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
A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis
Energies 2024, 17(10), 2331; https://doi.org/10.3390/en17102331 (registering DOI) - 12 May 2024
Abstract
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Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in
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Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.
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Open AccessArticle
Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units
by
Anping Wan, Chenyu Du, Wenbin Gong, Chao Wei, Khalil AL-Bukhaiti, Yunsong Ji, Shidong Ma, Fareng Yao and Lizheng Ao
Energies 2024, 17(10), 2330; https://doi.org/10.3390/en17102330 (registering DOI) - 11 May 2024
Abstract
To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine
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To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.
Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
Open AccessArticle
A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem
by
Slim Belhaiza and Sara Al-Abdallah
Energies 2024, 17(10), 2329; https://doi.org/10.3390/en17102329 (registering DOI) - 11 May 2024
Abstract
Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds
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Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.
Full article
(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity)
Open AccessArticle
Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution
by
Dong Liang, Long Ma, Peng Wang, Yuanxia Li and Yiping Luo
Energies 2024, 17(10), 2328; https://doi.org/10.3390/en17102328 (registering DOI) - 11 May 2024
Abstract
The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To
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The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.
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(This article belongs to the Section A: Sustainable Energy)
Open AccessArticle
Comparison of the Sample Preparation Strategies and Impacts on the Tensile Strength of Gas Shale with Variable Moisture Conditions
by
Liuqing Shu, Lingzhi Xie, Bo He and Yao Zhang
Energies 2024, 17(10), 2327; https://doi.org/10.3390/en17102327 (registering DOI) - 11 May 2024
Abstract
Moisture significantly affects the mechanical behavior of gas shale and further determines the hydraulic fracturing performance, as it is more attractive. Nevertheless, batch experiments have usually involved variable methodologies regarding the preparation of moisture-contained shale specimens in the sequence (and/or frequency) of drying
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Moisture significantly affects the mechanical behavior of gas shale and further determines the hydraulic fracturing performance, as it is more attractive. Nevertheless, batch experiments have usually involved variable methodologies regarding the preparation of moisture-contained shale specimens in the sequence (and/or frequency) of drying and soaking treatments. Accordingly, this work investigates how the preparation methodology influences the test results of moisture-contained shale samples. This study compares three commonly used shale sample preparation strategies for acquiring different moisture contents, that is, “dry-wet”, “dry-wet-dry”, and “wet-dry-wet” strategies, followed by a Brazilian splitting test for the mechanical parameters. The results show that under the same saturation conditions, the longer the soaking time during sample preparation, the higher the degradation degree of shale tensile strength. Meanwhile, prolonged soaking can lead to a more discrete distribution of strength values, and the failure mode may deviate from the Brazilian splitting theory model. Under the combined influence of moisture content and soaking time, the tensile strength of shale decreases approximately linearly with increasing saturation, while the degradation degree increases nonlinearly with increasing saturation, and the degradation rate changes from slow to fast. According to the observation of the microstructure of hydrated shale, prolonged soaking can lead to an increase in the expansion of clay minerals in shale by hydration, resulting in looser and more fragmented internal structure, and further degradation in shale strength. In order to weaken the interference of hydration when studying the effect of moisture content on the tensile strength of shale, the soaking time should be minimized as much as possible during the preparation process.
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(This article belongs to the Special Issue Application and Optimization of CCUS Technology in Shale Gas Production and Storage)
Open AccessArticle
A Practical Hybrid Hysteresis Model for Calculating Iron Core Losses in Soft Magnetic Materials
by
Xiaotong Fu, Shuai Yan, Zhifu Chen, Xiaoyu Xu and Zhuoxiang Ren
Energies 2024, 17(10), 2326; https://doi.org/10.3390/en17102326 (registering DOI) - 11 May 2024
Abstract
Accurately calculating the losses of ferromagnetic materials is crucial for optimizing the design and ensuring the safe operation of electrical equipment such as motors and power transformers. Commonly used loss calculation models include the Bertotti empirical formula and hysteresis models. In this paper,
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Accurately calculating the losses of ferromagnetic materials is crucial for optimizing the design and ensuring the safe operation of electrical equipment such as motors and power transformers. Commonly used loss calculation models include the Bertotti empirical formula and hysteresis models. In this paper, a new hybrid hysteresis model method is proposed to calculate losses—namely, the combination of the Jiles–Atherton hysteresis model (J–A) and the Fourier hysteresis model. The traditional Jiles–Atherton hysteresis model is mainly suitable for fitting the saturation hysteresis loop, but the fitting error is relatively large for internal minor hysteresis loops. In contrast, the Fourier hysteresis model is suitable for fitting the minor hysteresis loops because the corresponding magnetic induction strength or magnetic field is lower and the waveform distortion is small. Moreover, Fourier series expansion can be expressed with fewer terms, which is convenient for parameter fitting. Through examples, the results show that the hybrid hysteresis model can take advantage of the strengths of each model, not only reducing computational complexity, but also ensuring high fitting accuracy and loss calculation accuracy.
Full article
(This article belongs to the Section F3: Power Electronics)
Open AccessArticle
A Study on the Techno-Economics Feasibility of a 19.38 KWp Rooftop Solar Photovoltaic System at Al-Abrar Mosque, Saudi Arabia
by
Abdulaziz S. Alaboodi and Sultan J. Alharbi
Energies 2024, 17(10), 2325; https://doi.org/10.3390/en17102325 (registering DOI) - 11 May 2024
Abstract
This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic
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This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque’s energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque’s energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region.
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(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Open AccessArticle
Experimental Investigation of the Viscosity and Density of Microencapsulated Phase Change Material Slurries for Enhanced Heat Capacity and Transfer
by
Bartlomiej Nalepa, Krzysztof Dutkowski, Marcin Kruzel, Boguslaw Bialko and Bartosz Zajaczkowski
Energies 2024, 17(10), 2324; https://doi.org/10.3390/en17102324 (registering DOI) - 11 May 2024
Abstract
Working fluids that incorporate solid microencapsulated phase change materials (MPCMs) can benefit from properties such as density and viscosity, which are crucial for improving heat capacity and transfer. However, limited data are available on these parameters for specific slurry and mass ratios. In
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Working fluids that incorporate solid microencapsulated phase change materials (MPCMs) can benefit from properties such as density and viscosity, which are crucial for improving heat capacity and transfer. However, limited data are available on these parameters for specific slurry and mass ratios. In this study, we present a comparative analysis of the experimental results on the viscosity of three different MPCM aqueous dispersions, namely MPCM 31-S50, MPCM 25-S50, and Micronal 5428X. Varying MPCM mass ratios of distilled water were used to obtain different mass concentrations of the phase change material (PCM), and the resulting slurries were analysed at temperatures ranging from 15 to 40 °C. Our findings showed that all slurries exhibited non-Newtonian characteristics at low shear rates, with viscosity stabilising at higher shear rates, resulting in the characteristics of a Newtonian fluid. The viscosity results were highly dependent on the type of MPCM base dispersion, particularly at high mass ratios, with the slurries having viscosities higher than those of water. Furthermore, we conducted density experiments as a function of temperature, using a flow test setup and a Coriolis flowmeter (Endress+Hauser, Reinach, Switzerland) to determine the density of two MPCMs, namely MPCM 25-S50 and Micronal 5428X. The test samples were prepared at mass concentrations of 10%, 15%, and 20% of the phase change material. We found significant differences in density and viscosity for different MPCM slurries as a result of both the PCM concentration and the material studied. Our results also revealed an apparent PCM phase change process, in which the slurry density significantly decreased in the temperature range of the phase transition from solid to liquid.
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(This article belongs to the Collection Advances in Heat Transfer Enhancement)
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Open AccessArticle
Design, Simulation and Optimization of a Novel Transpired Tubular Solar Air Heater
by
Hossain Nemati
Energies 2024, 17(10), 2323; https://doi.org/10.3390/en17102323 (registering DOI) - 11 May 2024
Abstract
In this paper, a novel tubular solar air heater is introduced. In this air heater, the hot boundary layer is drawn into the absorber tube and can provide thermal energy at moderate temperatures. Several different cases were simulated and a correlation was proposed
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In this paper, a novel tubular solar air heater is introduced. In this air heater, the hot boundary layer is drawn into the absorber tube and can provide thermal energy at moderate temperatures. Several different cases were simulated and a correlation was proposed to predict the collector’s effectiveness as a function Rayleigh number and Reynolds number. An equation was derived to find the effectiveness of this collector. Finally, a real case was studied with non-uniform solar flux distribution, as well as radiation heat loss. Good agreement was found between the results and those derived by the proposed analytical method. For different suction values, the first-law and the second-law efficiencies were calculated. Based on the exergy analysis, exergy destruction in absorption is the dominant factor that is unavoidable in low-temperature collectors. It was shown that there is an optimum suction value at which the second-law efficiency is maximized. At the optimum point, temperature rise can reach 54 K, which is hardly possible with a flat plate collector. Based on the exergy analysis, the relation between tube wall temperature and air outlet temperature in their dimensionless forms at the optimum working condition was derived, and it was shown that effectiveness at the optimum working condition is around 0.5. This means that the air temperature rise shall be half of the temperature difference between collector wall and the ambient temperatures. A high outlet temperature besides the low cost of construction and maintenance are the main advantages of this air heater. With such a high temperature rise, this type of collector can increase the use of solar energy in domestic applications.
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(This article belongs to the Topic Advances in Solar Heating and Cooling)
Open AccessArticle
Time-Dependent Multi-Particle Model Describing the Hydrogen Absorption of Nanocrystalline Magnesium Powders: A Case Study
by
Ádám Révész and Áron Pintér
Energies 2024, 17(10), 2322; https://doi.org/10.3390/en17102322 (registering DOI) - 11 May 2024
Abstract
Classical kinetic models describing the hydrogen absorption of nanocrystalline metallic hydrides generally do not involve any parameter related to the change in the crystallite size during the hydrogenation at constant temperature. In the present investigation, ball-milled nanocrystalline Mg powders exhibiting lognormal crystallite size
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Classical kinetic models describing the hydrogen absorption of nanocrystalline metallic hydrides generally do not involve any parameter related to the change in the crystallite size during the hydrogenation at constant temperature. In the present investigation, ball-milled nanocrystalline Mg powders exhibiting lognormal crystallite size distribution have been subjected to hydrogen absorption in a Sievert-type apparatus. Partially absorbed states were achieved by interrupting the hydrogenation cycle at different hydrogen content, i.e., when 15%, 50%, and 90% of Mg powder transformed to MgH2. The evolution of the characteristic size of the nucleating MgH2 phase was determined from X-ray diffraction analysis. Considering the crystallite size distribution of the as-milled powder agglomerate as well as the growth during the isothermal hydrogenation process, a time-dependent multi-particle reaction function was developed. It was shown unambiguously for this case study that the measured hydrogen absorption curve of the ball-milled Mg powder shows the best correlation with this model when it is compared to classical kinetic functions or the previously developed multi-particle reaction function excluding the change in the average crystallite size during hydrogenation.
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(This article belongs to the Section A5: Hydrogen Energy)
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Open AccessArticle
Reservoir Simulations of Hydrogen Generation from Natural Gas with CO2 EOR: A Case Study
by
Krzysztof Miłek, Wiesław Szott, Jarosław Tyburcy and Alicja Lew
Energies 2024, 17(10), 2321; https://doi.org/10.3390/en17102321 (registering DOI) - 11 May 2024
Abstract
This paper addresses the problem of hydrogen generation from hydrocarbon gases using Steam Methane Reforming (SMR) with byproduct CO2 injected into and stored in a partially depleted oil reservoir. It focuses on the reservoir aspects of the problem using numerical simulation of
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This paper addresses the problem of hydrogen generation from hydrocarbon gases using Steam Methane Reforming (SMR) with byproduct CO2 injected into and stored in a partially depleted oil reservoir. It focuses on the reservoir aspects of the problem using numerical simulation of the processes. To this aim, a numerical model of a real oil reservoir was constructed and calibrated based on its 30-year production history. An algorithm was developed to quantify the CO2 amount from the SMR process as well as from the produced fluids, and optionally, from external sources. Multiple simulation forecasts were performed for oil and gas production from the reservoir, hydrogen generation, and concomitant injection of the byproduct CO2 back to the same reservoir. EOR from miscible oil displacement was found to occur in the reservoir. Various scenarios of the forecasts confirmed the effectiveness of the adopted strategy for the same source of hydrocarbons and CO2 sink. Detailed simulation results are discussed, and both the advantages and drawbacks of the proposed approach for blue hydrogen generation are concluded. In particular, the question of reservoir fluid balance was emphasized, and its consequences were presented. The presented technology, using CO2 from hydrogen production and other sources to increase oil production, also has a significant impact on the protection of the natural environment via the elimination of CO2 emission to the atmosphere with concomitant production of H2.
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(This article belongs to the Section A5: Hydrogen Energy)
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Open AccessArticle
Simulation and Experimental Verification of Dispersion and Explosion of Hydrogen–Methane Mixture in a Domestic Kitchen
by
Haidong Xu, Qiang Deng, Xiaomei Huang, Du Li and Fengwen Pan
Energies 2024, 17(10), 2320; https://doi.org/10.3390/en17102320 (registering DOI) - 11 May 2024
Abstract
Hydrogen is a carbon-free energy source that can be obtained from various sources. The blending of hydrogen represents a transitional phase in the shift from natural gas systems to hydrogen-based systems. However, concerns about the safety implications of introducing hydrogen have led to
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Hydrogen is a carbon-free energy source that can be obtained from various sources. The blending of hydrogen represents a transitional phase in the shift from natural gas systems to hydrogen-based systems. However, concerns about the safety implications of introducing hydrogen have led to extensive discussions. This paper utilizes Fluent 17.0 numerical simulation software to simulate the leakage of hydrogen-blended natural gas in a closed domestic kitchen and analyze the concentration distribution and its variation pattern after a leakage. An experimental platform is set up, and a mixture of nitrogen and helium gas is used as a substitute for hydrogen-blended natural gas for the simulations and experiments. The simulation results demonstrate that the leaked gas spreads and accumulates towards the top of the space, gradually filling the entire area as the leak persists. As the hydrogen content in the leaked gas increases, the dispersion capacity of the gas in the confined space also increases. Furthermore, as the flow rate of the leaked gas increases, the average concentration of the leaked gas rises, and the gas stratification in the confined kitchen diminishes. The concentration distribution observed in the experiments aligns with the simulation results. After establishing the feasibility conditions of the model, the dispersion of the hydrogen-blended natural gas in the kitchen is further simulated. The results suggest that blending hydrogen into the gas enhances the dispersion of the gas after a leak, leading to a wider distribution within the kitchen and an increased risk in the event of a leak. Additionally, this paper employs the CASD module of FLACS 11.0 software to construct a three-dimensional geometric model of the domestic kitchen for simulation studies on the explosion of hydrogen-blended natural gas in a confined space. By adjusting the hydrogen ratio in the combustible gases present in the space and examining the variations in hydrogen concentration and external conditions, such as opening or closing the door, the influence on parameters including the peak explosion pressure, explosion overpressure, explosion flame temperature, and explosion response time are examined. Furthermore, the extent of the explosion area is determined, and the effect of hydrogen on the blast is clarified.
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(This article belongs to the Special Issue Hydrogen Safety for Energy Applications)
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Open AccessArticle
A Study of the Thermal Management and Discharge Strategies of Lithium-Ion Batteries in a Wide Temperature Range
by
Kaixuan Li, Chen Sun, Mingjie Zhang, Shuping Wang, Bin Wei, Yifeng Cheng, Xing Ju and Chao Xu
Energies 2024, 17(10), 2319; https://doi.org/10.3390/en17102319 (registering DOI) - 11 May 2024
Abstract
The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery
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The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery module during the charge/discharge process. The effects of working temperature, current rate, and convective heat transfer coefficient are investigated by establishing an electrochemical and thermal model. The results are obtained by conducting numerous parameterized scans to analyze the system’s state across various operating conditions, enabling the determination of its temperature and the selection of appropriate cooling measures accordingly. Based on the internal and external conditions of battery operation, parameter selection corresponding to the operating range is divided into several stages, with thermal management strategies provided for each stage. The existing framework facilitates the design of battery packs equipped with efficient thermal management strategies, thereby enhancing the battery systems’ reliability and performance. Furthermore, it aids in establishing optimal operational and safety boundaries for batteries.
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(This article belongs to the Topic Battery Design and Management)
Open AccessArticle
Optimizing High-Voltage Direct Current Transmission Corridors: Dynamic Thermal Line Rating for Enhanced Renewable Generation and Greenhouse Gas Emission Reductions
by
Veenavi Pemachandra, Petr Musilek and Gregory Kish
Energies 2024, 17(10), 2318; https://doi.org/10.3390/en17102318 (registering DOI) - 11 May 2024
Abstract
Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the
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Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution.
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(This article belongs to the Section F: Electrical Engineering)
Open AccessArticle
Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid
by
Zhanfei Li, Zhenghong Tu, Zhongkai Yi and Ying Xu
Energies 2024, 17(10), 2317; https://doi.org/10.3390/en17102317 (registering DOI) - 11 May 2024
Abstract
In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development
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In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme.
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(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)
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Open AccessArticle
Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization
by
Zeyu Li and Junhua Xiong
Energies 2024, 17(10), 2316; https://doi.org/10.3390/en17102316 (registering DOI) - 11 May 2024
Abstract
The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply.
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The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.
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(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
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Open AccessArticle
Numerical Analysis on Performance Improvement of a Vertical Plate Indirect Evaporative Cooler with Baffles
by
Wenhe Zhou, Shuo Cheng, Jia Wang and Yong Liu
Energies 2024, 17(10), 2315; https://doi.org/10.3390/en17102315 (registering DOI) - 11 May 2024
Abstract
The performance of the Plate Indirect Evaporative Cooler (PIEC) can be effectively improved by incorporating baffles in the dry channel. However, in the dimensional influence of the baffles on PIEC performance there remains a research gap. In order to investigate the impact of
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The performance of the Plate Indirect Evaporative Cooler (PIEC) can be effectively improved by incorporating baffles in the dry channel. However, in the dimensional influence of the baffles on PIEC performance there remains a research gap. In order to investigate the impact of baffle dimensions on the wet bulb efficiency, namely the average heat transfer coefficient and the cooling capacity of the PIEC, this paper proposed and verified a three-dimensional numerical model and method based on the species transport model and the Euler wall film model. At the same time, in order to obtain the equilibrium point between the enhanced heat transfer performance and the additional resistance induced by baffles, a comprehensive performance evaluation index is introduced. The results indicate that, under the same conditions, (1) the baffle effect on PIEC performance is significant at a lower inlet air velocity, and the wet bulb efficiency of the PIEC with baffles can be improved by 22.8%; (2) the baffle effect on PIEC performance is negative if its relative length exceeds 60% or the primary air inlet velocity surpasses 4 m/s under the conditions specified in this paper; and (3) the baffle effect on PIEC performance is significant when its channel height is lower and its channel width is larger, and the wet bulb efficiency of the PIEC with baffles can be improved by 29.3%.
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(This article belongs to the Section J1: Heat and Mass Transfer)
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Open AccessArticle
The Impact of Replacing Synchronous Generators with Renewable-Energy Technologies on the Transient Stability of the Mangystau Power System: An Introduction to Flexible Automatic Dosage of Exposures
by
Yerzhan Aisayev, Kazhybek Tergemes, Algazy Zhauyt, Saken Sheryazov and Kairat Bakenov
Energies 2024, 17(10), 2314; https://doi.org/10.3390/en17102314 (registering DOI) - 11 May 2024
Abstract
Since the creation of the first parallel electrical power systems around the world, the rotor angle stability of synchronously operating generators has been one of the most crucial and challenging problems. In modern electricity networks, including in Kazakhstan, where renewable energy technologies are
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Since the creation of the first parallel electrical power systems around the world, the rotor angle stability of synchronously operating generators has been one of the most crucial and challenging problems. In modern electricity networks, including in Kazakhstan, where renewable energy technologies are rapidly emerging, the issue of stability takes on even greater importance due to the technical shortcomings of inverter-based generation. In this framework, an analysis of rotor angle transient stability was carried out when replacing existing synchronous generators with doubly fed induction generators under a certain pre-emergency mode. A critical proportion of active power generation by DFIG units was identified at which transient stability can still be maintained due to the sufficient stored kinetic energy of the synchronous machines remaining in operation. In addition, two simple solutions were investigated to improve transient stability, such as an increased time of the automatic reclosure operation and the use of special load-shedding automation. Finally, this paper proposes a novel type of flexible smart-grid automation that is capable of monitoring the main operating parameters and issuing control actions depending on inertia, the availability of wind resources, and the load of the system as a whole. For this analysis, a real power system from the Mangystau region in Kazakhstan was considered, and the PowerWorld software 23 was used.
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(This article belongs to the Section A: Sustainable Energy)
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Open AccessArticle
Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines
by
Hongxu Zhao, Xiang Wu and Zhou Zhou
Energies 2024, 17(10), 2313; https://doi.org/10.3390/en17102313 (registering DOI) - 10 May 2024
Abstract
The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of
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The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.
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(This article belongs to the Topic Advances in Power Science and Technology)
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An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems
by
Chao Tang, Yufeng Zhang, Fan Wu and Zhuo Tang
Energies 2024, 17(10), 2312; https://doi.org/10.3390/en17102312 (registering DOI) - 10 May 2024
Abstract
Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges
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Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.
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(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Energy and Environment Development)
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