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
Study on Traveling Wave Fault Localization of Transmission Line Based on NGO-VMD Algorithm
Energies 2024, 17(9), 2003; https://doi.org/10.3390/en17092003 - 23 Apr 2024
Abstract
To address the challenge of inaccurate fault location of variational mode decomposition (VMD) in practical engineering, due to poor choice of mode decomposition number K and quadratic penalty factor α, a traveling wave fault location method using Northern Goshawk optimization algorithm (NGO) to
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To address the challenge of inaccurate fault location of variational mode decomposition (VMD) in practical engineering, due to poor choice of mode decomposition number K and quadratic penalty factor α, a traveling wave fault location method using Northern Goshawk optimization algorithm (NGO) to optimize VMD was proposed. First, the NGO algorithm is used to optimize VMD, and the optimal K and α are obtained. Secondly, the optimal parameters are inputted into VMD for fault signal decomposition, and the eigenmode components are obtained. Due to the difficulty of identification of the traveling wave head in the process of traveling wave propagation, Hilbert transform is used to determine the time of initial arrival of the traveling wave head at both ends of the line, and the fault location is precisely calculated by using the two-ended traveling wave fault detection formula. Finally, simulation experiments are carried out to verify the accuracy of the proposed location method, which shows that the proposed location method can locate the fault more accurately and has good engineering application value.
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(This article belongs to the Section F: Electrical Engineering)
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Harnessing Geothermal Energy Potential from High-Level Nuclear Waste Repositories
by
Dauren Sarsenbayev, Liange Zheng, Dinara Ermakova, Rashid Sharipov and Haruko M. Wainwright
Energies 2024, 17(9), 2002; https://doi.org/10.3390/en17092002 - 23 Apr 2024
Abstract
The disposal of high-level nuclear waste (HLW) has been one of the most challenging issues for nuclear energy utilization. In this study, we have explored the potential of extracting decay heat from HLW, taking advantage of recent advances in the technologies to utilize
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The disposal of high-level nuclear waste (HLW) has been one of the most challenging issues for nuclear energy utilization. In this study, we have explored the potential of extracting decay heat from HLW, taking advantage of recent advances in the technologies to utilize low-temperature geothermal resources for the co-generation of electricity and heat. Given that geothermal energy entails extracting heat from natural radioactivity within the Earth, we may consider that our approach is to augment it with an anthropogenic geothermal source. Our study—for the first time—introduces a conceptual model of a binary-cycle geothermal system powered by the heat produced by HLW. TOUGHREACT V3.32 software was used to model the heat transfer resulting from radioactive decay to the surrounding geological media. Our results demonstrate the feasibility of employing the organic Rankine cycle (ORC) to generate approximately 108 kWe per HLW canister 30 years after emplacement and a heat pump system to produce 81 kWth of high-potential heat per canister for HVAC purposes within the same timeframe. The proposed facility has the potential to produce carbon-free power while ensuring the safe disposal of radioactive waste and removing the bottleneck in the sustainable use of nuclear energy.
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(This article belongs to the Section H: Geo-Energy)
Open AccessArticle
A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm
by
Yonggang Wang, Fuxian Li, Ruimin Xiao and Nannan Zhang
Energies 2024, 17(9), 2001; https://doi.org/10.3390/en17092001 - 23 Apr 2024
Abstract
With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power
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With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.
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(This article belongs to the Section F: Electrical Engineering)
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Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller
by
Gama Ali, Hamed Aly and Timothy Little
Energies 2024, 17(9), 2000; https://doi.org/10.3390/en17092000 - 23 Apr 2024
Abstract
Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid.
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Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.
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(This article belongs to the Special Issue Advances in Renewable Energy Power Forecasting and Integration)
Open AccessArticle
Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid
by
Muhammad Jamshed Abbass, Robert Lis, Muhammad Awais and Tham X. Nguyen
Energies 2024, 17(9), 1999; https://doi.org/10.3390/en17091999 - 23 Apr 2024
Abstract
The maintenance of an uninterrupted electricity supply to meet demand is of paramount importance for maintaining the stable operation of an electrical power system. Machine learning and deep learning play a crucial role in maintaining that stable operation. These algorithms have the ability
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The maintenance of an uninterrupted electricity supply to meet demand is of paramount importance for maintaining the stable operation of an electrical power system. Machine learning and deep learning play a crucial role in maintaining that stable operation. These algorithms have the ability to acquire knowledge from past data, enabling them to efficiently identify and forecast potential scenarios of instability in the future. This work presents a hybrid convolutional long short-term memory (ConvLSTM) technique for training and predicting nodal voltage stability in an IEEE 14-bus microgrid. Analysis of the findings shows that the suggested ConvLSTM model exhibits the highest level of precision, reaching a value of 97.65%. Furthermore, the ConvLSTM model has been shown to perform better compared to alternative machine learning and deep learning models such as convolutional neural networks, k-nearest neighbors, and support vector machine models, specifically in terms of accurately forecasting voltage stability. The IEEE 14-bus system tests indicate that the suggested method can quickly and accurately determine the stability status of the system. The comparative analysis obtained the results and further justified the efficiency and voltage stability of the proposed model.
Full article
(This article belongs to the Special Issue Application of Reinforcement Learning in Energy Management of Microgrids and Hybrid Energy Storage Systems)
Open AccessArticle
Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks
by
Dominik Łuczak
Energies 2024, 17(9), 1998; https://doi.org/10.3390/en17091998 - 23 Apr 2024
Abstract
Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are
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Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are proposed. The IMU6DoF-Time2GrayscaleGrid-CNN method converts the time series sensor data into a single grayscale image, leveraging the efficiency of a grayscale representation and the power of convolutional neural networks (CNNs) for feature extraction. The IMU6DoF-Time2RGBbyType-CNN method utilizes RGB images. The IMU6DoF-Time2RGBbyAxis-CNN method employs an RGB image where each channel corresponds to a specific axis (X, Y, Z) of the sensor data. This axis-aligned representation potentially allows the CNN to learn the relationships between movements along different axes. The performance of all three methods is evaluated through extensive training and testing on a dataset containing various operational states (idle, normal, fault). All methods achieve high accuracy in classifying these states. While the grayscale method offers the fastest training convergence, the RGB-based methods might provide additional insights. The interpretability of the models is also explored using Grad-CAM visualizations. This research demonstrates the potential of image-based approaches with CNNs for robust and interpretable machine fault diagnosis using sensor data.
Full article
(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
Open AccessArticle
SiC MOSFET Active Gate Drive Circuit Based on Switching Transient Feedback
by
Cheng Xu and Yiru Miao
Energies 2024, 17(9), 1997; https://doi.org/10.3390/en17091997 - 23 Apr 2024
Abstract
Due to the influence of parasitic internal parameters and junction capacitance, the silicon carbide (SiC) power devices are frequently marred by significant overshoots in current and voltage, as well as high-frequency oscillations during the switching process. These phenomena can severely compromise the reliability
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Due to the influence of parasitic internal parameters and junction capacitance, the silicon carbide (SiC) power devices are frequently marred by significant overshoots in current and voltage, as well as high-frequency oscillations during the switching process. These phenomena can severely compromise the reliability of SiC-based power electronic converters during operation. This study delves into the switching transient of the SiC MOSFET with the goal of establishing a quantitative correlation between the gate driving current and the overshoot in both the drain-source voltage and the drain current. In light of these findings, the innovative active gate drive (AGD) circuit, which features an adjustable gate current, is introduced. Throughout the switching process, the AGD circuit employs a dynamic monitoring and feedback mechanism that is responsive to the gate voltage and rate of change in the drain-source voltage and drain current of the SiC MOSFET. This adjustment enables gate driving current to be actively modified, thereby effectively mitigating the occurrence of overshoots and oscillations. To empirically validate the efficacy of the proposed AGD circuit in curbing voltage and current overshoots and oscillations, a double-pulse experimental setup was meticulously constructed and tested.
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(This article belongs to the Topic Advanced Energy and Propulsion Technology for Electric and Intelligent Transportation)
Open AccessArticle
Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model
by
Shengcai Zhang, Changsheng Zhu and Xiuting Guo
Energies 2024, 17(9), 1996; https://doi.org/10.3390/en17091996 - 23 Apr 2024
Abstract
Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional
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Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed.
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(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
Open AccessArticle
Potential Regenerative Impact of Implementation of Cultural Vernacular Elements (Rowshan) in Jeddah, Saudi Arabia
by
Ahmed Abdullah Mezaien and Juan-Carlos Baltazar
Energies 2024, 17(9), 1995; https://doi.org/10.3390/en17091995 - 23 Apr 2024
Abstract
The present study aims to explore rowshans as essential vernacular architectural elements in designing houses in very hot-dry climates such as Jeddah, Saudi Arabia, to determine their most significant effects on air movement, ventilation, and mitigating cooling loads. A comprehensive combination of building
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The present study aims to explore rowshans as essential vernacular architectural elements in designing houses in very hot-dry climates such as Jeddah, Saudi Arabia, to determine their most significant effects on air movement, ventilation, and mitigating cooling loads. A comprehensive combination of building performance simulation and computational fluid dynamics (CFD) analysis was used to model a room with six different sizes of rectangular openings and quantify rowshans’ potential as passive elements in providing occupants with comfort and reducing energy use. Analysis of the passive element revealed the thermal performance and natural ventilation in single-family homes for the Jeddah climate, created by outdoor and indoor temperature, airspeed, and pressure differences in the room model, were improved, lowering sensation temperature for inhabitants’ comfort. The results highlight the beneficial effects of rowshans in lowering a house’s temperature during the daytime: from November to April, at noon, indoor air temperature (IAT) could reach a 15% to 22% reduction in the north orientation. The findings also show that rowshans with 5 × 5 cm opening grids can keep the air volume flow rate within an acceptable range and keep the room in the comfort zone range for 42.3% of hours annually, equal to 3704 h. An implication of these results is the possibility of establishing housing design criteria that can enhance efficiency and thermal comfort conditions, lower the cost of operations, provide occupants with satisfaction, and reduce emissions to regenerate the environment, leading to affordability and sustainability in the Jeddah region.
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(This article belongs to the Section G: Energy and Buildings)
Open AccessArticle
Formation Rate and Energy Efficiency of Ice Plug in Pipelines Driven by the Cascade Utilization of Cold Energy
by
Minglei Hu, Wei Zhang, Ke Xu, Zijiang Yang, Liqun Wang, Yongqiang Feng and Hao Chen
Energies 2024, 17(9), 1994; https://doi.org/10.3390/en17091994 - 23 Apr 2024
Abstract
Ice plug technology is an effective method for isolating the pipeline system, which are promising methods utilized in the nuclear, chemical, and power industries. To reduce the cold energy consumption and temperature stress, the multi-stage (1–10) of time-dependent thermal boundary conditions was proposed
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Ice plug technology is an effective method for isolating the pipeline system, which are promising methods utilized in the nuclear, chemical, and power industries. To reduce the cold energy consumption and temperature stress, the multi-stage (1–10) of time-dependent thermal boundary conditions was proposed for the formation of ice plug, while the gradient cooling wall temperature of multi-stage was applied. A numerical model considering the liquid–solid phase change, heat transfer, and time-dependent thermal boundary condition has been established. The effects of the ratio of length and diameter of the cooling wall lc/d (1–9) on the formation rate and heat flux of ice plug in the pipe has been investigated. The fastest formation rate of ice plug with 800 mm in the axial direction (7.47 cm3/s) was observed in the pipe with the lc/d of 5. The formation rate of ice plug and the ice formation volume under unit energy consumption VE under various stages (1–10) of cooling wall temperature have been compared. The VE of eight temperature stages (1.45 cm3/kJ) was 1.16 times than the VE of one temperature stage, which satisfied the freezing rate at the same time. This investigation provides insight for proposing an energy-saving system for the formation of ice plug.
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(This article belongs to the Section B4: Nuclear Energy)
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Thermal Studies of Lithium-Ion Cells: Ensuring Safe and Efficient Energy Storage
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Beata Kurc, Xymena Gross, Ewelina Rudnicka and Łukasz Rymaniak
Energies 2024, 17(9), 1993; https://doi.org/10.3390/en17091993 - 23 Apr 2024
Abstract
This work investigated the impact of temperature on the diffusion of lithium ions within cells. To achieve this, electrochemical impedance spectroscopy (EIS) analysis was conducted at various temperatures across three distinct cells. These cells utilized an electrode composed of corn starch meringue and
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This work investigated the impact of temperature on the diffusion of lithium ions within cells. To achieve this, electrochemical impedance spectroscopy (EIS) analysis was conducted at various temperatures across three distinct cells. These cells utilized an electrode composed of corn starch meringue and were paired with three different electrolytes. Notably, one electrolyte included an additional 5% of starch. The objective of this study extends beyond merely determining resistance from graphical representations; it also entails performing a kinetic analysis of specific systems, with a particular emphasis on elucidating the significance of the lithium-ion diffusion coefficient as a critical parameter. The cell with 1 M LiPF6 in the EC/DMC/DEC electrolyte and corn starch-based electrode exhibited the most horizontally oriented Warburg curve, representing the smallest angle.
Full article
(This article belongs to the Special Issue Lithium-Ion Batteries: Latest Advances, Challenges and Prospects, Volume II)
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Open AccessReview
A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities
by
Michael Meiser and Ingo Zinnikus
Energies 2024, 17(9), 1992; https://doi.org/10.3390/en17091992 - 23 Apr 2024
Abstract
To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy
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To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.
Full article
(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)
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Open AccessArticle
Local DER Control with Reduced Loop Interactions in Active Distribution Networks
by
Giuseppe Fusco and Mario Russo
Energies 2024, 17(9), 1991; https://doi.org/10.3390/en17091991 - 23 Apr 2024
Abstract
Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present
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Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction’s sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network’s node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter’s pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio.
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(This article belongs to the Special Issue Control of Renewable Energy Sources in Power Systems and Smart Grids: 2nd Edition)
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Open AccessArticle
Qualification and Quantification of Porosity at the Top of the Fuel Pins in Metallic Fuels Using Image Processing
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Andrei V. Gribok, Fidelma G. Di Lemma, Jake Fay, Douglas L. Porter, Kyle M. Paaren and Luca Capriotti
Energies 2024, 17(9), 1990; https://doi.org/10.3390/en17091990 - 23 Apr 2024
Abstract
Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize
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Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias.
Full article
(This article belongs to the Section B4: Nuclear Energy)
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Open AccessArticle
Flow Characteristic Analysis of the Impeller Inlet Diameter in a Double-Suction Pump
by
Hyunjun Jang and Junho Suh
Energies 2024, 17(9), 1989; https://doi.org/10.3390/en17091989 - 23 Apr 2024
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Pumps are considered crucial mechanical devices in any industry. Especially, double-suction pumps, due to their high-flow design and high head, are utilized in diverse industrial sectors. However, despite these advantages, double-suction pumps are vulnerable to issues like losses, vibrations, and cavitation. Pump vibration
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Pumps are considered crucial mechanical devices in any industry. Especially, double-suction pumps, due to their high-flow design and high head, are utilized in diverse industrial sectors. However, despite these advantages, double-suction pumps are vulnerable to issues like losses, vibrations, and cavitation. Pump vibration is mainly caused by suction recirculation occurring at the impeller inlet. Therefore, this study investigates the impact of the impeller inlet design on enhancing the stability of double-suction pumps while expanding the operating flow range. Through a validated CFD study, the influence of the impeller inlet passage size on the head and efficiency was analyzed. Furthermore, research was conducted on the phenomenon of suction recirculation occurring at the impeller inlet, proposing design guidelines to minimize it, especially for operation at low flow rates. The results demonstrate that the ratio of the hub diameter to the shroud diameter at the impeller inlet significantly impacts the avoidance of recirculation at low flow rates. These findings are expected to contribute to improving the stability and efficiency of high-flow pumps.
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Open AccessArticle
Assessing the Potential of Teff Husk for Biochar Production through Slow Pyrolysis: Effect of Pyrolysis Temperature on Biochar Yield
by
Marcin Landrat, Mamo Abawalo, Krzysztof Pikoń, Paulos Asefa Fufa and Semira Seyid
Energies 2024, 17(9), 1988; https://doi.org/10.3390/en17091988 - 23 Apr 2024
Abstract
Environmental restoration and sustainable energy solutions require effective management and utilization of agricultural crop residues to reduce greenhouse gas emissions. Biowastes are a valuable resource that can be converted into biofuels and their byproducts, solving the energy crisis and reducing environmental impact. In
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Environmental restoration and sustainable energy solutions require effective management and utilization of agricultural crop residues to reduce greenhouse gas emissions. Biowastes are a valuable resource that can be converted into biofuels and their byproducts, solving the energy crisis and reducing environmental impact. In this study, teff husk, primarily generated in Ethiopia during the production of teff within the agro-industrial sector, is used as a feedstock for slow pyrolysis. Ethiopia generates an estimated annual production of over 1.75 million tons of teff husk, a significant portion of which is incinerated, resulting in significant pollution of the environment. This study focuses on assessing teff husk as a potential material for slow pyrolysis, a crucial stage in biochar production, to tap into its biochar-producing potential. To identify the composition of biomass, the teff husk underwent an initial analysis using thermogravimetry. The significant presence of fixed carbon indicates that teff husk is a viable candidate for pyrolytic conversion into biochar particles. The process of slow pyrolysis took place at three temperatures—specifically, 400, 450, and 500 °C. The maximum biochar yield was achieved by optimizing slow pyrolysis parameters including reaction time, temperature, and heating rate. The optimized reaction time, temperature, and heating rate of 120 min, 400 °C, and 4.2 °C/min, respectively, resulted in the highest biochar yield of 43.4 wt.%. Furthermore, biochar’s physicochemical, SEM-EDX, FTIR, and TGA characterization were performed. As the temperature of biochar increases, its carbon content and thermal stability increases as well. Unlike fuel recovery, the results suggest that teff-husk can be used as a feedstock for biochar production.
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(This article belongs to the Section A4: Bio-Energy)
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Open AccessReview
Insulation for Rotating Low-Voltage Electrical Machines: Degradation, Lifetime Modeling, and Accelerated Aging Tests
by
Xuanming Zhou, Paolo Giangrande, Yatai Ji, Weiduo Zhao, Salman Ijaz and Michael Galea
Energies 2024, 17(9), 1987; https://doi.org/10.3390/en17091987 - 23 Apr 2024
Abstract
The low-voltage electric machine (EM) is a core technology for transportation electrification, and features like high power density and compact volume are essential prerequisites. However, these requirements are usually in conflict with the reliability property of EM, especially in the safety-critical industry such
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The low-voltage electric machine (EM) is a core technology for transportation electrification, and features like high power density and compact volume are essential prerequisites. However, these requirements are usually in conflict with the reliability property of EM, especially in the safety-critical industry such as aviation. Therefore, an appropriate balance between high-performance and reliability needs to be found. Often, the over-engineering method is applied to ensure safety, although it might have a detrimental effect on the EM volume. To address this issue, the EM reliability assessment is included at the EM design stage through the physics of failure (PoF) theory. In EMs, the windings play a key role in electromechanical energy conversion, but their insulation system is subject to frequent failure and represents a reliability bottleneck. Therefore, in-depth research on the root causes of insulation breakdown is beneficial for EM reliability improvement purposes. Indeed, increasing awareness and knowledge on the mechanism of the insulation degradation process and the related lifetime modeling enables the growth of appropriate tools for achieving reliability targets since the first EM design steps. In this work, the main aspects of the insulation system, in terms of materials and manufacturing, are first reviewed. Then, the principal stresses experienced by the winding insulation system are deeply discussed with the purpose of building a profound understanding of the PoF. Finally, an overview of the most common insulation lifetime prediction models is presented, and their use for accomplishing the reliability-oriented design (RoD) and the remaining useful life (RUL) estimation are examined.
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(This article belongs to the Special Issue Reliability and Condition Monitoring of Electric Motors and Drives)
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Open AccessArticle
Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication
by
Carlo Fiorillo, Mattia Mauro, Atriya Biswas, Angelo Bonfitto and Ali Emadi
Energies 2024, 17(9), 1986; https://doi.org/10.3390/en17091986 - 23 Apr 2024
Abstract
Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to
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Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.
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(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)
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Open AccessReview
Digitalization in the Renewable Energy Sector
by
Musadag El Zein and Girma Gebresenbet
Energies 2024, 17(9), 1985; https://doi.org/10.3390/en17091985 - 23 Apr 2024
Abstract
This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines
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This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth.
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(This article belongs to the Section A: Sustainable Energy)
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Open AccessArticle
Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors
by
Hisahide Nakamura and Yukio Mizuno
Energies 2024, 17(9), 1984; https://doi.org/10.3390/en17091984 - 23 Apr 2024
Abstract
Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it
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Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it is critical to diagnose the condition of the windings. The objective of this article is to establish a method to identify the short-circuit fault points in concentrated stator windings based on the magnetic flux density distribution near the stator windings. Unlike with distributed windings, the coils are wound around the teeth in concentrated windings. Thus, it is expected that the accurate position specification of the short circuit can be realized if a detailed magnetic flux density distribution over the teeth is obtained with an appropriate magnetic field sensor. The problem of sensor positioning is solved with two stepper motors moving the search coil in the rotational and longitudinal directions independently at specified intervals. The excellent capability of the proposed system is verified through experiments using the stator winding employed in hybrid electric vehicles. The accuracy and sensitivity of the proposed identification system for short-circuit fault points may enable its practical application in industries, for example, shipping and periodic inspections as well as the production management of motors with concentrated stator windings.
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(This article belongs to the Section D: Energy Storage and Application)
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