Energies

24 pages, 14687 KB

Open AccessArticle

Comparative Investigation of Coupled Dynamic Mechanisms of Floating Vertical-Axis Wind Turbines Supported by Different Platform Configurations

by Haoda Huang, Qingsong Liu, Chun Li, Wanfu Zhang and Gregorio Iglesias

Energies 2026, 19(15), 3703; https://doi.org/10.3390/en19153703 - 6 Aug 2026

Abstract

Dedicated platforms for floating vertical-axis wind turbines (VAWTs) require an in-depth understanding of their strongly unsteady coupled dynamics, yet the effects of platform configuration on motion stability, aerodynamic loading, and wake recovery remain insufficiently clarified. This study develops a high-fidelity aero-hydro-mooring coupled framework [...] Read more.

Dedicated platforms for floating vertical-axis wind turbines (VAWTs) require an in-depth understanding of their strongly unsteady coupled dynamics, yet the effects of platform configuration on motion stability, aerodynamic loading, and wake recovery remain insufficiently clarified. This study develops a high-fidelity aero-hydro-mooring coupled framework base on computational fluid dynamics (CFD) to compare Φ-type floating VAWTs supported by semi-submersible and spar platforms under identical wind–wave excitation. The results show that the semi-submersible configuration, owing to its larger structural scale near the free surface, experiences stronger wave interaction and more pronounced wave-frequency heave, surge, and pitch responses. The spar configuration reduces wave-frequency hydrodynamic excitation because of its deep-draft slender structure, but it is more prone to mean pitch offset and sway–roll–yaw coupling. Mooring responses are governed by mean surge drift, mean pitch inclination, and wave-frequency motions, with the semi-submersible system exhibiting stronger tension fluctuations and the spar system showing a more uneven load distribution among the mooring lines. Under the examined wind–wave condition, the spar configuration exhibits larger fluctuations in instantaneous power, thrust and single-blade torque than the semi-submersible configuration. Wake analysis indicates that the semi-submersible system maintains stronger wake coherence, while the spar system enhances vortex breakdown, turbulent mixing, and velocity-deficit recovery. These findings support platform selection, load control, and array layout optimization for floating VAWTs. Full article

(This article belongs to the Special Issue Advanced Modeling, Simulation and Control Techniques for Offshore Renewable Energy Systems)

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29 pages, 3527 KB

Open AccessArticle

Neural Network-Based Optimized Control for Enhancing Voltage Support of Grid-Forming MMCs Under Voltage Sags

by Yi Lu, Feng Xu, Qian Chen, Fan Zhang, Mingyue Han and Guoteng Wang

Energies 2026, 19(15), 3702; https://doi.org/10.3390/en19153702 - 6 Aug 2026

Abstract

With the integration of renewable energy and power-electronic devices, grid-forming modular multilevel converters (GFM-MMCs) play a critical role in active grid support. An AC grid voltage sag can trigger a large support current, which may cause large voltage fluctuations in submodule capacitors and [...] Read more.

With the integration of renewable energy and power-electronic devices, grid-forming modular multilevel converters (GFM-MMCs) play a critical role in active grid support. An AC grid voltage sag can trigger a large support current, which may cause large voltage fluctuations in submodule capacitors and arm overmodulation, thereby threatening system safety. This paper proposes a multidimensional collaborative method to improve the support capability of grid-forming MMCs under severe grid voltage sags. The multidimensional physical constraints of internal energy fluctuation during fault transients are clarified. The corresponding safe operating boundaries are then established, after which a coordinated optimization strategy is developed. This approach integrates second-harmonic circulating current and zero-sequence voltage injections. Offline optimization utilizes a particle swarm optimization (PSO) algorithm across the full operating range. Expanding the safe P–Q operating region requires no extra hardware costs. A neural network enables a millisecond-level direct mapping control architecture. This architecture addresses the long online computation time of traditional heuristic algorithms by embedding offline optimization data into the network weights. The trained network performs rapid forward computation to generate optimized commands, which is verified by a hardware-in-the-loop (HIL) experiment. The experimental results verify the effectiveness of the proposed method, with clear performance improvements being observed. The strategy suppresses capacitor-voltage peak and prevents overmodulation. This directly improves the MMC support capability during severe faults. Full article

(This article belongs to the Special Issue Modular Multilevel Converters: Technologies, Control and Applications)

31 pages, 4917 KB

Open AccessArticle

A Physics-Informed Neural Network for PMSM Temperature Estimation Under Sparse Sampling Conditions

by Linxin Yu, Jianye Liang, Jing Ou, Mengran Ji and Hongwei Gao

Energies 2026, 19(15), 3701; https://doi.org/10.3390/en19153701 - 6 Aug 2026

Abstract

Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable [...] Read more.

Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable importance. However, existing data-driven methods generally rely heavily on high-frequency measurements, and their prediction accuracy tends to deteriorate under low-frequency sampling conditions. Moreover, purely data-driven models lack explicit physical constraints, which limits their interpretability and generalization capability. To address these issues, this study proposes a physics-informed long short-term memory model for permanent magnet temperature prediction. A physics-based loss function is formulated using the PMSM d–q-axis voltage balance equations, while the d- and q-axis inductances are treated as trainable parameters during network optimization. This design enables the temperature prediction task and the electromagnetic constraints to be optimized jointly. Multi-operating-condition experiments are conducted using a publicly available electric motor temperature dataset, and the proposed model is compared with CNN, GRU, MLP-PINN and TNN models. In addition, experiments involving different downsampling ratios, errors in the high-temperature region, parameter sensitivity, physical parameter identification, and input-feature effects are performed to comprehensively evaluate the proposed model. The results show that the PINN-LSTM model achieves the best overall prediction performance, with an MAE of 1.6048 °C, an RMSE of 2.1890 °C, and an R² of 0.9861, outperforming all comparison models. The model also maintains high prediction accuracy in the high-temperature region, with an MAE of 1.363 °C and an RMSE of 1.896 °C. Furthermore, the parameters learned by the model can effectively reconstruct the variation trends of the d- and q-axis voltages under the test operating conditions. Sensitivity analysis of the temperature coefficients further demonstrates that the model is robust to deviations in key physical parameters. These results indicate that the proposed method can achieve accurate and robust permanent magnet temperature prediction under low-frequency sampling conditions, providing an effective solution for motor thermal-state monitoring and health management. Full article

(This article belongs to the Special Issue Advanced Electrical Machines for Energy Conversion: Analysis, Design and Control)

29 pages, 4468 KB

Open AccessArticle

Power Quality Composite Disturbance Identification Based on CWT–STFT Dual-Modal Fusion and a Lightweight Network

by Yilin Jiang and Yan Zhang

Energies 2026, 19(15), 3700; https://doi.org/10.3390/en19153700 - 6 Aug 2026

Abstract

With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composite disturbances under strong noise remains a critical technical challenge. The existing [...] Read more.

With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composite disturbances under strong noise remains a critical technical challenge. The existing single time–frequency transformation methods cannot simultaneously capture transient time-domain details and fine frequency-domain features of steady-state harmonics, while mainstream deep learning classification networks contain redundant parameters and introduce excessive computational overhead, failing to meet the real-time deployment requirements of power edge terminals. To address these limitations, a lightweight Coordinate Attention ResNet network named ResNet–LCA is proposed based on the dual-modal time–frequency fusion of the Continuous Wavelet Transform and Short-Time Fourier Transform. First, the two transforms are implemented separately to generate two groups of complementary time–frequency maps, which are concatenated along the channel dimension to fully extract the coupling features between the steady-state harmonics and the transient impulses. Second, a Haar wavelet subband mean aggregation module is designed for dimensionality reduction with negligible information loss. This module eliminates the channel redundancy introduced by the multimodal fusion and reduces the overall computational overhead at the input stage. Finally, a lightweight residual network integrated with Coordinate Attention is constructed, with Grouped Half-Convolution adopted to compress the model parameters. CA offsets the feature attenuation induced by the lightweight structural design and further improves the model’s noise immunity. A simulation verification was carried out on a simulated dataset covering 25 types of single and superimposed composite disturbances. At a signal-to-noise ratio of 20 dB, the proposed method achieved an average classification accuracy of 97.92%, with only 5.32 M total parameters and a single-sample GPU inference latency of 0.33 ms. Compared with standard ResNet-18 under 20 dB noisy conditions, the total parameter volume was reduced by 52.7%, the inference latency was shortened by 0.13 ms, and the classification accuracy was improved by 0.60 percentage points. The proposed method achieves coordinated optimization of classification accuracy, noise immunity and inference efficiency, and it can provide lightweight technical support for online intelligent power quality monitoring at the edge nodes of microgrids and islanded power systems. Full article

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30 pages, 8007 KB

Open AccessArticle

Reliability-Constrained Planning Framework for Smart Distribution Systems Considering Generation Sufficiency for Virtual Microgrids

by Majed A. Alotaibi

Energies 2026, 19(15), 3699; https://doi.org/10.3390/en19153699 - 6 Aug 2026

Abstract

As modern electricity system evolve toward smart grids, increasing technological and regulatory complexity makes distribution system planning a challenging task. This paper develops new planning and reliability models to help local distribution companies (LDCs) achieve cost-effective, reliable solutions amid these changes. Furthermore, this [...] Read more.

As modern electricity system evolve toward smart grids, increasing technological and regulatory complexity makes distribution system planning a challenging task. This paper develops new planning and reliability models to help local distribution companies (LDCs) achieve cost-effective, reliable solutions amid these changes. Furthermore, this paper offers a novel iteration-based optimization model incorporating reliability and security considerations. Also, this work proposes the concept of virtual microgrids to enhance distributed generation (DG) penetration in weak areas, thereby improving reliability where it is most needed. The proposed model reduces an LDC’s total planning cost covering substation and feeder upgrades, market energy purchases, payments to DG owners, energy losses, and operations while ensuring that reliability indices and energy not supplied stay within regulatory limits. To ensure compliance with operational security limits as DG penetration rises, the DG capacity is split into normal operating capacity and reserve capacity. The results confirm the proposed algorithm’s superiority relative to current state-of-the-art planning methodologies. Full article

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35 pages, 392 KB

Open AccessReview

Non-Condensable Gas Injection in Late-Stage SAGD: A Critical Review

by Nima Shojaei, Rahman Miri, Mahmood Salimi and Alireza Nouri

Energies 2026, 19(15), 3698; https://doi.org/10.3390/en19153698 - 6 Aug 2026

Abstract

Steam-assisted gravity drainage (SAGD) makes a significant contribution to Canada’s heavy oil production. Yet, it faces notable efficiency challenges during late-life stages, characterized by increased Steam–Oil Ratios and environmental concerns. Non-condensable gas (NCG) injection has emerged as a promising strategy to address these [...] Read more.

Steam-assisted gravity drainage (SAGD) makes a significant contribution to Canada’s heavy oil production. Yet, it faces notable efficiency challenges during late-life stages, characterized by increased Steam–Oil Ratios and environmental concerns. Non-condensable gas (NCG) injection has emerged as a promising strategy to address these issues, particularly in late-life and post-steam SAGD phases. This review systematically examines the mechanisms, phase behavior, thermochemical interactions, field applications, and operational impacts of injecting NCGs such as methane, nitrogen, and carbon dioxide. This work exclusively synthesizes the application of NCG injections in mature SAGD reservoirs while outlining existing challenges. It delivers a unified perspective on this domain, introducing practical insights to improve NCG injection efficiency. Critical analysis of the existing literature reveals key benefits, including reservoir pressure maintenance, steam chamber stabilization, and viscosity reduction. However, literature gaps persist regarding long-term field-scale validation, complex drive mechanisms at the steam chamber flanks, thermochemical reactions, interactions with geological heterogeneity, and detailed thermodynamic modeling under non-equilibrium conditions. Emphasizing these gaps underscores the importance of further research and integrated modeling to optimize NCG utilization, thus enhancing recovery efficiency, reducing environmental footprints, and extending reservoir life. Full article

(This article belongs to the Section H: Geo-Energy)

23 pages, 34701 KB

Open AccessArticle

Dynamic Response and Load Transfer Mechanisms of Monopile Offshore Wind Turbines Subjected to Scour Effects

by Wanyong Zhang, Haoda Huang, Kunpeng Liu, Chun Li, Gregorio Iglesias and Musa Bashir

Energies 2026, 19(15), 3697; https://doi.org/10.3390/en19153697 - 6 Aug 2026

Abstract

Scour-induced degradation of soil–structure interactions (SSIs) may substantially amplify the dynamic response of monopile offshore wind turbines (OWTs) under combined environmental and seismic loads. To clarify this mechanism and provide guidance for the safety assessment of OWT foundations, this study establishes a coupled [...] Read more.

Scour-induced degradation of soil–structure interactions (SSIs) may substantially amplify the dynamic response of monopile offshore wind turbines (OWTs) under combined environmental and seismic loads. To clarify this mechanism and provide guidance for the safety assessment of OWT foundations, this study establishes a coupled numerical model considering wind, wave, earthquake, scour, and SSI effects. Based on this model, the DTU 10 MW OWT is selected as the research object, and its dynamic responses are investigated with different scour morphologies, scour depths, earthquake inputs, and selected soil parameter sets. The results show that scour reduces the lateral stiffness of the soil–structure system, shifts the structural natural frequency toward a lower-frequency range, and amplifies the displacement, acceleration, and stress responses under seismic excitation. With the selected M8 earthquake input, the tower top displacement reaches 4.18 m in the global-2D scour case, which is more than twice that in the non-scoured case. Global scour produces stronger response amplification than local scour because it weakens the SSI constraint around the full circumference of the pile. For the selected soil parameter sets and the adopted p–y formulations, the clayey foundation model produces smaller calculated displacement and stress responses than the sandy foundation model, reflecting differences in the prescribed lateral stiffness and ultimate soil resistance. These results provide comparative insights for the seismic assessment and scour-resistant design of monopile OWT foundations. Full article

(This article belongs to the Special Issue Advanced Modeling, Simulation and Control Techniques for Offshore Renewable Energy Systems)

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25 pages, 20499 KB

Open AccessArticle

Hybrid Energy Distance and Permutation Testing for Vibration-Based Wind Turbine Blade Fault Diagnosis

by Debela A. Teklemariyem, Nasir H. R. Syed, Wieslaw J. Staszewski and Phong B. Dao

Energies 2026, 19(15), 3696; https://doi.org/10.3390/en19153696 - 6 Aug 2026

Abstract

Wind turbine blade failure diagnosis is of critical importance, as the inability to reliably detect such faults can result in substantial financial losses, prolonged downtime, extensive repair activities, as well as increased operation and maintenance costs. This paper introduces a new application of [...] Read more.

Wind turbine blade failure diagnosis is of critical importance, as the inability to reliably detect such faults can result in substantial financial losses, prolonged downtime, extensive repair activities, as well as increased operation and maintenance costs. This paper introduces a new application of energy distance and permutation testing for wind turbine blade fault diagnosis based on vibration data analysis. The proposed approach combines a non-parametric energy distance and permutation testing for the identification and characterization of blade faults in wind turbines. Unlike conventional approaches, this method does not require the assumption of a normal data distribution, which is particularly important when analysing vibration signals, as such data often deviates from normality. The approach is also robust against the presence of outliers. The energy distance metric is employed to classify the blade condition of the wind turbine and to investigate subtle changes in blade behaviour by comparing healthy and corresponding faulty states while considering the entire data distribution rather than conventional summary statistics. In the second stage, a permutation test is applied to statistically validate the energy distance results via p-values. The proposed method is validated using experimental vibration datasets representing healthy and faulty blade conditions, including cracked, eroded, twisted, and imbalanced faults. These datasets are analysed under varying wind speed conditions to assess the robustness of the proposed method under different operating conditions. In the second stage, the permutation test confirms the statistical significance of the detected distributional differences, providing additional confidence in the diagnostic results. The findings indicate that the combined use of energy distance and permutation testing effectively identifies and detects the faults from the healthy blade behaviour across two different wind speed conditions. The findings of this study indicate the potential of the proposed approach as a simple, robust, and interpretable solution for wind turbine blade fault diagnosis based on vibration data analysis. Full article

(This article belongs to the Special Issue Latest Challenges in Wind Turbine Maintenance, Operation, and Safety)

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20 pages, 4513 KB

Open AccessArticle

A New Approach for the Measurement of the Complex Value of Fault-Loop Impedance in Low-Voltage Electrical Installations

by Grzegorz Hołdyński, Zbigniew Skibko and Wojciech Walendziuk

Energies 2026, 19(15), 3695; https://doi.org/10.3390/en19153695 - 6 Aug 2026

Abstract

The primary method for verifying the safety of electrical installations is the measurement of fault-loop impedance. The most common approach involves measuring the voltage under no-load and load conditions and determining the difference between them. However, this method has several drawbacks, including the [...] Read more.

The primary method for verifying the safety of electrical installations is the measurement of fault-loop impedance. The most common approach involves measuring the voltage under no-load and load conditions and determining the difference between them. However, this method has several drawbacks, including the need for complex instrumentation and measurement uncertainty that depends on the magnitude and nature of the load in the tested electrical system. Additionally, most existing measurement systems allow only the determination of the impedance modulus. This paper introduces a novel, theoretically grounded method for measuring fault-loop impedance. The proposed approach is based on measuring the RMS no-load voltage, followed by three voltage measurements under different loading conditions using reference impedances with known phase angles (defined by the resistance-to-reactance ratio). The complex value of the fault-loop impedance is then obtained using a second-degree polynomial approximation. The main advantage of the proposed method is that it reduces the influence of slowly varying operating loads under quasi-stationary network conditions of the network on measurement accuracy. This property requires experimental verification. It should be noted that the presented analysis is of a theoretical nature and constitutes a basis for further research and discussion within the scientific community. Full article

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

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26 pages, 2425 KB

Open AccessArticle

T-HMM-Based Transformer Fault Diagnosis in Grid-Connected Renewable Energy Systems

by Wei Li, Shanyun Gu, Lei Shen, Chen Wang, Yongkun Shi, Guanjian Zhu, Fei Zhuang, Jiaqiang Wang, Chao Liu, Zujun Ding, Xin Xia, Baolian Liu and Jie Ji

Energies 2026, 19(15), 3694; https://doi.org/10.3390/en19153694 - 6 Aug 2026

Abstract

Addressing multi-factor coupling, progressive degradation, and time-varying operating conditions in substation equipment under high-penetration renewable energy integration, this paper proposes a Time-varying Hidden Markov Model (T-HMM) for transformer fault diagnosis using multi-source heterogeneous data. Unlike conventional HMM with fixed transition matrices and initial [...] Read more.

Addressing multi-factor coupling, progressive degradation, and time-varying operating conditions in substation equipment under high-penetration renewable energy integration, this paper proposes a Time-varying Hidden Markov Model (T-HMM) for transformer fault diagnosis using multi-source heterogeneous data. Unlike conventional HMM with fixed transition matrices and initial parameter sensitivity, the proposed framework introduces a forgetting-factor-driven online transition matrix updating mechanism, enabling adaptive state tracking under varying operating conditions. A multi-dimensional feature system is constructed incorporating fundamental state, coupling correlation, and temporal evolution characteristics, with state-dependent sliding window standardization and adaptive wavelet denoising to enhance early-stage fault discriminability. Parameter optimization employs pre-clustering and multi-start strategies to circumvent local optima in the Baum–Welch algorithm, while an improved decoding strategy achieves real-time health state identification and multi-step probability prediction. Experimental results demonstrate that the proposed method accurately tracks state evolution trends and effectively identifies fault categories, achieving significantly superior state recognition accuracy and multi-step prediction hit rates compared to conventional HMM, with substantially reduced mean absolute error. The method effectively captures subtle precursors during normal-to-fault transitions, providing reliable theoretical foundations for early fault warning and condition-based maintenance. Full article

(This article belongs to the Special Issue Renewable Energy Microgrids for the Future of Electrical Grid—2nd Edition)

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18 pages, 3570 KB

Open AccessArticle

Temporal-Feature-Enhanced Reinforcement Learning Control with Adaptive Speed-Loop Gain for PMSM Drives

by Hongquan Zhang, Jingjun Cui, Zhihan Wu, Anqi Situ and Jiaqiang Yang

Energies 2026, 19(15), 3693; https://doi.org/10.3390/en19153693 - 5 Aug 2026

Abstract

The high-performance control of permanent magnet synchronous motor (PMSM) drives is challenged by operating uncertainties and coupled electromechanical dynamics. This paper proposes a temporal-feature-enhanced reinforcement learning control approach for a PMSM drive system with adaptive regulation of the speed-loop gain. The learning agent [...] Read more.

The high-performance control of permanent magnet synchronous motor (PMSM) drives is challenged by operating uncertainties and coupled electromechanical dynamics. This paper proposes a temporal-feature-enhanced reinforcement learning control approach for a PMSM drive system with adaptive regulation of the speed-loop gain. The learning agent is integrated into a dual-loop control structure to generate voltage control commands and update the speed-loop gain in real time. A multiobjective learning criterion is designed to balance tracking accuracy, torque smoothness, and gain boundedness. Temporal error features are further embedded in the state representation to capture short-term transient trends with limited additional complexity. Comparative simulations with conventional PI control and standard TD3 indicate that the proposed method improves transient response, reduces speed tracking error, and suppresses torque ripple. These results demonstrate the effectiveness of temporal-feature-enhanced reinforcement learning for coordinated control of PMSM drives. Full article

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

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26 pages, 3110 KB

Open AccessArticle

Real-World Combustion Emissions, Engine Size and Vehicle Mass Versus Euro-Class Access Criteria in Low-Emission Zones

by Katarzyna Turoń, Andrzej Kubik and Feng Chen

Energies 2026, 19(15), 3692; https://doi.org/10.3390/en19153692 - 5 Aug 2026

Abstract

Low-emission zones, known in Poland as clean transport zones, grant or deny access according to a vehicle’s Euro standard, age and fuel. These administrative labels, however, are only loosely connected to what an internal combustion engine (ICE) actually emits on the road. The [...] Read more.

Low-emission zones, known in Poland as clean transport zones, grant or deny access according to a vehicle’s Euro standard, age and fuel. These administrative labels, however, are only loosely connected to what an internal combustion engine (ICE) actually emits on the road. The founding assumption of a Euro-based zone is that the Euro stage is a usable proxy for the mass a vehicle emits, so admitting newer classes lowers the fleet emission inventory, the activity-weighted sum of class emission factors defined as the product of fleet frequency, in-zone activity and emission factor. This paper tests that assumption directly. A paradox follows: a newer, compliant, large and heavy vehicle can release more carbon dioxide (CO₂), nitrogen oxides (NOx) and particulate matter (PM) than an older, smaller vehicle that the same rule turns away. We develop a reproducible emission-accounting framework that pairs real-world emission factors, resolved by Euro standard, fuel, engine displacement and mass, with a mass-dependent treatment of non-exhaust particles. Given the number of petrol, diesel, liquefied-petroleum-gas (LPG) and electric vehicles in a fleet and the areas of the city and the zone, the model returns daily emissions and their density per square kilometre before and after a rule is applied. For a fleet parameterised on Polish statistics, the Warsaw and Kraków criterion removes about 80% of zone NOx, because the oldest vehicles are also the high-NOx diesels; the same rule, however, removes only about 40% of CO₂ and 48% of PM, and it shifts the admitted fleet toward heavier vehicles that produce more non-exhaust PM. Emission-based, fuel-based and hybrid criteria deliver larger and fairer reductions at the same level of stringency. Aligning zone access with real combustion emissions is therefore both more effective and more equitable, and it supports the wider goals of sustainable, low-carbon urban mobility. Full article

(This article belongs to the Special Issue Alternative Fuels, Combustion and Emissions of Internal Combustion Engines)

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17 pages, 4354 KB

Open AccessArticle

LCC-S vs. LCC-LCC: Efficient Wireless Charging for Underwater Drones Under Seawater Conditions

by Inmaculada Casaucao and Alicia Triviño

Energies 2026, 19(15), 3691; https://doi.org/10.3390/en19153691 - 5 Aug 2026

Abstract

Battery autonomy is one of the main factors limiting the endurance of autonomous underwater vehicles (AUVs). Conventional charging through electrical connectors is inconvenient in marine environments since connectors are exposed to corrosion and usually require manual intervention or docking procedures. Inductive wireless power [...] Read more.

Battery autonomy is one of the main factors limiting the endurance of autonomous underwater vehicles (AUVs). Conventional charging through electrical connectors is inconvenient in marine environments since connectors are exposed to corrosion and usually require manual intervention or docking procedures. Inductive wireless power transfer (WPT) avoids these drawbacks, although the conductive nature of seawater introduces additional effects, such as eddy current losses and parasitic capacitance between the coils. These effects modify the resonance conditions of the compensation network and, in turn, reduce the transfer efficiency. This paper presents the design and experimental assessment of an inductive charger for a commercial and specific underwater drone operating under seawater conditions. A square coil geometry, selected to match the available installation area on the vehicle, was analysed together with two compensation networks (LCC-S and LCC-LCC) and two coil designs with 20 and 25 turns. Based on an analytical characterisation, their performance was evaluated for different coil separations and operating temperatures. Among the analysed configurations, the LCC-S topology with 25 turns provided the best compromise between efficiency and tolerance to gap variations. A laboratory prototype was subsequently built and tested in saline water with NaCl concentrations of 2%, 3%, and 4%, reaching an efficiency close to 87% at 266 W. These results confirm that the proposed design is suitable for underwater wireless charging under representative marine salinity conditions. Full article

(This article belongs to the Special Issue Advances in Energy Efficiency for Wireless Power Transfer Systems)

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27 pages, 4772 KB

Open AccessArticle

An Explainable Deep Learning Framework with Multi-Head Attention and SHAP for Power Stability Monitoring in IoE-Enabled Smart Cities

by Hend Alshede

Energies 2026, 19(15), 3690; https://doi.org/10.3390/en19153690 - 5 Aug 2026

Abstract

The growing reliance of Internet of Energy (IoE)-enabled smart city infrastructures has significantly intensified the complexity of modern urban energy environments due to the integration of renewable resources, electric vehicles, and interconnected IoT devices. These highly dynamic environments introduce critical challenges related to [...] Read more.

The growing reliance of Internet of Energy (IoE)-enabled smart city infrastructures has significantly intensified the complexity of modern urban energy environments due to the integration of renewable resources, electric vehicles, and interconnected IoT devices. These highly dynamic environments introduce critical challenges related to power stability, operational reliability, and intelligent energy management. Therefore, developing accurate, adaptive, and explainable monitoring frameworks has become essential for ensuring resilient urban energy infrastructures. This paper proposes an Explainable Artificial Intelligence (XAI)-driven deep learning framework integrating Multi-Head Attention and SHapley Additive exPlanations (SHAP) for intelligent power stability monitoring in IoE-enabled smart cities. The proposed framework employs an attention-based deep learning architecture to classify stable and unstable operational conditions using multivariate operational power parameters. Furthermore, SHAP-based explainability analysis is incorporated to improve model transparency and identify influential operational factors affecting stability behavior. Using the Electrical Grid Stability Simulated Dataset, the proposed framework achieved 97.35% accuracy and 0.997 ROC-AUC, outperforming several traditional machine learning and deep learning baselines. The explainability results show that temporal response parameters have the strongest impact on stability decisions. This work offers not only high predictive performance but also valuable interpretability, which is essential for practical deployment in real-world smart city energy systems. Full article

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31 pages, 1599 KB

Open AccessArticle

A Techno-Economic Comparison of a Reference Energy Community Across European National Legislative Frameworks

by Elena Timofeeva, Arne Surmann, Patrick Selzam, Fabian Liesenhoff, Elias Dörre and Pierre Hülsemann

Energies 2026, 19(15), 3689; https://doi.org/10.3390/en19153689 - 5 Aug 2026

Abstract

Renewable energy communities are gaining importance as instruments for integrating decentralized renewable generation and enabling local flexibility provision. However, systematic comparisons of community performance under varying technical configurations and regulatory conditions remain scarce. This study combines a semi-structured literature review on system-level contributions [...] Read more.

Renewable energy communities are gaining importance as instruments for integrating decentralized renewable generation and enabling local flexibility provision. However, systematic comparisons of community performance under varying technical configurations and regulatory conditions remain scarce. This study combines a semi-structured literature review on system-level contributions of local energy systems with a quantitative techno-economic simulation of a reference urban energy community. The community—comprising eight apartment buildings equipped with rooftop photovoltaics, battery storage, electric vehicles, and heat pumps—was analyzed across 16 scenarios varying photovoltaic capacity, electric vehicle charging optimization, and energy-sharing incentives under four European regulatory frameworks (Germany, Austria, Spain, and Italy). Photovoltaic capacity emerges as the primary driver of energy autonomy, covering 16.3–30.8% of total community demand. Optimized electric vehicle charging significantly reduces grid dependency for vehicle loads (40.0–92.7%). Energy sharing contributes modestly to total consumption (0–3.4%) but substantially redistributes financial benefits among participants. Total community costs range from 23.6 k€ to 103.3 k€ across scenarios and countries, reflecting strong regulatory influence. The findings demonstrate that effective policy support requires coordinated design across sharing incentives, network tariffs, and flexibility pricing rather than isolated instrument deployment. Full article

(This article belongs to the Topic Renewable Energy Communities and Smart Grids: Building Loads and Renewables, Development of Optimization and Management Algorithms)

52 pages, 4885 KB

Open AccessArticle

Correlation-Aware Scenario Reduction for Wind-Power and Load Uncertainties via Density Denoising and Temporally Ordered Clustering

by Rong Hu, Yingrui Dong, Chong Shao, Cheng Xu, Weican Yuan, Xueli Yin and Weiqi Zhang

Energies 2026, 19(15), 3688; https://doi.org/10.3390/en19153688 - 5 Aug 2026

Abstract

The increasing penetration of wind-power introduces pronounced uncertainty and variability into modern renewable energy systems, while electric load exhibits strong temporal continuity and seasonal evolution. Directly using full-year chronological wind–load data in planning or operational studies may lead to excessive computational burden, whereas [...] Read more.

The increasing penetration of wind-power introduces pronounced uncertainty and variability into modern renewable energy systems, while electric load exhibits strong temporal continuity and seasonal evolution. Directly using full-year chronological wind–load data in planning or operational studies may lead to excessive computational burden, whereas conventional scenario reduction methods may distort load temporal patterns, weaken wind–load dependence, or be affected by isolated low-density samples. To address these issues, this paper proposes a correlation-aware scenario reduction method for wind-power and load uncertainties by integrating density-based denoising and temporally ordered clustering. First, the original wind-power and load profiles are normalized and processed using density-based spatial clustering of applications with noise (DBSCAN), so that isolated low-density samples can be identified before scenario extraction. Then, a temporally ordered clustering model is developed for load profiles, in which adjacent chronological samples are grouped into continuous load segments to preserve the intrinsic temporal evolution of demand. Based on the obtained load segments, conditional K-means clustering is further performed on the corresponding wind-power profiles, thereby capturing the wind-power distribution characteristics under different load states. Finally, representative joint wind–load scenarios are reconstructed with probability weights. Case studies based on annual wind-power and load data demonstrate that the proposed method reduces the original annual data into ten probability-weighted joint wind–load scenarios constructed from seven temporally ordered load states, while retaining the main statistical characteristics, temporal consistency, and multi-level wind–load dependence evaluated at the aggregate, intra-day, nonlinear, and short-lag levels. Compared with conventional clustering-based scenario reduction methods, the proposed approach provides more representative typical scenarios for uncertainty modeling in renewable energy system studies. Full article

(This article belongs to the Special Issue Intelligent Control and Management Systems for Energy-Efficient Buildings)

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32 pages, 8304 KB

Open AccessArticle

BO-PatchDiffFormer with Interpretable Feature Segments for State of Health Estimation of Lithium-Ion Batteries

by Yiming Xia, Songchang Xu, Ruiquan Hu, Jiquan Yang and Jianping Shi

Energies 2026, 19(15), 3687; https://doi.org/10.3390/en19153687 - 5 Aug 2026

Abstract

Reliable state of health (SOH) estimation provides important support for safety management and efficient operation of lithium-ion battery energy storage systems. To address the limitations of existing SOH estimation methods in terms of feature interpretability, joint modeling of local variations and overall morphological [...] Read more.

Reliable state of health (SOH) estimation provides important support for safety management and efficient operation of lithium-ion battery energy storage systems. To address the limitations of existing SOH estimation methods in terms of feature interpretability, joint modeling of local variations and overall morphological characteristics within each feature segment, and the rationality of model hyperparameter configuration, this study proposes a hybrid data-driven SOH estimation method integrating interpretable feature construction, an improved Transformer, and Bayesian optimization (BO). Specifically, raw charging data are first converted into incremental capacity (IC) curves based on incremental capacity analysis, and IC peaks are dynamically located in different cycles. Local voltage–capacity segments around the IC peak voltage are then extracted as interpretable input features closely related to battery aging. Subsequently, the Transformer encoder is improved by incorporating patch embedding and a multi-head differential self-attention mechanism, thereby enhancing the model’s ability to jointly capture local variations and overall morphological characteristics within each cycle-wise feature segment. BO is further employed to adaptively optimize key model hyperparameters. Experimental results on the CALCE-CS2 and CALCE-CX2 battery datasets show that the proposed BO-PatchDiffFormer model can provide accurate and stable SOH estimation in both comparative and generalization experimental scenarios. Compared with the best-performing baseline model, the maximum reductions in RMSE and MAE on the four CS2 test batteries reach 31.81% and 23.76%, respectively. In the generalization experiments, the average RMSE and MAE on the two CX2 test batteries are 1.6039% and 1.3081%, respectively. In addition, the maximum model size is only 2.07 MB, and the single-sample inference time remains within 0.84–1.18 ms, indicating good potential for practical deployment and application. Full article

(This article belongs to the Special Issue Advanced Battery Management Strategies)

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24 pages, 4959 KB

Open AccessArticle

Optical and Thermal Performance of Linear Fresnel Reflectors in Eastern Mediterranean Conditions

by Dimitrios Graikos, Lazaros Aresti, George Constandinides, Savvas Tassou, Toula Onoufriou and Paul Christodoulides

Energies 2026, 19(15), 3686; https://doi.org/10.3390/en19153686 - 5 Aug 2026

Abstract

Linear Fresnel Reflectors (LFR) are a type of concentrated solar collector that uses direct solar radiation (DSR) to provide useful heat for a range of applications, including power generation, heating and cooling. This study introduces a simplified analytical method for predicting the optical [...] Read more.

Linear Fresnel Reflectors (LFR) are a type of concentrated solar collector that uses direct solar radiation (DSR) to provide useful heat for a range of applications, including power generation, heating and cooling. This study introduces a simplified analytical method for predicting the optical and thermal performance of LFR systems using only the geometric parameters and meteorological data of a location. In this analysis, the Incident Angle Modifier (IAM) and DSR are calculated using long-term data from open-access weather datasets, offering analysis from minute-scale to annual resolution, without the requirement of computationally expensive methods such as ray-tracing or extensive onsite measurements. The model is validated against experimental data from an installed LFR system at the Cyprus Institute (CyI) in Nicosia, Cyprus, demonstrating good agreement in both solar and useful energy output. The latter is derived as the product of the predicted DSR, which closely matches pyrheliometer measurements over a four-year period, and the accurately reproduced IAM. Overall, the method provides a reliable estimation of the thermal performance, offering a practical tool for early-stage design for any location of interest, yielding results significantly faster and at a fraction of the computational cost compared to ray-tracing and CFD approaches. Full article

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32 pages, 927 KB

Open AccessArticle

Coordinated Scheduling of Distribution Networks and PEDF Microgrids via a Stackelberg Game with Wasserstein-DRO Reserve Requirements

by Haotian Zheng and Jing Lian

Energies 2026, 19(15), 3685; https://doi.org/10.3390/en19153685 - 5 Aug 2026

Abstract

High photovoltaic penetration increases reserve needs in active distribution networks, while PEDF microgrid flexibility is seldom coordinated as a reserve product. This study develops a complete-information Stackelberg framework in which a distribution system operator posts bounded prices for energy exchange, reactive support, and [...] Read more.

High photovoltaic penetration increases reserve needs in active distribution networks, while PEDF microgrid flexibility is seldom coordinated as a reserve product. This study develops a complete-information Stackelberg framework in which a distribution system operator posts bounded prices for energy exchange, reactive support, and directional reserve. A Wasserstein ambiguity set centered on the empirical source–load error distribution supports a worst-case conditional value-at-risk calculation of hourly requirements. PEDF reserve offers are constrained by available battery energy, flexible-load headroom, recoverable photovoltaic curtailment, shared converter capacity, and activated feeder states. KKT conditions and Big-M linearization yield a mixed-integer second-order cone program. On a modified IEEE 33-bus feeder, joint PEDF and external reserve procurement reduces system cost by 2.455% and aggregate PEDF net cost by 10.402%, with zero reserve shortages and a minimum voltage of 0.951284 p.u. Fixed-ratio, SAA-CVaR, Box-RO, and WDRO-CVaR comparisons reveal an economy–reliability tradeoff. Rolling out-of-sample tests identify 0.1 as the smallest tested radius multiplier meeting a 5% violation target in all three training windows. An IEEE 69-bus extension reduces system cost by 4.233%, and all 144 nonlinear AC power-flow states converge. The results support one-interval steady-state reserve coordination under the tested assumptions. Full article

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