 Mar 2026

Viewed by 351

Abstract

The paper examines the energy transition using Poland as a case study. The model was estimated based on annual data for Poland for the period of 1990–2024 (n = 35). The estimation was carried out using the OLS method with HAC correction, and [...] Read more.

The paper examines the energy transition using Poland as a case study. The model was estimated based on annual data for Poland for the period of 1990–2024 (n = 35). The estimation was carried out using the OLS method with HAC correction, and the statistical significance of parameters was assessed using statistical tests. Based on econometric analysis, the impact was examined throughout the entire research period, with additional analysis of the structural break dummy for 2015. It was verified whether this impact had changed since 2015 compared to the earlier period. The data were used to calculate indicators, arranged in three groups: (1) capacity availability indicators (for the availability of the overall power system and for the renewable energy sources (RES)); (2) indicator of emission intensity (the indicator was defined as the ratio of total greenhouse gases emission to real GDP); (3) indicator of the economy’s energy intensity (the indicator was defined as primary energy consumption per unit of GDP). Annual summaries of these indicators constituted the input data for econometric modelling. The aim of the empirical analysis was to deepen the identification of mechanisms shaping greenhouse gas emission intensity by incorporating into the model indicators of generation capacity availability and measures of the economy’s energy intensity. The data collection based on constructed greenhouse gas emission intensity and energy intensity indicators of the economy enables the analysis of the increase in emission intensity regardless of the scale of the economy, in the system of power availability for the entire energy system, as well as for renewable energy sources. This approach makes it possible to move away from the analysis of absolute volumes toward a structural perspective that better reflects the real production capabilities of the power system as well as the efficiency of energy use in the economy. The results indicate that economic energy intensity is the dominant determinant of greenhouse gas emission intensity in Poland during the research period. The econometric analysis estimates show a positive and statistically significant relationship between energy intensity and emissions intensity, whereas generation capacity availability indicators—both for the total power system and for renewable energy sources—do not exhibit statistically significant effects. However, it was found that this impact was not constant throughout the entire period (β is 0.455 for pre-2015 and 0.325 for post-2015). Sensitivity analysis based on point elasticities reveals that a 1% increase in energy intensity of GDP leads to an increase in greenhouse gas emission intensity (by approximately 1.18% pre-2015 and 0.85% post-2015), whereas analogous changes in total capacity availability and RES availability are associated with substantially smaller effects (0.10% and 0.20%, respectively). These findings suggest that improvements in economy-wide energy efficiency played a more decisive role in reducing emissions intensity than short-term variations in generation capacity availability. Full article

(This article belongs to the Special Issue Energy Policy and Sustainable Development: Challenges to Economic Development—2nd Edition)

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25 pages, 4390 KB

Open AccessArticle

Short-Term and Annual Variability of Continuously Monitored Biogas Yield from Sewage Sludge at a Wastewater Treatment Plant

by Wiktor Halecki, Anna Młyńska, Michał Gąsiorek, Agnieszka Petryk and Krzysztof Chmielowski

Energies 2026, 19(5), 1377; https://doi.org/10.3390/en19051377 - 9 Mar 2026

Viewed by 344

Abstract

Wastewater treatment plants increasingly rely on anaerobic digestion and biogas utilization to reduce operational costs, enhance energy self-sufficiency, and support circular-economy objectives. This study provides a comprehensive, year-round assessment of sludge production, sludge characteristics relevant to digestion, biogas generation, and energy performance at [...] Read more.

Wastewater treatment plants increasingly rely on anaerobic digestion and biogas utilization to reduce operational costs, enhance energy self-sufficiency, and support circular-economy objectives. This study provides a comprehensive, year-round assessment of sludge production, sludge characteristics relevant to digestion, biogas generation, and energy performance at a municipal wastewater treatment plant. The plant generated on average 68.0 m³/d of thickened primary sludge and 24.0 m³/d of excessive sludge (total 92 m³/d), with low daily variability throughout the year. Biogas production remained highly stable, with an annual average of approximately 1300 m³/d and limited daily variation. Although monthly averages ranged from 1004 to 1728 m³/d, within-month variability was low to moderate, indicating that digestion processes responded consistently to changes in sludge quantity and composition. The weak correlation between sludge volume and biogas output (r = 0.29) showed that, besides sludge quantity, factors such as organic content and digester operating conditions also influence biogas yield. Energy performance indicators demonstrated strong self-sufficiency potential: the facility produced 1,095,047 kWh of electricity, covering 56.72% of its annual demand. The high coefficient of determination for self-sufficiency (R² = 0.871) confirmed a strong linear relationship between biogas-derived energy production and reduced grid dependence. Operational correlations further highlighted system coherence, with cogenerator and boiler usage strongly inversely related (r = −0.85) and biogas production positively associated with heat output (r = 0.66). Overall, the results demonstrate a stable and efficient sludge-to-energy system and provide a detailed dataset supporting future optimization of anaerobic digestion processes. Full article

(This article belongs to the Special Issue Achieving Sustainable Energy Recovery and Environmental Protection Through Anaerobic Process Optimization)

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22 pages, 3072 KB

Open AccessArticle

A Coupled Multi-Mechanism Modeling Study for the Fractured Horizontal Well in Shale Oil Reservoirs

by Yilin Ren, Jianming Fan, Zunrong Xiao, Fulin Liu, Xuze Zhang, Yuan Zhang and Ye Tian

Energies 2026, 19(5), 1376; https://doi.org/10.3390/en19051376 - 9 Mar 2026

Viewed by 228

Abstract

Shale oil reservoirs are characterized by ultra-low matrix permeability. After large-scale hydraulic fracturing is applied to horizontal wells, fluid transport becomes highly complex, posing major challenges for accurately predicting production performance. In this study, a coupled multi-mechanism numerical model is developed for shale [...] Read more.

Shale oil reservoirs are characterized by ultra-low matrix permeability. After large-scale hydraulic fracturing is applied to horizontal wells, fluid transport becomes highly complex, posing major challenges for accurately predicting production performance. In this study, a coupled multi-mechanism numerical model is developed for shale oil reservoirs with complex fracture networks. Using the Embedded Discrete Fracture Model (EDFM), the mass transport between the fracture and matrix and within the hydraulic fracture network can be accurately quantified. Based on core analysis and fluid experimental data, the dynamic evolution of rock and fluid properties is characterized by incorporating nanopore confinement effects, stress sensitivity, and threshold pressure gradient behavior. Numerical simulations are then conducted to investigate the impacts of multiple mechanisms, including nanopore confinement effects, stress sensitivity, and threshold pressure gradient, as well as their coupling effects on shale oil production. A field application is carried out using Well H1 in the Qingcheng shale oil reservoir. Simulation results indicate that nanopore confinement reduces bubble-point pressure, leading to a 3.60% increase in cumulative oil production and a noticeable reduction in the producing gas–oil ratio. Stress sensitivity causes a 2.68% decrease in cumulative oil production and suppresses gas production. The threshold pressure gradient exerts the strongest negative impact, resulting in an 8.01% reduction in cumulative oil production and a slight decrease in gas–oil ratio. When all mechanisms are simultaneously considered, strong nonlinear interactions emerge, yielding a 7.09% reduction in cumulative oil production—significantly different from the linear superposition of individual effects. These results demonstrate the necessity of accounting for multi-mechanism coupling to achieve reliable production forecasting in fractured shale oil reservoirs. Full article

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

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26 pages, 3092 KB

Open AccessArticle

A Cluster- and Temperature-Aware Auto-Ensemble Model for Airport Cooling Load Forecasting

by Xiao-Yu Xie, Yu-Wei Fan, Yi-Zhou Wang, Jie-Ru Li and Xin-Rong Zhang

Energies 2026, 19(5), 1375; https://doi.org/10.3390/en19051375 - 9 Mar 2026

Viewed by 259

Abstract

Accurate cooling load forecasting supports energy-efficient operation in large public buildings such as airports. Cooling load time series are often nonlinear and temporally dependent, with frequent operating condition changes and pronounced thermal inertia, which limits the reliability of single-model forecasting. This study proposes [...] Read more.

Accurate cooling load forecasting supports energy-efficient operation in large public buildings such as airports. Cooling load time series are often nonlinear and temporally dependent, with frequent operating condition changes and pronounced thermal inertia, which limits the reliability of single-model forecasting. This study proposes a cluster- and temperature-aware auto-ensemble model (CATS-Ens) for short- and long-term cooling load prediction. CATS-Ens learns condition-dependent model contributions within temperature-based operating intervals and distinct load regimes, enabling collaborative prediction across complementary experts and avoiding reliance on a single globally optimal predictor. The proposed model is evaluated on a real-world hourly cooling load dataset collected from an airport terminal. Results show that CATS-Ens achieves consistently better performance than representative baselines under multiple metrics, including MAE, RMSE, MAPE, sMAPE, and

R^{2}

. Compared with the best individual baseline, CATS-Ens reduces MAE by 8.5%, RMSE by 8.4%, MAPE by 12.6%, and sMAPE by 7.1%, with an

R^{2}

of 0.967. The model maintains stable accuracy under varying operating conditions and alleviates false-positive predictions during zero-load and low-load periods, demonstrating its practical value for cooling load forecasting in complex building energy systems. Full article

(This article belongs to the Special Issue Urban Energy Systems: Challenges and Prospects for a Sustainable Future)

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15 pages, 3938 KB

Open AccessArticle

Construction of Transmission Line Segments Assessment Model Based on Correlation Analysis and Analytic Hierarchy Process Method

by Shizeng Liu, Yigang Ma, Wenbin Yu, Xianzhong E, Yang Huang, Jiahao Liu and Hongwei Mei

Energies 2026, 19(5), 1374; https://doi.org/10.3390/en19051374 - 9 Mar 2026

Viewed by 240

Abstract

The reliable operation of transmission lines is essential for grid stability. Growing electricity demand pushes existing lines to full capacity, while new construction is constrained by resources and the environment. Dynamic capacity increase technology addresses this by boosting transmission capacity without physical upgrades, [...] Read more.

The reliable operation of transmission lines is essential for grid stability. Growing electricity demand pushes existing lines to full capacity, while new construction is constrained by resources and the environment. Dynamic capacity increase technology addresses this by boosting transmission capacity without physical upgrades, with the identification of weak points along the line being central to its application. This study integrates correlation analysis and the Analytic Hierarchy Process to develop an evaluation method for transmission line segments, with a supporting software implementation also developed. A system of characteristic quantities was first established using operation and maintenance guidelines combined with correlation analysis. The Analytic Hierarchy Process was applied to score features and derive weights after consistency validation. Preprocessed line data were then weighted to calculate segment weakness levels, and fuzzy comprehensive evaluation was used for both qualitative and quantitative condition analysis. The model was validated through a case study, and its software implementation streamlines and enhances the assessment process. Full article

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

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22 pages, 5753 KB

Open AccessArticle

LiDAR-Referenced Inflow Wind Condition Estimation from SCADA Data Using a Deep Learning Model

by Shukai He, Hangyu Wang, Jie Yan, Kaibo Wang, Yongqian Liu, Jian Yue, Bo Xu and Guoqing Li

Energies 2026, 19(5), 1373; https://doi.org/10.3390/en19051373 - 8 Mar 2026

Viewed by 314

Abstract

Accurate inflow wind conditions are essential for operational wind farms. However, wind conditions from the Supervisory Control and Data Acquisition (SCADA) system are significantly affected by rotor-induced disturbances and thus cannot reliably represent the true inflow. Although LiDAR can directly measure inflow wind [...] Read more.

Accurate inflow wind conditions are essential for operational wind farms. However, wind conditions from the Supervisory Control and Data Acquisition (SCADA) system are significantly affected by rotor-induced disturbances and thus cannot reliably represent the true inflow. Although LiDAR can directly measure inflow wind conditions, its data availability is highly sensitive to environmental conditions, frequently leading to insufficient valid samples. Existing studies generally apply the Nacelle Transfer Function (NTF) to empirically correct SCADA wind speed, yet its accuracy remains limited. Consequently, this study proposes a deep learning model for LiDAR-referenced inflow wind condition estimation from SCADA data. First, variations in LiDAR data availability and their influencing factors are systematically analyzed. The deviations and correlations between SCADA data and LiDAR measurements are quantitatively characterized. Subsequently, a deep learning model is developed, employing a time–frequency dual-branch residual network to extract features from SCADA data, while incorporating the Gram matrix as an additional input to provide auxiliary information. Finally, the proposed method is validated using measurements from two offshore turbines with different rated capacities. The results demonstrate that the proposed approach outperforms comparative methods, enabling more accurate estimation of inflow wind speed and direction. Full article

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20 pages, 5063 KB

Open AccessArticle

Comparative Analysis of Surrogate Models for Organic Rankine Cycle Turbine Optimization

by Yeun-Seop Kim, Jong-Beom Seo, Ho-Saeng Lee and Sang-Jo Han

Energies 2026, 19(5), 1372; https://doi.org/10.3390/en19051372 - 8 Mar 2026

Viewed by 333

Abstract

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based [...] Read more.

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

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16 pages, 5328 KB

Open AccessArticle

Adaptive Hybrid Synchronization-Based Transient Stability Enhancement Strategy for Grid-Forming Converters in Weak Grid Scenarios

by Yanlin Wu, Chuang Yu, Ziyang Li, Xinyue Chen, Feng Jiang, Min Chen, Wei Wei and Hongda Cai

Energies 2026, 19(5), 1371; https://doi.org/10.3390/en19051371 - 8 Mar 2026

Viewed by 348

Abstract

Driven by the large-scale application of distributed power sources, power systems are facing escalating frequency stability challenges in terms of inertia reduction. In this weak grid scenario, grid-connected converters are increasingly required to operate as high-inertia grid-forming (GFM) units to participate in the [...] Read more.

Driven by the large-scale application of distributed power sources, power systems are facing escalating frequency stability challenges in terms of inertia reduction. In this weak grid scenario, grid-connected converters are increasingly required to operate as high-inertia grid-forming (GFM) units to participate in the regulation of grid frequency. However, this high inertia will seriously impair the transient stability of GFM converters. To resolve the conflict, an adaptive hybrid synchronization-based transient enhancement strategy is proposed. Through integrating the traditional droop phase angle with the phase-locked loop-locked grid phase angle, the proposed control can effectively enhance transient stability under the full fault range from mild to severe voltage sags (with a voltage sag depth of up to 90%) without sacrificing system inertia. Moreover, benefiting from this, the proposed hybrid synchronization scheme also avoids the secondary overcurrent issue that occurs after fault clearance in traditional GFM control. Finally, the simulation and experimental results under various voltage sags verify the effectiveness of the proposed control strategy. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control: 2nd Edition)

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31 pages, 5209 KB

Open AccessReview

AI-Driven Fault Detection and O&M for Wind Turbine Drivetrains: A Review of SCADA, CMS and Digital Twin Integration

by Ning Jia, Jiangzhe Feng, Zongyou Zuo, Zhiyi Liu, Tengyuan Wang, Chang Cai and Qingan Li

Energies 2026, 19(5), 1370; https://doi.org/10.3390/en19051370 - 7 Mar 2026

Viewed by 557

Abstract

The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many [...] Read more.

The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many existing studies remain fragmented and insufficiently address practical challenges such as heterogeneous data, sparse fault labels, and cross-site generalization. This review provides an engineering-oriented synthesis of AI-based methods for wind turbine fault detection and O&M, focusing on drivetrain diagnostics as a representative application. The literature is organized along an end-to-end O&M workflow, including SCADA-based condition monitoring, component-level fault diagnosis, health assessment and remaining useful life estimation, multi-modal blade inspection, and DT (Digital Twin) integration. Traditional ML (machine learning), ensemble methods, deep learning, physics-informed learning, and transfer learning are reviewed with respect to their data requirements, operational assumptions, and deployment constraints. Beyond algorithmic performance, this review discusses data governance, alarm design, model updating, and interpretability, and summarizes public datasets and emerging data resources. The aim is to bridge methodological advances and practical O&M requirements, supporting reliable and deployable AI applications in wind energy systems. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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22 pages, 2977 KB

Open AccessArticle

Risk Assessment of Distribution Network Operation Based on Generalized Load

by Ying Wang, Qikai Zhao, Mingshen Wang, Jiamin Lv, Manqian Yu and Yi Ru

Energies 2026, 19(5), 1369; https://doi.org/10.3390/en19051369 - 7 Mar 2026

Viewed by 272

Abstract

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical [...] Read more.

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation. Full article

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2 pages, 110 KB

Open AccessEditorial

Advanced Technologies for Energy-Efficient Buildings

by Martha Katafygiotou

Energies 2026, 19(5), 1368; https://doi.org/10.3390/en19051368 - 7 Mar 2026

Viewed by 235

Abstract

Dear Readers, [...] Full article

(This article belongs to the Special Issue Advanced Technologies for Energy-Efficient Buildings)

28 pages, 1033 KB

Open AccessPerspective

Re-Envisioning Electric Vehicle Charging Infrastructure and Sustainable Energy Transitions in the Gulf Cooperation Council Countries

by Madathodika Asna, Sanchari Deb and Hussain Shareef

Energies 2026, 19(5), 1367; https://doi.org/10.3390/en19051367 - 7 Mar 2026

Viewed by 393

Abstract

The Gulf Cooperation Council (GCC) countries are accelerating their transition toward sustainable mobility as part of broader national strategies to diversify economies and reduce dependence on hydrocarbons. This paper explores the development of electric vehicle (EV) charging infrastructures and their integration with renewable [...] Read more.

The Gulf Cooperation Council (GCC) countries are accelerating their transition toward sustainable mobility as part of broader national strategies to diversify economies and reduce dependence on hydrocarbons. This paper explores the development of electric vehicle (EV) charging infrastructures and their integration with renewable energy sources across the GCC countries. It highlights key government policies, renewable energy potential, and emerging technologies such as solar-powered charging, smart grids, and vehicle-to-grid systems. While progress is evident in nations like Saudi Arabia, the UAE, and Qatar, challenges persist, including limited charging infrastructure, high costs, and climatic constraints. The study identifies opportunities for advancing sustainability through microgrids, hydrogen mobility, and regional policy harmonisation. It concludes that the decarbonisation benefits of EV charging infrastructure depend on how closely its expansion is aligned with renewable energy growth in the GCC. Full article

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

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19 pages, 8303 KB

Open AccessArticle

Damage Evolution of Surface Soil and Buried Gas Pipelines Under Mining-Induced Subsidence in Goaf Areas

by Guozhen Zhao, Haoyan Liang, Jiadong Li and Yaochi Yang

Energies 2026, 19(5), 1366; https://doi.org/10.3390/en19051366 - 7 Mar 2026

Viewed by 251

Abstract

To address the potential threat of surface subsidence caused by coal mining to the safe operation of buried gas pipelines in goaf collapse areas, this study investigates the geological conditions of the Mugu Coal Mine in Shanxi Province, China, and a gas pipeline [...] Read more.

To address the potential threat of surface subsidence caused by coal mining to the safe operation of buried gas pipelines in goaf collapse areas, this study investigates the geological conditions of the Mugu Coal Mine in Shanxi Province, China, and a gas pipeline passing through its surface mining area. Using a combination of numerical simulations and physical analog modeling, the mechanical response and deformation characteristics of the pipeline under mining-induced influences were systematically analyzed from three perspectives: the failure mechanisms of surface soil, the pipe–soil interaction behavior, and the damage evolution of the pipeline within the goaf. The research reveals a separation-induced failure pattern of the gas pipeline in mining-affected areas, referring to the mechanism in which differential settlement causes pipe–soil detachment, leading to unsupported bending deformation and stress concentration. Results show that the subsidence basin expands rapidly when the working face advances between 150 m and 210 m. Before this stage, the pipeline and surface soil deform synergistically with a symmetric subsidence curve centered on the goaf and uniformly distributed loads, showing no significant damage. During this stage, non-synergistic deformation occurs, leading to separation between the pipeline and soil. The maximum subsidence point shifts away from the center, destroying symmetry and causing stress concentration at the mining boundary, the advancing working face, and the goaf center, resulting in severe bending and rapid failure. After this stage, the pipe–soil interaction restabilizes with reduced separation height and extent, though pipeline deformation and damage continue to increase gradually. These findings provide a theoretical basis for engineering design optimization, targeted reinforcement measures, and monitoring strategies for gas pipelines in similar goaf collapse areas. Full article

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18 pages, 5400 KB

Open AccessArticle

A Hybrid Optimal Modulation Strategy for Dual-Side Asymmetric Duty Cycles in a Dual-Active-Bridge Converter

by Biaoguang Sun and Zhenfeng Liu

Energies 2026, 19(5), 1365; https://doi.org/10.3390/en19051365 - 7 Mar 2026

Viewed by 283

Abstract

To address the issues of excessive current stress and the power dead zone associated with conventional phase-shift modulation in dual-active-bridge (DAB) converters, a hybrid optimized modulation strategy based on dual-side asymmetric duty modulation (ADM) is proposed. The proposed strategy aims to minimize the [...] Read more.

To address the issues of excessive current stress and the power dead zone associated with conventional phase-shift modulation in dual-active-bridge (DAB) converters, a hybrid optimized modulation strategy based on dual-side asymmetric duty modulation (ADM) is proposed. The proposed strategy aims to minimize the peak-to-peak current stress by introducing two distinct operating modes of the converter. A dynamic compensation mechanism based on mode switching is developed, enabling a coordinated dual-mode modulation to achieve minimum peak-to-peak current stress over the full power operating range. In addition, a virtual voltage control scheme is incorporated to enhance the dynamic response and stability of the system. Finally, experimental results obtained from a laboratory prototype verify that the proposed strategy effectively reduces the peak-to-peak current stress while significantly improving the dynamic performance of the DAB converter. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 3rd Edition)

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41 pages, 3705 KB

Open AccessReview

Bio-CO₂ as Feedstock for Renewable Methanol in Maritime Applications

by Michael Bampaou, Vasileios Mitrousis, Evangelia Koliamitra, Paraskevas Stratigousis, Henrik Schloesser, Ismael Matino, Valentina Colla and Kyriakos D. Panopoulos

Energies 2026, 19(5), 1364; https://doi.org/10.3390/en19051364 - 7 Mar 2026

Cited by 1 | Viewed by 513

Abstract

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources [...] Read more.

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector. Full article

(This article belongs to the Special Issue Renewable Hydrogen and Hydrogen Carriers for the Maritime Sector)

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42 pages, 1782 KB

Open AccessReview

Thermal Energy Storage in Renewable Energy Communities: A State-of-the-Art Review

by Tiago J. C. Santos, José M. Torres Farinha, Mateus Mendes and Jânio Monteiro

Energies 2026, 19(5), 1363; https://doi.org/10.3390/en19051363 - 7 Mar 2026

Viewed by 1019

Abstract

Renewable Energy Communities (RECs) are recognized as effective collective models to accelerate decarbonization through shared renewable generation, consumption, and local flexibility provision. However, their large-scale deployment remains constrained by the temporal mismatch between variable renewable generation and strongly time-dependent demand, particularly in buildings [...] Read more.

Renewable Energy Communities (RECs) are recognized as effective collective models to accelerate decarbonization through shared renewable generation, consumption, and local flexibility provision. However, their large-scale deployment remains constrained by the temporal mismatch between variable renewable generation and strongly time-dependent demand, particularly in buildings where heating and cooling dominate final energy use. This state-of-the-art review provides an integrated and comparative assessment of Thermal Energy Storage (TES) and Battery Energy Storage Systems (BESS) within RECs, with explicit focus on power-to-heat (PtH) pathways and phase change material (PCM)-based cooling storage. Based on a structured analysis of the peer-reviewed literature published between 2015 and 2025, the review shows that TES represents a cost-effective and durable complement to electrochemical storage in heating- and cooling-dominated communities. Reported results indicate that TES integration can reduce peak electrical demand by 20–35%, increase local renewable self-consumption by 15–40%, and significantly lower required battery capacity in hybrid configurations. While BESS remains indispensable for short-term electrical balancing and fast-response grid services, TES offers lower costs per kWh stored, longer operational lifetimes (often exceeding 25–40 years), and lower lifecycle greenhouse gas emissions, typically 70–85% lower than those of BESS when thermal energy is used directly. Among TES technologies, PCM-based systems demonstrate particular effectiveness in cooling-dominated RECs, enabling peak cooling power reductions of up to 30% through diurnal load shifting. Across climatic contexts, the literature converges on hybrid TES–BESS architectures as the most robust storage solution, with reported reductions in grid imports and renewable curtailment of up to 35–40%. In addition, TES uniquely enables seasonal energy shifting, for which no cost-competitive electrochemical alternative currently exists. Despite these advantages, the review identifies persistent gaps related to the limited availability of long-term operational data and the need for empirical validation of hybrid control strategies. Future research should prioritize multi-year field demonstrations, advanced data-driven energy management, and policy frameworks that explicitly recognize thermal flexibility and sector coupling within Renewable Energy Communities. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Conservation and Efficient Utilization for Thermal Energy)

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23 pages, 753 KB

Open AccessArticle

The European Union’s Energy Security Challenges: Import Dependency, Volatility, and Differences Across Member States

by László Török

Energies 2026, 19(5), 1362; https://doi.org/10.3390/en19051362 - 7 Mar 2026

Viewed by 462

Abstract

This study examines the evolution of the European Union’s (EU) energy security and import dependence over the period 2014–2023, shaped by global energy price shocks, the COVID-19 pandemic, and Russia’s war against Ukraine. This research aims to explore how the structure of energy [...] Read more.

This study examines the evolution of the European Union’s (EU) energy security and import dependence over the period 2014–2023, shaped by global energy price shocks, the COVID-19 pandemic, and Russia’s war against Ukraine. This research aims to explore how the structure of energy imports, domestic production capacities, and the composition of electricity generation shape the vulnerability of EU Member States. It highlights that energy is not only an economic input but also a determinant of social stability and political space. The analysis is based on Eurostat data for 27 Member States. This study combines several methods: panel regression to explore the structural determinants of energy dependence, absolute and relative volatility indicators to measure exposure to shocks, and K-means clustering to map heterogeneity across Member States. The comparison between the pre-2020 and post-2020 periods serves as a robustness check. The results point to three main conclusions. First, natural gas and oil imports remain the primary source of dependency, while domestic electricity generation and balanced gas supply mitigate vulnerability. Second, based on volatility, smaller Member States—particularly the Baltic States and Malta—are disproportionately exposed to shocks. Third, Member States can be grouped into three clusters, although the post-2020 crisis has partly rearranged the grouping of countries. The policy lesson is clear: reducing energy dependency requires diversification, targeted support for smaller Member States, strengthening crisis management capacities, and accelerating the green transition. Energy security and sustainability are not contradictory but mutually reinforcing objectives that will determine the future resilience of the EU. Full article

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

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38 pages, 6140 KB

Open AccessArticle

A Fully Automated Design of Experiments-Based Method for Rapidly Screening Near-Optimal CO₂ Injection Strategies

by Demis Diplas, Sofianos Panagiotis Fotias, Ismail Ismail, Spyridon Bellas and Vassilis Gaganis

Energies 2026, 19(5), 1361; https://doi.org/10.3390/en19051361 - 7 Mar 2026

Viewed by 333

Abstract

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection [...] Read more.

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization. Full article

(This article belongs to the Collection Feature Papers in Carbon Capture, Utilization, and Storage)

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27 pages, 4308 KB

Open AccessArticle

Lot Sizing Problem for Cold Supply Chain with Energy and Quality Considerations

by Simone Zanoni, Silvia Cardini, Beatrice Marchi and Lucio Enrico Zavanella

Energies 2026, 19(5), 1360; https://doi.org/10.3390/en19051360 - 7 Mar 2026

Viewed by 251

Abstract

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time–temperature-dependent quality degradation with energy consumption in refrigerated [...] Read more.

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time–temperature-dependent quality degradation with energy consumption in refrigerated warehouses. Unlike traditional approaches, energy is modeled as an endogenous function of warehouse filling level and warehouse temperature, allowing the interaction between inventory volume, energy efficiency, and quality preservation to be captured. The model is formulated under three coordination policies—Lot-for-Lot, traditional agreement, and consignment stock—and solved under joint decision making. Numerical results for chilled and frozen products show that neglecting energy and quality costs can lead to sub-optimal policies with total cost penalties exceeding 300% compared to the proposed integrated optimization. Results further indicate that a consignment stock agreement can reduce total system costs by up to 9% relative to traditional policies, while the optimal lot size is highly sensitive to energy prices, product value, and warehouse temperature. These findings highlight the critical role of jointly optimizing inventory, energy, and quality decisions in cold supply chains and provide actionable insights for designing more sustainable and energy-efficient production inventory systems. Full article

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28 pages, 5381 KB

Open AccessArticle

The Role of Hydropower in Climate-Resilient Energy Systems: Case Study of the Jeziorsko Reservoir (Poland)

by Mateusz Hämmerling, Tomasz Kałuża, Agnieszka A. Pilarska, Dariusz Graczyk and Kacper Konieczny

Energies 2026, 19(5), 1359; https://doi.org/10.3390/en19051359 - 7 Mar 2026

Viewed by 347

Abstract

Hydropower supports the energy transition by providing flexible, low-carbon generation, yet its performance is increasingly constrained by climate-driven variability in water availability. This study quantifies long-term hydroclimatic changes in the Warta River–Jeziorsko reservoir system (central Poland) and assess their implications for water resources, [...] Read more.

Hydropower supports the energy transition by providing flexible, low-carbon generation, yet its performance is increasingly constrained by climate-driven variability in water availability. This study quantifies long-term hydroclimatic changes in the Warta River–Jeziorsko reservoir system (central Poland) and assess their implications for water resources, hydropower generation, and reservoir operation. The analysis combines multi-decadal meteorological observations, daily river flows at the Sieradz gauge (1951–2022), and reservoir and plant operational records, with electricity production evaluated for 1995–2022. The results indicate significant warming and shorter snow-cover duration, while annual precipitation shows no consistent long-term trend. Hydrological drought has intensified, reflected by lower mean flows in recent decades and a strong increase in days with discharge below SNQ, particularly after 2015. Electricity production is highly variable and shows a significant downward trend, amplified by reduced usable storage following operating-rule changes. By linking long-term hydroclimatic indicators with site-specific operational and production data for a lowland multi-purpose reservoir under environmental constraints, this study provides evidence to support adaptive reservoir management balancing water security and hydropower reliability. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy in Light of Energy Transition Processes—3rd Edition)

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31 pages, 2797 KB

Open AccessArticle

Safe Soft Actor–Critic for Online Transmission Interface Power Flow Control

by Ji Zhang, Liudong Zhang, Qi Li, Di Shi and Yi Wang

Energies 2026, 19(5), 1358; https://doi.org/10.3390/en19051358 - 7 Mar 2026

Viewed by 299

Abstract

The rapid development of a new-type power system dominated by renewable energy has introduced growing complexity and variability into grid topology and dynamics, posing significant challenges for transmission interface power flow control. Traditional regulation methods based on operator experience and deterministic optimization often [...] Read more.

The rapid development of a new-type power system dominated by renewable energy has introduced growing complexity and variability into grid topology and dynamics, posing significant challenges for transmission interface power flow control. Traditional regulation methods based on operator experience and deterministic optimization often fail to achieve real-time optimality under such dynamic conditions. Leveraging its strong capability for autonomous learning and feature perception, deep reinforcement learning (DRL) offers a promising approach for addressing these challenges. This paper proposes a safe DRL-based control framework for online transmission interface power flow regulation. A Safe Soft Actor–Critic (SSAC) agent is developed, embedding power system security constraints directly into the decision process to ensure operational safety. A secure EMS-interactive training platform with containerized parallel learning is established to accelerate model convergence and improve adaptability to changing operating conditions. The developed SSAC agent is deployed in the Jiangsu Power Grid Energy Management System (EMS) for validation. Simulation and field test results demonstrate that the proposed method can generate control strategies online within milliseconds, achieving a 99.3% interface overload mitigation rate and 3.32% network loss reduction, outperforming conventional sensitivity-based optimization methods in both timeliness and economic efficiency. These results demonstrate strong real-time computational capability and compatibility with EMS-based dispatch workflows, indicating promising practical deployment potential for transmission interface control in renewable-dominated power systems. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, 2nd Edition)

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23 pages, 2495 KB

Open AccessArticle

Interactions Between Laminated Shale Oil Reservoir and Fracturing Fluid: A Case Study from the Chang 7₃ Member of the Triassic Heshui Area in the Ordos Basin, China

by Xuanming Zhang, Xiaorong Yu, Pengqi Yang, Jinchi Cai, Huan Yang and Gaoshen Su

Energies 2026, 19(5), 1357; https://doi.org/10.3390/en19051357 - 7 Mar 2026

Viewed by 287

Abstract

This study systematically investigates the reaction characteristics of laminated shale oil reservoirs in the 7₃ sub-member of the Yanchang Formation, Heshui area, Ordos Basin, under exposure to CNI-I nanoviscous fracturing fluid. The reservoir matrix comprises 84.85% brittle minerals and 15.15% clay minerals. [...] Read more.

This study systematically investigates the reaction characteristics of laminated shale oil reservoirs in the 7₃ sub-member of the Yanchang Formation, Heshui area, Ordos Basin, under exposure to CNI-I nanoviscous fracturing fluid. The reservoir matrix comprises 84.85% brittle minerals and 15.15% clay minerals. Fluid–rock interactions significantly dissolve calcite and dolomite, releasing Ca²⁺ and Mg²⁺ ions, while clay mineral reactions liberate substantial amounts of Na⁺. Post-reaction, fluid system stability is markedly reduced, elevating the risk of precipitate formation and pore-throat plugging. Exposure to fracturing fluid reduces the T₂ cutoff value of core samples from 3.29 ms to 1.72 ms, indicating a densification of the micro-pore-throat network and a decline in mobile fluid saturation, while fracture apertures exhibit widening. Based on empirical data, a discriminant criterion (R value) defined as the ratio of fracture aperture increment rate to pore-throat diameter reduction rate is established at 1.25, confirming that fracture propagation dominates over pore constriction. Dual-medium modeling yields a net permeability enhancement of 19.35%. Fluid–rock interactions induce overall degradation of rock mechanical properties with pronounced anisotropy: rock strength along the direction perpendicular to bedding declines by 37.546%, Young’s modulus decreases by 1.81%, and Poisson’s ratio increases by 0.02%—all significantly exceeding the degree of degradation parallel to bedding. This anisotropic mechanical degradation predisposes the near-wellbore region to shear slip and wall spalling, prompting the development of targeted engineering mitigation strategies. Full article

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

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34 pages, 4462 KB

Open AccessArticle

Multi-Scenario Dispatch Characteristics and Water Quality Parameter Sensitivity of Virtual Power Plants Based on Sewage–Sludge Co-Treatment

by Xiuyun Wang, Xunqi Yu and Rutian Wang

Energies 2026, 19(5), 1356; https://doi.org/10.3390/en19051356 - 7 Mar 2026

Viewed by 307

Abstract

With the accelerating urbanization process, traditional wastewater treatment plants are facing dual challenges of high energy consumption and high carbon emissions. To address the current research gaps in studies regarding the overlooked synergistic potential of sludge, the unclear quantification of regulation capacity, and [...] Read more.

With the accelerating urbanization process, traditional wastewater treatment plants are facing dual challenges of high energy consumption and high carbon emissions. To address the current research gaps in studies regarding the overlooked synergistic potential of sludge, the unclear quantification of regulation capacity, and the insufficient analysis of multi-scenario adaptability in wastewater treatment plants, this paper integrates carbon emission costs, wastewater grading, and a multi-energy complementary mechanism to establish a VPP dispatch optimization model incorporating sewage–sludge co-treatment. The superiority and robustness of the co-dispatch model are validated through simulations across multiple seasonal scenarios (dry, wet, and normal seasons) and various water quality parameters. The results indicate that the co-treatment mode can significantly enhance system revenue (with an increase of up to 34.3% in the wet season), reduce carbon emissions (with a reduction rate exceeding 57% across all seasons), and improve grid regulation potential (with upward and downward regulation potentials increasing by 248% and 288%, respectively, in the wet season). Furthermore, variations in water quality exert a notable nonlinear impact on the system’s economic performance, environmental benefits, and regulation capacity. As the water quality concentration increases, the system’s dispatch strategy gradually shifts from prioritizing “peak-shaving benefits” to prioritizing “carbon cost control”. Full article

(This article belongs to the Special Issue Wastewater Treatment and Energy Conversion)

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Energies, Volume 19, Issue 5 (March-1 2026) – 272 articles

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