Energies

7 pages, 450 KB

Open AccessEditorial

Editorial of Maximum Power Point Tracking Methods for PV Systems in Micro-Grids

by Yuanyuan Liu and Jun-Ho Huh

Energies 2025, 18(21), 5830; https://doi.org/10.3390/en18215830 (registering DOI) - 5 Nov 2025

In recent years, renewable energy has become one of the most critical pillars for addressing the dual challenges of global energy shortages and environmental degradation [...] Full article

(This article belongs to the Special Issue Overview of Maximum Power Point Tracking Methods for PV System in Micro-Grid)

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29 pages, 1699 KB

Open AccessArticle

Multi-Agent-Based Coordinated Voltage Regulation Technique in an Unbalanced Distribution System

by Swathi Tangi, Dattatraya N. Gaonkar, Ramakrishna S S Nuvvula, Ahmed Ali and Syed Riyaz Ahammed

Energies 2025, 18(21), 5829; https://doi.org/10.3390/en18215829 (registering DOI) - 5 Nov 2025

Abstract

Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve [...] Read more.

Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve the difficulties mentioned earlier. The proposed technique increases coordination between DGs and Shunt capacitors (SCs) to optimize the voltage profile and reduce overall power losses, along with voltage and current unbalanced factors in the proposed unbalanced 3-phase radial distribution network. To ensure improved real-time monitoring, PMUs (Phasor Measurement Units) measure the state parameters of the above-regulated distribution network in realtime. Because it does not necessitate the placement of PMUs at all nodes for total network observability, it is a cost-effective technique for estimating network state. The IEEE standard, a 25-bus unbalanced 3-phase distribution network feeder, is used to assess the viability of the recommended technique. MATLAB R2024a programming is used to simulate the case studies. Full article

(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)

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18 pages, 703 KB

Open AccessArticle

Türkiye’s GHG Emissions Towards the 2030 Target: MDAM and LSTM-Based Analysis with Key Energy Factors

by Gizem Göktaş Balkır and Nisa Özge Önal Tuğrul

Energies 2025, 18(21), 5828; https://doi.org/10.3390/en18215828 (registering DOI) - 4 Nov 2025

Abstract

Greenhouse gas (GHG) emission, caused by heat-trapping gases in the atmosphere, is a major driver of global warming. The World Economic Forum highlights rising emissions, ecosystem degradation, and climate-related disasters as long-term threats to global stability. The accurate modeling and prediction of GHG [...] Read more.

Greenhouse gas (GHG) emission, caused by heat-trapping gases in the atmosphere, is a major driver of global warming. The World Economic Forum highlights rising emissions, ecosystem degradation, and climate-related disasters as long-term threats to global stability. The accurate modeling and prediction of GHG emissions are crucial for evidence-based climate governance. This study investigates Türkiye’s GHG emissions by comparing two advanced time-series models: a deep learning-based Long Short-Term Memory (LSTM) network and the Multi Deep Assessment Model (MDAM), which incorporates Caputo fractional derivatives. Data from the Turkish Statistical Institute (TurkStat) and the Turkish Electricity Transmission Corporation (TEIAŞ) were used covering the period between 1993 and 2022. These datasets include information on GHG emissions, fossil-based generation, renewable energy, and electricity demand. Both models were trained to predict 2030 emissions and assess the contributing factors. Results show that MDAM achieved superior accuracy compared to LSTM. Both models projected 2030 emissions above Türkiye’s 695 Mt target, with 721.87 Mt (MDAM) and 709.49 Mt (LSTM), highlighting the need for stronger policy action. A no-COVID scenario yielded higher forecasts, confirming the pandemic’s suppressive effect. Impact factor analysis revealed that domestic electricity demand and fossil-based generation are the strongest drivers, while renewables mitigate emission. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Data Analysis Technologies in Modern Energy Systems)

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28 pages, 2196 KB

Open AccessArticle

Modeling Hybrid Renewable Microgrids in Remote Northern Regions: A Comparative Simulation Study

by Nurcan Kilinc-Ata and Liliana N. Proskuryakova

Energies 2025, 18(21), 5827; https://doi.org/10.3390/en18215827 (registering DOI) - 4 Nov 2025

Abstract

Remote northern regions face unique energy challenges due to geographic isolation, harsh climates, and limited access to centralized power grids. In response to growing environmental and economic pressures, there is a rising interest in hybrid energy systems that integrate renewable and conventional sources. [...] Read more.

Remote northern regions face unique energy challenges due to geographic isolation, harsh climates, and limited access to centralized power grids. In response to growing environmental and economic pressures, there is a rising interest in hybrid energy systems that integrate renewable and conventional sources. This study investigates sustainable and cost-effective energy supply strategies for off-grid northern communities through the modeling and simulation of multi-energy microgrids. Focusing on case studies from Yakutia (Russia), Hordaland (Norway), and Alaska (United States), the research employs a comprehensive methodology that combines a critical literature review, system design using HOMER Pro software (version 3.16.2), and a comparative analysis of simulation outcomes. Three distinct microgrid configurations are proposed, incorporating various combinations of solar photovoltaic (PV), wind energy, diesel generators, and battery storage systems. The findings reveal that integrating solar PV significantly enhances economic efficiency, particularly in regions with high solar irradiance, underscoring its pivotal role in shaping resilient, sustainable energy systems for remote northern areas. This study is innovative in its cross-regional comparative approach, linking techno-economic simulation with climatic variability analysis to identify context-specific energy strategies. The key findings highlight how hybrid microgrids combining PV, wind, and storage systems can reduce both costs and emissions by up to 35% compared to diesel-only systems, offering practical pathways toward sustainable electrification in high-latitude regions. Full article

(This article belongs to the Special Issue Advanced Grid Integration with Power Electronics: 2nd Edition)

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16 pages, 783 KB

Open AccessArticle

The Choice of Characteristics of the Components of the Power Plant of a Class C Hybrid Vehicle for Operation in the Russian Federation

by Kirill E. Karpukhin, Aleksei F. Kolbasov, Pablo Iturralde, Semen E. Zemtsev and Filipp K. Karpukhin

Energies 2025, 18(21), 5826; https://doi.org/10.3390/en18215826 (registering DOI) - 4 Nov 2025

Abstract

To ensure the transition to electric transport in order to reduce CO₂ emissions, a number of projects have been initiated to develop and introduce new types of locally produced vehicles. The Russian Federation market is quite conservative and has its own specifics [...] Read more.

To ensure the transition to electric transport in order to reduce CO₂ emissions, a number of projects have been initiated to develop and introduce new types of locally produced vehicles. The Russian Federation market is quite conservative and has its own specifics and a special consumer model. In addition, the component base of localized components for electric vehicles is relatively small, which is justified by relatively low demand and market volumes. To create the concept of a Class C passenger vehicle with electric traction, marketing research was conducted in a group of people who are potentially ready to abandon traditional vehicles and choose electric vehicles or hybrids. The purpose of the study is to evaluate the opinion of consumers and to form the technical characteristics of a Class C hybrid car based on localized components. Methods: To obtain the results, various components of the power unit were modeled, and a balanced solution was found that meets the requirements of consumers in the region. Mathematical modeling was carried out and analytical studies of the power reserve of various configurations of power units within the WLTC cycle were carried out in the digital environment of Siemens Amesim. Analytical work on the study of the composition of cars for calculating the masses of modern components and aggregates was carried out using Autodatas. Consumer opinions were assessed through a survey using the Yandex. Forms service. The relevance of the study is confirmed by the lack of domestic models of sequential hybrids on the market and the lack of similar studies, based on the opinion of a potential consumer. The result is the technical parameters of the main components and assemblies, which should ensure the optimal cost of the final product and meet the requirements of the consumer. Conclusion: As a result of the study, a concept of a combined sequential-type power unit was developed based on available components, meeting the main consumer properties, and the technical characteristics of the components were presented. Full article

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

15 pages, 1110 KB

Open AccessArticle

A Scalable and Standardized Methodology for the Comparative Cost–Benefit Evaluation of Smart Readiness Indicator (SRI) Technologies Across Europe

by Turkay Ersener, Paraskevas Koukaras, Dimosthenis Ioannidis, Christos Tjortjis, Byron Ioannou and Paris Fokaides

Energies 2025, 18(21), 5825; https://doi.org/10.3390/en18215825 (registering DOI) - 4 Nov 2025

Abstract

As the importance of energy efficiency and smart readiness in the building sector has been on the rise, the financial evaluation of smart-ready technologies (SRTs) remains a gap in this field. This study introduces a methodology that comparatively evaluates the cost–benefit relationship between [...] Read more.

As the importance of energy efficiency and smart readiness in the building sector has been on the rise, the financial evaluation of smart-ready technologies (SRTs) remains a gap in this field. This study introduces a methodology that comparatively evaluates the cost–benefit relationship between 11 different SRTs across three European countries—Cyprus, Italy and The Netherlands. Key performance indicators (KPIs) for energy-focused aspects such as Country-Specific Energy Savings Potential (CSESP) and Seasonal Smart Efficiency Coefficient (SSEC) and financial aspects such as Smart Readiness Cost Index (SRCI), Labor Cost Impact Factor (LCIF), Return on Smart Investment (RoSI), and Smart Investment Break-Even Period (SIBEP) were used to quantify the performance of the SRTs. The results indicate that regional labor rates, energy pricing, and climatic conditions—as well as relative technology cost–benefit tradeoffs—play a significant role in the economic viability of smart-ready devices. Having low labor costs and energy pricing, Cyprus exhibited the most cost-effective outcomes among the three countries. Italy showed strong returns although the initial investments were higher. The Netherlands was observed to benefit the most from heating-oriented technologies. The study comes to the conclusion that regionally specific methods are necessary for the adoption of SRTs and that techno-economic performance cannot be assessed separately from local market dynamics. The proposed framework supports stakeholders and policymakers in smart building investment and planning by offering a scalable method for device-level benchmarking. These indicators are developed specifically for this study and are not part of the official EU SRI (Smart Readiness Indicator) methodology. Their inclusion supports device-level evaluation and complements ongoing efforts toward SRI standardization. This research directly addresses Sustainable Development Goal (SDG) 7 on Affordable and Clean Energy, as well as SDG 11 on Sustainable Development, by evaluating how smart-ready technologies can contribute to energy efficiency and decarbonization in buildings. Based on the results, further research is needed to expand the indicator framework to additional technologies, include building typology effects, and integrate dynamic factors such as CO₂ pricing and real-time tariffs. Full article

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

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30 pages, 7297 KB

Open AccessArticle

Nanofluid Cooling Enhances PEM Fuel Cell Stack Performance via 3D Multiphysics Simulation

by Rashed Kaiser, Se-Min Jeong and Jong-Chun Park

Energies 2025, 18(21), 5824; https://doi.org/10.3390/en18215824 (registering DOI) - 4 Nov 2025

Abstract

The proton-exchange membrane fuel cell (PEMFC) generates a significant reaction and ohmic heat during operation, imposing stringent cooling requirements. This study employs a three-dimensional, non-isothermal, steady multiphase multiphysics model to investigate heat generation and transport in a three-cell PEMFC stack using deionized water, [...] Read more.

The proton-exchange membrane fuel cell (PEMFC) generates a significant reaction and ohmic heat during operation, imposing stringent cooling requirements. This study employs a three-dimensional, non-isothermal, steady multiphase multiphysics model to investigate heat generation and transport in a three-cell PEMFC stack using deionized water, CuO, and Al₂O₃ nanofluids (1 vol%) as coolants. The base (no-coolant) configuration was validated against a published polarization curve for a nine-cell stack. Introducing coolant channels increased the area-averaged current density from 2426 A m⁻² (no coolant) to 2613 A m⁻² (water), 2678 A m⁻² (CuO), and 2702 A m⁻² (Al₂O₃), representing up to an 11.4% performance improvement while reducing the peak cell temperature by approximately 7–8 °C. Among the examined coolants, Al₂O₃ nanofluid achieved the lowest maximum temperature and a favorable pressure drop, whereas water maintained the most uniform temperature field. A price-performance factor (PPF) was introduced to evaluate the techno-economic trade-off between cost and cooling benefit. This study highlights that, despite scale-related limitations between three-cell simulations and nine-cell experiments, nanofluid coolants offer a practical route toward thermally stable and high-performance PEMFC operation. Full article

(This article belongs to the Special Issue Advanced Studies for PEM Fuel Cells in Hydrogen-Fueled Vehicles: 2nd Edition)

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22 pages, 1924 KB

Open AccessReview

Review of Data-Driven Approaches Applied to Time-Series Solar Irradiance Forecasting for Future Energy Networks

by Xuan Jiao and Weidong Xiao

Energies 2025, 18(21), 5823; https://doi.org/10.3390/en18215823 (registering DOI) - 4 Nov 2025

Abstract

The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven [...] Read more.

The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven methods aims to predict the direction and level of power variation and indicate quick action. This article presents a comprehensive review and comparative analysis of data-driven approaches for time-series solar irradiance forecasting. It systematically evaluates nineteen representative models spanning from traditional statistical methods to state-of-the-art deep learning architectures across multiple performance dimensions that are critical for practical deployment. The analysis aims to provide actionable insights for researchers and practitioners when selecting and implementing suitable forecasting solutions for diverse solar energy applications. Full article

(This article belongs to the Special Issue Recent Advances in Renewable Energy Generation Technologies and Power Demand Response: 2nd Edition)

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22 pages, 1438 KB

Open AccessArticle

Deterioration of Exhaust Emissions in Ageing Gasoline Vehicles Assessed by RDE Testing

by Jacek Pielecha and Piotr Pryciński

Energies 2025, 18(21), 5822; https://doi.org/10.3390/en18215822 (registering DOI) - 4 Nov 2025

Abstract

The paper assesses the change in air pollutant emissions from a petrol passenger vehicle with changing mileage. The search for solutions enabling the assessment of the change in air pollutant emissions, considering the phenomenon of vehicle ageing, justifies the need to verify the [...] Read more.

The paper assesses the change in air pollutant emissions from a petrol passenger vehicle with changing mileage. The search for solutions enabling the assessment of the change in air pollutant emissions, considering the phenomenon of vehicle ageing, justifies the need to verify the actual air pollutant emissions from used vehicles. The fleet of vehicles used in Poland has an operational age exceeding 12 years, and the number of vehicles imported from Western Europe each year reaches almost 1 million. The research method used in the paper included conducting road tests, known as real driving emissions (RDE) tests of air pollutant emissions for a single vehicle, at different times and with various mileages. The petrol vehicle was operated by one driver whose driving style and routes were comparable and constant throughout the year. The RDE results were compared with data specifying the vehicle’s operating age and mileage to verify the research hypothesis, assuming increased emissions with increasing vehicle mileage. The emissions of basic air pollutants were determined as part of the research conducted using specialist equipment. The research results were obtained for one vehicle, and the experiment was carried out over several years. The results show differences in the emissions of selected chemical compounds depending on the petrol vehicle’s mileage and operating age while ensuring comparable driving technique and operation of one vehicle over a longer time period of 8 years. The vehicle’s age and mileage influence air pollutant emissions. The obtained results show a change in the emission of selected chemical compounds depending on the mileage, thereby confirming the validity of the adopted hypothesis. Full article

(This article belongs to the Special Issue Performance and Emissions of Vehicles and Internal Combustion Engines)

20 pages, 1501 KB

Open AccessArticle

Forecast-Driven Climate Control for Smart Greenhouses: Energy Optimization Using LSTM Model

by Abdulaziz Aborujilah, Mohammed Al-Sarem and Marwan Alabed Abu-Zanona

Energies 2025, 18(21), 5821; https://doi.org/10.3390/en18215821 (registering DOI) - 4 Nov 2025

Abstract

Greenhouses play a vital role in modern agriculture by providing controlled environments for year-round crop production. However, climate regulation within these structures accounts for a significant portion of their energy consumption, often exceeding 50% of operational costs. Current greenhouse systems predominantly rely on [...] Read more.

Greenhouses play a vital role in modern agriculture by providing controlled environments for year-round crop production. However, climate regulation within these structures accounts for a significant portion of their energy consumption, often exceeding 50% of operational costs. Current greenhouse systems predominantly rely on reactive control strategies, leading to energy inefficiency and unstable internal conditions. Addressing this gap, the present study develops a machine learning-based framework that leverages time series forecasting models—specifically Long Short-Term Memory (LSTM)—that predict key climate parameters and generate optimal actuator control recommendations. The system utilizes multivariate environmental data to forecast temperature, humidity, and CO₂ levels and minimize a composite energy proxy through proactive adjustments to heating, ventilation, and lighting systems. Experimental results demonstrate high prediction accuracy (R² = 0.9835) and significant improvements in energy efficiency. By integrating predictive analytics with real-time sensor feedback, the proposed approach supports intelligent, energy-aware decision-making and advances the development of smart agriculture through proactive greenhouse climate management. Full article

(This article belongs to the Special Issue Novel and Emerging Energy Systems)

32 pages, 1767 KB

Open AccessArticle

Managing Market Competition and Battery Disassembly Design in an Echelon Utilization Supply Chain: The Case of China Electric Vehicle Industry

by Senlin Zhao, Xinkang Wang and Hongchen Liu

Energies 2025, 18(21), 5820; https://doi.org/10.3390/en18215820 (registering DOI) - 4 Nov 2025

Abstract

The echelon utilization of electric vehicle batteries is regarded as an effective method for treating waste batteries, enabling the recycling and reuse of retired electric vehicle batteries. However, the efficiency of battery disassembly is a crucial factor that impacts the potential for battery [...] Read more.

The echelon utilization of electric vehicle batteries is regarded as an effective method for treating waste batteries, enabling the recycling and reuse of retired electric vehicle batteries. However, the efficiency of battery disassembly is a crucial factor that impacts the potential for battery recycling. When manufacturers take disassembly efficiency into account during the design phase of new electric vehicle batteries, they can significantly reduce disassembly costs at the time of decommissioning. This, in turn, incentivizes recycling and echelon utilization of waste batteries. Our research aims to promote the echelon use of waste batteries and analyze how market competition intensity and profits from battery echelon utilization influence decision-making within the battery recycling supply chain. This paper explores the effect of market competition on battery recycling and echelon utilization, while developing a supply chain model that includes a battery manufacturer responsible for determining the level of battery disassembly design and recycling waste batteries from the market, as well as a new energy vehicle manufacturer that focuses solely on recycling waste batteries. The findings indicate that as market competition increases, the battery manufacturer tends to lower both the level of battery disassembly design and the recycling price for waste batteries. Additionally, the recycling price for waste batteries offered by new energy vehicle manufacturers is also influenced by the intensity of market competition. In scenarios with low competition intensity, the recycling price tends to rise as competition intensifies. Conversely, in highly competitive markets, the recycling price decreases with increased competition. Furthermore, the overall volume of battery recycling is impacted by the intensity of market competition; in highly competitive markets, waste battery recycling is hindered. To enhance the echelon utilization of battery recycling, relevant government agencies should strive to maintain market competition at lower levels while also encouraging the recycling of batteries that do not meet usage standards. This dual approach will improve the benefits associated with the echelon utilization of waste batteries, thereby fostering greater enthusiasm for recycling among the involved enterprises. Full article

(This article belongs to the Special Issue Energy Storage, Energy Conversion, and Multifunctional Materials, 2nd Edition)

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19 pages, 3028 KB

Open AccessArticle

Investigating the Impact of E-Mobility on Distribution Grids in Rural Communities: A Case Study

by Marcus Brennenstuhl, Pawan Kumar Elangovan, Dirk Pietruschka and Robert Otto

Energies 2025, 18(21), 5819; https://doi.org/10.3390/en18215819 (registering DOI) - 4 Nov 2025

Abstract

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, [...] Read more.

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners. Full article

(This article belongs to the Special Issue Advanced Solutions for the Efficient Integration of Electric Vehicles in Electricity Grids: 2nd Edition)

19 pages, 2549 KB

Open AccessArticle

Optimal Aerial Imaging Parameters for UAV-Based Inspection and Maintenance of Photovoltaic Installations

by Eleftherios G. Vourkos, Eftychios G. Christoforou, Andreas S. Panayides, Soteris A. Kalogirou and Rafaela A. Agathokleous

Energies 2025, 18(21), 5818; https://doi.org/10.3390/en18215818 (registering DOI) - 4 Nov 2025

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with thermal and RGB cameras and enhanced by deep learning offer a powerful solution for autonomous photovoltaic (PV) system inspection. However, defect detection performance depends on flight parameters such as altitude, camera angles, speed, and solar position. This [...] Read more.

Unmanned Aerial Vehicles (UAVs) equipped with thermal and RGB cameras and enhanced by deep learning offer a powerful solution for autonomous photovoltaic (PV) system inspection. However, defect detection performance depends on flight parameters such as altitude, camera angles, speed, and solar position. This study examines the impact of various UAV flight parameters on the accurate detection of critical PV defects including hotspots, dirt from bird droppings, dust accumulation, and cell failures. For this purpose, two datasets were developed, comprising over 38,000 thermal infrared and RGB images. Using the YOLOv11 model, 21 flight configurations varying in altitude, camera tilt and pan angles, speed, and solar position were evaluated at four different times of day to assess the combined ambient and geometric effects on detection accuracy. Results indicate that low-altitude flights enhance small-object detection, while higher altitudes improve coverage at the expense of fine-detail accuracy. Dust detection is most effective when the camera aligns with the sun, whereas steep midday tilts cause reflective false positives. Thermal defect detection performs best during morning flights with moderate tilt angles. These findings emphasize the need to balance accuracy, coverage, efficiency, and safety, offering practical guidelines for effective and scalable PV inspection and maintenance. Full article

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23 pages, 5277 KB

Open AccessArticle

Numerical Analysis of FOWT Dynamics with Fully Coupled and Decoupled Methods: A Comparative Study

by Shi Liu, Yi Yang, Tao Tao, Zheng Huang, Wei Jiang, Chaohe Chen and Xinkuan Yan

Energies 2025, 18(21), 5817; https://doi.org/10.3390/en18215817 (registering DOI) - 4 Nov 2025

Abstract

The numerical analysis technique is one of the primary methods for the design and development of floating offshore wind turbines (FOWTs). This study presents a detailed investigation into the influences of fully coupled and decoupled numerical analysis methods on the dynamic responses of [...] Read more.

The numerical analysis technique is one of the primary methods for the design and development of floating offshore wind turbines (FOWTs). This study presents a detailed investigation into the influences of fully coupled and decoupled numerical analysis methods on the dynamic responses of a floating offshore wind turbine. The fully coupled analysis is implemented via bidirectional FAST-OrcaFlex co-simulation, considering the dynamic interaction between rotor operation and platform motions. The decoupled analysis is conducted using OrcaFlex for wave-induced response analysis, incorporating unidirectional imported FAST-based thrust time series. First, the numerical tools used for simulating fully coupled numerical model of OC5 DeepCwind are verified against published model test data, including free-decay test, white noise wave test and working condition test. Then, the fully coupled and decoupled numerical models are compared under wind fields of different turbulence intensities and wind speeds to reveal the dynamic coupling effects. The results indicate that the predictions of the decoupled model are more aligned with the experimental data compared to those of the fully coupled model under conditions of combined wave and steady winds. The differences between the fully coupled and decoupled models are minor under wave-only condition. However, under turbulent condition, the decoupled model overestimates surge by up to 10% and mooring tension by less than 5%, while pitch deviations can reach 17%. These findings support the use of the decoupled method in preliminary design stages—especially for mooring system optimal design—to save computational cost and time. For detailed designs involving turbulent winds, low-frequency structure response analysis or pitch-sensitive performance, the fully coupled approach is recommended to ensure accuracy. This study could offer practical guidance for selecting suitable numerical methods in FOWT design and analysis. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring for Energy Performance and Safety in Marine Renewable Systems)

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31 pages, 10145 KB

Open AccessArticle

Evaluating Sentiment and Factuality of Offshore Wind Technological Trends Using Large Language Models

by Holly Freed, Konstantina Vogiatzaki and Stephen Roberts

Energies 2025, 18(21), 5816; https://doi.org/10.3390/en18215816 - 4 Nov 2025

Abstract

The urgent pursuit of net-zero emissions presents a critical challenge for modern societies, necessitating a speedup of transformative shifts across sectors to mitigate climate change. Predicting trends and drivers in the integration of energy technologies is essential to addressing this challenge, as it [...] Read more.

The urgent pursuit of net-zero emissions presents a critical challenge for modern societies, necessitating a speedup of transformative shifts across sectors to mitigate climate change. Predicting trends and drivers in the integration of energy technologies is essential to addressing this challenge, as it informs policy decisions, strategic investments, and the deployment of innovative solutions crucial for transitioning to a sustainable energy future. Despite the importance of accurate forecasting, current methods remain limited, especially in leveraging the vast, unlabelled energy literature available. However, with the advent of large language models (LLMs), the ability to interpret and extract insights from extensive textual data has significantly advanced. Sentiment analysis, in particular, has just emerged as a vital tool for detecting scientific opinions from the energy literature, which can be harnessed to forecast energy trends. This study introduces a novel multi-agent framework, EnergyEval, to evaluate the sentiment and factuality of the energy literature. The core novelty of the multi-agent framework is found to be the use of heterogeneous energy-specialised roles with different LLMs. This investigation, using both multiple persona agents and different LLMs, provides a bespoke collaboration mechanism for multi-agent debate (MAD). In addition, we believe our approach can extend across the energy industry, where deep application of MAD is yet to be exploited. We apply EnergyEval to the case of UK offshore wind literature, assessing its predictive performance. Our findings indicate that the sentiment predicted by the EnergyEval effectively aligns with observed trends in increasing the installed capacity and reductions in Levelised Cost of Energy (LCOE). It also helps us to identify key drivers in offshore wind development. The advantage of employing a multi-agent LLM debate team allows us to achieve competitive accuracy compared to single-LLM-based methods, while significantly reducing computational costs. Overall, the results highlight the potential of EnergyEval as a robust tool for forecasting technology developments in the pursuit of net-zero emissions. Full article

(This article belongs to the Special Issue Advanced Wind Energy Systems: Comprehensive Insights into Analysis, Design, Control, and Optimization)

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17 pages, 6089 KB

Open AccessArticle

Numerical Simulation Analysis of the Temperature Field of Molten Salt Linear Fresnel Collector

by Linggang Kong, Yuan Niu, Duojin Fan, Minsen Shi and Ziyi Zheng

Energies 2025, 18(21), 5815; https://doi.org/10.3390/en18215815 - 4 Nov 2025

Abstract

A complex operating environment and high operating temperature lead to the uneven temperature field distribution of key components of the molten salt Linear Fresnel collector in a way that compromises the collector’s safety and stability. To investigate the influence of different working conditions [...] Read more.

A complex operating environment and high operating temperature lead to the uneven temperature field distribution of key components of the molten salt Linear Fresnel collector in a way that compromises the collector’s safety and stability. To investigate the influence of different working conditions on the temperature field of the molten salt Linear Fresnel collector under multi-physical field conditions, this study develops a three-dimensional numerical model based on ANSYS that integrates the loading of solar radiation and thermal–fluid coupling, compares and verifies the accuracy of the model through the collector field data of the actual operation, and systematically analyzes the distribution characteristics of the receiver tube and outlet temperature field and its rule of change. The results show that temperatures of the receiver tube and exit during operation exhibit pronounced non-uniform distribution characteristics, in which the inlet flow rate of the molten salt and intensity of solar irradiation have the most critical influence on the temperature distribution throughout the receiver tube and its exit, and the heat transfer temperature difference between the molten salt and heat conduit wall is reduced as the inlet temperature raises, which makes the receiver tube and molten salt outlet temperature gradient slightly reduced. This study not only supplements and improves the numerical simulation study of the molten salt Linear Fresnel collector under complex working conditions but also reveals the distribution law of the temperature field between the receiver tube and the outlet, which provides adequate numerical support for the safe and stable operation of the collector. Full article

(This article belongs to the Special Issue Recent Advances in Solar Thermal Technologies and Solar Energy Application)

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14 pages, 5677 KB

Open AccessArticle

Study on Power Frequency Breakdown and Decomposition Characteristics of SF₆/N₂ Gas Mixture

by Junbo Wang, Lijuan Liao, Yalong Xia, Jianyuan Xu, Xin Lin and Zhenxin Geng

Energies 2025, 18(21), 5814; https://doi.org/10.3390/en18215814 - 4 Nov 2025

Abstract

The SF₆/N₂ gas mixture is a key eco-friendly alternative to SF₆ in power equipment. However, its insulation and decomposition behaviors under realistic conditions of varying pressure and electric field non-uniformity remain insufficiently studied. This study establishes an experimental platform [...] Read more.

The SF₆/N₂ gas mixture is a key eco-friendly alternative to SF₆ in power equipment. However, its insulation and decomposition behaviors under realistic conditions of varying pressure and electric field non-uniformity remain insufficiently studied. This study establishes an experimental platform to systematically investigate the power–frequency breakdown and decomposition characteristics of a 3:7 SF₆/N₂ mixture. Breakdown tests and critical field strength calculations were conducted under different pressures and electric field uniformities, followed by gas composition analysis. Results demonstrate that the proposed critical breakdown field strength method effectively predicts the mixture’s breakdown voltage. Furthermore, a range of decomposition products, including NF₃, SOF₂, CO₂, and C₂F₆, were identified, whose concentrations generally decreased with rising pressure. These findings provide valuable experimental data and a predictive tool for optimizing the insulation design and condition monitoring of SF₆/N₂ gas-insulated equipment. Full article

(This article belongs to the Special Issue Dielectric Insulation in Medium- and High-Voltage Power Equipment—Degradation and Failure Mechanism, Diagnostics, and Electrical Parameters Improvement: 2nd Edition)

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50 pages, 16753 KB

Open AccessArticle

Spectral Energy of High-Speed Over-Expanded Nozzle Flows at Different Pressure Ratios

by Manish Tripathi, Sławomir Dykas, Mirosław Majkut, Krystian Smołka, Kamil Skoczylas and Andrzej Boguslawski

Energies 2025, 18(21), 5813; https://doi.org/10.3390/en18215813 - 4 Nov 2025

Abstract

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach:

1.1

to

1.3

). To that end, high-speed experiments in a blowdown open-channel [...] Read more.

This paper addresses the long-standing question of understanding the origin and evolution of low-frequency unsteadiness interactions associated with shock waves impinging on a turbulent boundary layer in transonic flow (Mach:

1.1

to

1.3

). To that end, high-speed experiments in a blowdown open-channel wind tunnel have been performed across a convergent–divergent nozzle for different expansion ratios (PR = 1.44, 1.6, and 1.81). Quantitative evaluation of the underlying spectral energy content has been obtained by processing time-resolved pressure transducer data and Schlieren images using the following spectral analysis methods: Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), as well as coherence and time-lag evaluations. The images demonstrated the presence of increased normal shock-wave impact for PR = 1.44, whereas the latter were linked with increased oblique

λ

-foot impact. Hence, significant disparities associated with the overall stability, location, and amplitude of the shock waves, as well as quantitative assertions related to spectral energy segregation, have been inferred. A subsequent detailed spectral analysis revealed the presence of multiple discrete frequency peaks (magnitude and frequency of the peaks increasing with PR), with the lower peaks linked with large-scale shock-wave interactions and higher peaks associated with shear-layer instabilities and turbulence. Wavelet transform using the Morlet function illustrates the presence of varying intermittency, modulation in the temporal and frequency scales for different spectral events, and a pseudo-periodic spectral energy pulsation alternating between two frequency-specific events. Spectral analysis of the pixel densities related to different regions, called spatial FFT, highlights the increased influence of the feedback mechanism and coupled turbulence interactions for higher PR. Collation of the subsequent coherence analysis with the previous results underscores that lower PR is linked with shock-separation dynamics being tightly coupled, whereas at higher PR values, global instabilities, vortex shedding, and high-frequency shear-layer effects govern the overall interactions, redistributing the spectral energy across a wider spectral range. Complementing these experiments, time-resolved numerical simulations based on a transient 3D RANS framework were performed. The simulations successfully reproduced the main features of the shock motion, including the downstream migration of the mean position, the reduction in oscillation amplitude with increasing PR, and the division of the spectra into distinct frequency regions. This confirms that the adopted 3D RANS approach provides a suitable predictive framework for capturing the essential unsteady dynamics of shock–boundary layer interactions across both temporal and spatial scales. This novel combination of synchronized Schlieren imaging with pressure transducer data, followed by application of advanced spectral analysis techniques, FFT, CWT, spatial FFT, coherence analysis, and numerical evaluations, linked image-derived propagation and coherence results directly to wall pressure dynamics, providing critical insights into how PR variation governs the spectral energy content and shock-wave oscillation behavior for nozzles. Thus, for low PR flows dominated by normal shock structure, global instability of the separation zone governs the overall oscillations, whereas higher PR, linked with dominant

λ

-foot structure, demonstrates increased feedback from the shear-layer oscillations, separation region breathing, as well as global instabilities. It is envisaged that epistemic understanding related to the spectral dynamics of low-frequency oscillations at different PR values derived from this study could be useful for future nozzle design modifications aimed at achieving optimal nozzle performance. The study could further assist the implementation of appropriate flow control strategies to alleviate these instabilities and improve thrust performance. Full article

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20 pages, 5094 KB

Open AccessArticle

Research Trends in Renewable Energy Sources in Academic Infrastructure: A Bibliometric Analysis

by Kacper Bednarz, Agnieszka Stec and Daniel Słyś

Energies 2025, 18(21), 5812; https://doi.org/10.3390/en18215812 - 4 Nov 2025

Abstract

A growing interest in renewable energy sources has been observed for over a decade. This stems from the need to reduce the use of fossil fuels, the processing of which leads to significant environmental pollution. Implementing unconventional energy technologies is not only crucial [...] Read more.

A growing interest in renewable energy sources has been observed for over a decade. This stems from the need to reduce the use of fossil fuels, the processing of which leads to significant environmental pollution. Implementing unconventional energy technologies is not only crucial but also their proper management and optimization. This is particularly crucial in hybrid systems based on multiple energy sources. With this in mind, a comprehensive bibliometric analysis of the scientific literature was conducted, relating to research on these systems implemented in academic facilities between 2009 and 2025. The focus was on these facilities because universities, and particularly their research laboratories, increasingly play a key role in the creation, research, development, and implementation of hybrid energy systems. The study utilized professional software: Bibliometrix and VOSviewer. The bibliographic database was created using the most popular international scientific platforms, Web of Science and Scopus. Evolution of research trends and intensive development of research on the management and optimization of hybrid energy systems implemented in academic infrastructure were observed. Analysis of the results showed that, since 2009, there has been a transition from basic research on the efficiency and optimization of renewable energy sources, through the integration of various technologies and the development of management methods, to contemporary issues related to digitalization, energy storage, and economic analysis. Full article

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