Energies

26 pages, 3274 KB

Open AccessArticle

An Integrated Assessment of Battery and Hydrogen Electric Vehicles for Urban and Interurban Service Operations

by Giuseppe Napoli, Salvatore Micari, Antonio Comi, Ippolita Idone, Antonio Polimeni, Valerio Gatta and Edoardo Marcucci

Energies 2026, 19(4), 1113; https://doi.org/10.3390/en19041113 - 23 Feb 2026

Viewed by 476

Abstract

Urban freight and service operations represent a critical challenge for cities, contributing to greenhouse gas emissions, congestion, and competition for curb space. In addition to parcel deliveries, many service trips combine transport with installation, maintenance, or packaging recovery, generating long vehicle dwell times [...] Read more.

Urban freight and service operations represent a critical challenge for cities, contributing to greenhouse gas emissions, congestion, and competition for curb space. In addition to parcel deliveries, many service trips combine transport with installation, maintenance, or packaging recovery, generating long vehicle dwell times and inefficient use of public space. This paper investigates alternative operational scenarios for such activities, evaluating technological and organizational options that can reduce their environmental and spatial impacts. The study compares a diesel LCV baseline with four zero-emission configurations: battery electric LCVs; battery electric LCVs integrated with micro-hubs and cargo e-bikes; hydrogen fuel cell LCVs for long-range operations, and hydrogen fuel cell LCVs combined with cargo e-bikes via micro-hubs. The methodological framework is based on a vehicle routing problem (VRP) formulation supported by empirical data from Rome. It integrates indicators of energy use, carbon emissions, and curb-side occupation, and it includes the spatial representation of routes on urban and inter-urban maps to highlight operational differences across the five scenarios. Results indicate that zero-emission vehicles can eliminate tailpipe emissions, while logistics reorganization through decoupling improves the use of public space and enables the recovery of packaging materials. Battery solutions appear best suited to short and medium distances, whereas hydrogen is advantageous for longer routes. Overall, the study shows that combining technological and organizational measures provides a robust pathway toward sustainable logistics and more efficient service operations in metropolitan contexts. Full article

(This article belongs to the Special Issue Smart and Sustainable Mobility Systems: Advances in Electric and Hybrid Vehicles)

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29 pages, 3549 KB

Open AccessPerspective

Development and New Challenges of Sensorless Control for Permanent Magnet Synchronous Motors

by Quntao An, Mengji Zhao, Yuzhuo Lu, Hongwei Wang, Shiling Zhu and Xiangxu Zhang

Energies 2026, 19(4), 1112; https://doi.org/10.3390/en19041112 - 23 Feb 2026

Viewed by 745

Abstract

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency and power density. To further enhance reliability and reduce cost and volume, sensorless control techniques have been extensively investigated over the past few decades. This article provides [...] Read more.

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency and power density. To further enhance reliability and reduce cost and volume, sensorless control techniques have been extensively investigated over the past few decades. This article provides a review of major sensorless control methods, categorizing them into low-speed and high-speed methods. Virtual frequency methods, saliency-based methods, and model-based methods, along with their developments, are analyzed and compared. In addition, application-oriented analysis, implementation insights, as well as the challenges and future development trends are discussed at the end of the article. Full article

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

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23 pages, 2371 KB

Open AccessArticle

Analog Duty Cycle Peak-Shaving Control for Inverter Air Conditioners Considering User Comfort Under Prolonged High Temperatures

by Xiuzheng Wu, Chengxin Li, Xiaohan Dong and Xin Liang

Energies 2026, 19(4), 1111; https://doi.org/10.3390/en19041111 - 23 Feb 2026

Viewed by 310

Abstract

Current research on the participation of inverter-based air conditioners in demand response often prioritizes system performance during regulation periods yet frequently overlooks the prolonged high indoor temperatures that follow. Furthermore, oversimplified user comfort constraints limit the accurate evaluation of peak-shaving potential. To address [...] Read more.

Current research on the participation of inverter-based air conditioners in demand response often prioritizes system performance during regulation periods yet frequently overlooks the prolonged high indoor temperatures that follow. Furthermore, oversimplified user comfort constraints limit the accurate evaluation of peak-shaving potential. To address these limitations, this paper proposes a novel control framework. First, a differential user comfort evaluation model is established to quantify the adjustable temperature range under varying scenarios. Second, an analog duty cycle grouped rotation control model is developed. By leveraging the variable-frequency characteristics of inverter ACs, this method optimized peak-shaving potential while preventing indoor temperatures from remaining at their upper limits for extended durations. Third, to ensure fairness, a user selection model incorporating a User Impact Factor is introduced as a dynamic ranking criterion for participation priority. Finally, to address the inevitable parameter mismatch in practical engineering, the control strategy is upgraded to a feedforward–feedback closed-loop framework. Simulation results demonstrate the superiority of the proposed ADC strategy over existing methods. Specifically, compared to existing methods, it achieved a 45–50% reduction in the high-temperature influence factor and a 67% decrease in the standard deviation of user impact, indicating significantly improved thermal comfort and fairness. Furthermore, the framework exhibits strong robustness; even under 20% parameter uncertainty, it restricted the duration of temperature exceedance to within 0.8%, strictly outperforming traditional open-loop approaches in preventing user discomfort. These improvements ensure a more uniform distribution of comfort impacts among users, thereby enhancing both the precision and sustainability of demand-side peak shaving. Full article

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

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18 pages, 999 KB

Open AccessArticle

Image-Based Fault Detection and Severity Classification of Broken Rotor Bars in Induction Motors Using EfficientNetB3

by Shahil Kumar, Meshach Kumar and Rahul Ranjeev Kumar

Energies 2026, 19(4), 1110; https://doi.org/10.3390/en19041110 - 23 Feb 2026

Viewed by 401

Abstract

Broken rotor bar faults (BRBFs) in induction motors (IMs) present significant challenges in industrial applications, particularly due to the need for large labeled datasets and fast processing. This study addresses these issues by leveraging transfer learning with classical diagnostic techniques, using experimental 3-phase [...] Read more.

Broken rotor bar faults (BRBFs) in induction motors (IMs) present significant challenges in industrial applications, particularly due to the need for large labeled datasets and fast processing. This study addresses these issues by leveraging transfer learning with classical diagnostic techniques, using experimental 3-phase current and 3-axes vibration signals. The Gramian Angular Field (GAF) technique has been utilized to transform time series data into 2D images, enabling fine-tuning of an EfficientNetB3 model, which achieved 99.83% accuracy in classifying five BRBF severity levels. The proposed strategy also outperforms the state-of-the-art methods using the same experimental data. Similarly, validation with features extracted using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) further confirmed its reliability and superiority. This study also offers enhanced interpretability through Grad-CAM visualizations of the best model, which highlights the critical regions contributing to fault classification. These visualizations enable deeper and simpler understanding of fault mechanisms and support subsequent risk analysis, making the developed model actionable and user-friendly for industrial applications. Full article

(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)

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21 pages, 4708 KB

Open AccessArticle

Optimal Wind Farm Layout in a Complex Terrain by Varying Turbine Hub Heights: Case Study of Yeongdeok, South Korea

by Joon Heon Lee, SooHwan Kim and Jun Hyung Ryu

Energies 2026, 19(4), 1109; https://doi.org/10.3390/en19041109 - 22 Feb 2026

Viewed by 373

Abstract

In this study, we investigated the optimization of a wind farm layout on complex mountainous terrain in Yeongdeok, South Korea, with varying hub heights. Specifically, the energy performance of mixing two commonly used commercial models with different heights, i.e., Vestas V82 and V162, [...] Read more.

In this study, we investigated the optimization of a wind farm layout on complex mountainous terrain in Yeongdeok, South Korea, with varying hub heights. Specifically, the energy performance of mixing two commonly used commercial models with different heights, i.e., Vestas V82 and V162, was evaluated. The impact of site scale in terms of farm area (ranging from 1 to 9 km²) on power generation and wake effects was also determined. The results obtained using WindPRO and the Wind Atlas Analysis and Application Program demonstrated that, with increased wind farm area, the annual energy production increased while wake losses decreased. Compared with the case employing hubs with a uniform height, the mixed-height case showed a decrease in wake losses of up to 1.7% while maintaining comparable AEP. The findings of this study demonstrate that combining turbines of different hub heights provides more energy-efficient layouts, even in complex mountainous terrains. Insights from these findings can be further utilized to expand wind power in complex terrain in other countries. Full article

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

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30 pages, 8182 KB

Open AccessArticle

From Invasive Alien Species to Resource: Hydrothermal Carbonization of Myriophyllum aquaticum

by Federica Barontini, Marco Landi, Nicola Silvestri, Sandra Vitolo and Monica Puccini

Energies 2026, 19(4), 1108; https://doi.org/10.3390/en19041108 - 22 Feb 2026

Viewed by 375

Abstract

The invasive aquatic plant Myriophyllum aquaticum represents both an ecological threat and a wet biomass disposal challenge. This study investigates hydrothermal carbonization (HTC) as a strategy for its valorisation into energy-dense hydrochar. A Design of Experiments–Response Surface Methodology (DoE-RSM) approach was applied to [...] Read more.

The invasive aquatic plant Myriophyllum aquaticum represents both an ecological threat and a wet biomass disposal challenge. This study investigates hydrothermal carbonization (HTC) as a strategy for its valorisation into energy-dense hydrochar. A Design of Experiments–Response Surface Methodology (DoE-RSM) approach was applied to elucidate the combined influence of temperature (200–260 °C), residence time (30–210 min), and solid load (5–25 wt%) on hydrochar yield and properties. Hydrochar yields ranged from 48.8% to 65.6%, with the highest yields achieved at 200 °C, 30 min, and 25 wt% solids. Higher heating values of hydrochars spanned from 12.14 to 14.53 MJ/kg, corresponding up to +19% energy densification at higher process severity. Carbon and energy yields reached 69.7% and 68.6%, respectively, with maximum values attained under low-severity, high-solid-load conditions. The predictive models exhibited strong agreement with experimental data, enabling optimisation of HTC parameters for targeted hydrochar applications. Two hydrochars, “peat-like” and “lignite-like”, were further characterised for their potential use as soil amendments. The lignite-like hydrochar complied with EU contaminant limits and showed no phytotoxicity, confirming its suitability for agronomic use. Overall, HTC of M. aquaticum provides an effective waste-to-resource pathway, transforming wet invasive biomass into value-added carbon materials. Full article

(This article belongs to the Special Issue Advances in Thermal Chemical Conversion of Biomass/Organic Waste/Coal)

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20 pages, 2367 KB

Open AccessArticle

Time-Resolved Analysis of Photovoltaic–Building Energy Matching Using Dynamic Time Warping

by Arkadiusz Małek, Katarzyna Piotrowska, Michalina Gryniewicz-Jaworska and Andrzej Marciniak

Energies 2026, 19(4), 1107; https://doi.org/10.3390/en19041107 - 22 Feb 2026

Viewed by 475

Abstract

The increasing share of photovoltaic (PV) generation in building energy systems highlights the importance of understanding not only the magnitude but also the temporal structure of energy mismatch between PV production and building demand. This study proposes a Dynamic Time Warping (DTW)-based framework [...] Read more.

The increasing share of photovoltaic (PV) generation in building energy systems highlights the importance of understanding not only the magnitude but also the temporal structure of energy mismatch between PV production and building demand. This study proposes a Dynamic Time Warping (DTW)-based framework for the analysis of daily temporal mismatch patterns in a building-integrated photovoltaic system using high-resolution measurement data. Daily temporal signatures are constructed from normalized PV generation and building load profiles, allowing the analysis to focus exclusively on temporal deformation rather than absolute energy values. Pairwise DTW distances are used to construct a distance matrix that captures similarities between daily mismatch structures over an entire month. The resulting DTW distance matrix enables not only pairwise comparison of daily mismatch patterns, but also the identification of representative, transitional, and extreme days through ranking and hierarchical organization of temporal signatures. Hierarchical clustering with average linkage reveals distinct families of days characterized by similar types of temporal deformation, while a ranking based on average DTW distance provides a compact diagnostic summary of monthly variability. The findings demonstrate that PV–building energy matching is inherently time-structured, forming recurrent temporal families of days that cannot be identified using aggregate energy metrics alone. The proposed framework provides a robust diagnostic layer for time-aware energy analysis and supports the development of advanced control and management strategies that explicitly address temporal mismatch in building-integrated photovoltaic systems. Full article

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

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22 pages, 3981 KB

Open AccessArticle

Rotating Electric Machine Fault Diagnosis with Magnetic Flux Measurement Using Deep Learning Models

by Obinna Onodugo, Innocent Enyekwe and Emmanuel Agamloh

Energies 2026, 19(4), 1106; https://doi.org/10.3390/en19041106 - 22 Feb 2026

Viewed by 841

Abstract

This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending [...] Read more.

This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending the service life of the machine. The existing diagnostic tools face issues, including false indication of faults using classical methods, and the proposed data-driven methods based on machine learning lack transferability of model knowledge on an unseen dataset from different motor types or power ratings due to structural differences. To overcome these diagnostic drawbacks of statistical ML classifiers and classical approaches, innovative feature selection methods were employed in this work to preprocess the measured magnetic flux into a spectrogram image, and the transfer learning (TL) technique was applied to fine-tune convolution neural networks (CNNs) ImageNet pretrained models. The experimental results show the trained statistical ML classifiers and traditional CNN performance on unseen BU data and on the external data, and the performance demonstrated a lack of generalization on external datasets of different power ratings or structures. Models with such drawbacks cannot be used for developing effective diagnostic systems. The TL technique was employed on different deep CNN ImageNet pretrained models with spectrogram images as inputs to the deep CN network. This approach demonstrated an advanced and improved electric machine diagnostic system that addresses the drawbacks of the current ML-based diagnostic systems. The generalized model developed using CNN ResNet50 outperformed other deep CNN ImageNet models in correctly diagnosing faults on both the dataset generated from the authors’ lab and on an external dataset of a different machine from another research lab. Full article

(This article belongs to the Special Issue Innovations in Electric Motor Drives: Exploring the Future of High-Performance, Energy Efficiency and Reliability)

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35 pages, 941 KB

Open AccessArticle

Bioenergy from Maize Silage by Anaerobic Digestion: Batch Kinetics in Relation to Biochemical Composition

by Krzysztof Pilarski, Agnieszka A. Pilarska, Michał B. Pietrzak and Bartłomiej Igliński

Energies 2026, 19(4), 1105; https://doi.org/10.3390/en19041105 - 22 Feb 2026

Viewed by 531

Abstract

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the [...] Read more.

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the agricultural energy sector. To convert this potential into stable energy production, operators require kinetic models that translate routine silage quality indicators into concrete guidance for digester operation and control. Therefore, the aim of this article was to evaluate the batch kinetics of anaerobic digestion (AD) of maize silage and to select an adequate model for describing biochemical methane potential (BMP) profiles and associated energy recovery in the context of start-up, organic loading rate (OLR), hydraulic retention time (HRT) and feedstock preparation. Ten batches of silage (A–J) were examined, covering a realistic range of pH, electrical conductivity (EC), dry and volatile solids, ash, protein–fat–fibre fractions, fibre composition (NDF, ADF and ADL), derived fractions (hemicellulose, cellulose, and residual organic matter (OM)), C/N ratio and macro-/micronutrient profiles, including trace elements relevant to methanogenesis (Ni, Co, Mo, and Se). BMP tests were carried out in batch mode, and the resulting curves were fitted using the modified Gompertz and a first-order kinetic model. Methane yields of approx. 100–120 m³ CH₄/Mg fresh matter (FM) and 336–402 m³ CH₄/Mg volatile solids (VS), with CH₄ contents of 52–57% v/v, were typical for energy-grade maize silage. Kinetic and energetic behaviours were governed mainly by residual OM and hemicellulose (shortening the lag phase and increasing the maximum methane production rate), the ADL/cellulose ratio (controlling the slower hydrolytic tail), EC and Na/Cl/S (extending the lag phase), and C/N together with Ni/Co/Mo/Se (stabilising methanogenesis). The modified Gompertz model reproduced BMP curves with a pronounced lag phase and asymmetry more accurately (lower error and better information criterion values), and its parameters directly support start-up design, OLR ramp-up and energetic performance optimisation in bioenergy reactors. The novelty of this work lies in combining batch BMP tests, comparative kinetic modelling and detailed silage characterisation to establish quantitative links between kinetic parameters and routine maize silage quality indicators that are directly relevant for biogas plant operation and renewable energy production. Full article

(This article belongs to the Section A4: Bio-Energy)

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24 pages, 1964 KB

Open AccessReview

Survey of Blockchain Technology Deployment in Electric Power Industry in Indonesia

by Jauzak Hussaini Windiatmaja, Budi Sudiarto, Muhammad Salman, Riri Fitri Sari and Nugroho Adi Triyono

Energies 2026, 19(4), 1104; https://doi.org/10.3390/en19041104 - 22 Feb 2026

Viewed by 458

Abstract

This study investigates the potential adoption of blockchain technology within the Indonesian electricity sector to address key challenges in digital infrastructure. Blockchain technology has the potential to address the challenges by facilitating immutable and distributed storage of data across multiple network points. A [...] Read more.

This study investigates the potential adoption of blockchain technology within the Indonesian electricity sector to address key challenges in digital infrastructure. Blockchain technology has the potential to address the challenges by facilitating immutable and distributed storage of data across multiple network points. A two-stage methodology comprising a comprehensive literature review and selection of case studies is employed to conduct the survey. Research from reputable databases is reviewed by focusing on blockchain applications in energy systems. Key criteria such as Regulation, Implementation Readiness, Urgency, Technology Readiness Level, and Business Maturity Level are analyzed to assess deployment readiness across the main use cases in the Indonesian landscape. The review finds that five main use cases in Indonesia can be enhanced by blockchain technology, including peer-to-peer energy trading, renewable energy certificate trading, electronic billing of electricity, microgrid transactions, and electric vehicle charging transactions. Furthermore, the deployment readiness analysis suggests that electronic billing and electric vehicle charging transactions emerge as the most viable options. It is supported by conducive regulations, high urgency, and existing technological infrastructure. Full article

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

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29 pages, 14512 KB

Open AccessArticle

ANFIS-Based Controller and Associated Cybersecurity Issues with Hybrid Energy Storage Used in EV-Connected Microgrid System

by Md Nahin Islam and Mohd. Hasan Ali

Energies 2026, 19(4), 1103; https://doi.org/10.3390/en19041103 - 22 Feb 2026

Viewed by 431

Abstract

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to [...] Read more.

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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28 pages, 3689 KB

Open AccessArticle

A Coordinated Optimal Operation Method for Distribution Networks and Multiple Microgrids Based on Flexibility Margin Assessment

by Xiuyu Yang, Chongbi Li, Hao Zhang, Ying Wang and Chang Liu

Energies 2026, 19(4), 1102; https://doi.org/10.3390/en19041102 - 22 Feb 2026

Viewed by 322

Abstract

Under the “dual carbon” goals, the large-scale integration of distributed photovoltaics (DPVs) presents a challenge for the flexibility supply–demand mismatch in distribution systems. To address the issue of accurately matching flexibility supply and demand in the process of DPV consumption, this paper proposes [...] Read more.

Under the “dual carbon” goals, the large-scale integration of distributed photovoltaics (DPVs) presents a challenge for the flexibility supply–demand mismatch in distribution systems. To address the issue of accurately matching flexibility supply and demand in the process of DPV consumption, this paper proposes a coordinated optimization method for the distribution network (DN)- multi-microgrid (MMG) system, based on flexibility margin assessment. First, the mechanism of flexibility supply–demand imbalance under high penetration of DPV is analyzed, and a flexibility margin index considering network constraints is developed to quantify the flexibility surplus or deficit at different levels and periods. Next, within the framework of energy interaction between the DN-MMG systems, a centralized collaborative optimization model is established, aiming to enhance global flexibility margins. This model coordinates power exchange between nodes and the inter-temporal dispatch of energy storage, achieving the collaborative utilization of various flexibility resources. Finally, a case study based on a 10 kV distribution system with MMGs in a northern region is presented. The results show that the proposed method can effectively improve the spatiotemporal matching of system flexibility, reduce the risks of solar power curtailment and load shedding, while enhancing the economic performance of the system and the capacity for DPV integration. Full article

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

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19 pages, 3565 KB

Open AccessArticle

Short-Term Demand Forecasting and Supply Assurance Evaluation for Natural Gas Pipeline Networks Based on Uncertainty Quantification and Deep Learning

by Jinghua Chen, Yuxuan He, Qi Xiang, Haiyang You, Weican Wang, Pengcheng Li, Zhiwei Zhao, Zhaoming Yang, Huai Su and Jinjun Zhang

Energies 2026, 19(4), 1101; https://doi.org/10.3390/en19041101 - 22 Feb 2026

Viewed by 380

Abstract

Natural gas pipeline networks are subject to supply instability due to random fluctuations. Current forecasting methodologies often suffer from limited accuracy, inadequate uncertainty quantification, and poor integration with dynamic network evaluation mechanisms. To address these challenges, this study presents an integrated framework that [...] Read more.

Natural gas pipeline networks are subject to supply instability due to random fluctuations. Current forecasting methodologies often suffer from limited accuracy, inadequate uncertainty quantification, and poor integration with dynamic network evaluation mechanisms. To address these challenges, this study presents an integrated framework that bridges short-term demand forecasting with supply assurance assessment. A deep learning model that combines a graph convolutional network and a bidirectional long short-term memory network is developed to produce accurate 72 h demand forecasts. Forecasting uncertainty is quantified using the cumulative distribution function. Based on the probabilistic forecasts, a supply assurance evaluation model is constructed that accounts for the dynamic regulation capability of line pack. The comprehensive indicator system incorporates key metrics such as user satisfaction and the line pack demand−storage ratio. A case study was conducted with the proposed method based on a regional real-world pipeline network. The results demonstrate that the proposed model outperforms conventional baselines, achieving a mean absolute percentage error of less than 1%. The uncertainty quantification captures the risk probability associated with demand fluctuations. The proposed evaluation method identifies vulnerable sections and assesses supply margins under various scenarios, thus providing effective decision support for operational scheduling and supply assurance. Full article

(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)

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17 pages, 3327 KB

Open AccessArticle

Coordinated Inertia Synthesis and Stability Design for PV Systems Utilizing DC-Link Capacitors

by Qi Hua, Lunbo Deng, Qiao Peng and Yongheng Yang

Energies 2026, 19(4), 1100; https://doi.org/10.3390/en19041100 - 22 Feb 2026

Viewed by 309

Abstract

The increasing penetration of inverter-based resources (IBRs) has been reducing system inertia and intensifying frequency stability challenges. Hence, various grid demands have been imposed on grid-connected systems, e.g., requiring the provision of an auxiliary service to the grid. In this context, this paper [...] Read more.

The increasing penetration of inverter-based resources (IBRs) has been reducing system inertia and intensifying frequency stability challenges. Hence, various grid demands have been imposed on grid-connected systems, e.g., requiring the provision of an auxiliary service to the grid. In this context, this paper investigates the provision of synthesized inertia from the DC-link capacitors in grid-connected photovoltaic (PV) systems. For this configuration, the PV converter adopts a frequency–voltage droop control (FVDC) strategy, while a virtual synchronous generator (VSG) is employed on the grid side to emulate a synchronous generator, to enable the DC-link energy to contribute to primary frequency support. To quantify the virtual inertia and evaluate the closed-loop stability, a small-signal model of the inverter system is established. An eigenvalue analysis reveals that while increasing the DC-link voltage or capacitance enhances the achievable virtual inertia, it simultaneously narrows the stability margin. As such, comparative stability assessments under different parameter settings are performed, highlighting the distinct impacts of the DC-link voltages and capacitances on the emulated inertia and stability margins. The study provides insights into the maximum virtual inertia achievable via DC-link capacitors and offers practical guidelines for coordinating the controller and DC-link design to enhance frequency robustness in low-inertia power systems. Real-time hardware-in-the-loop (RT-HIL) tests validate the analytical findings. 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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16 pages, 3570 KB

Open AccessArticle

Electromagnetic Analysis of Double-Rotor Direct-Drive Permanent Magnet Generators Under Eccentricity Faults

by Marios Salinas, Alexandros Sergakis, Markus Mueller and Konstantinos N. Gyftakis

Energies 2026, 19(4), 1099; https://doi.org/10.3390/en19041099 - 22 Feb 2026

Viewed by 398

Abstract

Permanent Magnet Synchronous Generators (PMSGs) have acquired a pivotal role in recent years, owing to their high-power density, high efficiency, and ability to operate in direct-drive configurations. Despite these advantages, such machines are susceptible to mechanical faults, particularly airgap eccentricity, with axial flux [...] Read more.

Permanent Magnet Synchronous Generators (PMSGs) have acquired a pivotal role in recent years, owing to their high-power density, high efficiency, and ability to operate in direct-drive configurations. Despite these advantages, such machines are susceptible to mechanical faults, particularly airgap eccentricity, with axial flux topologies being more vulnerable due to their high ratio of axial to radial length. Given the rapidly increasing deployment rates of these generators, this paper focuses on the electromagnetic analysis of a coreless axial flux dual-rotor direct-drive PMSG, with the analysis focusing on eccentricity faults. Static (SE) and dynamic (DE) eccentricities are investigated under a specific load condition using 3D finite element analysis (FEA) models. For the investigation of the fault scenarios, this work utilizes traditional signature analysis methods, namely Current Fast Fourier Transform (FFT), Voltage FFT, and Electromagnetic Torque Analysis. Full article

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

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20 pages, 2577 KB

Open AccessArticle

MSR Fuel and Thermohydraulic: Modeling of Energy Well Experimental Loop in TRACE Code

by Giacomo Longhi, Guglielmo Lomonaco, Tomáš Melichar and Guido Mazzini

Energies 2026, 19(4), 1098; https://doi.org/10.3390/en19041098 - 21 Feb 2026

Viewed by 355

Abstract

The transition toward carbon-neutral energy systems has revived interest in nuclear technologies, particularly small and micro modular reactors (SMRs and MMRs) as flexible, safe and efficient alternatives to conventional large-scale power plans. In the Czech Republic, Centrum výzkumu Řez (CVŘ) is developing Energy [...] Read more.

The transition toward carbon-neutral energy systems has revived interest in nuclear technologies, particularly small and micro modular reactors (SMRs and MMRs) as flexible, safe and efficient alternatives to conventional large-scale power plans. In the Czech Republic, Centrum výzkumu Řez (CVŘ) is developing Energy Well (EW), a molten salt-cooled micro modular reactor concept employing FLiBe (Fluoride Lithium Beryllium) as primary and secondary coolant and a supercritical CO₂ (sCO₂) tertiary loop. A dedicated experimental facility was built to reproduce EW operating conditions and provide critical data on thermohydraulic behavior, fuel properties and heat-transfer mechanisms. This paper presents the development and assessment of a TRACE (TRAC/RELAP Advanced Computational Engine) model of the experimental facility, including specific methodologies for the main heater and the heat exchanger. Model accuracy was assessed through comparison with experimental commissioning data. The simulations demonstrated overall model consistency, especially regarding the heat exchanger and the main heater general performances, while some discrepancies were observed inside the main heater graphitic core. Other discrepancies were observed along the loop, mainly resulting from modeling simplifications and lack of information regarding certain experimental loop phenomena. In particular, the pressure calculation showed large inconsistencies mainly connected to the complexity of pressure measurements in molten salt circuits and the lack of specific head loss correlations. This study also helped identify broader issues in both the code (persistent error in generating CO₂ property tables and instabilities resulting from FLiBe interactions with non-condensable gases) and the experimental loop (defect in the heat exchanger filling and uncertainties on sensors location), also contributing to resolving sensor-related inconsistencies in the facility. Results confirm TRACE as a reliable tool for modeling molten salt systems, regarding the temperature distribution and the heat transfer. However, depending on the specific experimental case, this paper introduces specific limitations, such as some inconsistencies in the pressure drops distribution, in order to support the future development of TRACE code. Beyond technical advances, this work provides unique experimental data and fosters international collaboration in advancing SMR and molten salt reactor technologies. Full article

(This article belongs to the Special Issue Nuclear Fuel and Fuel Cycle Technology)

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22 pages, 8147 KB

Open AccessArticle

Development of a Resonance Velocity-Driven Energy Harvester Using Triple-Layer Piezoelectric

by Mojtaba Ghodsi, Morteza Mohammadzaheri, Payam Soltani and Jebraeel Gholinezhad

Energies 2026, 19(4), 1097; https://doi.org/10.3390/en19041097 - 21 Feb 2026

Viewed by 311

Abstract

This research aims to establish design guidelines for a cantilever triple-layer piezoelectric harvester (CTLPH) with tip mass and tip excitation, operating under resonance conditions. The guideline is derived by combining constitutive equations with Euler–Bernoulli beam theory to identify the effective parameters of the [...] Read more.

This research aims to establish design guidelines for a cantilever triple-layer piezoelectric harvester (CTLPH) with tip mass and tip excitation, operating under resonance conditions. The guideline is derived by combining constitutive equations with Euler–Bernoulli beam theory to identify the effective parameters of the CTLPH and, subsequently, the storage voltage after rectification using a germanium diode bridge. The analysis shows that excitation frequency, piezoelectric coefficients, geometrical dimensions, and the mechanical properties of the layers all significantly influence CTLPH performance. The effects of storage capacitance and excitation frequency were experimentally validated through the design, fabrication, and testing of a prototype. Furthermore, the LTC3588 energy storage module was employed to store the generated charge from resonance motion. An advanced non-contact optical method was employed to determine the bending stiffness of the CTLPH. The output power after the energy storage module was measured across a range of resistive loads at frequencies near the resonance condition (f = 65 Hz). Results demonstrate that both excitation frequency and external resistance affect the maximum harvested power. The developed CTLPH achieved an optimum output power of 46.18 ± 0.98 μW at an external resistance of 3 kΩ, which is sufficient to supply micropower sensors. Full article

(This article belongs to the Section B2: Clean Energy)

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28 pages, 1749 KB

Open AccessArticle

A Minimally Intrusive Methodology for Power Loss Identification in Electric Powertrains for Physics-Based Analytical Modeling

by Pascal Brejaud, Guillaume Colin and Pascal Higelin

Energies 2026, 19(4), 1096; https://doi.org/10.3390/en19041096 - 21 Feb 2026

Viewed by 280

Abstract

This paper presents a minimally intrusive experimental methodology for identifying and modeling power losses in the electric powertrain of a battery electric vehicle, including the inverter, electric motor and speed reducer. Measurements are performed on a roller test bench equipped with an eddy [...] Read more.

This paper presents a minimally intrusive experimental methodology for identifying and modeling power losses in the electric powertrain of a battery electric vehicle, including the inverter, electric motor and speed reducer. Measurements are performed on a roller test bench equipped with an eddy current brake, using two complementary approaches to determine the mechanical power at the wheel: (i) a direct measurement based on an onboard rotary torque sensor integrated into a driveshaft; (ii) an indirect estimation derived from brake power measurements corrected for bench losses and tire longitudinal slip. The two approaches are systematically compared in order to quantify the accuracy loss associated with brake-based measurements and to identify the operating conditions under which they can reliably substitute direct torque measurements. The experimental results show that brake-based estimations provide acceptable accuracy at moderate–high torque levels, while significant deviations occur at low torque. Based on the experimental dataset, an overall power loss model is identified using a polynomial function of motor torque and speed. Two fitting strategies are investigated: an unconstrained least-squares approach, allowing all coefficients to vary freely, and a constrained formulation enforcing physically admissible (non-negative) loss terms; while the unconstrained method slightly improves the numerical fit, it may lead to non-physical coefficients and invalid efficiency predictions. In contrast, the constrained approach preserves physical interpretability and ensures consistent loss and efficiency maps. Finally, a step-by-step practical guide is provided to facilitate the implementation of the proposed methodology for powertrain loss identification on electric vehicles without extensive mechanical disassembly. Full article

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

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22 pages, 1811 KB

Open AccessArticle

A Dynamic Decision-Making Framework for Prioritizing Renewable Energy Technologies in Smart Cities Using Deep Learning and Hybrid Multi-Criteria Decision-Making

by Rashid Nasimov, Shukhrat Kamalov, Azamat Kakhorov, Jamila Kamalova and Rahma Aman

Energies 2026, 19(4), 1095; https://doi.org/10.3390/en19041095 - 21 Feb 2026

Viewed by 435

Abstract

Rapid energy planning in cities needs decision-support tools that can change based on the supply of renewable resources and the needs of stakeholders. This paper introduces an innovative adaptive decision-support framework that integrates Long Short-Term Memory (LSTM)-based short-term renewable energy forecasting with an [...] Read more.

Rapid energy planning in cities needs decision-support tools that can change based on the supply of renewable resources and the needs of stakeholders. This paper introduces an innovative adaptive decision-support framework that integrates Long Short-Term Memory (LSTM)-based short-term renewable energy forecasting with an interval-valued Pythagorean fuzzy Best-Worst Method–TOPSIS (IVPF-BWM–TOPSIS). This enables forecast-driven and temporally adaptive prioritisation of urban energy technologies, as opposed to static expert-based evaluation. Using criteria based on forecasted technical feasibility and scalability, the five green energy options that are looked at are rooftop solar, wind energy, smart grids, solar-integrated electric vehicle infrastructure, and battery energy storage. The best score is for rooftop solar (RDC = 0.65), followed by solar-integrated EV infrastructure (RDC = 0.566), and finally smart grids (RDC = 0.55). Wind energy gets the lowest score because it will not be very useful in cities. Sensitivity analysis (±20% weight change) and 15 scenario-based stress tests show that the framework is strong and does not change the order of the ranks. The results show that the proposed mixed AI and fuzzy method can be used to make plans for renewable energy in smart cities that are both based on data and can be used by many people. Full article

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

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20 pages, 6375 KB

Open AccessArticle

Thermal Analysis of a Series Thyristor Module Prototype for Realizing Repetitive Operation of a Compact Torus Injector

by Xingyu Fang, Mingsheng Tan, Xin Huang, Xiaopeng Wang, Yang Ye, Fubin Zhong, Chengming Qu, Xiaohui Zhang, Jin Zhang, Erfei Wang, Wenzhe Mao, Haixia Hu, Taixun Fang, Defeng Kong and Shoubiao Zhang

Energies 2026, 19(4), 1094; https://doi.org/10.3390/en19041094 - 21 Feb 2026

Viewed by 323

Abstract

Pulse thyristors are extensively utilized in pulsed plasma discharge applications. In this study, a pulse switch prototype is built using two parallel valve groups, each consisting of seven series-connected thyristors. Each thyristor is equipped with an anti-parallel protection diode, a static voltage-sharing resistor, [...] Read more.

Pulse thyristors are extensively utilized in pulsed plasma discharge applications. In this study, a pulse switch prototype is built using two parallel valve groups, each consisting of seven series-connected thyristors. Each thyristor is equipped with an anti-parallel protection diode, a static voltage-sharing resistor, and an RCD (resistor-capacitor-diode) dynamic voltage-sharing circuit. The prototype withstands 24 kV, delivers 150 kA peak current, operates at 10 Hz, and can run continuously for 1 s. Thermal analysis is essential under narrow-pulse high-current conditions to avoid failure from localized overheating. By investigating the expansion process of the conduction zone during thyristor turn-on, a single-thyristor turn-on model and a finite-element model of the multi-layer series thyristor module are established to analyze transient temperature distributions. Results show a non-uniform temperature profile across the silicon wafer, with the hottest zone near the gate ring. During repetitive pulses, the silicon temperature fluctuates rapidly, while the copper base heats up gradually. At a spreading speed of 30 m/s, the gate terminal temperature rises about 38 °C—within safe limits for now, but projected to exceed them under future operating conditions. Thus, improved thermal management will be critical in further development. Full article

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35 pages, 5430 KB

Open AccessArticle

A Multi-Fidelity Modeling and Optimization Framework for Designing Grid-Tied Hybrid AC Battery Systems

by Abdul Mannan Rauf, Thomas Geury and Omar Hegazy

Energies 2026, 19(4), 1093; https://doi.org/10.3390/en19041093 - 21 Feb 2026

Viewed by 329

Abstract

AC battery systems (ACBSs) based on multilevel converters (MLCs) have gained considerable attention in recent times for the provision of grid services due to high-power (HP) and high-energy (HE) capabilities. In a hybrid ACBS, multiple low-voltage ports provide DC interfaces for battery modules [...] Read more.

AC battery systems (ACBSs) based on multilevel converters (MLCs) have gained considerable attention in recent times for the provision of grid services due to high-power (HP) and high-energy (HE) capabilities. In a hybrid ACBS, multiple low-voltage ports provide DC interfaces for battery modules from the same or different chemistries, enabling flexible operation across a wide range of grid services. However, the design complexity increases substantially, due to (i) higher electrothermal coupling between heterogeneous battery modules and power electronic (PE) switches, (ii) grid compliance constraints and (iii) power quality requirements, which often leads to conservative oversizing and, consequently, increased total cost of ownership (TCO). To address these challenges, this paper proposes a co-design optimization framework for the sizing and selection of battery modules, PE components, and MLC architecture. A multi-fidelity modeling approach is presented to co-simulate the battery modules and MLC. The model captures electrochemical behavior, degradation dynamics, and power losses to enable accurate estimation of system-level energy efficiency. The framework then leverages a multi-objective nondominated sorting genetic algorithm (NSGA-II) to perform optimal cell-to-module sizing across different chemistries and MLC levels, while incorporating the inter-module balancing and AC power quality constraints. Comparative simulation studies show that the proposed co-design framework achieves life-cycle TCO reduction of 3.5%, 4.5% and 20% relative to non-hybrid (single chemistry) configurations based on LFP, NMC and LTO chemistries, respectively. The test results validate the effectiveness of the proposed co-design methodology for the optimal design of grid-tied AC battery systems. Full article

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20 pages, 1791 KB

Open AccessSystematic Review

Energy-Efficient Innovations in Agricultural and Food Systems: A Systematic Review of Productivity and Sustainability Outcomes and Adoption Trends

by Siyabonga Gasa, Asanda Sokombela, Nyasha E. Chiuta and Charles S. Mutengwa

Energies 2026, 19(4), 1092; https://doi.org/10.3390/en19041092 - 21 Feb 2026

Viewed by 549

Abstract

Agriculture and food systems are among the world’s greatest energy consumers and emitters of greenhouse gases (GHGs), highlighting the importance of energy-efficient strategies that maintain a balance between productivity and sustainability. This study used the PRISMA-ScR methodology and the Biblioshiny platform to conduct [...] Read more.

Agriculture and food systems are among the world’s greatest energy consumers and emitters of greenhouse gases (GHGs), highlighting the importance of energy-efficient strategies that maintain a balance between productivity and sustainability. This study used the PRISMA-ScR methodology and the Biblioshiny platform to conduct a systematic review and evaluation of renewable energy integration and digital advances in agriculture and food systems. Fifty-one peer-reviewed research articles published between 2009 and 2025 were examined to determine technology trends, performance outcomes, and adoption challenges. The findings identified two significant innovation pathways: renewable energy technology such as solar-powered irrigation, biogas generation, and agrivoltaic systems, and digital solutions such as precision agriculture, Internet of Things (IoT)-enabled monitoring, and automation. Results indicate yield improvements of 10–25%, irrigation water savings of up to 40%, and yearly GHG emissions reductions of 0.3 to 0.6 tonnes of CO₂ per hectare. However, adoption remains uneven across regions, restricted by infrastructural constraints, capital costs, and inadequate policy support especially in underdeveloped countries. Overall, combining renewable energy and digital technology improves productivity, resource-use efficiency, and environmental performance while promoting various SDGs. Furthermore, integrating these two types of technologies leads to digital economic transformation in agriculture and food systems. These findings show the innovative potential of energy-efficient solutions in enabling sustainable intensification and climate resilience in agriculture. Full article

(This article belongs to the Special Issue Renewable Energy Integration into Agricultural and Food Engineering)

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20 pages, 1420 KB

Open AccessArticle

High-Level Synthesis (HLS)-Enabled Field-Programmable Gate Array (FPGA) Algorithms for Latency-Critical Plasma Diagnostics and Neural Trigger Prototyping in Next-Generation Energy Projects

by Radosław Cieszewski, Krzysztof Poźniak, Ryszard Romaniuk and Maciej Linczuk

Energies 2026, 19(4), 1091; https://doi.org/10.3390/en19041091 - 21 Feb 2026

Viewed by 522

Abstract

Large-scale advanced energy systems, including fusion devices, high-power plasma sources, and accelerator-driven energy platforms, increasingly depend on real-time, hardware-level data processing for diagnostics, control, and protection. In such installations, ultra-low latency, deterministic throughput, and multi-decade operational lifetimes are not optional design goals but [...] Read more.

Large-scale advanced energy systems, including fusion devices, high-power plasma sources, and accelerator-driven energy platforms, increasingly depend on real-time, hardware-level data processing for diagnostics, control, and protection. In such installations, ultra-low latency, deterministic throughput, and multi-decade operational lifetimes are not optional design goals but strict system-level requirements. While similar timing constraints exist in high-energy physics infrastructures, energy applications place a stronger emphasis on long-term stability, maintainability, and reproducibility of digital signal processing pipelines. This work investigates whether high-level synthesis (HLS) provides a practical and sustainable design methodology for implementing both classical pattern-based and compact neural network (NN) trigger logic on Field-Programmable Gate Arrays (FPGAs) under realistic energy-system constraints. Using representative commercial toolchains (Intel HLS and hls4ml) as reference workflows, we demonstrate the capabilities of fixed-point, fully pipelined streaming architectures, while also identifying critical shortcomings of pragma-driven HLS approaches in terms of architecture transparency, long-term portability, and systematic multi-objective design-space exploration, all of which are crucial for long-lived energy projects and plasma diagnostic systems. These limitations directly motivate the development of a custom, vendor-agnostic, extensible HLS framework (PyHLS), specifically oriented toward deterministic latency, reproducibility, and physics-grade verification demands of advanced energy infrastructures. Gas Electron Multipliers (GEMs) are modern gaseous detectors increasingly employed in plasma diagnostics, radiation monitoring, and high-power energy experiments, where high rate capability, fine spatial resolution, and radiation tolerance are required. Their massively parallel signal structure and continuous data streams make GEMs a representative and demanding benchmark for FPGA-based real-time trigger and preprocessing systems in energy-related environments. The primary objective of this study is to establish a pragmatic technological baseline, demonstrating that contemporary HLS workflows can reliably support both template-based and neural inference-based trigger architectures within strict timing, resource, and power constraints typical for advanced energy installations. Furthermore, we outline a scalable development path toward multi-channel and two-dimensional (pixelated) GEM readout architectures, directly applicable to fusion diagnostics, plasma accelerators, beam–plasma interaction studies, and radiation-hard energy monitoring platforms. Although the proposed methodology remains fully transferable to large-scale physics trigger systems, its principal relevance is directed toward real-time diagnostics and protection layers in next-generation energy systems. Full article

(This article belongs to the Special Issue Modern High-Performance Electronic Systems for Advanced Energy Projects and Large-Scale Research Infrastructures)

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17 pages, 9090 KB

Open AccessArticle

Design and Numerical Analysis of a Novel Vortex-Induced Vibration Bladeless Wind Turbine with Cylindrical Cam Mechanical Conversion

by Nicolas Saba, Charbel Makhlouf, Amin Raad, Christopher Abi Frem and Macole Sabat

Energies 2026, 19(4), 1090; https://doi.org/10.3390/en19041090 - 21 Feb 2026

Viewed by 470

Abstract

Global efforts to mitigate climate change and reduce reliance on fossil fuels have intensified interest in sustainable, urban-compatible wind energy technologies. Conventional wind turbines, however, remain limited in densely populated environments due to acoustic emissions, mechanical complexity, cost, and risks to avian wildlife. [...] Read more.

Global efforts to mitigate climate change and reduce reliance on fossil fuels have intensified interest in sustainable, urban-compatible wind energy technologies. Conventional wind turbines, however, remain limited in densely populated environments due to acoustic emissions, mechanical complexity, cost, and risks to avian wildlife. This study proposes and numerically evaluates a bladeless wind turbine concept based on vortex-induced vibrations (VIVs) as a simplified alternative to conventional bladed systems. The proposed design replaces rotating blades with a vertical mast that undergoes wind-induced oscillations, which are passively converted into unidirectional rotational motion using a cylindrical cam (CCAM) mechanism. The aerodynamic behavior and structural response of the system are investigated using computational fluid dynamics (CFD) and finite element analysis (FEA) under low-wind-speed conditions representative of urban environments. The numerical results indicate well-defined flow separation and wake formation conducive to VIV, along with low stress and displacement levels in the mast, supporting reliable mechanical engagement with the CCAM mechanism. These findings demonstrate the feasibility of mechanically rectified VIV-based bladeless wind turbines and highlight their potential as low-noise, low-impact solutions for decentralized and urban wind energy applications. Full article

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

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31 pages, 4069 KB

Open AccessArticle

Bio-Solar Green Roofs for Urban Heat Adaptation: A Case in Point

by Azhar Iqbal and Shoaib Rauf

Energies 2026, 19(4), 1089; https://doi.org/10.3390/en19041089 - 21 Feb 2026

Viewed by 480

Abstract

Urban heat islands (UHIs) increase the cooling load and reduce the performance of rooftop photovoltaic (PV) systems; thus, the co-benefits of integrating bio-solar green roofs require quantification and real-world demonstration to encourage the uptake of this technology. Consequently, this study compares the thermal [...] Read more.

Urban heat islands (UHIs) increase the cooling load and reduce the performance of rooftop photovoltaic (PV) systems; thus, the co-benefits of integrating bio-solar green roofs require quantification and real-world demonstration to encourage the uptake of this technology. Consequently, this study compares the thermal and electrical performances of four simultaneously installed roof assemblies, namely conventional roof (CR), green roof (GR), photovoltaic roof (pCR), and bio-solar green roof (pGR), under clear-sky summer periods in Lahore, Pakistan. The experiment equipped the same insulated test cells with meteorological, thermal, moisture, and PV power gauging to collect data every 1 min; standardized layers were built, and the PV tilt was set to 22°. The results show that pGR always performs better compared with other roof assemblies: the temperature on the outer surface is lower, the diurnal amplitude is the most reduced (ΔDF ≈ +19% vs. CR), the thermal response is the most delayed (ΔTL ≈ −21%), and TPI improves by 6.5–7%. All of these results indicate a new, field-validated synergy between evapotranspiration and PV shading/ventilation that could translate into practical value through reduced peak cooling loads (demand control), lower day-to-day cooling energy, and incremental PV gains. These are critical factors for achieving positive techno-economic outcomes in hot, sunny cities, with the aim of realizing UHI mitigation and resilient building energy systems. Full article

(This article belongs to the Special Issue Smart Energy Management and Sustainable Urban Communities, 2nd Edition)

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19 pages, 4792 KB

Open AccessArticle

Development of a Physics-Based Digital Twin Framework for a 3 MW Class Wind Turbine

by Changhyun Kim

Energies 2026, 19(4), 1088; https://doi.org/10.3390/en19041088 - 20 Feb 2026

Viewed by 388

Abstract

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital [...] Read more.

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital twin framework for the real-time health monitoring of a 3 MW class wind turbine. A physics-based numerical model was developed using Modelica 4.0.0 to simulate the electrical and mechanical behaviors of the wind turbine based on supervisory control and data acquisition (SCADA) inputs. Data preprocessing and wind speed calibration strategies were applied to reconcile nacelle-measured SCADA data with the turbine design specifications. Furthermore, reduced-order models (ROMs) were integrated with the physics-based numerical model to predict the thermal states of the generator and gearbox. Key operational parameters were selected through correlation analysis to enable accurate temperature prediction. Validation results demonstrate that the proposed digital twin accurately reproduces the dynamic behavior of the wind turbine, with the ROM-based temperature predictions showing agreement with SCADA measurements. The overall framework achieves a computation time within one second, indicating its suitability for real-time diagnostic and predictive maintenance applications. Full article

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

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33 pages, 6014 KB

Open AccessReview

From Biomass Waste to Green Fuel: Biochar-Based Catalysts for Hydrogen Production

by Karoll M. Rubiano, Asim Jilani and Hussameldin Ibrahim

Energies 2026, 19(4), 1087; https://doi.org/10.3390/en19041087 - 20 Feb 2026

Viewed by 523

Abstract

With the increasing demand for energy given by the effects of extreme weathers in the last years, the need to expand the access to renewable energy such as green hydrogen has become a priority in current research. However, one of the main challenges [...] Read more.

With the increasing demand for energy given by the effects of extreme weathers in the last years, the need to expand the access to renewable energy such as green hydrogen has become a priority in current research. However, one of the main challenges for hydrogen production is the elevated cost of catalysts due to the consumption and lack of availability of rare metals. To reinforce sustainability, biochar, a carbon-rich material has emerged with huge potential. Its properties such as a high surface area and abundant functional groups facilitate catalyst adsorption and the dispersion of active sites, besides the mineral content and surface chemistry tunability, allow the activation and metal impregnation to improve hydrogen production. Considering these characteristics, this paper will highlight all the potential of biochar as a catalyst and catalyst support, the current advances identifying biochar as catalyst in hydrogen production, and the key characteristics that make it adequate in these applications. Finally, the remaining challenges and limitations are described, providing a perspective on future opportunities and research directions. Full article

(This article belongs to the Special Issue Hydrogen Production: Technical-Economic, Upscaling, and Industrialization Perspectives for Sustainable and Secure Energy Production)

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36 pages, 1420 KB

Open AccessReview

Advances in CO₂ Injection for Enhanced Hydrocarbon Recovery: Reservoir Applications, Mechanisms, Mobility Control Technologies, and Challenges

by Mazen Hamed and Ezeddin Shirif

Energies 2026, 19(4), 1086; https://doi.org/10.3390/en19041086 - 20 Feb 2026

Viewed by 523

Abstract

Carbon dioxide injection is one of the most advanced and commercially proven methods of enhanced hydrocarbon recovery, and CO₂ injection has been shown to be very effective in conventional oil reservoirs and is gaining attention in gas, unconventional, and coalbed methane reservoirs. [...] Read more.

Carbon dioxide injection is one of the most advanced and commercially proven methods of enhanced hydrocarbon recovery, and CO₂ injection has been shown to be very effective in conventional oil reservoirs and is gaining attention in gas, unconventional, and coalbed methane reservoirs. The advantages of CO₂ injection lie in the favorable phase properties and interactions with reservoir fluids, such as swelling, reduction in oil viscosity, reduction in interfacial tension, and miscible displacement in favorable cases. But the low viscosity and density of CO₂ compared to the reservoir fluids result in unfavorable mobility ratios and gravity override, resulting in sweep efficiency limitations. This review offers a broad and EOR-centric evaluation of the various CO₂ injection methods for a broad array of reservoir types, such as depleted oil reservoirs, gas reservoirs for the purpose of gas recovery, tight gas/sands, as well as coalbed methane reservoirs. Particular attention will be given to the use of mobility control/sweep enhancement techniques such as water alternating gas (CO₂-WAG), foam-assisted CO₂ injection, polymer-assisted WAG processes, as well as hybrid processes that combine the use of CO₂ injection with low salinity or engineered waterflood. Further, recent developments in compositional simulation, fracture-resolving simulation, hysteresis modeling, and data-driven optimization techniques have been highlighted. Operational challenges such as injectivity reduction, asphaltene precipitation, corrosion, and conformance problems have been reviewed, along with the existing methods to mitigate such issues. Finally, key gaps in the current studies have been identified, with an emphasis on the development of EHR processes using CO₂ in complex and low-permeability reservoirs, enhancing the resistance of chemical and foam methods in realistic conditions, and the development of reliable methods for optimizing the process on the field scale. This review article will act as an aid in the technical development process for the implementation of CO₂ injection projects for the recovery of hydrocarbons. Full article

(This article belongs to the Special Issue Advances in Carbon Dioxide (CO₂) Enhanced Oil Recovery (EOR) and Carbon Capture and Storage (CCS))

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58 pages, 2592 KB

Open AccessReview

Waste-to-Energy Technologies in Saudi Arabia: A Case Study and Review of Waste Conversion and Energy Recovery

by Mohammed F. M. Abushammala, Sultan Almuaythir, Tharaa M. Al-Zghoul and Motasem Y. D. Alazaiza

Energies 2026, 19(4), 1085; https://doi.org/10.3390/en19041085 - 20 Feb 2026

Viewed by 556

Abstract

This study provides a comprehensive evaluation of waste-to-energy (WtE) technologies in Saudi Arabia, focusing on municipal solid waste (MSW) across various cities, in alignment with Saudi Vision 2030. Saudi Arabia generates approximately 16 million tons of MSW annually, primarily composed of organic matter [...] Read more.

This study provides a comprehensive evaluation of waste-to-energy (WtE) technologies in Saudi Arabia, focusing on municipal solid waste (MSW) across various cities, in alignment with Saudi Vision 2030. Saudi Arabia generates approximately 16 million tons of MSW annually, primarily composed of organic matter (37–57%), followed by paper (11–28%) and plastics (5–36%). According to Vision 2030 projections, MSW generation is expected to increase to approximately 30 million tons per year by 2033, driven by population growth, urbanization, and increased tourism activities. Waste quantities notably increase during the Hajj and Ramadan seasons. The study assesses three main WTE technologies: biochemical, chemical, and thermochemical processes. Anaerobic digestion (AD) effectively converts organic waste into biogas with a methane content of 60% to 80%, potentially yielding up to 2.99 TWh annually. Transesterification efficiently targets fats in waste, generating around 244.2 GWh per year. Thermochemical processes, including incineration, gasification, and pyrolysis, are suitable for high-calorific waste. Incineration can significantly reduce waste volume and generate up to 2073 MW while lowering GHG emissions. Economic assessments reveal that biochemical methods are the most cost-effective for managing organic waste, while thermochemical methods, despite higher capital costs, achieve significant energy recovery. Integrating WTE technologies with recycling is crucial for enhancing environmental sustainability and supporting Saudi Arabia’s Vision 2030 objectives. Full article

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

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28 pages, 842 KB

Open AccessReview

AI-Driven Virtual Power Plants: A Comprehensive Review

by Jian Li, Chenxi Wang and Yonghe Liu

Energies 2026, 19(4), 1084; https://doi.org/10.3390/en19041084 - 20 Feb 2026

Viewed by 1247

Abstract

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous [...] Read more.

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous assets and enabling coordinated control, market participation, and grid-support functions. Recent advances in artificial intelligence (AI) have further elevated the scalability, autonomy, and responsiveness of VPP operations. This paper presents a comprehensive review of AI for VPPs, organized around a taxonomy of machine learning, deep learning, reinforcement learning, and hybrid approaches, and examines how these methods map to core VPP functions such as forecasting, scheduling, market bidding, aggregation, and ancillary services. In parallel, we analyze enabling architectural frameworks—including centralized cloud, distributed edge, hybrid cloud–edge collaboration, and emerging 5G/LEO satellite communication infrastructures—that support real-time data exchange and scalable deployment of intelligent control. By integrating methodological, functional, and architectural perspectives, this review highlights the evolution of VPPs from rule-based coordination to intelligent, autonomous energy ecosystems. Key research challenges are identified in data quality, model interpretability, multi-agent scalability, cyber-physical resilience, and the integration of AI with digital twins and edge-native computation. These findings outline promising directions for next-generation intelligent VPPs capable of delivering secure, flexible, and self-optimizing DER aggregation at scale. Full article

(This article belongs to the Collection Review Papers in Energy and Environment)

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