Energies

20 pages, 2495 KB

Open AccessArticle

Ele-LLM: A Systematic Evaluation and Adaptation of Large Language Models for Very Short-Term Power Load Forecasting

by Yansheng Chen, Miao Chen, Chenchao Hu, Jinxi Wu and Ruilin Qin

Energies 2026, 19(3), 631; https://doi.org/10.3390/en19030631 (registering DOI) - 26 Jan 2026

Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series [...] Read more.

Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework and conducts a systematic empirical study on various LLMs, including GPT-4, Claude-3, Gemini, the Llama series, DeepSeek, and Qwen, comparing them with traditional methods such as ARIMA, BiLSTM, MICN, TimesNet, and VMD-BiLSTM. Furthermore, Ele-LLM, a specialized model based on the Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning strategy, is proposed. Experimental results show that Ele-LLM achieves the best forecasting performance (MAPE = 1.04%), significantly outperforming the best traditional baseline. LLMs also demonstrate notable advantages in few-shot learning, long-sequence dependency modeling, and generalization in complex scenarios. This study provides an evaluation benchmark and practical guidelines for applying LLMs in very short-term power load forecasting, proving their great potential and practical value as an emerging technological pathway. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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17 pages, 2695 KB

Open AccessArticle

Bottom Ash from Biomass Combustion in Fluidized Bed Boilers in the Context of the Circular Economy

by Alicja Uliasz-Bocheńczyk and Eugeniusz Mokrzycki

Energies 2026, 19(3), 630; https://doi.org/10.3390/en19030630 (registering DOI) - 26 Jan 2026

Abstract

This paper presents a comprehensive characterization of bottom ash generated during biomass combustion in fluidized boilers, with a focus on its potential use in a circular economy. Two biomass bottom ash samples (BBA 1 and BBA 2) from commercial combined heat and power [...] Read more.

This paper presents a comprehensive characterization of bottom ash generated during biomass combustion in fluidized boilers, with a focus on its potential use in a circular economy. Two biomass bottom ash samples (BBA 1 and BBA 2) from commercial combined heat and power plants were tested. The scope of this study included the determination of chemical composition, phase composition, and leachability testing of selected impurities. The results showed that the bottom ashes tested are calcium silicate materials with varying proportions of calcium phases (anhydrite, portlandite, and calcite) and silica phases (quartz), depending on the type of biomass and combustion technology. Thermal analysis confirmed the presence of characteristic dehydration, decarbonation, and polymorphic transformations of quartz, with a low organic content. Leachability tests showed low mobility of most trace elements and heavy metals, with increased solubility of sulfates, chlorides, and alkali ions, typical for fluidized ash. The concentrations of As, Cd, Cr, Cu, Pb, Zn, and Hg in the eluates were low or below the limit of quantification, indicating the favorable chemical stability of the tested waste. The results obtained suggest that bottom ashes from biomass combustion in fluidized boilers may be a promising secondary raw material for engineering applications, especially in binding materials and bonded layers, and potentially also in selected agricultural applications, provided that the contents of sulfates, chlorides, and pH are controlled. Full article

(This article belongs to the Special Issue New Advances in Renewable Energy and Sustainable Energy Storage Systems)

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18 pages, 1264 KB

Open AccessArticle

Comprehensive Methodology for the Design of Fuel Cell Vehicles: A Layered Approach

by Swantje C. Konradt and Hermann S. Rottengruber

Energies 2026, 19(3), 629; https://doi.org/10.3390/en19030629 (registering DOI) - 26 Jan 2026

Abstract

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and [...] Read more.

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

(This article belongs to the Special Issue Advances in Fuel Cells: Materials, Technologies, and Applications)

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17 pages, 4618 KB

Open AccessArticle

A Method for Identification and Adjustment of Key Variables for Power Flow Convergence in Bulk Power Systems Based on Unbalanced Power Characteristics of Intermediate Power Flow

by Yuxi Fan and Yibo Zhou

Energies 2026, 19(3), 628; https://doi.org/10.3390/en19030628 (registering DOI) - 25 Jan 2026

Abstract

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power [...] Read more.

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power flow convergence, it is imperative to accurately identify the key variables inducing non-convergence and provide physical justifications. For this purpose, this paper proposes a data-driven key variable identification and adjustment method: firstly, based on the blocking cut-set theory and the characteristic that the active unbalanced power ΔP of intermediate power flow exhibits opposite signs at the sending and receiving ends of the cut-set, a blocking cut-set identification method leveraging the characteristics of the active unbalanced power of intermediate power flow is developed; secondly, relying on the feature that the reactive unbalanced power ΔQ of intermediate power flow is less than zero, a key variable identification method based on the characteristics of the reactive unbalanced power of intermediate power flow is presented; finally, a key variable adjustment method grounded in the numerical value of ΔQ is proposed. The validity of the proposed approach was validated via simulated computations using both the IEEE 39 bus system and a practical bulk power system. Full article

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

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20 pages, 2986 KB

Open AccessArticle

Thermal Stratification in Solar Storage Tanks: Long-Term Modelling and Efficiency Analysis

by Barbara Król and Krzysztof Kupiec

Energies 2026, 19(3), 627; https://doi.org/10.3390/en19030627 (registering DOI) - 25 Jan 2026

Abstract

The storage tank plays a key role in solar thermal installations, as thermal stratification allows high temperatures to be maintained in the upper region while keeping the return temperature to the collectors low. This study analyses the influence of thermal stratification on short- [...] Read more.

The storage tank plays a key role in solar thermal installations, as thermal stratification allows high temperatures to be maintained in the upper region while keeping the return temperature to the collectors low. This study analyses the influence of thermal stratification on short- and long-term performance of solar domestic hot water systems using a multi-node storage tank model. An algorithm was developed to compute temperature profiles along the height of a storage tank operating under time-varying temperature and flow-rate conditions. Time courses of temperatures and heat fluxes in a solar domestic hot water system were determined. In addition, the seasonal variation in the optimal locations for supplying the tank with water from the solar collector was identified. Annual simulations were performed for the climate of Kraków (Poland) and the domestic hot water demand of a single-family household. The results show that the effect of the degree of stratification on solar fraction and solar efficiency is small. It was also demonstrated that the effect of thermal stratification within the tank on stabilizing the temperature of the produced water is more significant than the effect associated with increasing the tank volume. Full article

(This article belongs to the Special Issue Advances in the Design and Application of Solar Energy in Buildings)

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34 pages, 7114 KB

Open AccessArticle

CFD Analysis of Equivalence Ratio Effects on Combustion and Emissions in a Methanol–Diesel Dual-Fuel Marine Engine

by Van Chien Pham, Van Vang Le, Jae-Hyuk Choi and Won-Ju Lee

Energies 2026, 19(3), 626; https://doi.org/10.3390/en19030626 (registering DOI) - 25 Jan 2026

Abstract

Methanol is a promising alternative marine fuel due to its favorable combustion characteristics and potential to reduce exhaust emissions under increasingly stringent International Maritime Organization (IMO) regulations. This study presents a three-dimensional computational fluid dynamics (CFD) analysis of a four-stroke, medium-speed marine engine [...] Read more.

Methanol is a promising alternative marine fuel due to its favorable combustion characteristics and potential to reduce exhaust emissions under increasingly stringent International Maritime Organization (IMO) regulations. This study presents a three-dimensional computational fluid dynamics (CFD) analysis of a four-stroke, medium-speed marine engine operating in methanol–diesel dual-fuel (DF) mode. Simulations were performed using AVL FIRE for a MAN B&W 6H35DF engine, covering the in-cylinder process from intake valve closing to exhaust valve opening. Nine operating cases were investigated, including seven methanol–diesel DF cases with equivalence ratios (Φ) from 0.18 to 0.30, one methane–diesel DF case (Φ = 0.22), and one pure diesel baseline. A power-matched condition (IMEP ≈ 20 bar) enabled consistent comparison among fueling strategies. The results show that methanol–diesel DF operation reduces peak in-cylinder pressure, heat-release rate, turbulent kinetic energy, and wall heat losses compared with diesel operation. At low to moderate Φ, methanol DF combustion significantly suppresses nitric oxide (NO), soot, and carbon monoxide (CO emissions), while carbon dioxide (CO₂) emissions increase with Φ and approach diesel levels under power-matched conditions. These results highlight methanol’s potential as a viable low-carbon fuel for marine engines. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

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23 pages, 3106 KB

Open AccessArticle

Evaluating a Meanline Model for Partially Admitted Axial Turbines Operating in Organic Rankine Cycles: Extensive Experimental Validation with Multiple Working Fluids and Mixtures

by Robin Gautier and Nicolas Tauveron

Energies 2026, 19(3), 625; https://doi.org/10.3390/en19030625 (registering DOI) - 25 Jan 2026

Abstract

This study aims to evaluate the robustness of the well proved Aungier meanline model, originally developed for air and steam turbines, on Organic Rankine Cycles (ORC) turbines. More specifically, the study focuses on two pure-impulse axial turbines with partial admission and using various [...] Read more.

This study aims to evaluate the robustness of the well proved Aungier meanline model, originally developed for air and steam turbines, on Organic Rankine Cycles (ORC) turbines. More specifically, the study focuses on two pure-impulse axial turbines with partial admission and using various working fluids, including zeotropic mixtures. To this end, a three-part numerical model was developed to adapt this type of meanline model to a prediction-oriented methodology rather than a design-oriented one. Using inlet and outlet pressures, inlet temperature, and rotational speed as inputs, the model provides the resulting mass flow rate through the turbine as well as its performance characteristics. The model predictions are compared against an extensive experimental dataset comprising more than 300 operating points obtained with three pure fluids—R1233zd(E), NOVEC™ 649, and HFE7000—and three zeotropic mixtures. The model demonstrates good predictive accuracy over a wide range of operating conditions, including very low velocity ratios corresponding to severe off-design operation. Specifically, the mass flow rate is predicted with a Mean Absolute Percentage Error (MAPE) ranging from 1.23% to 3.31%, depending on the working fluid. Furthermore, over an experimental specific work range of 5 to 15 kJ/kg, the predicted numerical work exhibits a MAPE of 7.04% for 102 experimental points corresponding to the main dataset (R1233zd(E)). Finally, the total-to-total efficiency is predicted within ±4 efficiency points, showing a very good trend over a velocity ratio range from 0.06 to 0.36. Full article

(This article belongs to the Section J2: Thermodynamics)

15 pages, 702 KB

Open AccessArticle

Modeling of Electromagnetic Fields Along the Route of a Gas-Insulated Line Feeding Traction Substations

by Andrey Kryukov, Hristo Beloev, Dmitry Seredkin, Ekaterina Voronina, Aleksandr Kryukov, Iliya Iliev, Ivan Beloev and Konstantin Suslov

Energies 2026, 19(3), 624; https://doi.org/10.3390/en19030624 (registering DOI) - 25 Jan 2026

Abstract

Power supply for traction substations (TSs) of AC railways has traditionally been provided by 110–220 kV overhead transmission lines (OHL). These OHLs can be damaged during strong winds and ice formation. Furthermore, these lines generate significant electromagnetic fields (EMFs), which adversely affect maintenance [...] Read more.

Power supply for traction substations (TSs) of AC railways has traditionally been provided by 110–220 kV overhead transmission lines (OHL). These OHLs can be damaged during strong winds and ice formation. Furthermore, these lines generate significant electromagnetic fields (EMFs), which adversely affect maintenance personnel, the public, and the environment. Mitigating the resulting damages requires the establishment of protection zones, necessitating significant land allocation. Enhancing the reliability of power supply to traction substations and reducing EMF levels can be achieved through the use of gas-insulated lines (GIL), whose application in the power industry of many countries is continuously increasing. The aim of the research presented in this article was to develop computer models for determining the EMF of a GIL supplying a group of traction substations, taking into account actual traction loads characterized by non-sinusoidal waveforms and asymmetry. To solve this problem, an approach implemented in the Fazonord AC-DC software package, based on the use of phase coordinates, was applied. This allowed for the correct accounting of the skin effect and proximity effect in the massive current-carrying parts of the GIL, as well as the influence of asymmetry and harmonic distortions. The simulation results showed that the use of GIL brings the voltage unbalance factors at the 110 kV busbars of the traction substations within the permissible range, with the maximum values of these coefficients not exceeding 2%. The results of the harmonic distortion assessment demonstrated a significant reduction in harmonic distortion factors in the 110 kV network for the GIL compared to the OHL. The performed electromagnetic field calculations confirmed that the GIL generates magnetic field strengths one order of magnitude lower than those of the OHL. The obtained results lead to the conclusion that the use of gas-insulated lines for powering traction substations is highly effective, ensuring increased reliability, improved power quality, and a reduced negative impact of EMF on personnel, the public, the environment, and electronic equipment. Full article

18 pages, 5275 KB

Open AccessArticle

Interference Characteristics of a Primary–Secondary Integrated Distribution Switch Under Lightning Strike Conditions Based on a Field-Circuit Hybrid Full-Wave Model

by Ge Zheng, Shilei Guan, Yilin Tian, Changkai Shi, Hui Yin, Chengbo Jiang, Meng Yuan, Yijun Fu, Yiheng Chen, Shen Lai and Shaofei Wang

Energies 2026, 19(3), 623; https://doi.org/10.3390/en19030623 (registering DOI) - 25 Jan 2026

Abstract

As distribution networks become increasingly intelligent, primary–secondary integrated distribution switches are replacing the traditional electromagnetic type. However, the high degree of integration intensifies inherent electromagnetic compatibility (EMC) challenges. This paper presents a field-circuit hybrid full-wave model to investigate switch characteristics during lightning strikes. [...] Read more.

As distribution networks become increasingly intelligent, primary–secondary integrated distribution switches are replacing the traditional electromagnetic type. However, the high degree of integration intensifies inherent electromagnetic compatibility (EMC) challenges. This paper presents a field-circuit hybrid full-wave model to investigate switch characteristics during lightning strikes. A 3D full-wave model of the switch and a distributed parameter circuit model of the connecting lines are coupled via a network parameter matrix. This approach comprehensively accounts for the impacts of transmission lines and structural components on electromagnetic disturbances. Simulation and experimental results reveal that lightning strikes induce high-frequency damped oscillatory waves, primarily caused by traveling wave reflections along overhead lines. The characteristic frequency of disturbance is inversely proportional to the transmission line length. Additionally, internal components significantly influence this frequency; specifically, a larger voltage dividing capacitance in the voltage transformer results in a lower frequency. Model validation was performed using a 20 m transmission line setup. A 75 kV standard lightning impulse was injected into Phase B. At a distance of 500 mm from the voltage transformer, the measured radiated electric field amplitude was 14.12 kV/m (deviation < 5%), and the characteristic frequency was 1.11 MHz (deviation < 20%). These findings offer vital guidance for the lightning protection and EMC design of primary–secondary integrated distribution switches. Full article

(This article belongs to the Topic EMC and Reliability of Power Networks)

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19 pages, 1894 KB

Open AccessArticle

Modeling the Characteristics of an Alkaline Electrolyzer When Powered by a Rectangular Pulse Train

by Krzysztof Górecki, Michał Lewandowski and Przemysław Ptak

Energies 2026, 19(3), 622; https://doi.org/10.3390/en19030622 (registering DOI) - 25 Jan 2026

Abstract

This paper presents the results of modeling the DC and dynamic characteristics of an alkaline electrolyzer. A model of such an electrolyzer is proposed as a subcircuit for the SPICE software. This model describes DC and dynamic current–voltage characteristics of the electrolyzer, taking [...] Read more.

This paper presents the results of modeling the DC and dynamic characteristics of an alkaline electrolyzer. A model of such an electrolyzer is proposed as a subcircuit for the SPICE software. This model describes DC and dynamic current–voltage characteristics of the electrolyzer, taking into account the effect of solution concentration on the electrolyzer internal resistance and electrolyte capacitance, as well as the resistance and inductance of the leads. Using this model, one can calculate the voltage and current waveforms across the electrolyzer, as well as the gas flow rate produced by the electrolyzer. The correctness of the developed model was experimentally verified by powering the electrolyzer using a DC source and by powering the device using a voltage source, generating a rectangular pulse train with an adjustable frequency and duty cycle. The measurement system is described, and the obtained calculation and measurement results are presented and discussed. It was shown that the obtained calculation results differed minimally from the measurement results across a wide range of frequencies (from 0 to 50 kHz), duty cycles (from 0.3 to 0.7) of the supply voltage, and concentrations of the electrolyte (from 0.1 to 10%). The mean square error, normalized to peak measured values of each considered quantity, does not exceed 4%. Full article

(This article belongs to the Section A5: Hydrogen Energy)

27 pages, 1343 KB

Open AccessReview

Review of Data-Driven Personal Thermal Comfort Modeling and Its Integration into Building Environment Control

by Wenping Xue, Xiaotian He, Guibin Chen and Kangji Li

Energies 2026, 19(3), 621; https://doi.org/10.3390/en19030621 (registering DOI) - 25 Jan 2026

Abstract

With the increasingly prominent demand for building energy efficiency and occupant-centric design, accurate and reliable personal thermal comfort models (PTCMs) are playing an important role in various residential and energy applications (e.g., building energy-saving design, indoor environmental regulation, and health and well-being improvement). [...] Read more.

With the increasingly prominent demand for building energy efficiency and occupant-centric design, accurate and reliable personal thermal comfort models (PTCMs) are playing an important role in various residential and energy applications (e.g., building energy-saving design, indoor environmental regulation, and health and well-being improvement). In recent years, data-driven and artificial intelligence (AI) technologies have attracted considerable attention in the field of personal thermal comfort modeling. This study systematically reviews recent progress in data-driven personal thermal comfort modeling, emphasizing contact-based and non-contact data collection ways, correlation analysis of feature data, modeling methods based on machine learning and deep learning. Considering the high cost and limited scale of collection experiments, as well as noise, ambiguity, and individual differences in subjective feedback, special attention is put on the data-efficient thermal comfort modeling in data scarcity scenarios using a transfer learning (TL) strategy. Characteristics and suitable occasions of four transfer methods (model-based, instance-based, feature-based, and ensemble methods) are summarized to provide a deep perspective for practical applications. Furthermore, integration of PTCM into building environment control is summarized from aspects of the integration framework, modeling method, control strategy, actuator, and control effect. Performance of the integrated systems is analyzed in terms of improving personal thermal comfort and promoting building energy efficiency. Finally, several challenges faced by PTCMs and future directions are discussed. Full article

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

21 pages, 3957 KB

Open AccessArticle

Integration Optimization and Annual Performance of a Coal-Fired Power System Retrofitted with a Solar Tower

by Junjie Wu, Ximeng Wang, Yun Li, Jiawen Liu and Yu Han

Energies 2026, 19(3), 620; https://doi.org/10.3390/en19030620 (registering DOI) - 25 Jan 2026

Abstract

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. [...] Read more.

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

(This article belongs to the Special Issue Solar Energy Conversion and Storage Technologies)

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35 pages, 24985 KB

Open AccessArticle

From Blade Loads to Rotor Health: An Inverse Modelling Approach for Wind Turbine Monitoring

by Attia Bibi, Chiheng Huang, Wenxian Yang, Oussama Graja, Fang Duan and Liuyang Zhang

Energies 2026, 19(3), 619; https://doi.org/10.3390/en19030619 (registering DOI) - 25 Jan 2026

Abstract

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in [...] Read more.

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

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

27 pages, 1677 KB

Open AccessArticle

Energy Leaders: The Catalyst for Strategic Energy Management

by Kalie Miera, Indraneel Bhandari, Subodh Chaudhari, Senthil Sundaramoorthy and Thomas Wenning

Energies 2026, 19(3), 618; https://doi.org/10.3390/en19030618 (registering DOI) - 25 Jan 2026

Abstract

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, [...] Read more.

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future. Full article

(This article belongs to the Special Issue From Consumer Behavior to Business Innovation: Paths to a Sustainable Energy Future)

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35 pages, 3075 KB

Open AccessReview

Agentic Artificial Intelligence for Smart Grids: A Comprehensive Review of Autonomous, Safe, and Explainable Control Frameworks

by Mahmoud Kiasari and Hamed Aly

Energies 2026, 19(3), 617; https://doi.org/10.3390/en19030617 (registering DOI) - 25 Jan 2026

Abstract

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, [...] Read more.

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

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29 pages, 2200 KB

Open AccessArticle

Method of Comparative Analysis of Energy Consumption in Passenger Car Fleets with Internal Combustion, Hybrid, Battery Electric, and Hydrogen Powertrains in Long-Term European Operating Conditions

by Lech J. Sitnik and Monika Andrych-Zalewska

Energies 2026, 19(3), 616; https://doi.org/10.3390/en19030616 (registering DOI) - 25 Jan 2026

Abstract

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex [...] Read more.

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

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

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12 pages, 1517 KB

Open AccessArticle

High Volumetric Capacity Lithium Primary Battery via CuO and FeS₂ All-Active-Material Cathodes

by Chen Cai, Byeongcheol Min and Gary M. Koenig, Jr.

Energies 2026, 19(3), 615; https://doi.org/10.3390/en19030615 (registering DOI) - 24 Jan 2026

Abstract

Low-voltage primary batteries broadly power small electronics used in health, biomedical, and wearable applications. These devices are generally more sensitive to volumetric capacity than gravimetric capacity. The current state-of-the-art button battery is Zn-Ag₂O, where contributors that limit volumetric capacity include the [...] Read more.

Low-voltage primary batteries broadly power small electronics used in health, biomedical, and wearable applications. These devices are generally more sensitive to volumetric capacity than gravimetric capacity. The current state-of-the-art button battery is Zn-Ag₂O, where contributors that limit volumetric capacity include the incorporation of inactive materials in the electrode microstructure such as gelling agents, binders, and conductive additives. Herein, cathode materials of CuO and FeS₂ will be described for small form factor coin/button cells. When paired with Li metal anodes, the operating voltage is similar to Zn-Ag₂O. The key innovation is that they will be processed into all-active-material (AAM) electrode architectures, where the electrodes will comprise only electroactive materials and pores that are filled with electrolyte during cell fabrication. The AAM architecture significantly enhanced electroactive material volume utilization, and thus volumetric capacity. FeS₂ and CuO were processed into AAM electrodes under various processing conditions, and Li-FeS₂ and Li-CuO primary batteries were fabricated and evaluated. At the cell level, volumetric capacity of 1300 mAh cm⁻³ was achieved, and in a button cell form factor 395/927, nearly 100 mAh was delivered, which compares favorably with commercially available options, which typically range from 27 to 55 mAh. Full article

(This article belongs to the Special Issue High Performance Lithium Batteries: Prospects, Theories and Key Technologies)

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22 pages, 931 KB

Open AccessArticle

Coordinated Capacity Configuration Method for Distributed Resources of Virtual Power Plants Considering Time-Varying Power Coupling

by Lili Yao, Kaixin Zhao, Jun Shen, Liangwu Xu and Lingxiang Shen

Energies 2026, 19(3), 614; https://doi.org/10.3390/en19030614 (registering DOI) - 24 Jan 2026

Abstract

This paper proposes a coordinated capacity configuration method for Virtual Power Plant (VPP) distributed resources that considers time-varying power coupling. The method addresses the inadequate economic efficiency and reliability of existing configuration schemes, which stems from insufficient attention to the time-varying power coupling [...] Read more.

This paper proposes a coordinated capacity configuration method for Virtual Power Plant (VPP) distributed resources that considers time-varying power coupling. The method addresses the inadequate economic efficiency and reliability of existing configuration schemes, which stems from insufficient attention to the time-varying power coupling characteristics of Distributed Energy Resources (DERs). Firstly, we define the concepts of direct and indirect power coupling among DERs, derive a Lagrange multiplier-based coupling coefficient model, and realize the quantification of time-varying coupling coefficients through sliding time window correlation analysis (STWCA). Next, a capacity correlation matrix integrating technical and economic synergies is constructed to map coupling characteristics to capacity configuration. Then, a coordinated configuration model with time-varying coupling constraints is established to minimize life-cycle cost and maximize power supply reliability, validated by case simulation. The results demonstrate that the proposed method effectively reduces VPP operation cost and improves resource utilization and reliability, providing theoretical support for the scientific configuration of DERs in VPPs. Full article

(This article belongs to the Special Issue Recent Progress in Virtual Power Plants)

22 pages, 6646 KB

Open AccessArticle

Optimal Design of Horizontal-Axis Tidal Turbine Rotor Based on the Orthogonal Test Method

by Xiaojun Zhang, Yan Liu, Cui Wang, Wankun Wang and Honggang Fan

Energies 2026, 19(3), 613; https://doi.org/10.3390/en19030613 (registering DOI) - 24 Jan 2026

Abstract

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an [...] Read more.

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an improved Blade Element Momentum (BEM) approach, in which tip and hub loss factors are employed to enhance the modeling accuracy, and these results are employed to construct a parametric model of the original rotor. Due to its simplified assumptions and inability to capture three-dimensional flow effects, computational fluid dynamics (CFD) simulations were carried out to evaluate the hydrodynamic performance and flow analysis of the designed rotor. Further, the orthogonal test method was used to optimize the hydraulic performance of the rotor. Three optimization parameters, namely hub diameter, airfoil type, and maximum airfoil thickness, were set with three levels. Based on the orthogonal design scheme, nine rotor configurations were generated, and their energy capture characteristics and flow fields were subsequently evaluated through numerical simulations. The analysis indicates that the choice of airfoil exerts the strongest impact on the rotor’s energy capture efficiency, while the influences of maximum airfoil thickness and hub diameter follow in descending order. Consequently, the optimized rotor adopts a NACA63-415 airfoil with a reduced maximum thickness of 0.9 T₀ and an intermediate hub diameter of 15%R, achieving a power coefficient of 0.445 at the design tip-speed ratio of 4, corresponding to a 3.08% improvement compared with the original design. Flow field analysis demonstrates that the optimized geometry promotes a more uniform spanwise pressure distribution and effectively suppresses flow separation, thereby enhancing the overall hydrodynamic efficiency. Full article

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

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