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
Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials
Energies 2024, 17(6), 1468; https://doi.org/10.3390/en17061468 (registering DOI) - 19 Mar 2024
Abstract
Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those
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Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.
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(This article belongs to the Special Issue Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry)
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Industrial Chain, Supply Chain and Value Chain in the Energy Industry: Opportunities and Challenges
by
Jiachao Peng, Le Wen, Jianzhong Xiao, Ming Yi and Mingyue Selena Sheng
Energies 2024, 17(6), 1467; https://doi.org/10.3390/en17061467 (registering DOI) - 19 Mar 2024
Abstract
Ongoing geopolitical conflicts, frequent energy trade wars, and related issues significantly undermine the globalization of the energy market [...]
Full article
(This article belongs to the Special Issue Industrial Chain, Supply Chain and Value Chain in the Energy Industry: Opportunities and Challenges)
Open AccessArticle
Thermodynamic Reactivity Study during Deflagration of Light Alcohol Fuel-Air Mixtures with Water
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Rafał Porowski, Arief Dahoe, Robert Kowalik, Joanna Sosnowa and Katarzyna Zielinska
Energies 2024, 17(6), 1466; https://doi.org/10.3390/en17061466 (registering DOI) - 19 Mar 2024
Abstract
In this paper, a thermodynamic and reactivity study of light alcohol fuels was performed, based on experimental and numerical results. We also tested the influence of water addition on fundamental properties of the combustion reactivity dynamics in closed vessels, like the maximum explosion
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In this paper, a thermodynamic and reactivity study of light alcohol fuels was performed, based on experimental and numerical results. We also tested the influence of water addition on fundamental properties of the combustion reactivity dynamics in closed vessels, like the maximum explosion pressure, maximum rate of pressure rise and the explosion delay time of alcohol–air mixtures. The substances that we investigated were as follows: methanol, ethanol, n-propanol and iso-propanol. All experiments were conducted at initial conditions of 323.15 K and 1 bar in a 20 dm3 closed testing vessel. We investigated the reactivity and thermodynamic properties during the combustion of liquid fuel–air mixtures with equivalence ratios between 0.3 and 0.7 as well as some admixtures with water, to observe water mitigation effects. All light alcohol samples were prepared at the same initial conditions on a volumetric basis by mixing the pure components. The volumetric water content of the admixtures varied from 10 to 60 vol%. The aim of water addition was to investigate the influence of thermodynamic properties of light alcohols and to discover to which extent a water addition may accomplish mitigation of combustion dynamics and thermodynamic reactivity.
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(This article belongs to the Special Issue Advances in Fuels and Combustion)
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Open AccessCommunication
Comparative Analysis of Methods for Predicting Brine Temperature in Vertical Ground Heat Exchanger—A Case Study
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Joanna Piotrowska-Woroniak, Krzysztof Nęcka, Tomasz Szul and Stanisław Lis
Energies 2024, 17(6), 1465; https://doi.org/10.3390/en17061465 (registering DOI) - 19 Mar 2024
Abstract
This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory
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This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory (RST), and Support Regression Trees (SRTs), in the context of determining the temperature of brine from vertical ground heat exchangers used by a heat pump heating system. The subject of the analysis was a public building located in Poland, in a temperate continental climate zone. The results of this study indicate that the models based on Rough Set Theory (RST) and Artificial Neural Networks (ANNs) achieved the highest accuracy in predicting brine temperature, with the choice of the preferred method depending on the input variables used for modeling. Using three independent variables (mean outdoor air temperature, month of the heating season, mean solar irradiance), Rough Set Theory (RST) was one of the best models, for which the evaluation rates were as follows: CV RMSE 21.6%, MAE 0.3 °C, MAPE 14.3%, MBE 3.1%, and R2 0.96. By including an additional variable (brine flow rate), Artificial Neural Networks (ANNs) achieved the most accurate predictions. They had the following evaluation rates: CV RMSE 4.6%, MAE 0.05 °C, MAPE 1.7%, MBE 0.4%, and R2 0.99.
Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System, Volume II)
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Quantitative Analysis of Balancing Range for Single-Phase 3L-NPC Converters
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Ziying Wang, Ning Jiao, Shunliang Wang, Junpeng Ma, Rui Zhang and Tianqi Liu
Energies 2024, 17(6), 1464; https://doi.org/10.3390/en17061464 (registering DOI) - 19 Mar 2024
Abstract
Multiple techniques have been suggested to achieve control balance in single-phase three-level neutral-point clamped (3L-NPC) converters. Nevertheless, there is a deficiency of quantitative calculations related to the extent of balancing. Operating beyond the balancing range may result in a sequence of safety incidents.
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Multiple techniques have been suggested to achieve control balance in single-phase three-level neutral-point clamped (3L-NPC) converters. Nevertheless, there is a deficiency of quantitative calculations related to the extent of balancing. Operating beyond the balancing range may result in a sequence of safety incidents. This paper presents a conceptualization of the 3L-NPC converter as two cascaded H-bridges. By employing power conservation principles, the balancing range for the NPC converter is derived, and two novel methods are investigated to broaden the balance range in accordance with the calculated balance range. A comparison is made among the balancing ranges under different balancing control methods. This study establishes a theoretical foundation to ensure the secure and stable operation of the NPC converter.
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(This article belongs to the Section F3: Power Electronics)
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Demand-Side Management Optimization Using Genetic Algorithms: A Case Study
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Lauro Correa dos Santos Junior, Jonathan Muñoz Tabora, Josivan Reis, Vinicius Andrade, Carminda Carvalho, Allan Manito, Maria Tostes, Edson Matos and Ubiratan Bezerra
Energies 2024, 17(6), 1463; https://doi.org/10.3390/en17061463 (registering DOI) - 18 Mar 2024
Abstract
This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering
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This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption.
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(This article belongs to the Section F: Electrical Engineering)
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A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power
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Wei Dai, Yang Gao, Hui Hwang Goh, Jiangyi Jian, Zhihong Zeng and Yuelin Liu
Energies 2024, 17(6), 1462; https://doi.org/10.3390/en17061462 (registering DOI) - 18 Mar 2024
Abstract
AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC
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AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch.
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(This article belongs to the Section F3: Power Electronics)
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European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice
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Inna Tryhuba, Anatoliy Tryhuba, Taras Hutsol, Vasyl Lopushniak, Agata Cieszewska, Oleh Andrushkiv, Wiesław Barabasz, Anna Pikulicka, Zbigniew Kowalczyk and Vyacheslav Vasyuk
Energies 2024, 17(6), 1461; https://doi.org/10.3390/en17061461 - 18 Mar 2024
Abstract
Based on the analysis conducted on the state of theory and practice, the expediency of assessing the relationships between the functional indicators of bioenergy production systems using the organic waste of residential areas is substantiated in the projects of the European Green Deal.
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Based on the analysis conducted on the state of theory and practice, the expediency of assessing the relationships between the functional indicators of bioenergy production systems using the organic waste of residential areas is substantiated in the projects of the European Green Deal. It is based on the use of existing results published in scientific works, as well as on the use of methods of system analysis and mathematical modeling. The proposed approach avoids limitations associated with the one-sidedness of sources or subjectivity of data and also ensures complete consideration of various factors affecting the functional indicators of the bioenergy production system from the organic waste of residential areas. Four types of organic waste generated within the territory of residential areas are considered. In our work, we used passive experimental methods to collect data on the functional characteristics of bioenergy production systems, mathematical statistics methods to process and interpret trends in the functional characteristics of bioenergy production systems using municipal organic waste, and mathematical modeling methods to develop mathematical models that reflect the patterns of change in the functional characteristics of bioenergy production systems. The results indicate the presence of dependencies with close correlations. The resulting dependencies can be used to optimize processes and increase the efficiency of bioenergy production. It was found that: (1) yard waste has the highest volume of the total volume of solid organic substances but has a low yield of biogas and low share of methane production; (2) food waste has the highest yield of biogas and, accordingly, the highest share of methane production; (3) mixed organic waste has the lowest volume of the total volume of solid organic substances and the lowest content of volatile organic substances. The amount of electricity and thermal energy production varies by type of organic waste, with mixed organic waste having a higher average amount of electricity production compared to other types of waste. It was established that the production volume of the solid fraction (biofertilizer) is also different for different types of organic waste. Less solid fraction is produced from food waste than from yard waste. The obtained research results are of practical importance for the development of sustainable bioenergy production from organic waste in residential areas during the implementation of the European Green Deal projects. They provide further research on the development of effective models for determining the rational configuration of bioenergy production systems using organic waste for given characteristics of residential areas.
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(This article belongs to the Section A4: Bio-Energy)
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An Assessment of CO2 Capture Technologies towards Global Carbon Net Neutrality
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Amith Karayil, Ahmed Elseragy and Aliyu M. Aliyu
Energies 2024, 17(6), 1460; https://doi.org/10.3390/en17061460 - 18 Mar 2024
Abstract
Carbon dioxide, the leading contributor to anthropogenic climate change, is released mainly via fossil fuel combustion, mostly for energy generation. Carbon capture technologies are employed for reducing the emissions from existing huge point sources, along with capturing them from direct air, to reduce
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Carbon dioxide, the leading contributor to anthropogenic climate change, is released mainly via fossil fuel combustion, mostly for energy generation. Carbon capture technologies are employed for reducing the emissions from existing huge point sources, along with capturing them from direct air, to reduce the existing concentration. This paper provides a quantitative analysis of the various subtypes of carbon capture technologies with the aim of providing an assessment of each from technological, social, geo-political, economic, and environmental perspectives. Since the emissions intensity and quantity, along with the social–political–economic conditions, vary in different geographic regions, prioritising and finding the right type of technology is critical for achieving ambitious net-zero targets. Four main types of carbon capture technology were analysed (adsorption, absorption, membrane, and cryogenic) under four scenarios depending on the jurisdiction. The Technique for Order of Preference by Similarity to Ideal Solution (also known as the TOPSIS method) was used to establish a quantitative ranking of each, where weightages were allocated according to the emissions status and economics of each depending on the jurisdiction. Furthermore, forecasting the trends for technology types vis à vis carbon neutral targets between 2040 and 2050 was carried out by applying regression analysis on existing data and the emissions footprint of major contributing countries. The study found the membrane score to be the highest in the TOPSIS analysis in three of the four scenarios analysed. However, absorption remains the most popular for post-combustion capture despite having the highest energy penalty per ton of CO2 capture. Overall, capture rates are well short of projections for carbon neutrality; the methodology put forward for prioritising and aligning appropriate technologies and the region-by-region analysis will help highlight to technocrats, governments, and policymakers the state of the art and how to best utilise them to mitigate carbon emissions—critical in achieving the net-zero goals set at various international agreements on climate change.
Full article
(This article belongs to the Collection Feature Papers in Carbon Capture, Utilization, and Storage)
Open AccessArticle
Small Disturbance Stability Analysis of Onshore Wind Power All-DC Power Generation System Based on Impedance Method
by
Tao Wang, Fengting Li, Chunya Yin and Guixin Jin
Energies 2024, 17(6), 1459; https://doi.org/10.3390/en17061459 - 18 Mar 2024
Abstract
The Onshore Wind Power All-DC Generation System (OWDCG) is designed to integrate with renewable energy sources by modifying the grid structure. This adaptation supports the grid infrastructure and addresses the challenges of large-scale wind power AC collection and harmonic resonance during transmission. Crucially,
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The Onshore Wind Power All-DC Generation System (OWDCG) is designed to integrate with renewable energy sources by modifying the grid structure. This adaptation supports the grid infrastructure and addresses the challenges of large-scale wind power AC collection and harmonic resonance during transmission. Crucially, small disturbance stability parameters are essential for ensuring the system’s stable operation. Unlike conventional power systems, the OWDCG exhibits strong coupling between subsystems, accentuating the small disturbance stability issue due to the dynamic nature of its converter control system. The impedance method facilitates the decomposition of such systems into subsystems, offering insights into the destabilization mechanism through the lens of negative impedance contribution. This approach is conducive to conducting small disturbance stabilization analyses. To tackle this issue, the initial step involves deriving the input and output equivalent impedance models of the subsystem, considering the topological structure, control features, and operational dynamics of the OWDCG. Subsequently, the impact of circuit and control parameters on the system’s impedance characteristics and small-disturbance stability is examined through Bode diagrams and Nyquist curves. This analysis identifies critical parameters for small-disturbance stability, guiding the stable operation and parameter optimization of the OWDCG. The analysis highlights that the main control strategies for stability are the Modular Multilevel Converter (MMC) DC voltage control and the inner-loop current control gain. Validation of the theoretical findings is achieved through simulation results using PSCAD/EMTDC.
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(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning
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Jianpeng Zhao, Qi Wang, Wei Rong, Jingbo Zeng, Yawen Ren and Hui Chen
Energies 2024, 17(6), 1458; https://doi.org/10.3390/en17061458 - 18 Mar 2024
Abstract
Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results
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Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.
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(This article belongs to the Special Issue Exploring Hydrocarbons in Carbonate Reservoirs)
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An Ethane-Based CSI Process and Two Types of Flooding Process as a Hybrid Method for Enhancing Heavy Oil Recovery
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Yishu Li, Zhongwei Du, Bo Wang, Jiasheng Ding and Fanhua Zeng
Energies 2024, 17(6), 1457; https://doi.org/10.3390/en17061457 - 18 Mar 2024
Abstract
Combining multiple secondary oil recovery (SOR)/enhanced oil recovery (EOR) methods can be an effective way to maximize oil recovery from heavy oil reservoirs; however, previous studies typically focus on single methods. In order to optimize the combined process of ethane-based cyclic solvent injection
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Combining multiple secondary oil recovery (SOR)/enhanced oil recovery (EOR) methods can be an effective way to maximize oil recovery from heavy oil reservoirs; however, previous studies typically focus on single methods. In order to optimize the combined process of ethane-based cyclic solvent injection (CSI) and water/nanoparticle-solution flooding, a comprehensive understanding of the impact of injection pressure, water, and nanoparticles on CSI performance is crucial. This study aims to provide such understanding through experimental evaluation, advancing the knowledge of EOR methods for heavy oil recovery. Three approaches (an ethane-based CSI process, water flooding, and nanoparticle-solution flooding) were applied through a cylindrical sandpack model with a length of 95.0 cm and a diameter of 3.8 cm. Test 1 conducted an ethane-based CSI process only. Test 2 conducted a combination approach of CSI–water flooding–CSI–nanoparticle-solution flooding–CSI. Specifically, the injection pressure of the first CSI phase in Test 2 was gradually increased from 3500 to 5500 kPa. The second and the third CSI phases had the same injection pressure as Test 1 at 5500 kPa. The CSI process ceased once the oil recovery was less than 0.5% of the original oil in place (OOIP) in a single cycle. Results show that the ethane-based CSI process is sensitive to injection pressure. A high injection pressure is crucial for optimal oil recovery. The first CSI phase in Test 2, where the injection pressure was increased gradually, resulted in a 2.9% lower oil recovery and five times as much ethane consumption compared to Test 1, which applied a high injection pressure. It was also found that water flooding improved the oil recovery in the CSI process. In Test 2, the oil recovery factor of the second CSI phase increased by 57% after the water flooding process, which is likely due to the formation of water channels and a dispersed oil phase that increased the contact area between ethane and oil. Although the nanoparticle-solution flooding only had 0.3% oil recovery, after that the third CSI phase stimulated another 10.8% of OOIP even when the water saturation achieved more than 65%. This demonstrated that the addition of nanoparticles can maintain the stability of the foam and enhance the transfer of ethane to the heavy oil. Finally, Test 2 reached a total oil recovery factor of 76.1% on a lab scale, an increase of 45% compared to the single EOR method, which proved the combination process is an efficient method to develop a heavy oil field.
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(This article belongs to the Section H1: Petroleum Engineering)
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An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers
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Wenhui Li, Qianqian Zhou, Yuzhong Zhang, Jianming Xie, Wei Zhao, Jinglun Li and Hui Cui
Energies 2024, 17(6), 1456; https://doi.org/10.3390/en17061456 - 18 Mar 2024
Abstract
(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or
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(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or the criminal smuggling of nuclear materials. High count rates can lead to signal accumulation, negatively affecting the performance of gamma spectrometers, and in severe cases, even damaging the detectors. Higher pulse throughput with better energy resolution is the ultimate goal of a gamma-ray spectrometer. Traditionally, pileup pulses, which cause dead time and affect throughput, are rejected to maintain good energy resolution. (2) Method: In this paper, an ultra-throughput boost (UTB) off-line processing method was used to improve the throughput and reduce the pileup effect of the spectrometer. Firstly, by fitting the impulse signal of the detector, the response matrix was built by the functional model of a dual exponential tail convolved with the Gaussian kernel; then, a quadratic programming method based on a non-negative least squares (NNLS) algorithm was adopted to solve the constrained optimization problem for the inversion. (3) Results: Both the simulated and experimental results of the UTB method show that most of the impulses in the pulse sequence from the scintillator detector were restored to δ-like pulses, and the throughput of the UTB method for the NaI(Tl) spectrometer reached 207 kcps with a resolution of 7.71% @661.7 keV. A reduction was also seen in the high energy pileup phenomenon. (4) Conclusions: We conclude that the UTB method can restore individual and piled-up pulses to δ-like sequences, effectively boosting pulse throughput and suppressing high-energy tailing and sum peaks caused by the pileup effect at the cost of a slight loss in energy resolution.
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(This article belongs to the Special Issue Advancements in Nuclear Energy Technology)
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Scalable and Quench-Free Processing of Metal Halide Perovskites in Ambient Conditions
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Carsen Cartledge, Saivineeth Penukula, Antonella Giuri, Kayshavi Bakshi, Muneeza Ahmad, Mason Mahaffey, Muzhi Li, Rui Zhang, Aurora Rizzo and Nicholas Rolston
Energies 2024, 17(6), 1455; https://doi.org/10.3390/en17061455 - 18 Mar 2024
Abstract
With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth-abundant materials; however, scalability,
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With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth-abundant materials; however, scalability, stability, and durability remain key challenges. In order to transition from small-scale processing in inert environments to higher throughput processing in ambient conditions, the fundamentals of perovskite crystallization must be understood. Classical nucleation theory, the LaMer relation, and nonclassical crystallization considerations are discussed to provide a mechanism by which a gellan gum (GG) additive—a nontoxic polymeric saccharide—has enabled researchers to produce quality halide perovskite thin-film blade coated in ambient conditions without a quench step. Furthermore, we report on the improved stability and durability properties inherent to these films, which feature improved morphologies and optoelectronic properties compared to films spin-coated in a glovebox with antisolvent. We tune the amount of GG in the perovskite precursor and study the interplay between GG concentration and processability, morphological control, and increased stability under humidity, heat, and mechanical testing. The simplicity of this approach and insensitivity to environmental conditions enable a wide process window for the production of low-defect, mechanically robust, and operationally stable perovskites with fracture energies among the highest obtained for perovskites.
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(This article belongs to the Collection Featured Papers in Solar Energy and Photovoltaic Systems Section)
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Research on Adaptive Droop Control Strategy for a Solar-Storage DC Microgrid
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Zhanpeng Xu, Fuxin Chen, Kewei Chen and Qinfen Lu
Energies 2024, 17(6), 1454; https://doi.org/10.3390/en17061454 - 18 Mar 2024
Abstract
When the solar-storage DC microgrid operates in islanded mode, the battery needs to stabilize the bus voltage and keep the state of charge (SOC) balanced in order to extend the service life of the battery and the islanded operation time. When there are
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When the solar-storage DC microgrid operates in islanded mode, the battery needs to stabilize the bus voltage and keep the state of charge (SOC) balanced in order to extend the service life of the battery and the islanded operation time. When there are multiple energy storage units in the DC microgrid, it is necessary to solve the problem of unbalanced circulation and the state of charge between batteries using a reasonable droop control method. In this paper, firstly, the DC–DC charging and discharging circuit of the battery is designed, and the unbalanced SOC of the battery caused by the different impedances of the line is analyzed. Secondly, an adaptive droop control method is proposed to solve the problems of SOC imbalance and current circulation between the batteries. Thirdly, based on MATLAB/SIMULINK R2021b simulation software, the proposed control method is modeled and simulated. Compared with the traditional droop control, the effectiveness of the proposed method is validated.
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(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Study on the Influence of International Economic Law of Carbon Emission Trading on Environmental Sustainable Development
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Ziying Chen and Jin-Tae Kim
Energies 2024, 17(6), 1453; https://doi.org/10.3390/en17061453 - 18 Mar 2024
Abstract
With the continuous development of global economic and trade activities, environmental problems have become an important factor restricting the sustainable development of all countries. How to realize the coordinated development of international trade and environmental protection has become a major issue facing the
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With the continuous development of global economic and trade activities, environmental problems have become an important factor restricting the sustainable development of all countries. How to realize the coordinated development of international trade and environmental protection has become a major issue facing the international community. Since China joined the WTO, its share of international trade has been increasing continuously. In order to deeply analyze the influence of international carbon emission trading policy on domestic carbon emissions, we use an input–output model and a GTAP analysis method to theoretically calculate the carbon emissions of the international trade of various departments in Shandong Province. At the same time, the implicit carbon emission index of various industries in 2022 is calculated through the direct energy consumption coefficient. The results show that there are significant differences in the impact of the carbon tariff system on different industries. In terms of the carbon emission index, the food processing industry showed a decrease of 18.99 Mt, while the implied carbon emission of the tobacco, textile and leather manufacturing industry reached 30.56 Mt due to the continuous expansion of trade scale. In contrast, the implied carbon emission level of the metal product processing industry reached 5.3 Mt, while the carbon emission of traditional trading industries such as coal mining was almost unaffected by international trade, and its carbon emission index reached the highest level of 5.89 in 2020. In terms of trade impact, high-trade industries such as the food processing industry are significantly affected by the carbon tariff policy, and their share has dropped from 5.89% to 3.95% in the past decade. The carbon emissions generated by GDP growth established by the GTAP model are more convincing. This model can directly reflect the energy efficiency of a region from the side. Based on the present situation of international trade, this paper analyzes the inequality of the current carbon tariff system, and puts forward some policies to optimize the energy structure to reduce carbon emissions and expand domestic demand to reduce the dependence on international trade. Through the GTAP model, we put forward policy suggestions to optimize the energy structure to reduce carbon emissions and the dependence on international trade by expanding domestic demand.
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(This article belongs to the Special Issue Advances in Marketing Researches for Sustainable Development of Energy Economic)
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Open AccessArticle
Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning
by
Fernando Ulloa-Vásquez, Victor Heredia-Figueroa, Cristóbal Espinoza-Iriarte, José Tobar-Ríos, Fernanda Aguayo-Reyes, Dante Carrizo and Luis García-Santander
Energies 2024, 17(6), 1452; https://doi.org/10.3390/en17061452 - 18 Mar 2024
Abstract
The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals
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The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO2 reduction.
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(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)
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Open AccessArticle
Analyzing Supply Reliability Incentive in Pricing Regulation of Electricity Distribution Operators
by
Joel Seppälä and Pertti Järventausta
Energies 2024, 17(6), 1451; https://doi.org/10.3390/en17061451 - 18 Mar 2024
Abstract
In support of the global green transition, numerous policies have been introduced to efficiently address the increasing demand for reliable electricity. However, the impacts of these policies have received limited attention, despite the potential for unsuccessful policy targets to introduce inefficiencies into the
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In support of the global green transition, numerous policies have been introduced to efficiently address the increasing demand for reliable electricity. However, the impacts of these policies have received limited attention, despite the potential for unsuccessful policy targets to introduce inefficiencies into the energy system, subsequently diminishing societal wealth. This study bridges this research gap by conducting a comprehensive examination of a supply reliability incentive within electricity pricing regulation, aiming to contribute new insights for policy assessments. Analyzing data from all electricity distribution operators within a single jurisdiction, the study investigates the volume and distribution of economic steering to elucidate the overall societal impact. The findings suggest a rewarding system for positive developments in indices, regardless of the absolute interruption index levels, highlighting the importance of precise variable definitions in implementing incentive mechanisms. The assessment tools developed for this study will be valuable for further regulation and policy assessments.
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(This article belongs to the Section F: Electrical Engineering)
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Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models
by
Paraskevas Koukaras, Akeem Mustapha, Aristeidis Mystakidis and Christos Tjortjis
Energies 2024, 17(6), 1450; https://doi.org/10.3390/en17061450 - 18 Mar 2024
Abstract
The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building
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The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using , root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.
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(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
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Spinning of Carbon Nanofiber/Ni–Cu–S Composite Nanofibers for Supercapacitor Negative Electrodes
by
Qiong Li, Yu Wang, Ganghui Wei, Xiaorong Fang, Ni Lan, Yonggang Zhao, Qiming Liu, Shumei Lin and Deyan He
Energies 2024, 17(6), 1449; https://doi.org/10.3390/en17061449 - 18 Mar 2024
Abstract
The preparation of composite carbon nanomaterials is one of the methods for improving the electrochemical performance of carbon-based electrode materials for supercapacitors. However, traditional preparation methods are complicated and time-consuming, and the binder also leads to an increase in impedance and a decrease
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The preparation of composite carbon nanomaterials is one of the methods for improving the electrochemical performance of carbon-based electrode materials for supercapacitors. However, traditional preparation methods are complicated and time-consuming, and the binder also leads to an increase in impedance and a decrease in specific capacitance. Therefore, in this work, we reduced Ni-Cu nanoparticles on the surface of nitrogen-doped carbon nanofibers (CNFs) by employing an electrostatic spinning method combined with pre-oxidation and annealing treatments. At the same time, Ni-Cu nanoparticles were vulcanized to Ni–Cu–S nanoparticles without destroying the structure of the CNFs. The area-specific capacitance of the CNFs/Ni–Cu–S–300 electrode reaches 1208 mF cm−2 at a current density of 1 mA cm−2, and the electrode has a good cycling stability with a capacitance retention rate of 76.5% after 5000 cycles. As a self-supporting electrode, this electrode can avoid the problem of the poor adhesion of electrode materials and the low utilization of active materials due to the inactivity of the binder and conductive agent in conventional collector electrodes, so it has excellent potential for application.
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(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)
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