Journal Description
Energies
Energies
is a peer-reviewed, open access journal of related scientific research, technology development, engineering policy, and management studies related to the general field of energy, from technologies of energy supply, conversion, dispatch, and final use to the physical and chemical processes behind such technologies. Energies is published semimonthly online by MDPI. The European Biomass Industry Association (EUBIA), Association of European Renewable Energy Research Centres (EUREC), Institute of Energy and Fuel Processing Technology (ITPE), International Society for Porous Media (InterPore), CYTED and others are affiliated with Energies and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, RePEc, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: CiteScore - Q1 (Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 41 topical sections.
- Testimonials: See what our editors and authors say about Energies.
- Companion journals for Energies include: Fuels, Gases, Nanoenergy Advances and Solar.
Impact Factor:
3.2 (2022);
5-Year Impact Factor:
3.3 (2022)
Latest Articles
Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning
Energies 2024, 17(10), 2290; https://doi.org/10.3390/en17102290 (registering DOI) - 9 May 2024
Abstract
Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction
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Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.
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(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)
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The Cost Reduction Analysis of Green Hydrogen Production from Coal Mine Underground Water for Circular Economy
by
Małgorzata Magdziarczyk, Andrzej Chmiela, Roman Dychkovskyi and Adam Smoliński
Energies 2024, 17(10), 2289; https://doi.org/10.3390/en17102289 - 9 May 2024
Abstract
The novelty of the paper is the analysis of the possibilities of reducing the operating costs of a mine water pumping station in an abandoned coal mine. To meet the energy needs of the pumping station and reduce the carbon footprint, “green” energy
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The novelty of the paper is the analysis of the possibilities of reducing the operating costs of a mine water pumping station in an abandoned coal mine. To meet the energy needs of the pumping station and reduce the carbon footprint, “green” energy from a photovoltaic farm was used. Surplus green energy generated during peak production is stored in the form of green hydrogen from the water electrolysis process. Rainwater and process water are still underutilized sources for increasing water resources and reducing water stress in the European Union. The article presents the possibilities of using these waters, after purification, in the production of green hydrogen by electrolysis. The article also presents three variants that ensure the energy self-sufficiency of the proposed concepts of operation of the pumping station.
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(This article belongs to the Special Issue Bioenergy Economics: Analysis, Modeling and Application)
Open AccessArticle
A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon
by
Benxiang Lin, Chao Wei, Fuyong Feng and Tao Liu
Energies 2024, 17(10), 2288; https://doi.org/10.3390/en17102288 - 9 May 2024
Abstract
Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of
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Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.
Full article
(This article belongs to the Special Issue Energy Management Control of Hybrid Electric Vehicles)
Open AccessArticle
Solvent Exsolution and Liberation from Different Heavy Oil–Solvent Systems in Bulk Phases and Porous Media: A Comparison Study
by
Wei Zou and Yongan Gu
Energies 2024, 17(10), 2287; https://doi.org/10.3390/en17102287 - 9 May 2024
Abstract
In this paper, experimental and numerical studies were conducted to differentiate solvent exsolution and liberation processes from different heavy oil–solvent systems in bulk phases and porous media. Experimentally, two series of constant-composition-expansion (CCE) tests in a PVT cell and differential fluid production (DFP)
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In this paper, experimental and numerical studies were conducted to differentiate solvent exsolution and liberation processes from different heavy oil–solvent systems in bulk phases and porous media. Experimentally, two series of constant-composition-expansion (CCE) tests in a PVT cell and differential fluid production (DFP) tests in a sandpacked model were performed and compared in the heavy oil–CO2, heavy oil–CH4, and heavy oil–C3H8 systems. The experimental results showed that the solvent exsolution from each heavy oil–solvent system in the porous media occurred at a higher pressure. The measured bubble-nucleation pressures (Pn) of the heavy oil–CO2 system, heavy oil–CH4 system, and heavy oil–C3H8 system in the porous media were 0.24 MPa, 0.90 MPa, and 0.02 MPa higher than those in the bulk phases, respectively. In addition, the nucleation of CH4 bubbles was found to be more instantaneous than that of CO2 or C3H8 bubbles. Numerically, a robust kinetic reaction model in the commercial CMG-STARS module was utilized to simulate the gas exsolution and liberation processes of the CCE and DFP tests. The respective reaction frequency factors for gas exsolution (rffe) and liberation (rffl) were obtained in the numerical simulations. Higher values of rffe were found for the tests in the porous media in comparison with those in the bulk phases, suggesting that the presence of the porous media facilitated the gas exsolution. The magnitudes of rffe for the three different heavy oil–solvent systems followed the order of CO2 > CH4 > C3H8 in the bulk phases and CH4 > CO2 > C3H8 in the porous media. Hence, CO2 was exsolved from the heavy oil most readily in the bulk phases, whereas CH4 was exsolved from the heavy oil most easily in the porous media. Among the three solvents, CH4 was also found most difficult to be liberated from the heavy oil in the DFP test with the lowest rffl of 0.00019 min−1. This study indicates that foamy-oil evolution processes in the heavy oil reservoirs are rather different from those observed from the bulk-phase tests, such as the PVT tests.
Full article
(This article belongs to the Special Issue Exploration and Development of Unconventional Oil and Gas Resources: Latest Advances and Prospects)
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Open AccessArticle
Electrical Machine Winding Performance Optimization by Multi-Objective Particle Swarm Algorithm
by
François S. Martins, Bernardo P. Alvarenga and Geyverson T. Paula
Energies 2024, 17(10), 2286; https://doi.org/10.3390/en17102286 - 9 May 2024
Abstract
The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine
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The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented.
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(This article belongs to the Topic Advanced Electrical Machine Design and Optimization Ⅱ)
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Non-Integrated and Integrated On-Board Battery Chargers (iOBCs) for Electric Vehicles (EVs): A Critical Review
by
Fatemeh Nasr Esfahani, Ahmed Darwish, Xiandong Ma and Peter Twigg
Energies 2024, 17(10), 2285; https://doi.org/10.3390/en17102285 - 9 May 2024
Abstract
The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical
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The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors’ knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis.
Full article
(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)
Open AccessArticle
Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods
by
Cheng Qin, Jianing Li, Chen Yang, Bin Ai and Yecheng Zhou
Energies 2024, 17(10), 2284; https://doi.org/10.3390/en17102284 - 9 May 2024
Abstract
In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W
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In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method.
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(This article belongs to the Special Issue Photovoltaic Solar Cells and Systems: Fundamentals and Applications)
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Anti-Gravity 3D Pulsating Heat Pipe for Cooling Electric Vehicle Batteries
by
Ji-Su Lee, Su-Jong Kim, Woo-Sung Han and Seok-Ho Rhi
Energies 2024, 17(10), 2283; https://doi.org/10.3390/en17102283 - 9 May 2024
Abstract
This study proposes an anti-gravity 3D pulsating heat pipe (PHP) for cooling pouch batteries in electric vehicles. The 3D PHP envelops the battery cells and rapidly transfers heat generated from the batteries to the bottom cold plate. While the batteries generate heat on
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This study proposes an anti-gravity 3D pulsating heat pipe (PHP) for cooling pouch batteries in electric vehicles. The 3D PHP envelops the battery cells and rapidly transfers heat generated from the batteries to the bottom cold plate. While the batteries generate heat on their frontal surface during charging and discharging, structural characteristics lead to localized heat accumulation at the electrode lead tabs. Therefore, to address frontal heating, Pattern A with a consistent height for the 3D PHP and Pattern B with varying heights to enhance heat transfer in the localized heating area were designed. The target application involved creating a battery simulator for 340 × 100 mm pouch battery cells, considering the battery’s heat generation characteristics. The experiments for the thermal characteristics were conducted, considering factors such as the working fluid (methanol, Novec7100), filling ratio, supplied heat, and orientation. Additionally, to observe internal flow mechanisms, a special experimental apparatus was used, employing transparent fluorine rubber tubes to observe the flow mechanism of the 3D PHP. In the results of the thermal characteristics, the optimal filling ratio was 15% when heat generation levels of 50 W and 100 W were supplied and 20% when 150 W was supplied. The impact of orientation yielded varied results depending on the pattern and working fluid, attributed to the complex interplay of flow momentum due to orientation changes and the influence of the working fluid’s buoyancy under anti-gravity conditions. Pattern B, designed with the goal of applying a localized heat model, exhibited relatively decreased heat transfer performance in areas with varying heights. As the distance from the varying height portion increased, temperature oscillations and heat transfer became more active. These results suggest that variations in the shape of the 3D PHP could be a primary design variable for crafting localized heat models. Observations of internal flow revealed that the 3D PHP, with its unique shape and operation under anti-gravity conditions, exhibits longer and more irregular cycles compared to gravity-assist PHPs, transferring heat through rapid oscillations of internal working fluid liquid/vapor slug/plug. The potential of 3D PHPs for cooling electric vehicle batteries is suggested by these findings, and further experimentation is planned to evaluate the optimal design and applicability.
Full article
(This article belongs to the Topic Advanced Battery Thermal Management Solution for Electric Vehicles)
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The Influence of Solid Heat Carrier Load of Char on Pyrolysis Characteristics of Pulverized Coal in a Fluidized Bed Reactor
by
Xinli Li, Xiaobin Qi, Rui Chen, Zhiping Zhu and Xiaofang Wang
Energies 2024, 17(10), 2282; https://doi.org/10.3390/en17102282 - 9 May 2024
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Pulverized coal pyrolysis based on solid heat carrier has a huge advantage in high tar yield. In this study, pyrolysis experiments on pulverized coal were conducted in a lab-scale fluidized bed reactor at 650 °C, utilizing char as the solid heat carrier. The
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Pulverized coal pyrolysis based on solid heat carrier has a huge advantage in high tar yield. In this study, pyrolysis experiments on pulverized coal were conducted in a lab-scale fluidized bed reactor at 650 °C, utilizing char as the solid heat carrier. The influence of mass ratio of char to coal (RATIO) was investigated. Results show that the incorporation of solid heat carrier of char significantly enhanced the primary pyrolysis reaction in coal pyrolysis, resulting in increasing yields of tar and gas but reducing one of char. The yield of tar maximally reached 148.80–262.22% of the Gray–King analysis value at the RATIO of 14.52 g/g. As the RATIO increased, the tar contained more light component content, indicating that incorporating solid heat carriers improved the tar quality. These findings offer significant insights for the design of fluidized bed pyrolysis unit utilizing char as solid heat carrier.
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Open AccessArticle
Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances
by
Enrique Reyes-Archundia, Wuqiang Yang, Jose A. Gutiérrez Gnecchi, Javier Rodríguez-Herrejón, Juan C. Olivares-Rojas and Aldo V. Rico-Medina
Energies 2024, 17(10), 2281; https://doi.org/10.3390/en17102281 - 9 May 2024
Abstract
Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a
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Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.
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(This article belongs to the Special Issue Power Quality and Disturbances in Modern Distribution Networks)
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Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications
by
Richard Pravin Antony, Pongiannan Rakkiya Goundar Komarasamy, Narayanamoorthi Rajamanickam, Roobaea Alroobaea and Yasser Aboelmagd
Energies 2024, 17(10), 2280; https://doi.org/10.3390/en17102280 - 9 May 2024
Abstract
The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design
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The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient.
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(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
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Open AccessArticle
Sand-Laden Wind Erosion Pair Experimental Analysis of Aerodynamic Performance of the Wind Turbine Blades
by
Daqian Wan, Songli Chen, Danlan Li, Qi Zhen and Bo Zhang
Energies 2024, 17(10), 2279; https://doi.org/10.3390/en17102279 - 9 May 2024
Abstract
In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs,
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In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs, this study delves into the specific impact of sand-laden wind erosion on the aerodynamic performance of scaled-down wooden wind turbine blades. The experiment conducts vehicle-mounted tests on scaled models of 1.5 MW wind turbine blades that have been eroded by wind-sand flows from different zones, analyzing the changes in aerodynamic performance of wind turbines caused by the erosion. The results indicate that with an increase in the angle of installation, both the overall power output and the wind energy utilization coefficient of the wind turbines show a declining trend. The power outputs of both the partially eroded group and the fully eroded group are unable to reach the rated power level of 100 W. Compared to the uneroded group, the leading-edge eroded group demonstrated higher power output and wind energy utilization coefficients across most wind speed ranges. This finding verifies the possibility that the drag-reducing effect caused by pits from leading-edge erosion has a positive impact on the aerodynamic performance of the blades. It also provides a new research perspective and strong evidence for the study of erosion effects on wind turbine blades and the optimization of their aerodynamic performance.
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(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC
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Nan Li, Jiafei Wu, Lili Shan and Luan Yi
Energies 2024, 17(10), 2278; https://doi.org/10.3390/en17102278 - 9 May 2024
Abstract
In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number
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In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples from the distribution. The decomposition method based on an improved error-correcting output coding algorithm is applied to convert the multi-class problem into a decision fusion issue for binary models. This method improves the overall performance of the multi-class model, particularly significantly increasing the recognition accuracy of discrimination against oscillatory unstable samples and reducing the safety hazards in the operation of power systems. The simulation validation was conducted on the IEEE 39-bus and IEEE 140-bus systems to confirm the effectiveness of the proposed model.
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(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)
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Implementation of a Microgrid System with a Four-Phase Inductor Coupled Interleaved Boost Converter for EV Charging Stations
by
Kommoju Naga Durga Veera Sai Eswar, Mohan Arun Noyal Doss, Mohammed Alruwaili and Waleed Mohammed Abdelfattah
Energies 2024, 17(10), 2277; https://doi.org/10.3390/en17102277 - 9 May 2024
Abstract
Electric vehicle charging stations are essential to enable broad reception due to the rise in electric vehicles in the transportation industry because they will lessen range anxiety concerns about distance. The primary objective of this work is to design a microgrid that is
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Electric vehicle charging stations are essential to enable broad reception due to the rise in electric vehicles in the transportation industry because they will lessen range anxiety concerns about distance. The primary objective of this work is to design a microgrid that is effective and affordable for an electric vehicle charging station that combines a photovoltaic, wind, and utility grid energy system (optional) as a principal source of energy. The proposed study employs a four-phase inductor coupled interleaved boost converter which is compact and effective with high power output which results in charging a vehicle within 33 min. A perturb and observe MPPT approach based on DC converters is used along with the digital 2PI controller to increase the effectiveness and performance of distributed energy systems. To make the converter a hassle-free operation, an interleaving technique is applied to the developed converter which results in ripple reduction, which results in an increase in the output current and voltage gain, with high power density and efficiency. For better understanding, real-time data for 2W/3W/4W are acquired and tested for various conditions and the maximum state of charge for the battery is gained within one-third of the usual time. At present, the interleaved converter’s operation is theoretically examined, and the behavior of the converter and the charging conditions of several electric vehicle systems are compared and shown in the simulation analysis.
Full article
(This article belongs to the Special Issue Electric Vehicle Charging: Social and Technical Issues Ⅱ)
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Open AccessArticle
Accuracy of Simscape Solar Cell Block for Modeling a Partially Shaded Photovoltaic Module
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Tihomir Betti, Ante Kristić, Ivan Marasović and Vesna Pekić
Energies 2024, 17(10), 2276; https://doi.org/10.3390/en17102276 - 9 May 2024
Abstract
With half-cut photovoltaic (PV) modules being the dominant technology on the market, there is an increasing necessity for accurate modeling of this module type. Circuit simulators such as Simulink are widely used to study different topics regarding photovoltaics, often employing a solar cell
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With half-cut photovoltaic (PV) modules being the dominant technology on the market, there is an increasing necessity for accurate modeling of this module type. Circuit simulators such as Simulink are widely used to study different topics regarding photovoltaics, often employing a solar cell block available from the Simcape library. The purpose of this work is to validate this model against measurements for a partially shaded half-cut PV module. Diverse shading scenarios are created by varying the number of shaded substrings, the number of shaded solar cells in the substring, and the shading level. For every shading scenario, the PV module’s I-V curve is measured, along with in-plane irradiance, air temperature, and module temperature. A comprehensive evaluation of simulation accuracy is presented. The results confirm a high accuracy of the model with mean nRMSE values of 2.2% for I-V curves and 2.8% when P-V curves are considered. It is found that the simulation errors tend to increase when increasing the number of shaded substrings. At the same time, no obvious dependency of simulation accuracy on the shading level or the number of shaded solar cells in the substring is found.
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(This article belongs to the Special Issue Advances in Solar Systems and Energy Efficiency)
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Open AccessArticle
Efficient Design of Battery Thermal Management Systems for Improving Cooling Performance and Reducing Pressure Drop
by
Kai Chen, Ligong Yang, Yiming Chen, Bingheng Wu and Mengxuan Song
Energies 2024, 17(10), 2275; https://doi.org/10.3390/en17102275 - 9 May 2024
Abstract
The air-cooled system is one of the most widely used battery thermal management systems (BTMSs) for the safety of electric vehicles. In this study, an efficient design of air-cooled BTMSs is proposed for improving cooling performance and reducing pressure drop. Combining with a
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The air-cooled system is one of the most widely used battery thermal management systems (BTMSs) for the safety of electric vehicles. In this study, an efficient design of air-cooled BTMSs is proposed for improving cooling performance and reducing pressure drop. Combining with a numerical calculation method, a strategy with a varied step length of adjustments (∆d) is developed to optimize the spacing distribution among battery cells for temperature uniformity improvement. The optimization results indicate that the developed strategy reduces the optimization time by about 50% compared with a strategy using identical ∆d values while maintaining good performance of the optimized system. Furthermore, the system’s pressure drop does not increase after the spacing optimization. Based on this characteristic, a structural design strategy is proposed to improve the cooling performance and reduce the pressure drop simultaneously. First, the appropriate flow pattern is arranged and the secondary outlet is added to reduce the pressure drop of the system. The results show that the BTMS with U-type flow combined with a secondary outlet against the original outlet can effectively reduce the pressure drop of the system. Subsequently, this BTMS is further improved using the developed cell spacing optimization strategy with varied ∆d values while the pressure drop is fixed. It is found that the final optimized BTMS achieves a battery temperature difference below 1 K for different inlet airflow rates, with the pressure drop being reduced by at least 45% compared with the BTMS before the optimization.
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(This article belongs to the Special Issue Thermal Management of Energy-Saving and New Energy Vehicles: Technology and Application)
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Research on the Thermal Aging Characteristics of Crosslinked Polyethylene Cables Based on Polarization and Depolarization Current Measurement
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Yamei Li, Zhaowei Peng, Dangguo Xu, Shiyang Huang, Yanfeng Gao and Yuan Li
Energies 2024, 17(10), 2274; https://doi.org/10.3390/en17102274 - 9 May 2024
Abstract
Although XLPE cables are widely used in power transmission and distribution systems, their insulating properties are susceptible to degradation due to thermal aging. In order to clarify the influence law of the thermal aging process on the structural and dielectric properties of XLPE
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Although XLPE cables are widely used in power transmission and distribution systems, their insulating properties are susceptible to degradation due to thermal aging. In order to clarify the influence law of the thermal aging process on the structural and dielectric properties of XLPE cables, this paper investigates the thermal aging characteristics of XLPE cables by using polarization and depolarization current measurement. Results show that when the XLPE cable is aged at 140 °C, the crystallinity of the insulation layer appears to increase and then decrease. With the increase in aging time, micron-sized microvoids appear on the surface of the XLPE. At the same time, the DC conductivity and 0.1 Hz dielectric loss factor of the insulating layer increase with the aging time. The average DC conductivity increased from 2.26 × 10−16 S/m for new cables to 4.47 × 10−16 S/m after aging for 432 h, while the dielectric loss increased from 0.11% to 0.42%. The polarization characteristics of thermal-aged cables were further analyzed using the extended Debye model. Results indicate that the time constant of the third branch of the model increased significantly with increasing aging time. A correspondence between this parameter and the thermal aging time of the cable was established. Thermal aging can damage the crystalline structure of XLPE, so that the number of interfaces between the crystalline and amorphous regions of the material increases, resulting in structural damages and a decline in the dielectric properties of the cable insulation.
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(This article belongs to the Special Issue Recent Progress, Challenges and Outlooks of Insulation System in HVDC)
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Open AccessReview
Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries
by
Wenwei Wang, Shuaibang Liu, Xiao-Ying Ma, Jiuchun Jiang and Xiao-Guang Yang
Energies 2024, 17(10), 2273; https://doi.org/10.3390/en17102273 - 8 May 2024
Abstract
Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these
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Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.
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(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System—2nd Edition)
Open AccessArticle
Performance Analysis Based on Fuel Valve Train Control Optimization of Ammonia-Fuel Ships
by
Lim Seungtaek, Lee Hosaeng and Seo Youngkyun
Energies 2024, 17(10), 2272; https://doi.org/10.3390/en17102272 - 8 May 2024
Abstract
In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this,
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In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this, eco-friendly fuel propulsion technology is being developed, and the development of an ammonia fuel supply system is in progress. Among them, fuel valve train (FVT) technology was researched for the final supply and cutoff of fuel and purging through nitrogen for ammonia engines. In this paper, we analyzed the change in ammonia supply due to FVT opening and the change in nitrogen supply due to closure. In addition, a plan to minimize risk factors was presented by applying a control method to remove residual fuel in FVT. According to the presented FVT model, the difference in the flow rate of supplied fuel was as much as 17.8 kg/s. Additionally, by opening the gas bleed valve at intervals during the closing process and purging about 0.28 kg of nitrogen, the internal fuel could be completely discharged. This is expected to have an impact on improving the marine environment through the application of eco-friendly fuels and the development of fuel supply system technology.
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(This article belongs to the Special Issue Advances in Fuel Energy)
Open AccessArticle
Global Energy Transition and the Efficiency of the Largest Oil and Gas Companies
by
Sami Jarboui and Hind Alofaysan
Energies 2024, 17(10), 2271; https://doi.org/10.3390/en17102271 - 8 May 2024
Abstract
The challenges posed by climate change and global warming loom large, necessitating a critical initial step towards the long-term growth and the enhancement of both environmental and operational efficiency. Within the energy sector, renewable energy sources are gaining increasing prominence. Consequently, traditional oil
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The challenges posed by climate change and global warming loom large, necessitating a critical initial step towards the long-term growth and the enhancement of both environmental and operational efficiency. Within the energy sector, renewable energy sources are gaining increasing prominence. Consequently, traditional oil and gas companies (OGC) are undergoing a gradual transformation into comprehensive energy corporations, aligning themselves with energy transition policies. This paper examines two types of efficiency measures—operational and environmental—for the 20 largest OGC during the period of 2010–2019. Secondly, this research aims to explore the effect of the global energy transition on both environmental and operational efficiency. Based on three estimation methods, two estimation steps are used in this research. In the first step, the True Fixed Effect (TFE) model and the Battese and coelli (1995) SFA model are applied to evaluate, measure and compare the environmental and operational efficiency scores. In the second step, the TFE model and GMM approach for the dynamic panel data model are used to explore, evaluate and verify the effect of global energy transition on the environmental and operational efficiency of the largest 20 OGC in the world. The results reveal that the average operational efficiency of major OGC measured using the BC.95 model and TFE model is 66% and 85%, respectively, and the overall average level of environmental efficiency for OGC over a 10-year period is 31% (based to B.C.95 model) and 13% (based to TFE model). Our findings reveal that biofuels, solar and hydropower contribute to promote the operational and environmental efficiency of the largest 20 OGC. However, the analysis suggests that while the global energy transition significantly influences and bolsters environmental efficiency, its effect on operational efficiency among these major OGC remains less pronounced and insufficient.
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(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System, Volume II)
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