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 (Control and Optimization)
- 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
Performance Analysis of Vermiculite–Potassium Carbonate Composite Materials for Efficient Thermochemical Energy Storage
Energies 2024, 17(12), 2847; https://doi.org/10.3390/en17122847 (registering DOI) - 9 Jun 2024
Abstract
In this study, the preparation of the composite material consisting of expanded vermiculite (EV) and potassium carbonate (K2CO3) was conducted using a solution impregnation method. Sorption and desorption experiments were undertaken to investigate the dynamic and thermodynamic properties of
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In this study, the preparation of the composite material consisting of expanded vermiculite (EV) and potassium carbonate (K2CO3) was conducted using a solution impregnation method. Sorption and desorption experiments were undertaken to investigate the dynamic and thermodynamic properties of the EV/K2CO3 composites with varying salt contents. The findings suggest that the EV/K2CO3 composites effectively address the issues of solution leakage resulting from the deliquescence and excessive hydration of pure K2CO3 salt, thereby substantially improving the water sorption capacity and overall stability of the composite materials. The salt content plays a vital role in the sorption and desorption processes of EV/K2CO3 composites. As the salt content rises, the resistance to sorption mass transfer increases, resulting in a decline in the average sorption rate. Concurrently, as the salt content increases, there is a corresponding increase in the average desorption rate, water uptake, and heat storage density. Specifically, at a temperature of 30 °C and a relative humidity of 60%, the EVPC40 composite with a salt content of 67.4% demonstrates water uptake, mass energy density, and volumetric energy density values of 0.68 g/g, 1633.6 kJ/kg, and 160 kWh/m3, respectively. In comparison to pure K2CO3 salt, the utilization of EV/K2CO3 composites under identical heat demand conditions results in a 57% reduction in the required reaction material. This study offers essential empirical evidence and theoretical backing for the utilization and development of EV/K2CO3 composites within thermochemical energy storage systems.
Full article
(This article belongs to the Topic Thermal Energy Transfer and Storage)
Open AccessReview
Non-Linear Phenomena in Voltage and Frequency Converters Supplying Non-Thermal Plasma Reactors
by
Grzegorz Karol Komarzyniec, Henryka Danuta Stryczewska and Oleksandr Boiko
Energies 2024, 17(12), 2846; https://doi.org/10.3390/en17122846 (registering DOI) - 9 Jun 2024
Abstract
Atmospheric pressure cold plasmas have recently been the subject of intense research and applications for solving problems in the fields of energy, environmental engineering, and biomedicine. Non-thermal atmospheric pressure plasma sources, with dielectric barrier discharges, plasma jets, and arc discharges, are non-linear power
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Atmospheric pressure cold plasmas have recently been the subject of intense research and applications for solving problems in the fields of energy, environmental engineering, and biomedicine. Non-thermal atmospheric pressure plasma sources, with dielectric barrier discharges, plasma jets, and arc discharges, are non-linear power loads. They require special power systems, which are usually designed separately for each type of plasma reactor, depending on the requirements of the plasma-chemical process, the power of the receiver, the type of process gas, the current, voltage and frequency requirements, and the efficiency of the power source. This paper presents non-linear phenomena accompanying plasma generation in the power supply plasma reactor system, such as harmonic generation, resonance, and ferroresonance of currents and voltages, and the switching of overvoltages and pulse generation. When properly applied, this can support the operation of the above-mentioned reactors by providing improved discharge ignition depending on the working gas, thus increasing the efficiency of the plasma process and improving the cooperation of the plasma-generation system with the power supply.
Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering 2024)
Open AccessArticle
Study on Discharge Characteristic Performance of New Energy Electric Vehicle Batteries in Teaching Experiments of Safety Simulation under Different Operating Conditions
by
Meilin Gong, Jiatao Chen, Jianming Chen and Xiaohuan Zhao
Energies 2024, 17(12), 2845; https://doi.org/10.3390/en17122845 (registering DOI) - 9 Jun 2024
Abstract
High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the
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High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the laboratory testing of driving conditions of electric vehicles, this paper simulated and compared the discharge characteristics of two common batteries (lithium iron phosphate (LFP) battery and nickel–cobalt–manganese (NCM) ternary lithium battery) in three different operating conditions. The operating conditions are the NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure) and CLTC-P (China light vehicle test cycle) for normal driving of electric vehicles. LFP batteries have a higher maximum voltage and lower minimum voltage under the same initial voltage conditions, with a maximum voltage difference variation of 11 V. The maximum current of WLTP is significantly higher than NEDC and CLTC-P operating conditions (>20 A). Low current discharge conditions should be emulated in teaching simulation and experiments for safety reasons. The simulation data showed that the LFP battery had good performance in maintaining the voltage plateau and discharge voltage stability, while the NCM battery had excellent energy density and long-term endurance.
Full article
(This article belongs to the Special Issue Advances in Hybrid Vehicles: Volume II)
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Open AccessArticle
Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation
by
Dimitrios Kontogiannis, Dimitrios Bargiotas, Athanasios Fevgas, Aspassia Daskalopulu and Lefteri H. Tsoukalas
Energies 2024, 17(12), 2844; https://doi.org/10.3390/en17122844 (registering DOI) - 9 Jun 2024
Abstract
Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant
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Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption forecasting models need to capture feature nonlinearities, analyze system dynamics and conserve evolving temporal patterns in order to minimize the impact of noise and adapt to concept drift. Prominent estimators and standalone decomposition-based approaches may not fully address those challenges as they often yield small error rate improvements and omit optimal time series evolution. Therefore, in this work we propose a combinatorial component decomposition method focused on the selection of important renewable generation component sequences extracted from the combined output of seasonal-trend decomposition using locally estimated scatterplot smoothing, singular spectrum analysis and empirical mode decomposition methods. The proposed method was applied on five well-known kernel models in order to evaluate day-ahead consumption forecasts on linear, tree-based and neural network structures. Moreover, for the assessment of pattern conservation, an intuitive metric function, labeled as Weighted Average Unanchored Chain Divergence (WAUCD), based on distance scores and unanchored time series chains is introduced. The results indicated that the application of the combinatorial component method improved the accuracy and the pattern conservation capabilities of most models substantially. In this examination, the long short-term memory (LSTM) and deep neural network (DNN) kernels reduced their mean absolute percentage error by 46.87% and 42.76% respectively and predicted sequences that consistently evolved over 30% closer to the original target in terms of daily and weekly patterns.
Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering 2024)
Open AccessArticle
Influence of γ-Fe2O3 Nanoparticles Added to Gasoline–Bio-Oil Blends Derived from Plastic Waste on Combustion and Emissions Generated in a Gasoline Engine
by
Paul Palmay, Diego Barzallo, Cesar Puente, Ricardo Robalino, Dayana Quinaluisa and Joan Carles Bruno
Energies 2024, 17(12), 2843; https://doi.org/10.3390/en17122843 (registering DOI) - 9 Jun 2024
Abstract
The environmental pressure to reduce the use of fossil fuels such as gasoline generates the need to search for new fuels that have similar characteristics to conventional fuels. In this sense, the objective of the present study is the use of commercial gasoline
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The environmental pressure to reduce the use of fossil fuels such as gasoline generates the need to search for new fuels that have similar characteristics to conventional fuels. In this sense, the objective of the present study is the use of commercial gasoline in mixtures with pyrolytic oil from plastic waste and the addition of γ-Fe2O3 nanoparticles (NPs) in a spark ignition engine to analyze both the power generated in a real engine and the emissions resulting from the combustion process. The pyrolytic oil used was obtained from thermal pyrolysis at low temperatures (450 °C) of a mixture composed of 75% polystyrene (PS) and 25% polypropylene (PP), which was mixed with 87 octane commercial gasoline in 2% and 5% by volume and 40 mg of γ-Fe2O3 NPs. A standard sample was proposed, which was only gasoline, one mixture of gasoline with bio-oil, and a gasoline, bio-oil, and NPs mixture. The bio-oil produced from the pyrolysis of PS and PP enhances the octane number of the fuel and improves the engine’s power performance at low revolutions. In contrast, the addition of iron NPs significantly improves gaseous emissions with a reduction in emissions of CO (carbon monoxide), NOx (nitrogen oxide), and HCs (hydrocarbons) due to its advantages, which include its catalytic effect, presence of active oxygen, and its large surface area.
Full article
(This article belongs to the Section I1: Fuel)
Open AccessArticle
A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition
by
Peifang Tian, Zhiyuan Gao and Yu Hao
Energies 2024, 17(12), 2842; https://doi.org/10.3390/en17122842 (registering DOI) - 9 Jun 2024
Abstract
Abstract: Energy transition, as a crucial aspect of the country’s high-value-added economic development, involves the construction of an energy transition index system and empirical analysis using methods such as the entropy weighting method, kernel density estimation, Markov chain, Dagum Gini coefficient, σ-convergence, and
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Abstract: Energy transition, as a crucial aspect of the country’s high-value-added economic development, involves the construction of an energy transition index system and empirical analysis using methods such as the entropy weighting method, kernel density estimation, Markov chain, Dagum Gini coefficient, σ-convergence, and β-convergence. This study measures the level of energy transition in 280 Chinese cities from 2010 to 2019 and analyzes their evolutionary trends, regional disparities, structural differences, and convergence. The findings reveal that China’s energy transition generally exhibits characteristics of “improvement in development levels and reduction in absolute disparities”. The disparities in energy transition primarily stem from developmental differences among the three major regions, displaying typical σ-convergence and β-convergence characteristics. This analysis contributes to understanding the real level and distribution features of China’s energy transition, providing a basis for identifying focal points for enhancing energy transition in the current and future stages.
Full article
(This article belongs to the Section C: Energy Economics and Policy)
Open AccessArticle
A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell
by
Valery A. Danilov, Gunther Kolb and Carsten Cremers
Energies 2024, 17(12), 2841; https://doi.org/10.3390/en17122841 (registering DOI) - 9 Jun 2024
Abstract
A dynamic tanks-in-series model has been developed for the coupled heat, mass, and charge transfer processes in a high-temperature proton exchange membrane fuel cell. The semi-empirical model includes the heat and mass balance equations in the gas channels and the membrane electrode assembly
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A dynamic tanks-in-series model has been developed for the coupled heat, mass, and charge transfer processes in a high-temperature proton exchange membrane fuel cell. The semi-empirical model includes the heat and mass balance equations in the gas channels and the membrane electrode assembly together with the charge balance at the electrode/membrane interfaces. The outputs of the tanks-in-series model are the concentration, the temperature, and the current density with a step change from tank to tank. The dynamic non-isothermal model is capable of predicting both the transient and steady-state behavior of the fuel cell and reproducing impedance data under harmonic perturbations of the cell potential together with a comprehensive interpretation of experimental data.
Full article
(This article belongs to the Special Issue Solid Oxide Fuel Cells: Modelling and Research)
Open AccessArticle
Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland
by
Aleksei Vakhnin, Ivan Ryzhikov, Christina Brester, Harri Niska and Mikko Kolehmainen
Energies 2024, 17(12), 2840; https://doi.org/10.3390/en17122840 (registering DOI) - 9 Jun 2024
Abstract
Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak
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Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests.
Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Efficient Buildings)
Open AccessArticle
Trace Elements in Maize Biomass Used to Phyto-Stabilise Iron-Contaminated Soils for Energy Production
by
Mirosław Wyszkowski and Natalia Kordala
Energies 2024, 17(12), 2839; https://doi.org/10.3390/en17122839 (registering DOI) - 8 Jun 2024
Abstract
The aim of the study was to determine the feasibility of using maize biomass for the phyto-stabilisation of iron-contaminated soils under conditions involving the application of humic acids (HAs). The biomass yield content of maize trace elements was analysed. In the absence of
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The aim of the study was to determine the feasibility of using maize biomass for the phyto-stabilisation of iron-contaminated soils under conditions involving the application of humic acids (HAs). The biomass yield content of maize trace elements was analysed. In the absence of HAs, the first dose of Fe-stimulated plant biomass growth was compared to the absence of Fe contamination. The highest soil Fe contamination resulted in a very large reduction in maize biomass yield, with a maximum of 93%. The addition of HAs had a positive effect on plant biomass, with a maximum of 53%, and reduced the negative effect of Fe. There was an almost linear increase in maize biomass yield with increasing doses of HAs. Analogous changes were observed in dry matter content in maize. Soil treatment with Fe caused a significant increase in its content in maize biomass, with a maximum increase of three times in the series without HAs. There was also a decrease in Co, Cr and Cd content (by 17%, 21% and 44%, respectively) and an increase in Cu, Ni, Pb, Zn and Mn accumulation (by 32%, 63%, 75%, 97% and 203%, respectively). The application of HAs to the soil reduced the content of this trace element and its growth in the biomass of this plant under the influence of Fe contamination. They had a similar effect on other trace elements contained in the maize biomass. HAs contributed to a decrease in the level of most of the tested trace elements (except Ni and Pb) in the maize biomass. The reduction ranged from 11% (Cr and Mn) to 72% (Cd). The accumulation of Ni and Pb in the maize biomass was higher in the objects with HAs application than in the series without their addition. Humic acid application is a promising method for the reduction of the effects of soil Fe contamination on plants.
Full article
(This article belongs to the Special Issue Biomass and Bio-Energy—2nd Edition)
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Open AccessArticle
Advancing Industrial Process Electrification and Heat Pump Integration with New Exergy Pinch Analysis Targeting Techniques
by
Timothy Gordon Walmsley, Benjamin James Lincoln, Roger Padullés and Donald John Cleland
Energies 2024, 17(12), 2838; https://doi.org/10.3390/en17122838 (registering DOI) - 8 Jun 2024
Abstract
The process integration and electrification concept has significant potential to support the industrial transition to low- and net-zero-carbon process heating. This increasingly essential concept requires an expanded set of process analysis tools to fully comprehend the interplay of heat recovery and process electrification
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The process integration and electrification concept has significant potential to support the industrial transition to low- and net-zero-carbon process heating. This increasingly essential concept requires an expanded set of process analysis tools to fully comprehend the interplay of heat recovery and process electrification (e.g., heat pumping). In this paper, new Exergy Pinch Analysis tools and methods are proposed that can set lower bound work targets by acutely balancing process heat recovery and heat pumping. As part of the analysis, net energy and exergy load curves enable visualization of energy and exergy surpluses and deficits. As extensions to the grand composite curve in conventional Pinch Analysis, these curves enable examination of different pocket-cutting strategies, revealing their distinct impacts on heat, exergy, and work targets. Demonstrated via case studies on a spray dryer and an evaporator, the exergy analysis targets net shaft-work correctly. In the evaporator case study, the analysis points to the heat recovery pockets playing an essential role in reducing the work target by 25.7%. The findings offer substantial potential for improved industrial energy management, providing a robust framework for engineers to enhance industrial process and energy sustainability.
Full article
(This article belongs to the Special Issue Advanced Research on Heat Exchangers Networks and Heat Recovery)
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Open AccessArticle
Energy Poverty and Democratic Values: A European Perspective
by
Aleksy Kwilinski, Oleksii Lyulyov and Tetyana Pimonenko
Energies 2024, 17(12), 2837; https://doi.org/10.3390/en17122837 (registering DOI) - 8 Jun 2024
Abstract
This paper explores the complex relationship between energy poverty and the maintenance of democratic values within the European Union (EU), suggesting that energy poverty not only impacts economic stability and health outcomes but also poses significant challenges to democratic engagement and equity. To
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This paper explores the complex relationship between energy poverty and the maintenance of democratic values within the European Union (EU), suggesting that energy poverty not only impacts economic stability and health outcomes but also poses significant challenges to democratic engagement and equity. To measure energy poverty, a composite index is developed using the entropy method, which surpasses traditional measures focused solely on access to energy or its developmental implications. To assess the level of democratic governance in EU countries, the voice and accountability index (VEA), which is part of the World Governance Indicators compiled by the World Bank, is utilized. By analyzing EU data from 2006 to 2022, the findings suggest that a 1% improvement in VEA quality, represented by a coefficient of 0.122, is correlated with a notable improvement in the energy poverty index. This suggests that the EU should focus on enhancing transparency and public participation in energy decision-making, along with ensuring accountability in policy implementation. The research also differentiates between full and flawed democracies, noting that tailored approaches are needed. In full democracies, leveraging economic prosperity and trade is crucial due to their significant positive impacts on the energy poverty index. In contrast, in flawed democracies, enhancing governance and accountability is more impactful, as evidenced by a higher coefficient of 0.193. Strengthening legal and regulatory frameworks, improving regulatory quality, and ensuring public engagement in governance could substantially mitigate energy poverty in these contexts. In addition, this paper demonstrates that this relationship is influenced by factors such as income inequality, energy intensity, and trade openness.
Full article
(This article belongs to the Special Issue Environmental Footprint of Energy Production and Storage Systems Based on Renewable Energy Sources)
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Open AccessReview
Comprehensive Overview of Recent Research and Industrial Advancements in Nuclear Hydrogen Production
by
Venizelos Venizelou and Andreas Poullikkas
Energies 2024, 17(12), 2836; https://doi.org/10.3390/en17122836 (registering DOI) - 8 Jun 2024
Abstract
As new sources of energy and advanced technologies are used, there is a continuous evolution in energy supply, demand, and distribution. Advanced nuclear reactors and clean hydrogen have the opportunity to scale together and diversify the hydrogen production market away from fossil fuel-based
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As new sources of energy and advanced technologies are used, there is a continuous evolution in energy supply, demand, and distribution. Advanced nuclear reactors and clean hydrogen have the opportunity to scale together and diversify the hydrogen production market away from fossil fuel-based production. Nevertheless, the technical uncertainties surrounding nuclear hydrogen processes necessitate thorough research and a solid development effort. This paper aims to position pink hydrogen for nuclear hydrogen production at the forefront of sustainable energy-related solutions by offering a comprehensive review of recent advancements in nuclear hydrogen production, covering both research endeavors and industrial applications. It delves into various pink hydrogen generation methodologies, elucidating their respective merits and challenges. Furthermore, this paper analyzes the evolving landscape of pink hydrogen in terms of its levelized cost by comparatively assessing different production pathways. By synthesizing insights from academic research and industrial practices, this paper provides valuable perspectives for stakeholders involved in shaping the future of nuclear hydrogen production.
Full article
(This article belongs to the Special Issue Advances in Hydrogen Energy III)
Open AccessArticle
A Two-Stage Twisted Blade μ-Vertical Axis Wind Turbine: An Enhanced Savonius Rotor Design
by
Andrés Pérez-Terrazo, Martin Moreno, Iván Trejo-Zúñiga and José Alberto López
Energies 2024, 17(12), 2835; https://doi.org/10.3390/en17122835 (registering DOI) - 8 Jun 2024
Abstract
Wind turbines are a solution for sustainable energy, significantly reducing carbon emissions and fostering a circular economy for more cost-effective and cleaner power generation, in line with worldwide environmental aspirations. In this context, this research aims to explore a novel two-stage, twisted-blade micro-Vertical-Axis
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Wind turbines are a solution for sustainable energy, significantly reducing carbon emissions and fostering a circular economy for more cost-effective and cleaner power generation, in line with worldwide environmental aspirations. In this context, this research aims to explore a novel two-stage, twisted-blade micro-Vertical-Axis Wind Turbine ( -VAWT)alternative inspired by the Savonius Rotor (SR). This investigation utilizes the SST turbulence model to explore the power coefficient ( ) and torque coefficient ( ), finding values ranging from 0.02 to 0.08 across the turbine by altering the free stream velocity (V). analysis further delves into four specific sections, highlighting areas of particular interest. These results are validated by examining velocity contours, pressure contours, and streamlines in four horizontal sections, demonstrating that the proposed turbine model exhibits minimal torque fluctuation. Moreover, the analysis of vertical wind streamlines illustrates very low interference with various wind turbine proposals, underscoring the turbine’s efficiency and potential for integration into diverse wind energy projects.
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(This article belongs to the Special Issue Low Carbon Energy Generation and Utilization Technologies)
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Open AccessArticle
Framework to Develop Electric School Bus Vehicle-to-Grid (ESB V2G) Systems Supplied with Solar Energy in the United States
by
Francisco Haces-Fernandez
Energies 2024, 17(12), 2834; https://doi.org/10.3390/en17122834 (registering DOI) - 8 Jun 2024
Abstract
Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction
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Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction in greenhouse gases, aiding in the fight against climate change. However, due to their significant size and long routes, ESBs require large batteries with significant electricity demand. If this additional electricity demand is supplied to hundreds of thousands of EBSs at peak consumption times, the strain on the grid may be detrimental, while transportation costs for schools could dramatically increase. Furthermore, if EBSs are charged using traditional hydrocarbon generation, the environmental benefits of these projects may be significantly reduced. Therefore, applying renewable energy presents a host of synergistic opportunities to reduce emissions while providing inexpensive electricity to schools. Solar energy is abundant in large portions of the US, potentially providing many schools with ample inexpensive and sustainable electricity to power their transportation equipment and meet other requirements at their facilities. This research developed a novel framework to integrate publicly available big data provided by federal and state agencies in the US, as well as National Laboratories, to provide stakeholders with actionable information to develop EBS grid-to-vehicle (V2G) systems across the US. Geographic Information Systems, data analytics and Business Intelligence were applied to assess and characterize solar energy generation and consumption patterns. The novel integration of the systems in the proposed framework provided encouraging results that have practical implications for stakeholders to develop successful and sustainable ESB V2G facilities. These results identified many schools across the US that would significantly benefit from the use of solar energy and be able to supply their local communities during idle times with renewable energy through V2G. The renewable energy resource would be capable of charging ESBs at a low cost for operational availability as required. The results indicate that the proposed ESB V2G system will provide significant benefits to both schools and their local communities. The feasibility of the proposed endeavor was validated by the results of the study, providing both school and solar energy stakeholders with insights into how to better manage such a complex system.
Full article
(This article belongs to the Section D: Energy Storage and Application)
Open AccessArticle
Thermodynamic Analysis of the Combustion Process in Hydrogen-Fueled Engines with EGR
by
Stanislaw Szwaja, Andrzej Piotrowski, Magdalena Szwaja and Dorota Musial
Energies 2024, 17(12), 2833; https://doi.org/10.3390/en17122833 (registering DOI) - 8 Jun 2024
Abstract
This article presents a novel approach to the analysis of heat release in a hydrogen-fueled internal combustion spark-ignition engine with exhaust gas recirculation (EGR). It also discusses aspects of thermodynamic analysis common to modeling and empirical analysis. This new approach concerns a novel
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This article presents a novel approach to the analysis of heat release in a hydrogen-fueled internal combustion spark-ignition engine with exhaust gas recirculation (EGR). It also discusses aspects of thermodynamic analysis common to modeling and empirical analysis. This new approach concerns a novel method of calculating the specific heat ratio (cp/cv) and takes into account the reduction in the number of moles during combustion, which is characteristic of hydrogen combustion. This reduction in the number of moles was designated as a molar contraction. This is particularly crucial when calculating the average temperature during combustion. Subsequently, the outcomes of experimental tests, including the heat-release rate, the initial combustion phase (denoted CA0-10) and the main combustion phase (CA10-90), are presented. Furthermore, the impact of exhaust gas recirculation on the combustion process in the engine is also discussed. The efficacy of the proposed measures was validated by analyzing the heat-release rate and calculating the mean combustion temperature in the engine. The application of EGR in the range 0-40% resulted in a notable prolongation of both the initial and main combustion phases, which consequently influenced the mean combustion temperature.
Full article
(This article belongs to the Special Issue Computational and Data-Driven Modeling of Combustion in Reciprocating Engines or Gas Turbines, Volume II)
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Open AccessArticle
Suitability Analysis of Selected Methods for Modelling Infrasound and Low-Frequency Noise from Wind Turbines
by
Bartłomiej Stępień, Tadeusz Wszołek, Dominik Mleczko, Paweł Małecki, Paweł Pawlik, Maciej Kłaczyński and Marcjanna Czapla
Energies 2024, 17(12), 2832; https://doi.org/10.3390/en17122832 (registering DOI) - 8 Jun 2024
Abstract
Wind turbines emit infrasound and low-frequency noise (ILFN), which can be annoying for people living near wind farms. To assess the acoustic impact of wind turbines on the environment, it is essential to model ILFN propagation during the forecasting stage. This study assesses
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Wind turbines emit infrasound and low-frequency noise (ILFN), which can be annoying for people living near wind farms. To assess the acoustic impact of wind turbines on the environment, it is essential to model ILFN propagation during the forecasting stage. This study assesses the effectiveness of three commonly used sound propagation models (ISO 9613-2, CNOSSOS-EU for favourable propagation conditions, Nord2000) in predicting ILFN generated by wind turbines. The performance of these models in modelling ILFN is generally not validated or guaranteed. The analysis covers octave frequency bands ranging from 4 Hz to 250 Hz, and comparisons are made against measurements conducted at a wind farm in Poland. Non-parametric statistical tests were used with a significance level of to determine significant differences between measured and predicted results. The results show that the Nord2000 method provides accurate calculations, while the ISO 9613-2 method can be used for simplified assessments of ILFN generated by wind turbines during the investment preparation phase.
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(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
Open AccessArticle
Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models
by
Minan Tang, Changyou Wang, Jiandong Qiu, Hanting Li, Xi Guo and Wenxin Sheng
Energies 2024, 17(12), 2831; https://doi.org/10.3390/en17122831 (registering DOI) - 8 Jun 2024
Abstract
The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to
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The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability.
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(This article belongs to the Section G1: Smart Cities and Urban Management)
Open AccessArticle
A Modified Variable Power Angle Control for Unified Power Quality Conditioner in a Distorted Utility Source
by
Krittapas Chaiyaphun, Phonsit Santiprapan and Kongpol Areerak
Energies 2024, 17(12), 2830; https://doi.org/10.3390/en17122830 (registering DOI) - 8 Jun 2024
Abstract
The distorted supply voltage degrades the control performance of a unified power quality conditioner (UPQC). This problem causes incorrect calculations in the harmonic identification and reference signal generation processes. This paper proposes a modified harmonic identification of the UPQC. The reference compensating current
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The distorted supply voltage degrades the control performance of a unified power quality conditioner (UPQC). This problem causes incorrect calculations in the harmonic identification and reference signal generation processes. This paper proposes a modified harmonic identification of the UPQC. The reference compensating current calculation for the shunt active power filter (shunt APF) is developed using the sliding window with the Fourier analysis (SWFA) method. In addition, the variable power angle control (PAC) is applied to operate the reference signal generation of the series APF and the shunt APF of the UPQC. Under the distorted voltage and nonlinear load conditions, the proposed approach can provide accurate reference compensating signals and successfully share the load reactive power compensation between the shunt APF and the series APF. In this work, a three-phase, three-wire power system with linear and nonlinear loads was implemented. The proposed method was validated using the processor-in-the-loop technique on an eZdsp™ F28335 board and the MATLAB/Simulink program. The testing results indicated that SWFA has excellent filtering performance and enhances harmonic identification compared to the operation without any filter or with low pass filters (LPF). With the proposed approach, the percentage of total harmonic distortion of voltage and current could be maintained within the IEEE519-2022 standard, and the magnitude of the RMS voltage across the load was in the recommended range specified by ANSI C84.1-2016.
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(This article belongs to the Section F: Electrical Engineering)
Open AccessArticle
The Inter-Regional Embodied Carbon Flow Pattern in China Based on Carbon Peaking Stress
by
Qianqian Xiao, Zi’ang Chu and Changfeng Shi
Energies 2024, 17(12), 2829; https://doi.org/10.3390/en17122829 (registering DOI) - 8 Jun 2024
Abstract
Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows
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Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows can more clearly show what causes the carbon peaking stress and which carbon flow paths are more critical. In this study, the decoupling index of carbon emissions and economy development was applied to characterize the carbon peaking stress in each region, and the environmental extended multi-regional input–output model was applied to re-evaluate the criticality of regional embodied carbon flows. The results showed that the carbon peaking stress in China improved from 2007 to 2012, but the rebound of carbon peaking stress in 2017 made most regions reverse the previous downward trend. The stress to reach carbon peaks varies considerably from region to region, and the stress in the northwest is much higher than that in developed eastern China. Considering the heterogeneity of carbon peaking stress, additional concerns should be given to the net embodied carbon output in the northwestern, northern, and central regions, which can help avoid the dilemma between outsourcing embodied carbon and reducing carbon emissions from production. The policy to reduce emissions should be implemented in all regions that benefit from the net embodied carbon output of the northern and northwestern regions, where the carbon peaking stress is higher. The focus should be on the actual improvement of the carbon peaking stress, not just on the transfer of stress. The increasing urgency of achieving carbon peaking targets and unequal stress for regional peaking emissions calls for differentiated regional mitigation measures to help the Chinese government scientifically and in an orderly manner promote the overall and local carbon peaking work.
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(This article belongs to the Section B: Energy and Environment)
Open AccessArticle
Skin and Proximity Effect Calculation of a System of Rectangular Conductors Using the Proper Generalized Decomposition Technique
by
Barzan Tabei, Aniruddha M. Gole and Behzad Kordi
Energies 2024, 17(12), 2828; https://doi.org/10.3390/en17122828 (registering DOI) - 8 Jun 2024
Abstract
This paper presents the application of a numerical approach known as proper generalized decomposition (PGD) to calculate the per-unit length (PUL) ac resistance of rectangular conductors. PGD has been successfully used in areas such as fluid mechanics and biomedical applications. It solves a
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This paper presents the application of a numerical approach known as proper generalized decomposition (PGD) to calculate the per-unit length (PUL) ac resistance of rectangular conductors. PGD has been successfully used in areas such as fluid mechanics and biomedical applications. It solves a partial differential equation (PDE) by decomposing the answer into a set of unknown one-dimensional (1D) functions in an iterative approach until it reaches a predetermined convergence. In this paper, a frequency-dependent meshing scheme is employed in the PGD technique at each frequency to properly take skin and proximity effects into account. One of the main advantages of PGD over traditional numerical approaches such as finite element or finite difference methods is that it confines the answers within a set of one-dimensional functions, which require fewer computational resources. Different examples of single and multiple rectangular conductors are considered to study skin and proximity effects. The PGD results are compared with those obtained using a commercial finite element method (FEM) software to verify the accuracy of the model. This approach can be used in applications such as white box modeling of transformers, EMC analysis, hairpin winding design used in electric vehicles, and busbar simulation.
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(This article belongs to the Section F3: Power Electronics)
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