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Energies, Volume 16, Issue 15 (August-1 2023) – 268 articles

Cover Story (view full-size image): Radio frequency energy harvesting (RFEH) is a specific type of wireless energy harvesting that enables wireless power transfer by utilizing RF signals. RFEH holds immense potential for extending the lifespan of wireless sensors and wearable electronics that require low-power operation. This literature review focuses on three key areas: materials, antenna design, and power management, to delve into the research challenges of RFEH comprehensively. By providing an up-to-date review of research findings on RFEH, this review aims to shed light on the critical challenges, potential opportunities, and existing limitations. Moreover, it emphasizes the importance of further research and development in RFEH to advance its state-of-the-art and offer a vision for future trends in this technology. View this paper
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45 pages, 3914 KiB  
Review
Evaluating Green Solvents for Bio-Oil Extraction: Advancements, Challenges, and Future Perspectives
by Muhammad Usman, Shuo Cheng, Sasipa Boonyubol and Jeffrey S. Cross
Energies 2023, 16(15), 5852; https://doi.org/10.3390/en16155852 - 7 Aug 2023
Cited by 5 | Viewed by 4024
Abstract
The quest for sustainable and environmentally friendly fuel feedstocks has led to the exploration of green solvents for the extraction of bio-oil from various biomass sources. This review paper provides a comprehensive analysis of the challenges and future research outlooks for different categories [...] Read more.
The quest for sustainable and environmentally friendly fuel feedstocks has led to the exploration of green solvents for the extraction of bio-oil from various biomass sources. This review paper provides a comprehensive analysis of the challenges and future research outlooks for different categories of green extraction solvents, including bio-based solvents, water-based solvents, supercritical fluids, and deep eutectic solvents (DES). The background of each solvent category is discussed, highlighting their potential advantages and limitations. Challenges such as biomass feedstock sourcing, cost fluctuations, solvent properties variability, limited compatibility, solute solubility, high costs, and potential toxicity are identified and examined in detail. To overcome these challenges, future research should focus on alternative and abundant feedstock sources, the development of improved solubility and separation techniques, optimization of process parameters, cost-effective equipment design, standardization of DES compositions, and comprehensive toxicological studies. By addressing these challenges and advancing research in these areas, the potential of green extraction solvents can be further enhanced, promoting their widespread adoption and contributing to more sustainable and environmentally friendly industrial processes. Full article
(This article belongs to the Section A4: Bio-Energy)
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Graphical abstract

Graphical abstract
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<p>Schematic diagram of the SE.</p>
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<p>The scheme of green solvent classification.</p>
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<p>The production of ethyl lactate process from renewable sources.</p>
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<p>The schematic diagram of 2-MeTHF and GVL production from biomass.</p>
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<p>Overall Reactions mechanism for DES Production.</p>
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<p>Key properties of the green solvents.</p>
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<p>Various aspects for the Performance Evaluation of the green solvents.</p>
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<p>Different parameters or causes of green solvents for the reduced energy consumption.</p>
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21 pages, 8119 KiB  
Article
Improvement of a Hybrid Solar-Wind System for Self-Consumption of a Local Object with Control of the Power Consumed from the Grid
by Olexandr Shavolkin, Iryna Shvedchykova, Michal Kolcun and Dušan Medveď
Energies 2023, 16(15), 5851; https://doi.org/10.3390/en16155851 - 7 Aug 2023
Viewed by 1057
Abstract
Improvement of the principles of the implementation of a hybrid solar-wind system equipped with a battery for self-consumption of a local object, with the control of power consumed from the grid, is considered. The aim is to increase the degree of energy use [...] Read more.
Improvement of the principles of the implementation of a hybrid solar-wind system equipped with a battery for self-consumption of a local object, with the control of power consumed from the grid, is considered. The aim is to increase the degree of energy use from renewable energy sources for consumption while limiting the degree of battery discharge, taking into account deviations in the load schedule and generation of energy sources relative to the calculated (forecast) values. The possibility of compensating for deviations in the load schedule and renewable energy sources generation relative to the calculated (forecast) values is shown when electricity consumption decreases and the degree of energy use increases. Compliance of the schedule of the battery state of charge with the calculated schedule is achieved by correcting the consumption of active power according to the deviation of the state of charge with a given discreteness of time. The algorithm of the control was improved by taking into account the measured value of the load power with an increase in the degree of energy use. Also, the use of correction allows you to limit the depth of discharge of the battery at the accepted value. A mathematical 24 h model of energy processes was developed, taking into account the error in estimating the state of charge. The results of the modeling using archival data on renewable sources generation confirm that the proposed solutions are effective. For the considered application with average monthly generation in February, the correction allows reducing electricity consumption by 16–21% and payment costs at three tariffs by 24–27%. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Figure 1

Figure 1
<p>Simplified structure of the PV-WG system.</p>
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<p>Daily load schedule of LO.</p>
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<p>Block diagram of the algorithm for implementing changes in the structure of the control system.</p>
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<p>Block diagram of the algorithm of calculation.</p>
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<p>Oscillograms of <span class="html-italic">Q*</span>, battery current <span class="html-italic">I<sub>B</sub></span>, RES power <span class="html-italic">P<sub>R</sub></span>, load power <span class="html-italic">P<sub>L</sub></span>, inverter power <span class="html-italic">P<sub>C</sub></span>: (<b>a</b>) <span class="html-italic">W*<sub>PV</sub></span> = 1, <span class="html-italic">P*<sub>LAV</sub></span> = 1. (<b>b</b>) <span class="html-italic">W*<sub>PV</sub></span> = 0.9, <span class="html-italic">P*<sub>LAV</sub></span> = 1; (<b>c</b>) <span class="html-italic">W*<sub>PV</sub></span> = 0.9, <span class="html-italic">P*<sub>LAV</sub></span> = 0.877 with correction and <span class="html-italic">Q</span><sup>1</sup><span class="html-italic"><sub>M</sub></span> = 1.05.</p>
Full article ">Figure 6
<p>Oscillograms of <span class="html-italic">Q*</span>, battery current <span class="html-italic">I<sub>B</sub></span>, RES power <span class="html-italic">P<sub>RM</sub></span> for MPPT mode, RES power taking into account regulation <span class="html-italic">P<sub>R</sub></span>, load power <span class="html-italic">P<sub>L</sub></span>, inverter power <span class="html-italic">P<sub>C</sub></span>: (<b>a</b>) <span class="html-italic">W*<sub>PV</sub></span> = 1.1, <span class="html-italic">P*<sub>LAV</sub></span> = 0.877. (<b>b</b>) <span class="html-italic">W*<sub>PV</sub></span> = 1.1, <span class="html-italic">P*<sub>LAV</sub></span> = 0.877 with correction and <span class="html-italic">Q</span><sup>1</sup><span class="html-italic"><sub>M</sub></span> = 1.05.</p>
Full article ">Figure 7
<p>Oscillograms of <span class="html-italic">Q*</span>, battery current <span class="html-italic">I<sub>B</sub></span>, RES power <span class="html-italic">P<sub>RM</sub></span> for MPPT mode, RES power taking into account regulation <span class="html-italic">P<sub>R</sub></span>, load power <span class="html-italic">P<sub>L</sub></span>, inverter power <span class="html-italic">P<sub>C</sub></span>: (<b>a</b>) <span class="html-italic">W*<sub>PV</sub></span> = 1.0, <span class="html-italic">P*<sub>LAV</sub></span> = 0.9, (<b>b</b>) <span class="html-italic">W*<sub>PV</sub></span> = 1.0, <span class="html-italic">P*<sub>LAV</sub></span> = 0.9 with correction and <span class="html-italic">Q</span><sup>1</sup><span class="html-italic"><sub>M</sub></span> = 1.05.</p>
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<p>Oscillograms of <span class="html-italic">Q*</span>, battery current <span class="html-italic">I<sub>B</sub></span>, RES power <span class="html-italic">P<sub>RM</sub></span> for MPPT mode, RES power taking into account regulation <span class="html-italic">P<sub>R</sub></span>, load power <span class="html-italic">P<sub>L</sub></span>, inverter power <span class="html-italic">P<sub>C</sub></span>: (<b>a</b>) <span class="html-italic">W*<sub>PV</sub></span> = 0.81, <span class="html-italic">P*<sub>LAV</sub></span> = 1, (<b>b</b>) <span class="html-italic">W*<sub>PV</sub></span> = 0.8, <span class="html-italic">P*<sub>LAV</sub></span> = 1.065 with correction and <span class="html-italic">Q</span><sup>1</sup><span class="html-italic"><sub>M</sub></span> = 1.05.</p>
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17 pages, 9359 KiB  
Article
Modelling of Floor Heating and Cooling in Residential Districts
by Xenia Kirschstein, Joscha Reber, Rouven Zeus, Miriam Schuster and Nadja Bishara
Energies 2023, 16(15), 5850; https://doi.org/10.3390/en16155850 - 7 Aug 2023
Cited by 3 | Viewed by 1151
Abstract
In this study, a method is proposed to expand the utilization of an existing calculation model for a floor heat exchanger (HX) from room scale to small district scale. The model, namely Trnsys Type 653, is typically employed for the simulation of single [...] Read more.
In this study, a method is proposed to expand the utilization of an existing calculation model for a floor heat exchanger (HX) from room scale to small district scale. The model, namely Trnsys Type 653, is typically employed for the simulation of single or simultaneously controlled parallel heating circuits. It uses a simplified approach to calculate the heat exchange between fluid and screed, taking the HX effectiveness as an input. In order to calculate the effectiveness based on the HX design, fluid properties and mass flow rate, a Python model is developed to be coupled with Type 653. The results are compared to a reference finite element model set up in COMSOL® and depend on the HX design. The highest deviations range from over 1 K for 35 min to over 2 K for 175 min, while the lowest deviations range from below 0.5 K to below 1 K. Furthermore, the simplification of the floor HX model is analyzed by summarizing heating circuits from single rooms to a whole flat and from single flats to a whole floor. This approach results in deviations of approximately 2 and 4%, respectively, in the overall transferred heat over longer periods of time, while the switch-on frequency of the controller in an exemplary day is halved. While further analysis is required, the described simplifications seem promising for detailed district simulations with relatively low computational effort. Full article
(This article belongs to the Section G: Energy and Buildings)
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Figure 1

Figure 1
<p>Geometries of the reference models.</p>
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<p>Summary of the heating circuits of one flat and one floor, respectively.</p>
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<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 1, d = 10 cm, inlet temperature 18 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature 26 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">Figure 4
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 1, d = 35 cm, inlet temperature 40 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature 20 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
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<p>Simplification results from single rooms (detailed) to whole flat (simplified) over one year.</p>
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<p>Simplification results from single rooms (detailed) to whole flat (simplified) over one day.</p>
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<p>Simplification results from flats (detailed) to whole floor (simplified) over one year.</p>
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<p>Simplification results from flats (detailed) to whole floor (simplified) over one day.</p>
Full article ">Figure A1
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 1, d = 35 cm, inlet temperature <math display="inline"><semantics><mrow><mn>18</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature <math display="inline"><semantics><mrow><mn>26</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">Figure A2
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 2, d = 10 cm, inlet temperature <math display="inline"><semantics><mrow><mn>18</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature <math display="inline"><semantics><mrow><mn>26</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">Figure A2 Cont.
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 2, d = 10 cm, inlet temperature <math display="inline"><semantics><mrow><mn>18</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature <math display="inline"><semantics><mrow><mn>26</mn><msup><mspace width="3.33333pt"/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">Figure A3
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL® as well as their difference at varying inlet velocities for room 1, pipe distance 10 cm, inlet temperature 40 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature 20 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">Figure A4
<p>Fluid outlet temperatures calculated in Trnsys and COMSOL<sup>®</sup> as well as their difference at varying inlet velocities for room 2, pipe distance 10 cm, inlet temperature 40 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>, room temperature 20 <math display="inline"><semantics><mrow><msup><mrow/><mo>∘</mo></msup><mi mathvariant="normal">C</mi></mrow></semantics></math>.</p>
Full article ">
18 pages, 8960 KiB  
Article
Numerical Analysis of Crashworthiness on Electric Vehicle’s Battery Case with Auxetic Structure
by Liviu I. Scurtu, Ioan Szabo and Marius Gheres
Energies 2023, 16(15), 5849; https://doi.org/10.3390/en16155849 - 7 Aug 2023
Cited by 3 | Viewed by 1687
Abstract
Due to the reduction in pollutant emissions, the number of electric vehicles has experienced rapid growth in worldwide traffic. Vehicles equipped with batteries represent a greater danger of explosion and fire in the case of traffic accidents, which is why new protective systems [...] Read more.
Due to the reduction in pollutant emissions, the number of electric vehicles has experienced rapid growth in worldwide traffic. Vehicles equipped with batteries represent a greater danger of explosion and fire in the case of traffic accidents, which is why new protective systems and devices have been designed to improve impact safety. Through their design and construction, auxetic structures can ensure the efficient dissipation of impact energy, reducing the risk of battery damage and maintaining the safety of vehicle occupants. In this paper, we analyze the crashworthiness performance of a battery case equipped with an energy absorber with a particular shape based on a re-entrant auxetic model. Simulations were performed at a velocity of 10 m/s and applied to the battery case with a rigid impact pole, a configuration justified by most accidents occurring at a low velocity. The results highlight that by using auxetic structures in the construction of the battery case, the impact can be mitigated by the improved energy absorber placed around the battery case, which leads to a decrease in the number of damaged cells by up to 35.2%. In addition, the mass of the improved energy absorbers is lower than that of the base structure. Full article
(This article belongs to the Special Issue Performance Analysis and Simulation of Electric Vehicles)
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Figure 1

Figure 1
<p>Flow chart of the numerical analysis process.</p>
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<p>Developed surface and geometric details of the cylindrical cell of the re-entrant unit.</p>
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<p>The re-entrant auxetic cylindrical models used in simulation (length: (<b>a</b>) 17 mm, (<b>b</b>) 24 mm, and (<b>c</b>) 31 mm).</p>
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<p>Geometric dimensions and top and side views of the battery pack model.</p>
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<p>The design and components of the battery pack.</p>
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<p>FE initial setup of the battery case impact simulation.</p>
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<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and base cylindrical elements (CYL1) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 7 Cont.
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and base cylindrical elements (CYL1) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 8
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and re-entrant auxetic cylindrical elements (AUX1) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 9
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and base cylindrical elements (CYL2) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 9 Cont.
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and base cylindrical elements (CYL2) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 10
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and re-entrant auxetic cylindrical elements (AUX2) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 10 Cont.
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and re-entrant auxetic cylindrical elements (AUX2) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 11
<p>Details of the effects of displacement, mechanical stress, and energy balance on modules and base cylindrical elements (CYL3) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 12
<p>Details of the effects of displacement, mechanical stress, and energy balance on re-entrant auxetic cylindrical elements (AUX3) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">Figure 12 Cont.
<p>Details of the effects of displacement, mechanical stress, and energy balance on re-entrant auxetic cylindrical elements (AUX3) at a 10 m/s impact velocity ((<b>a</b>) von Mises stress; (<b>b</b>) energy balance; (<b>c</b>) displacement; (<b>d</b>) top view of the von Mises stress).</p>
Full article ">
15 pages, 4077 KiB  
Article
Mechanism of Low-Frequency Oscillation When Electric Multiple Units Pass Neutral Zone, and Suppression Method
by Jixing Sun, Kun Zhang, Jiyong Liu, Kaixuan Hu, Jindong Huo, Shengchun Yan and Yan Zhang
Energies 2023, 16(15), 5848; https://doi.org/10.3390/en16155848 - 7 Aug 2023
Viewed by 864
Abstract
This article addresses the problem of the contact voltage increase caused by the low-frequency oscillation of the train-grid system in the phase-separation process of EMUs. The article establishes the EMU-contact line-traction substation model, reveals the mechanism of low-frequency oscillation, and ascertains the relationship [...] Read more.
This article addresses the problem of the contact voltage increase caused by the low-frequency oscillation of the train-grid system in the phase-separation process of EMUs. The article establishes the EMU-contact line-traction substation model, reveals the mechanism of low-frequency oscillation, and ascertains the relationship between the phase angle when the pantograph leaves the line, and low-frequency oscillations. Methods to suppress overvoltage during the low-frequency oscillation are proposed. The research indicated that a significant voltage amplitude was observed in the neutral zone, when the phase angle of the pantograph to the contact line separation power supply fell within the range of 60–90° and 240–270°. The maximum voltage amplitude reached 69.75 kV, and there was an occurrence of low-frequency oscillation in the neutral zone, where electrical phase separation takes place. During this oscillation, the voltage of the contact network in the neutral zone mainly operated at one-third of the power frequency (16.7 Hz). However, after installing an RC suppression device in the neutral zone, when low-frequency oscillation occurred, the absolute value of the peak voltage dropped below 37 kV as soon as the EMU entered electric phase separation. Furthermore, compared to situations without a connected suppression device, there was nearly a 30% reduction in the absolute value of the peak voltage. The study provides a basis for the design of the neutral zone of the contact line, and the selection of high-voltage equipment for the EMU. Full article
(This article belongs to the Section F: Electrical Engineering)
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Figure 1

Figure 1
<p>Transient process of the EMU passing the phase-division zone.</p>
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<p>Train operating current and voltage (RMS value).</p>
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<p>Train overcurrent split-phase voltage waveform.</p>
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<p>Train overcurrent split-phase voltage waveform.</p>
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<p>Harmonic frequency analysis of the voltage in the neutral zone.</p>
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<p>Structure of the double-wire full parallel AT traction net.</p>
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<p>Equivalent circuit diagram of the overphase division of the EMU.</p>
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<p>A source, power-supply line, and train-system simulation model.</p>
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<p>The EMU enters the electric split-phase simulation model.</p>
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<p>Frequency spectrum of low-frequency oscillation network voltage in the phase-separated neutral zone.</p>
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<p>Abnormal low-frequency oscillation phenomenon in the neutral zone.</p>
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<p>Simple equivalent circuit diagram.</p>
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<p>Step response under different capacitance values (the resistance R2 at 200 Ω remains unchanged).</p>
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<p>Step response under different resistance and capacitance values.</p>
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<p>Equivalent circuit diagram of the EMU in the neutral zone (with an additional RC device).</p>
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<p>Voltage waveform of the neutral zone.</p>
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11 pages, 6210 KiB  
Article
A ZnO Gas Sensor with an Abnormal Response to Hydrogen
by Hao Sun, Yachi Yao, Ruixuan Yang, Zhaonan Yan, Chen Cao, Yanwen Deng, Shengjiang Wu, Shuhai Liu, Qi Xu and Yong Qin
Energies 2023, 16(15), 5847; https://doi.org/10.3390/en16155847 - 7 Aug 2023
Cited by 1 | Viewed by 1292
Abstract
ZnO is a commonly used material for hydrogen gas sensors. In this study, a ZnO nanofiber film with a diameter of approximately 60 nm was synthesized by the electrospinning method. Compared to previously reported ZnO hydrogen gas sensors, an abnormal phenomenon was observed [...] Read more.
ZnO is a commonly used material for hydrogen gas sensors. In this study, a ZnO nanofiber film with a diameter of approximately 60 nm was synthesized by the electrospinning method. Compared to previously reported ZnO hydrogen gas sensors, an abnormal phenomenon was observed here, where the resistance of the ZnO nanofiber film increased upon exposure to hydrogen gas in the temperature range from 210 °C to 330 °C. The physical mechanism of this phenomenon was explored through microstructure analysis and DFT simulation calculations that showed a total charge transfer of 0.65 e for the hydrogen molecule. This study can push forward the understanding of ZnO hydrogen sensing. Full article
(This article belongs to the Special Issue Advanced Materials for Sustainable Energy Applications)
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Figure 1

Figure 1
<p>(<b>a</b>) Schematic diagram of electrospinning method, (<b>b</b>) actual photographic (I) and schematic diagram (II) of ZnO nanofiber film sensor, and (<b>c</b>) illustration of the testing system of gas sensing.</p>
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<p>XRD patterns of ZnO nanofiber film.</p>
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<p>SEM images of polymer solution film (<b>a</b>), ZnO nanofiber film (<b>b</b>), and the EDS spectrum of ZnO nanofiber film (<b>c</b>).</p>
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<p>(<b>a</b>–<b>e</b>) Dynamic response graph (blue) of current after introducing H<sub>2</sub> to ZnO nanofiber film at different temperatures. Illustration: dynamic response curves (red). (<b>f</b>) The temperature-dependent response curve of ZnO nanofiber film to H<sub>2</sub>. The label “H<sub>2</sub> on” in the figure represents the test atmosphere of 20 ppm H<sub>2</sub> (composition: 20% O<sub>2</sub> + 80% N<sub>2</sub> + 20 ppm H<sub>2</sub>), and “H<sub>2</sub> off” represents the simulated air atmosphere (composition: 20% O<sub>2</sub> + 80% N<sub>2</sub>).</p>
Full article ">Figure 5
<p>The current response curve (blue) (<b>a</b>) and the repeatability response curve (blue) (<b>b</b>) of the ZnO nanofiber film in a 20 ppm H<sub>2</sub> atmosphere. The red area indicates the introduction of H<sub>2</sub> gas, the label “H<sub>2</sub>” in the figure represents the test atmosphere of 20 ppm H<sub>2</sub>.</p>
Full article ">Figure 6
<p>(<b>a</b>–<b>c</b>) Dynamic response graph (blue) of current after introducing H<sub>2</sub> to ZnO nanofiber film at different concentrations. Illustration: dynamic response curves (red). The label “H<sub>2</sub> on” in the figure represents the test atmosphere of 20 ppm H<sub>2</sub> (composition: 20% O<sub>2</sub> + 80% N<sub>2</sub> + 20 ppm H<sub>2</sub>), and “H<sub>2</sub> off” represents the simulated air atmosphere (composition: 20% O<sub>2</sub> + 80% N<sub>2</sub>).</p>
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<p>(<b>a</b>) XPS spectra of ZnO nanofiber film in a full 1200 eV binding energy range and XPS Zn2p (<b>b</b>) and O1s (<b>c</b>) lines for ZnO nanofiber film after deconvolution using Gauss fitting. (<b>b</b>,<b>c</b>) Test curve (grey), fit curve (black).</p>
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<p>Ortho-position (<b>a</b>), bridge location (<b>b</b>), and oxygen vacancy (<b>c</b>) in adsorption configuration. 1 and 2 of figure indicate the adsorption of H atoms at different sites.</p>
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28 pages, 792 KiB  
Article
Energy-Efficient City Transportation Solutions in the Context of Energy-Conserving and Mobility Behaviours of Generation Z
by Kalina Grzesiuk, Dorota Jegorow, Monika Wawer and Anna Głowacz
Energies 2023, 16(15), 5846; https://doi.org/10.3390/en16155846 - 7 Aug 2023
Cited by 1 | Viewed by 1656
Abstract
Undertaking various activities aimed at sustainable development, especially energy conservation, is becoming one of the challenges of modern economies, including developing urban areas. One of the most widely promoted activities is designing and implementing energy-conserving solutions for urban mobility. People play a vital [...] Read more.
Undertaking various activities aimed at sustainable development, especially energy conservation, is becoming one of the challenges of modern economies, including developing urban areas. One of the most widely promoted activities is designing and implementing energy-conserving solutions for urban mobility. People play a vital role in this regard, especially young people, represented here by Generation Z. Their attitudes and behaviours regarding sustainability can significantly impact the effectiveness of energy-efficient technological solutions. The purpose of this article is to examine the nature of the relationship between the assessment of the importance of energy-efficient transportation solutions available in the city and the attitudes and behaviours of representatives of Generation Z relating to the idea of sustainability, broken down into two categories, i.e., energy-conserving behaviour and mobility. In this study, a diagnostic survey method was used. Based on the literature review, we designed a research tool in the form of a questionnaire. Four hundred and ninety representatives of Generation Z participated in the study. To verify the hypotheses, first, a qualitative analysis was carried out for the three study areas using measures of central tendency; then, a correlation analysis was performed based on Pearson’s chi-square independence test, and to determine the strength of the relationship, the following symmetric measures were used: Cramer’s V and the Contingency Coefficient. The normalisation of the data, giving them a quantitative character, allowed the possibility of examining the correlation using Pearson’s test and the directionality of the analysed relationships based on simple and multiple linear regression results. Ananalys is of the obtained results allows us to conclude that energy-related sustainable behaviours in the acquisition of electrical appliances, their use and disposal, and mobility-related energy-conserving behaviours, resulting from the choice of means of transportation for moving in the city, influence the assessment of the importance of available energy-efficient mobility solutions. City administrations could use the study results as a guideline for the implementation of energy-conserving solutions in urban transportation, as well as the planning and promotion of appropriate activities related to the mobility of Generation Z, that are adequate to the attitudes and behaviours of young people. Full article
(This article belongs to the Special Issue Energy Consumption and Smart Cities)
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<p>Graphic presentation of the research model. Source: own elaboration.</p>
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21 pages, 584 KiB  
Article
The Driving Factors of Italy’s CO2 Emissions Based on the STIRPAT Model: ARDL, FMOLS, DOLS, and CCR Approaches
by Dulal Chandra Pattak, Farian Tahrim, Mahdi Salehi, Liton Chandra Voumik, Salma Akter, Mohammad Ridwan, Beata Sadowska and Grzegorz Zimon
Energies 2023, 16(15), 5845; https://doi.org/10.3390/en16155845 - 7 Aug 2023
Cited by 28 | Viewed by 3194
Abstract
As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. [...] Read more.
As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. Using the extended Stochastic Regression on Population, Affluence, and Technology (STIRPAT) framework, the yearly data from 1972 to 2021 are analyzed in this paper through an Autoregressive Distributed Lag (ARDL) framework. The reliability of the study is also examined by employing Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimators and also the Granger causality method which is used to see the directional relationship among the indicators. The investigation confirms the findings of previous studies by showing that in the longer period, rising Italian GDP and non-green energy by 1% can lead to higher CO2 emissions by 8.08% and 1.505%, respectively, while rising alternative and nuclear energy by 1% can lead to falling in CO2 emission by 0.624%. Although population and green energy adversely influence the upsurge of CO2, they seem insignificant. Robustness tests confirm these longer-period impacts. This analysis may be helpful in planning and developing strategies for future financial funding in the energy sector in Italy, which is essential if the country is to achieve its goals of sustainable development. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>CUSUM and CUSUM square tests.</p>
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17 pages, 2953 KiB  
Article
Impact of Iron Oxide Nanoparticles on Anaerobic Co-Digestion of Cow Manure and Sewage Sludge
by Tariq Alkhrissat, Ghada Kassab and Mu’tasim Abdel-Jaber
Energies 2023, 16(15), 5844; https://doi.org/10.3390/en16155844 - 7 Aug 2023
Cited by 3 | Viewed by 1198
Abstract
Supplementation with iron oxide nanoparticles has been suggested as a potential method for improving energy generation through anaerobic digestion, specifically by enhancing the rate of methane production. This investigation examined the effects of iron oxide (Fe3O4) nanoparticles (NPs) on [...] Read more.
Supplementation with iron oxide nanoparticles has been suggested as a potential method for improving energy generation through anaerobic digestion, specifically by enhancing the rate of methane production. This investigation examined the effects of iron oxide (Fe3O4) nanoparticles (NPs) on anaerobic co-digestion of cow manure (CM) and sewage sludge (SS) through batch testing conducted under mesophilic conditions (35 °C) using a RESPIROMETRIC Sensor System 6 Maxi—BMP (RSS-BMP). The use of Fe3O4 nanoparticles at doses of 40, 80, 120, and 160 mg/L (batches M1, M2, M3, and M5) was studied. The use of 160 mg/L Fe3O4 nanoparticles in combination with mixtures of different ratios (M4, M5, and M6) was further investigated. The findings indicate that the addition of Fe3O4 nanoparticles at a concentration of 40 mg/L to anaerobic batches did not significantly impact the hydrolysis process and subsequent methane production. Exposing the samples to Fe3O4 NPs at concentrations of 80, 120, and 160 mg/L resulted in a similar positive effect, as evidenced by hydrolysis percentages of approximately 94%, compared to 60% for the control (C2). Furthermore, methane production also increased. The use of Fe3O4 nanoparticles at a concentration of 160 mg/L resulted in biodegradability of 97.3%, compared to 51.4% for the control incubation (C2). Moreover, the findings demonstrate that supplementing anaerobic batches with 160 mg/L Fe3O4 NPs at varying mixture ratios (M4, M5, and M6) had a significant impact on both hydrolysis and methane production. Specifically, hydrolysis percentages of 94.24, 98.74, and 96.78% were achieved for M4, M5, and M6, respectively, whereas the percentages for the control incubation (C1, C2, and C3) were only 56.78, 60.21, and 58.74%. Additionally, the use of 160 mg/L Fe3O4 NPs in mixtures M4, M5, and M6 resulted in biodegradability percentages of 78.4, 97.3, and 88.3%, respectively. In contrast, for the control incubation (C1, C2, and C3) biodegradability was only 44.24, 51.4, and 49.1%. Full article
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<p>DLS particle size analysis curve for Fe<sub>3</sub>O<sub>4</sub> nanoparticles.</p>
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<p>Effect of different Fe<sub>3</sub>O<sub>4</sub> NP doses on soluble COD.</p>
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<p>Effect of Fe<sub>3</sub>O<sub>4</sub> magnetite NPs on VFA production: (<b>a</b>) acetate, (<b>b</b>) propionate, and (<b>c</b>) butyrate.</p>
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<p>Cumulative methane production with different Fe<sub>3</sub>O<sub>4</sub> NP doses: (<b>a</b>) experimental data, (<b>b</b>) modified Gompertz model fit.</p>
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<p>Effects of different Fe<sub>3</sub>O<sub>4</sub> NP doses on maximum methane production, computed from a modified Gompertz model.</p>
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<p>Effect of Fe<sub>3</sub>O<sub>4</sub> NPs on soluble COD in mixtures C1, C2, C3, M4, M5, and M6.</p>
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<p>Effect of Fe<sub>3</sub>O<sub>4</sub> NPs on VFA production in mixtures C1, C2, C3, M4, M5, and M6: (<b>a</b>) acetate, (<b>b</b>) propionate, and (<b>c</b>) butyrate.</p>
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<p>Cumulative methane production in mixtures C1, C2, C3, M4, M5, and M6 with 160 mg/L of Fe<sub>3</sub>O<sub>4</sub> NPs: (<b>a</b>) experimental data, (<b>b</b>) modified Gompertz model fit.</p>
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<p>Effect of 160 mg/L Fe<sub>3</sub>O<sub>4</sub> NPs on maximum methane production in mixtures C1, C2, C3, M4, M5, and M6 computed from a modified Gompertz model.</p>
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27 pages, 5869 KiB  
Article
On the Benefits of Active Aerodynamics on Energy Recuperation in Hybrid and Fully Electric Vehicles
by Petar Georgiev, Giovanni De Filippis, Patrick Gruber and Aldo Sorniotti
Energies 2023, 16(15), 5843; https://doi.org/10.3390/en16155843 - 7 Aug 2023
Cited by 1 | Viewed by 1953
Abstract
In track-oriented road cars with electric powertrains, the ability to recuperate energy during track driving is significantly affected by the frequent interventions of the antilock braking system (ABS), which usually severely limits the regenerative torque level because of functional safety considerations. In high-performance [...] Read more.
In track-oriented road cars with electric powertrains, the ability to recuperate energy during track driving is significantly affected by the frequent interventions of the antilock braking system (ABS), which usually severely limits the regenerative torque level because of functional safety considerations. In high-performance vehicles, when controlling an active rear wing to maximize brake regeneration, it is unclear whether it is preferable to maximize drag by positioning the wing into its stall position, to maximize downforce, or to impose an intermediate aerodynamic setup. To maximize energy recuperation during braking from high speeds, this paper presents a novel integrated open-loop strategy to control: (i) the orientation of an active rear wing; (ii) the front-to-total brake force distribution; and (iii) the blending between regenerative and friction braking. For the case study wing and vehicle setup, the results show that the optimal wing positions for maximum regeneration and maximum deceleration coincide for most of the vehicle operating envelope. In fact, the wing position that maximizes drag by causing stall brings up to 37% increased energy recuperation over a passive wing during a braking maneuver from 300 km/h to 50 km/h by preventing the ABS intervention, despite achieving higher deceleration and a 2% shorter stopping distance. Furthermore, the maximum drag position also reduces the longitudinal tire slip power losses, which, for example, results in a 0.4% recuperated energy increase when braking from 300 km/h to 50 km/h in high tire–road friction conditions at a deceleration close to the limit of the vehicle with passive aerodynamics, i.e., without ABS interventions. Full article
(This article belongs to the Section E: Electric Vehicles)
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<p>Schematic of the powertrain configuration of the case study HEV. The two front on-board EMs are mechanically independent, and each is connected to a gearbox; the rear motor is installed between the ICE and the rear transmission, including a gearbox and a differential.</p>
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<p>(<b>a</b>) Lift and (<b>b</b>) drag coefficients, <math display="inline"><semantics><mrow><msub><mi>C</mi><mrow><mi>z</mi><mo>,</mo><mi>w</mi><mo>,</mo><mi>r</mi></mrow></msub></mrow></semantics></math> and <math display="inline"><semantics><mrow><msub><mi>C</mi><mrow><mi>x</mi><mo>,</mo><mi>w</mi><mo>,</mo><mi>r</mi></mrow></msub></mrow></semantics></math>, of the rear wing as a function of the angle of attack, <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math>.</p>
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<p>(<b>a</b>) Schematic of the test rig setup at the University of Surrey, including electric machines emulating the road load (Dyno motors); power supplies for the test piece and test rig inverters; inverters of the electric axle (eAxle); eAxle components, namely gearboxes (Gbx) and EMs, where the notations ‘fl’ and ‘fr’ refer to the front left and front right powertrains; and (<b>b</b>) test rig with test piece installation.</p>
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<p>(<b>a</b>) Torque loss (<math display="inline"><semantics><mrow><msub><mi>T</mi><mrow><mi>l</mi><mi>o</mi><mi>s</mi><mi>s</mi><mo>,</mo><mi>f</mi></mrow></msub></mrow></semantics></math>) map of each front gearbox, referred to at the EM shaft level, as a function of EM torque (<math display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math>) and speed (<math display="inline"><semantics><mrow><msub><mi>ω</mi><mi>m</mi></msub></mrow></semantics></math>); and (<b>b</b>) power loss map (<math display="inline"><semantics><mrow><msub><mi>P</mi><mrow><mi>l</mi><mi>o</mi><mi>s</mi><mi>s</mi><mo>,</mo><mi>m</mi></mrow></msub></mrow></semantics></math>) of the individual inverter and EM.</p>
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<p>Schematic of the 3-DoF vehicle model with indications of the main variables and their sign conventions. The black arrows refer to accelerations, the red arrows refer to forces, and the orange arrows refer to moments.</p>
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<p>(<b>a</b>) Front and rear axle lift coefficients, <math display="inline"><semantics><mrow><msub><mi>C</mi><mrow><mi>z</mi><mo>,</mo><mi>a</mi><mi>e</mi><mi>r</mi><mo>,</mo><mi>f</mi></mrow></msub></mrow></semantics></math> and <math display="inline"><semantics><mrow><msub><mi>C</mi><mrow><mi>z</mi><mo>,</mo><mi>a</mi><mi>e</mi><mi>r</mi><mo>,</mo><mi>r</mi></mrow></msub></mrow></semantics></math>, as a function of <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math>; and (<b>b</b>) drag coefficient at the vehicle level, <math display="inline"><semantics><mrow><msub><mi>C</mi><mrow><mi>x</mi><mo>,</mo><mi>a</mi><mi>e</mi><mi>r</mi></mrow></msub></mrow></semantics></math>, as a function of <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math>.</p>
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<p>Simplified block diagram of the time domain simulation architecture.</p>
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<p>Workflow diagram from the quasi-static optimizations to the time domain feedforward control block.</p>
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<p>Results of the Problem 1 optimization: maximum deceleration, <math display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>x</mi><mo>,</mo><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow></semantics></math>, as a function of <math display="inline"><semantics><mi>V</mi></semantics></math> for the Passive and Active configurations.</p>
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<p><math display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>x</mi><mo>,</mo><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow></semantics></math> as a function of <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math> for different values of <math display="inline"><semantics><mi>μ</mi></semantics></math>, for (<b>a</b>) the airfoil used in the target vehicle of this study (see <a href="#energies-16-05843-f002" class="html-fig">Figure 2</a>) and (<b>b</b>) an airfoil similar to the one in [<a href="#B25-energies-16-05843" class="html-bibr">25</a>].</p>
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<p>(<b>a</b>) Contour plot of the percentage increase in regenerative power, <math display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>P</mi><mrow><mi>m</mi><mo>,</mo><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></mrow></semantics></math>, as a function of <math display="inline"><semantics><mi>V</mi></semantics></math> and <math display="inline"><semantics><mrow><msub><mi>a</mi><mi>x</mi></msub></mrow></semantics></math>. The dashed and continuous lines are the maximum decelerations for Passive and Active. (<b>b</b>) Equivalent decelerations—see (41)–(44)—corresponding to the EM limits (‘Motor Limit’), regeneration functional safety limit (‘Safety Limit’), battery limit (‘Battery Limit’), actual front EM regenerative level (‘Front Actual’), and actual deceleration associated with the regenerative braking effect (‘Total Actual’), for <math display="inline"><semantics><mrow><msub><mi>a</mi><mi>x</mi></msub><mo>=</mo></mrow></semantics></math> −11 m/s<sup>2</sup>.</p>
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<p>Contour plots of (<b>a</b>) optimal front-to-total brake force distribution ratio, <math display="inline"><semantics><mrow><msub><mi>b</mi><mrow><mi>f</mi><mi>t</mi></mrow></msub></mrow></semantics></math> (expressed in percentage), for Active and (<b>b</b>) optimal <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math> for Active.</p>
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<p>Contour plot of the ratio of the friction brake force to the total braking force, <math display="inline"><semantics><mrow><msub><mi>b</mi><mrow><mi>f</mi><mi>r</mi><mi>i</mi><mi>c</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></msub></mrow></semantics></math> (expressed in percentage), for Active.</p>
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<p>Spider chart showing the benefit (braking distance reduction, peak deceleration increase, and regenerated energy increase) of Active w.r.t. Passive for Tests 1–5.</p>
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<p>Comparison of the Passive and Active vehicle configurations along Test 5. Time profiles of (<b>a</b>) <math display="inline"><semantics><mrow><msub><mi>a</mi><mi>x</mi></msub></mrow></semantics></math>; and (<b>b</b>) total regenerative power, <math display="inline"><semantics><mrow><msub><mi>P</mi><mrow><mi>r</mi><mi>e</mi><mi>g</mi><mi>e</mi><mi>n</mi><mo>,</mo><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></mrow></semantics></math>.</p>
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<p>Time profiles of the total braking force contributions, <math display="inline"><semantics><mrow><msub><mi>F</mi><mrow><mi>x</mi><mo>,</mo><mi>c</mi><mi>t</mi><mi>r</mi><mi>l</mi><mo>,</mo><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></mrow></semantics></math>, from the EMs (‘Motors’), and the combination of EMs and friction brakes (‘Motors + Friction Brakes’), for (<b>a</b>) Passive and (<b>b</b>) Active.</p>
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<p>Time profiles of the front and rear slip ratios, <math display="inline"><semantics><mrow><msub><mi>σ</mi><mi>i</mi></msub></mrow></semantics></math>, for (<b>a</b>) the front (‘f’) and rear (‘r’) corners of Passive and (<b>b</b>) the front and rear corners of Active.</p>
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<p>Time profile of <math display="inline"><semantics><mrow><msub><mi>ϕ</mi><mi>r</mi></msub></mrow></semantics></math>.</p>
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<p>Time profiles of the equivalent regeneration force limits, <math display="inline"><semantics><mrow><msub><mi>F</mi><mrow><mi>x</mi><mo>,</mo><mi>h</mi><mi>s</mi><mo>,</mo><mi>c</mi><mi>t</mi><mi>r</mi><mi>l</mi><mo>,</mo><mi>l</mi><mi>i</mi><mi>m</mi></mrow></msub></mrow></semantics></math>, related to the EM torque characteristics (‘Motor’); the feedforward (‘FF’) output from the map resulting from the Problem 2 optimization; and the feedforward output with the feedback correction (‘FF + FB’) induced by the ABS intervention with variable <math display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>x</mi><mo>,</mo><mi>e</mi><mi>q</mi><mo>,</mo><mi>l</mi><mi>i</mi><mi>m</mi><mo>,</mo><mi>s</mi><mi>a</mi><mi>f</mi></mrow></msub></mrow></semantics></math>.</p>
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13 pages, 1349 KiB  
Article
Hydropower Unit Commitment Using a Genetic Algorithm with Dynamic Programming
by Shuangquan Liu, Pengcheng Wang, Zifan Xu, Zhipeng Feng, Congtong Zhang, Jinwen Wang and Cheng Chen
Energies 2023, 16(15), 5842; https://doi.org/10.3390/en16155842 - 7 Aug 2023
Cited by 1 | Viewed by 1042
Abstract
This study presents a genetic algorithm integrated with dynamic programming to address the challenges of the hydropower unit commitment problem, which is a nonlinear, nonconvex, and discrete optimization, involving the hourly scheduling of generators in a hydropower system to maximize benefits and meet [...] Read more.
This study presents a genetic algorithm integrated with dynamic programming to address the challenges of the hydropower unit commitment problem, which is a nonlinear, nonconvex, and discrete optimization, involving the hourly scheduling of generators in a hydropower system to maximize benefits and meet various constraints. The introduction of a progressive generating discharge allocation enhances the performance of dynamic programming in fitness evaluations, allowing for the fulfillment of various constraints, such as unit start-up times, shutdown/operating durations, and output ranges, thereby reducing complexity and improving the efficiency of the genetic algorithm. The application of the genetic algorithm with dynamic programming and progressive generating discharge allocation at the Manwan Hydropower Plant in Yunnan Province, China, showcases increased flexibility in outflow allocation, reducing spillages by 79%, and expanding high-efficiency zones by 43%. Full article
(This article belongs to the Special Issue Advanced Modeling and Control of Hydropower Generation Systems)
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<p>Flowchart of GA with DP to solve the HUC problem.</p>
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<p>Generating discharge for each unit and spillage for Manwan with DP-1.</p>
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<p>Generating discharge for each unit and spillage with DP-2.</p>
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<p>Converging process of GA-1DP (<b>a</b>) and GA-2DP (<b>b</b>).</p>
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<p>The generating discharge for each unit and spillage with GA-2DP.</p>
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15 pages, 4230 KiB  
Article
Vacuum Insulation Panel: Evaluation of Declared Thermal Conductivity Value and Implications for Building Energy
by Fred Edmond Boafo, Jin-Hee Kim, Jong-Gwon Ahn, Sang-Myung Kim and Jun-Tae Kim
Energies 2023, 16(15), 5841; https://doi.org/10.3390/en16155841 - 7 Aug 2023
Viewed by 1044
Abstract
Policymakers regularly implement stricter building energy-efficiency codes towards curtailing building energy use. Inevitably, super-insulating materials such as Vacuum Insulation Panels (VIPs) are essential to satisfy such codes. VIPs have been applied to buildings for over two decades now, with many lessons learned. Generally, [...] Read more.
Policymakers regularly implement stricter building energy-efficiency codes towards curtailing building energy use. Inevitably, super-insulating materials such as Vacuum Insulation Panels (VIPs) are essential to satisfy such codes. VIPs have been applied to buildings for over two decades now, with many lessons learned. Generally, the thermal conductivity values of VIPs often reported in the literature are the center-of-panel thermal conductivity (λcop) and effective thermal conductivity (λeff), factoring thermal bridges. However, there are other indexes, such as λ90/90 (declared value in the 90% percentile with a confidence of 90%) and λcop,90/90,aged (factoring aging), that increase consistently and reliably in the declared thermal conductivity value for VIPs. These indexes are scarcely computed and hardly reported. The main aim of this study was to examine the different declared thermal conductivity values of VIP-based guidelines, such as draft ISO DIS 16478, and evaluate their implications on annual building energy consumption. The main study constitutes four parts: (1) experimental evaluation of the thermal properties of pristine and aged VIP samples, (2) computation of thermal conductivity indexes, (3) numerical investigation of thermal conductivity indexes based on a reference building, and (4) related building energy implications. The mean λcop for 10 VIP samples was 0.0042 W/(mK) and increased to 0.0073 W/(mK) for λ90/90, bridge, aged. Results show a significant bearing on building energy performance of as much as 2.1 GJ. Full article
(This article belongs to the Section G: Energy and Buildings)
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<p>Schematic showing heat flow meter instrumentation [<a href="#B28-energies-16-05841" class="html-bibr">28</a>].</p>
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<p>Picture of test apparatus: (<b>a</b>) VIP sample, (<b>b</b>) heat flow meter (Netzsch 436—<b>top left</b>; EKO HC-074—<b>top right</b>), and (<b>c</b>) temperature-humidity climatic chamber (external view—<b>bottom left</b>; internal chamber—<b>bottom right</b>).</p>
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<p>Reference building: (<b>a</b>) real pictures and (<b>b</b>) a rendered 3D image showing the sun’s path and cast shadows.</p>
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<p>Floor plan layout showing various thermal zones. (<b>a</b>) First floor unit; (<b>b</b>) Second floor unit.</p>
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<p>Fluctuation of solar radiation and ambient air temperature profiles for typical winter (8 February) and summer (16 September) days.</p>
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<p>Heating energy demand.</p>
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<p>Cooling energy demand.</p>
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15 pages, 3256 KiB  
Article
Tracking Photovoltaic Power Output Schedule of the Energy Storage System Based on Reinforcement Learning
by Meijun Guo, Mifeng Ren, Junghui Chen, Lan Cheng and Zhile Yang
Energies 2023, 16(15), 5840; https://doi.org/10.3390/en16155840 - 7 Aug 2023
Cited by 1 | Viewed by 1034
Abstract
The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement [...] Read more.
The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the photovoltaic and energy storage hybrid system and the mathematical model of the hybrid system are briefly introduced, and the tracking control problem is defined. Then, power generation plans on different days are clustered into four scenarios by the K-means clustering algorithm. The mean, standard deviation, and kurtosis of the power generation plant are used as the features. Based on the clustered results, the state, action, and reward required for reinforcement learning are set. In the constraint conditions of various variables, to increase the accuracy of the hybrid system for tracking the new generation schedule, the proximal policy optimization (PPO) algorithm is used to optimize the charging/discharging power of the energy storage system (ESS). Finally, the proposed control method is applied to a photovoltaic power station. The results of several valid experiments indicate that the average errors of tracking using the Proportion Integral Differential (PID), model predictive control (MPC) method, and the PPO algorithm in the same condition are 0.374 MW, 0.609 MW, and 0.104 MW, respectively, and the computing time is 1.134 s, 2.760 s, and 0.053 s, respectively. The consequence of these indicates that the proposed deep reinforcement learning-based control strategy is more competitive than the traditional methods in terms of generalization and computation time. Full article
(This article belongs to the Section D: Energy Storage and Application)
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<p>Photovoltaic and energy storage hybrid system.</p>
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<p>Solution flow chart.</p>
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<p>Reinforcement learning.</p>
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<p>Control strategy based on the PPO algorithm.</p>
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<p>Clustering results.</p>
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<p>Power generation plans in different 4 scenarios.</p>
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<p>Tracking results of the power generation plan.</p>
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<p>Output power of the ESS.</p>
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<p>SOC of the ESS.</p>
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<p>The average errors of tracking for no storage system, and two storage system with PID, MPC, and PPO.</p>
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18 pages, 821 KiB  
Article
Impact of Environmental Policy Mix on Carbon Emission Reduction and Social Welfare: Scenario Simulation Based on Private Vehicle Trajectory Big Data
by Wenjie Chen, Xiaogang Wu and Zhu Xiao
Energies 2023, 16(15), 5839; https://doi.org/10.3390/en16155839 - 7 Aug 2023
Cited by 2 | Viewed by 1014
Abstract
Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict [...] Read more.
Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector. Full article
(This article belongs to the Collection Feature Papers in Energy, Environment and Well-Being)
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<p>Sensitivity analysis of the number of nodes in the hidden layer.</p>
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<p>Nearest neighbor matching common support domain.</p>
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20 pages, 799 KiB  
Article
Prospects for Wood Pellet Production in Kazakhstan: A Case Study on Business Model Adjustment
by Timur Kogabayev, Anne Põder, Henrik Barth and Rando Värnik
Energies 2023, 16(15), 5838; https://doi.org/10.3390/en16155838 - 7 Aug 2023
Viewed by 1668
Abstract
Biomass and renewable resources are becoming substitutes for fossil-based resources, providing opportunities for more sustainable environmental management and reductions in environmental damage. This paper studies the prospects for wood pellet production in Kazakhstan through the lens of business model adjustment in a microenterprise [...] Read more.
Biomass and renewable resources are becoming substitutes for fossil-based resources, providing opportunities for more sustainable environmental management and reductions in environmental damage. This paper studies the prospects for wood pellet production in Kazakhstan through the lens of business model adjustment in a microenterprise in Kazakhstan. This study focuses on answering the following questions: (1) How do microenterprises propose, create, deliver and capture value through business models in the wood industry? (2) What are the opportunities and challenges relating to these business models in the context of wood pellet production in Kazakhstan? Kazakhstan has a high potential for biomass production, providing a particularly interesting case for analysing how microenterprises can tap into this potential to create value. This paper combines an analysis of bioenergy and forestry trends with a qualitative case study. The analysis of the business model is based on Osterwalder’s business model canvas. The value proposition of the enterprise studied herein is to provide a local biomass-based alternative to fossil fuels. The overall growth of wood-based industries in Kazakhstan and the national movement towards renewable energy create favourable prospects for microenterprises engaged in the production of wood pellets; however, these industries are also characterised by high institutional and regulatory dependencies. Full article
(This article belongs to the Section A: Sustainable Energy)
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<p>Osterwalder and Pigneur’s business model canvas explanation [<a href="#B32-energies-16-05838" class="html-bibr">32</a>].</p>
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<p>Share of primary energy derived from low-carbon sources, 2022 [<a href="#B70-energies-16-05838" class="html-bibr">70</a>].</p>
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<p>Forest fund and standing timber stock [<a href="#B67-energies-16-05838" class="html-bibr">67</a>].</p>
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6 pages, 658 KiB  
Brief Report
Phenomenological Material Model for First-Order Electrocaloric Material
by Sabrina Unmüßig, David Bach, Youri Nouchokgwe, Emmanuel Defay and Kilian Bartholomé
Energies 2023, 16(15), 5837; https://doi.org/10.3390/en16155837 - 7 Aug 2023
Viewed by 942
Abstract
Caloric cooling systems are potentially more efficient than systems based on vapour compression. Electrocaloric cooling systems use a phase transformation from the paraelectric to the ferroelectric state by applying or removing an electric field to pump heat. Lead scandium tantalate (PST) materials show [...] Read more.
Caloric cooling systems are potentially more efficient than systems based on vapour compression. Electrocaloric cooling systems use a phase transformation from the paraelectric to the ferroelectric state by applying or removing an electric field to pump heat. Lead scandium tantalate (PST) materials show a first-order phase transition and are one of the most promising candidates for electrocaloric cooling. To model caloric cooling systems, accurate and thermodynamically consistent material models are required. In this study, we use a phenomenological model based on an analytical equation for the specific heat capacity to describe the material behaviour of bulk PST material. This model is fitted to the experimental data, showing a very good agreement. Based on this model, essential material properties such as the adiabatic temperature change and isothermal entropy change of this material can be calculated. Full article
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<p>Results after fitting the model to the experimental data. The experimental data is represented by dots; the model is depicted by solid lines. (<b>a</b>) Specific heat capacity versus temperature for various electric field strengths. (<b>b</b>) Adiabatic temperature change versus the sample temperature for various electric field strengths.</p>
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21 pages, 4686 KiB  
Article
A Comprehensive Analysis on the Influence of the Adopted Cumulative Peak Current Distribution in the Assessment of Overhead Lines Lightning Performance
by Daiane Conceição, Rafael Alipio, Ivan J. S. Lopes and William Chisholm
Energies 2023, 16(15), 5836; https://doi.org/10.3390/en16155836 - 7 Aug 2023
Cited by 1 | Viewed by 1222
Abstract
Backflashover rate (BFR) is strongly dependent on the cumulative peak current distribution (CCD) adopted in the calculations. An original aspect of the present work is that such dependence is simultaneously assessed in estimating the probability of the critical current being exceeded as well [...] Read more.
Backflashover rate (BFR) is strongly dependent on the cumulative peak current distribution (CCD) adopted in the calculations. An original aspect of the present work is that such dependence is simultaneously assessed in estimating the probability of the critical current being exceeded as well as in the annual number of flashes to the line. An IEEE brochure recommends that the distribution values that characterize the atmospheric characteristic of the region under study as accurately as possible be used. The objective of this article is to evaluate the impact of the use of different CCDs, related to several measurements carried out around the world, in the estimation of the lightning performance of transmission lines (TLs). Structures of 138, 230 and 500 kV were analyzed. In the simulations, representative curves of lightning associated with measurements taken at Monte San Salvatore (MSS), Morro do Cachimbo (MCS) and TLs in Japan (TLJ) were considered. The distributions recommended by the IEEE and by the CIGRE and the distributions of Berger obtained from MSS, MCS and TLJ were considered. The presented results indicate differences of up to 100% between the considered work distributions and the IEEE one for certain values of tower footing impedance. Full article
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<p>Comparison between different cumulative peak current distributions (<b>a</b>) Range from 0 to 200 kA and probability 0 to 1. (<b>b</b>) Range from 80 to 200 kA and probability from 0 to 0.1.</p>
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<p>(<b>a</b>) The 138 kV tower geometry; the sags of the phase cables and shield wires are respectively 11.20 m and 7.2 m. (<b>b</b>) The 230 kV tower geometry; the sags of the phase cables and shield wires are respectively 18.165 m and 14.44 m. (<b>c</b>) The 500 kV tower geometry; the sags of the phase cables and shield wires are respectively 21.17 m and 13.61 m.</p>
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<p>Lateral attractive radius of a transmission line for a specific stroke peak current.</p>
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<p>Representation of first stroke current waveforms.</p>
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<p>Typical overvoltage waveforms across the external insulator of the 230 kV line, considering the injection of the lightning currents depicted in <a href="#energies-16-05836-f004" class="html-fig">Figure 4</a> at the tower top and assuming tower footing impedances of (<b>a</b>) 10 Ω, (<b>b</b>) 30 Ω, (<b>c</b>) 60 Ω.</p>
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<p>Typical overvoltage waveforms across the external insulator of the 230 kV line, considering the injection of the lightning currents depicted in <a href="#energies-16-05836-f004" class="html-fig">Figure 4</a> at the tower top and assuming tower footing impedances of (<b>a</b>) 10 Ω, (<b>b</b>) 30 Ω, (<b>c</b>) 60 Ω.</p>
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<p>Critical current versus tower footing impedance for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV and (<b>c</b>) 500 kV lines.</p>
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<p>Critical current versus tower footing impedance for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV and (<b>c</b>) 500 kV lines.</p>
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<p>Backflashover rate for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV, (<b>c</b>) 500 kV lines, considering the concept of average attractive radius and different CCDs. On the left, the graph considering impedances from 10 to 60 Ω, on the right a zoomed-in view considering impedances from 10 to 30 Ω.</p>
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<p>Backflashover rate for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV, (<b>c</b>) 500 kV lines, considering the concept of average attractive radius and different CCDs. On the left, the graph considering impedances from 10 to 60 Ω, on the right a zoomed-in view considering impedances from 10 to 30 Ω.</p>
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<p>Differences between the backflashover rates estimated considering the IEEE distribution compared to the use of different CCDs for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV, (<b>c</b>) 500 kV lines.</p>
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<p>Annual number of flashes to the 230 kV line assuming the concept of average attractive radius and considering the influence of different cumulative current distributions.</p>
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<p>Product <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub><mi>p</mi><mfenced><mrow><msub><mi>I</mi><mi>P</mi></msub><mo>&gt;</mo><msub><mi>I</mi><mrow><mi>c</mi><mi>r</mi><mi>i</mi><mi>t</mi></mrow></msub></mrow></mfenced></mrow></semantics></math> as a function of <math display="inline"><semantics><mrow><msub><mi>I</mi><mi>P</mi></msub></mrow></semantics></math> for the 230 kV line considering (<b>a</b>) the concept of average attractive radius to compute <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub></mrow></semantics></math> and (<b>b</b>) the influence of CCD to compute <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub></mrow></semantics></math>.</p>
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<p>Product <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub><mi>p</mi><mfenced><mrow><msub><mi>I</mi><mi>P</mi></msub><mo>&gt;</mo><msub><mi>I</mi><mrow><mi>c</mi><mi>r</mi><mi>i</mi><mi>t</mi></mrow></msub></mrow></mfenced></mrow></semantics></math> as a function of <math display="inline"><semantics><mrow><msub><mi>I</mi><mi>P</mi></msub></mrow></semantics></math> for the 230 kV line considering (<b>a</b>) the concept of average attractive radius to compute <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub></mrow></semantics></math> and (<b>b</b>) the influence of CCD to compute <math display="inline"><semantics><mrow><msub><mi>N</mi><mrow><mi>T</mi><mi>L</mi></mrow></msub></mrow></semantics></math>.</p>
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<p>Backflashover for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV, (<b>c</b>) 500 kV lines, considering the simultaneous influence of the CCDs. On the right, the graph considering impedances from 10 to 60 Ω, on the left a zoomed-in view considering impedances from 10 to 30 Ω.</p>
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<p>Differences between the backflashover rates estimated considering the IEEE distribution compared to the use of different CCDs for (<b>a</b>) 138 kV, (<b>b</b>) 230 kV, (<b>c</b>) 500 kV lines.</p>
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26 pages, 4479 KiB  
Article
Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor
by Muhammad Yousaf Arshad, Muhammad Azam Saeed, Muhammad Wasim Tahir, Halina Pawlak-Kruczek, Anam Suhail Ahmad and Lukasz Niedzwiecki
Energies 2023, 16(15), 5835; https://doi.org/10.3390/en16155835 - 7 Aug 2023
Cited by 5 | Viewed by 1528
Abstract
This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance [...] Read more.
This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors. Full article
(This article belongs to the Special Issue Plasma Application in Fuel Conversion Processes)
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<p>Discharge barrier NTP technology characteristics for gasification tar reduction, based on [<a href="#B44-energies-16-05835" class="html-bibr">44</a>].</p>
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<p>Working cycle for current TAC decomposition in NTP DBD reactor for kinetic modeling, reactor simulation and machine learning modeling.</p>
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<p>Experimental trends for tar analogue compound benzene reduction in DBD reactor (adapted from [<a href="#B36-energies-16-05835" class="html-bibr">36</a>]). (<b>a</b>) Power input (W) vs. benzene decomposition (%) and methane decomposition (%). Pin = 5–40 W, Tin = ambient conditions, Qin = 40 mL/min, concentration = 36 g/Nm<sup>3</sup> and t = 2.86 s. (<b>b</b>) Residence time (s) vs. benzene decomposition (%). Tin = ambient conditions, pin = 20 W, concentration = 36 g/Nm<sup>3</sup> and t = 0.8–2.86 s. (<b>c</b>) Benzene concentration (g/Nm<sup>3</sup>) vs. benzene decomposition (%). Tin = ambient conditions, pin = 15 W, concentration = 18, 36, 64 g/Nm<sup>3</sup> and t = 2.86 s.</p>
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<p>Novel suggested strategy for kinetic modeling, reactor assessment and machine learning methodology.</p>
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<p>Decomposition rate-constant calculation.</p>
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<p>MATLAB simulation results for TAC reactant concentration (<span class="html-italic">C<sub>A</sub></span>) change along the reactor length to product (<span class="html-italic">C<sub>B</sub></span>) at t = 2.86 s, <span class="html-italic">P<sub>in</sub></span> = 15 W, Simulation 1 for <span class="html-italic">C<sub>A</sub></span> = 0.178 kmol/m<sup>3</sup>. Simulation 2 for <span class="html-italic">C<sub>A</sub></span> = 0.401 kmol/m<sup>3</sup> in comparison to experiments of Faisal et al. [<a href="#B36-energies-16-05835" class="html-bibr">36</a>] with simulation. Simulation 3 for <span class="html-italic">C<sub>A</sub></span> = 0.801 kmol/m<sup>3</sup>.</p>
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<p>MATLAB simulation results for benzene decomposition at <span class="html-italic">t</span> = 2.86 s, <span class="html-italic">P<sub>in</sub></span> = 5–40 W, simulation for <span class="html-italic">C<sub>A</sub></span> = 36 g/Nm<sup>3</sup> (0.401 kmol/m<sup>3</sup>) for experimental and reactor model comparisons of experiments of Faisal et al. [<a href="#B36-energies-16-05835" class="html-bibr">36</a>] with simulation.</p>
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<p>Linear regression machine learning error quantification graphical representation.</p>
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<p>Machine learning linear regression modeling flowchart for Scikit Learn Library and generalized machine learning linear regression algorithm.</p>
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<p>Power input (<span class="html-italic">P<sub>in</sub></span>, W) vs. tar analogue compound decomposition experimental dataset machine learning study (%). (<b>a</b>) Heat map for data analysis. (<b>b</b>) Paired plot of data preprocessing. (<b>c</b>) Experimental dataset testing plot. (<b>d</b>) Experimental training set data plot for shortlisted variable at experimental power input (<span class="html-italic">P<sub>in</sub></span>, W). Experimental decomposition 0–100%, residence time = 2.86 s, concentration = 36 g/Nm<sup>3</sup>.</p>
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<p>Power input (P<sub>in</sub>, W) vs. tar analogue compound decomposition reactor model—simulation dataset machine learning study (%). (<b>a</b>) Heat map for data analysis. (<b>b</b>) Paired plot of data preprocessing. (<b>c</b>) Experimental dataset testing plot. (<b>d</b>) Experimental training set data plot for shortlisted variable at experimental power input (<span class="html-italic">P<sub>in</sub></span>, W) in reactor model and simulation dataset conditions. Power input P<sub>in</sub> 5–40 W, reactor model and simulation dataset decomposition 0–100%, residence time = 2.86 s, concentration = 36 g/Nm<sup>3</sup>.</p>
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23 pages, 11712 KiB  
Article
Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia
by Jake Anderson and Ashish P. Agalgaonkar
Energies 2023, 16(15), 5834; https://doi.org/10.3390/en16155834 - 7 Aug 2023
Viewed by 1429
Abstract
Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective [...] Read more.
Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer-owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modeled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process, and quasi-dynamic simulation (QDSL) models were used to represent the dynamic volt–watt and volt–var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear relationships were revealed between the number of PV systems on a feeder and various network characteristics. Regression was used to form trend equations that represent the linear relationships for each scenario analyzed. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer-owned rooftop solar PV without the need for detailed modeling. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs))
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<p>Energy Queensland’s micro-embedded generation volt–var connection requirement [<a href="#B33-energies-16-05834" class="html-bibr">33</a>].</p>
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<p>Energy Queensland’s micro-embedded generation volt–watt connection requirement [<a href="#B33-energies-16-05834" class="html-bibr">33</a>].</p>
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<p>LV network modeling.</p>
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<p>LV network tracing process.</p>
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<p>Constructing LV networks script flowchart.</p>
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<p>Constructing LV networks in DIgSILENT PowerFactory.</p>
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<p>DIgSILENT PowerFactory distribution transformer model before LV network creation.</p>
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<p>DIgSILENT PowerFactory distribution transformer model after LV network creation.</p>
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<p>Network analysis script flowchart.</p>
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<p>DIgSILENT PowerFactory QDSL models—Simulation Procedure.</p>
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<p>DIgSILENT PowerFactory QDSL element volt–var and volt–watt parameters.</p>
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<p>DIgSILENT PowerFactory QDSL element volt–var and volt–watt load flow script.</p>
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<p>Bolden Hill distribution feeder—PV system voltage.</p>
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<p>Bolden Hill distribution feeder—PV system active power output.</p>
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<p>Bolden Hill distribution feeder—PV system reactive power response.</p>
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<p>Urban distribution feeders’ minimum active power level.</p>
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<p>Urban distribution feeders’ minimum active power level—trend line.</p>
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<p>Rural distribution feeders’ minimum active power level.</p>
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<p>Rural distribution feeders’ minimum active power level—trend line.</p>
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<p>Urban distribution feeders’ reactive power contribution from PV.</p>
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<p>Urban distribution feeders’ reactive power contribution from PV—trend line.</p>
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<p>Rural distribution feeders’ reactive power contribution from PV.</p>
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<p>Rural distribution feeders reactive power contribution from PV—trend line.</p>
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<p>Variation in power factor on urban distribution feeders.</p>
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<p>Variation in power factor on rural distribution feeders.</p>
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14 pages, 2911 KiB  
Article
Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties
by Yetuo Tan, Yongming Zhi, Zhengbin Luo, Honggang Fan, Jun Wan and Tao Zhang
Energies 2023, 16(15), 5833; https://doi.org/10.3390/en16155833 - 7 Aug 2023
Cited by 4 | Viewed by 1446
Abstract
The emission reduction of global greenhouse gases is one of the key steps towards sustainable development. Demand response utilizes the resources of the demand side as an alternative of power supply which is very important for the power network balance, and the virtual [...] Read more.
The emission reduction of global greenhouse gases is one of the key steps towards sustainable development. Demand response utilizes the resources of the demand side as an alternative of power supply which is very important for the power network balance, and the virtual power plant (VPP) could overcome barriers to participate in the electricity market. In this paper, the optimal scheduling of a VPP with a flexibility margin considering demand response and uncertainties is proposed. Compared with a conventional power plant, the cost models of VPPs considering the impact of uncertainty and the operation constraints considering demand response and flexibility margin characteristics are constructed. The orderly charging and discharging strategy for electric vehicles considering user demands and interests is introduced in the demand response. The research results show that the method can reduce the charging cost for users participating in reverse power supply using a VPP. The optimizing strategy could prevent overload, complete load transfer, and realize peak shifting and valley filling, solving the problems of the new peak caused by disorderly power utilization. Full article
(This article belongs to the Special Issue Power System Analysis Control and Operation)
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<p>Basic structure of VPP.</p>
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<p>Prices of different energy.</p>
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<p>Charging load of electric vehicle benchmark.</p>
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<p>Power balance in traditional model without VPP.</p>
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<p>Power balance in the model with VPP.</p>
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<p>Power balance in the model with VPP and electric vehicle.</p>
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<p>Power interaction between power grid and electric vehicle.</p>
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<p>The annual profit of VPP with different charging prices.</p>
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<p>The incomes of different participators.</p>
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21 pages, 17766 KiB  
Article
Research on the Dynamic Response of a Steel Catenary Riser in the Touchdown Zone under Pigging Conditions
by Xiaoxiao Zhu, Yunlei Fu, Yutao Wang, Lulu Wang and Liyun Lao
Energies 2023, 16(15), 5832; https://doi.org/10.3390/en16155832 - 6 Aug 2023
Viewed by 1096
Abstract
A periodic pigging operation performed to clean off sediment and provide operators with detailed health information for a pipeline is mandatorily required. The research on pigging-induced issues for the steel catenary riser (SCR), one of the key parts in offshore hydrocarbon recovery pipelines [...] Read more.
A periodic pigging operation performed to clean off sediment and provide operators with detailed health information for a pipeline is mandatorily required. The research on pigging-induced issues for the steel catenary riser (SCR), one of the key parts in offshore hydrocarbon recovery pipelines between the floating production system and the seabed, has been scarce until now. As a result, there is an urgent need for theories to guide the pigging operation to ensure safe pigging is achieved in deepwater risers. In this paper, a study aiming to determine the effects of the pigging impact load and the pigging-induced slugging load on the dynamic response of the riser is reported. A SCR pigging model was established and proposed based on the finite element analysis (FEA) method. The stress distribution and displacement of the SCR were investigated under the pigging conditions, with the consideration of the effects of waves, currents, and floating platform movements. It was found that the pigging load has large effects on the stress and displacement of the touchdown zone (TDZ), especially the touchdown point (TDP). The displacement of the TDZ in the Y (vertical) direction is more significant than that in the X (horizontal) direction under pigging conditions, and the maximum displacement of the TDZ in the Y direction is proportional to the weight of the pig, as well as the length of the pigging-induced slugging. Full article
(This article belongs to the Special Issue Multiphase Flow in Energy and Process Systems)
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<p>Schematic diagram of the SCR structure.</p>
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<p>Finite element model of an SCR.</p>
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<p>Comparison of the overall shape of the catenary riser.</p>
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<p>Force diagram of the SCR pigging process without considering the slug load.</p>
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<p>Schematic diagram of an SCR applied with the pigging impact load and slugging load.</p>
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<p>The experimental rig of the SCR under pigging conditions.</p>
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<p>Time traces of top tension values.</p>
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<p>Stress values at different nodes.</p>
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<p>Comparison of equivalent stress values of the SCR with and without the pigging impact load at different nodes.</p>
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<p>Variations in the mean and maximum equivalent stress values with different pigging impact loads at different nodes.</p>
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<p>Comparison of the absolute displacements of different nodes with the effects of different pigging impact loads.</p>
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<p>Variations in the average and maximum displacements of the TDZ in the X direction at different pigging velocity rates.</p>
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<p>Variations in the average and maximum displacements of the TDZ in the Y direction at different pigging velocity rates.</p>
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<p>Schematic diagram of the pigging impact load and the pigging-induced slug load on the riser.</p>
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<p>Comparison of equivalent stress values of the TDZ with and without considering the pigging load.</p>
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<p>Variations in mean and maximum equivalent stress values of the TDZ with and without slug loads at different nodes.</p>
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<p>Comparison of the absolute displacement values of different nodes with the effects of different pigging loads.</p>
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<p>Variations in average and maximum displacements of the touchdown zone in the X direction at different pigging velocity values while considering the slug flow.</p>
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<p>Variations in average and maximum displacements of the touchdown zone in the Y direction at different pigging velocity values while considering the slug flow.</p>
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<p>SCR geometrical morphologies at different velocities when the slug flow is considered and the pig is located at node 990.</p>
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14 pages, 1895 KiB  
Article
Synergistic Effect of Water-Soluble Hydroxylated Multi-Wall Carbon Nanotubes and Graphene Nanoribbons Coupled with Tetra Butyl Ammonium Bromide on Kinetics of Carbon Dioxide Hydrate Formation
by Shu-Li Wang, Yan-Yun Xiao, Shi-Dong Zhou, Kun Jiang, Yi-Song Yu and Yong-Chao Rao
Energies 2023, 16(15), 5831; https://doi.org/10.3390/en16155831 - 6 Aug 2023
Cited by 1 | Viewed by 1072
Abstract
In this work, the thermodynamics and kinetics of hydrate formation in 9.01 wt% tetra butyl ammonium bromide (TBAB) mixed with water-soluble hydroxylated multi-wall carbon nanotube (MWCNTol) systems were characterized by measuring hydrate formation conditions, induction time, and final gas consumption. The results showed [...] Read more.
In this work, the thermodynamics and kinetics of hydrate formation in 9.01 wt% tetra butyl ammonium bromide (TBAB) mixed with water-soluble hydroxylated multi-wall carbon nanotube (MWCNTol) systems were characterized by measuring hydrate formation conditions, induction time, and final gas consumption. The results showed that MWCNTols had little effect on the phase equilibrium of CO2 hydrate formation. Nanoparticles (graphene nanoribbons (GNs) and MWCNTols) could significantly shorten the induction time. When the concentration was ≤0.06 wt%, MWCNTols had a better effect on the induction time than the GN system, and the maximum reduction in induction time reached 44.22%. The large surface area of MWCNTols could provide sites for heterogeneous nucleation, thus shortening the induction time of hydrate formation. Furthermore, adding different concentrations of nanoparticles to the 9.01 wt% TBAB solution effectively increased the final gas consumption, and the maximum increase was 10.44% of the 9.01 wt% TBAB + 0.08 wt% GN system. Meanwhile, the suitable initial pressure and experimental temperature could also promote the hydrate formation and increase the motivation in hydrate formation. The 9.01 wt% TBAB + 0.02 wt% MWCNTol system had the best effect at 3.5 MPa and 277.15 K. The induction time was reduced by 66.67% and the final gas consumption was increased by 284.11% compared to those of the same system but at a different initial pressure and experimental temperature. This work helps to promote the industrial application of hydrate technology in CO2 capture and storage. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>Schematic diagram of the experimental apparatus.</p>
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<p>Phase equilibrium curves for TBAB + MWCNTols varying with different MWCNTols concentrations.</p>
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<p>The induction time of hydrate formation in the 9.01 wt% TBAB solution with different concentrations of MWCNTol and GN systems.</p>
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<p>Effect of TBAB + MWCNTol and GN systems with different concentrations on the amount of CO<sub>2</sub> consumed: (<b>a</b>) the curves of CO<sub>2</sub> consumed in 9.01 wt% TBAB + different concentration GN systems; (<b>b</b>) the curves of CO<sub>2</sub> consumed in 9.01 wt% TBAB + different concentration MWCNTol systems; (<b>c</b>) the bar graph of CO<sub>2</sub> consumed in 9.01 wt% TBAB + different concentration GN systems; (<b>d</b>) the bar graph of CO<sub>2</sub> consumed in 9.01 wt% TBAB + different concentration MWCNTol systems.</p>
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<p>Pressure changes during the CO<sub>2</sub> hydrate formation in three additive systems at 277.15 K and different initial pressures: (<b>a</b>) 9.01 wt% TBAB; (<b>b</b>) 9.01 wt% TBAB + 0.02 wt% GN; (<b>c</b>) 9.01 wt% TBAB + 0.02 wt% MWCNTols.</p>
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<p>Effect of initial pressure on (<b>a</b>) induction time and (<b>b</b>) gas consumption for the CO<sub>2</sub> hydrate formation in three additive systems.</p>
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<p>Pressure changes during the CO<sub>2</sub> hydrate formation in three additive systems at 2.5 MPa and different experimental temperatures. (<b>a</b>) 9.01 wt% TBAB; (<b>b</b>) 9.01 wt% TBAB + 0.02 wt% GN; (<b>c</b>) 9.01 wt% TBAB + 0.02 wt% MWCNTols.</p>
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<p>Effect of experimental temperature on (<b>a</b>) induction time and (<b>b</b>) gas consumption for the CO<sub>2</sub> hydrate formation in three additive systems.</p>
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26 pages, 31757 KiB  
Article
Optimal Design and Operation of Hybrid Renewable Energy Systems for Oakland University
by Edrees Yahya Alhawsawi, Hanan Mikhael D. Habbi, Mansour Hawsawi and Mohamed A. Zohdy
Energies 2023, 16(15), 5830; https://doi.org/10.3390/en16155830 - 6 Aug 2023
Cited by 9 | Viewed by 2145
Abstract
This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical, economic, and environmental aspects of integrating renewable energy technologies. The research [...] Read more.
This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical, economic, and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university’s electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, and environmental factors and addressing unmet load, this research contributes to advancing renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design, and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University’s search for energy resilience and environmental surveillance, which has a total peak load of 9.958 MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of USD 30 M, with a levelized energy cost (LCOE) of 0.00274 USD/kWh. In addition, the minimum desired load will be unmetered on some days in September. Full article
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<p>Monthly average solar GHI data for Michigan State.</p>
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<p>Monthly average wind speed data (NASA Prediction of Worldwide Energy Resource).</p>
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<p>Load profile for OU “from facility management department”.</p>
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<p>Selected electrical load served daily profile (HOMER Pro software).</p>
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<p>Available areas for PV and wind turbines within the university campus (CHP <span class="html-fig-inline" id="energies-16-05830-i001"><img alt="Energies 16 05830 i001" src="/energies/energies-16-05830/article_deploy/html/images/energies-16-05830-i001.png"/></span>, PV <span class="html-fig-inline" id="energies-16-05830-i002"><img alt="Energies 16 05830 i002" src="/energies/energies-16-05830/article_deploy/html/images/energies-16-05830-i002.png"/></span>, and WT <span class="html-fig-inline" id="energies-16-05830-i003"><img alt="Energies 16 05830 i003" src="/energies/energies-16-05830/article_deploy/html/images/energies-16-05830-i003.png"/></span>).</p>
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<p>System 1: grid and CHP design.</p>
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<p>Output power (System 1: grid and CHP).</p>
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<p>System 1: grid and CHP (<b>a</b>) generator power output and (<b>b</b>) energy purchased from grid.</p>
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<p>System 2: grid, CHP, and PV design.</p>
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<p>Output power (System 2: grid, CHP, and PV).</p>
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<p>System 2: grid, CHP, and PV (<b>a</b>) energy purchased from grid, (<b>b</b>) energy sold to grid, (<b>c</b>) CHP output power, and (<b>d</b>) PV power output.</p>
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<p>System 3: grid, CHP PV, and ESS design.</p>
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<p>Output power (System 3: grid, CHP, PV, and ESS).</p>
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<p>System 3: grid, CHP, PV, and ESS (<b>a</b>) energy purchased from grid, (<b>b</b>) energy sold to grid, (<b>c</b>) CHP output power, (<b>d</b>) PV power output, and (<b>e</b>) state of charge.</p>
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<p>System 4: grid, CHP, and wind design.</p>
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<p>Output power (System 4: grid, CHP, and wind).</p>
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<p>System 4: grid, CHP, and wind, (<b>a</b>) energy purchased from grid, (<b>b</b>) energy sold to grid, (<b>c</b>) CHP output power, and (<b>d</b>) wind power output.</p>
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<p>System 5: grid, CHP, wind, PV, and ESS design.</p>
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<p>Output power (System 5: grid, CHP, PV, ESS, and wind).</p>
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<p>System 5: grid, CHP, PV, ESS, and wind (<b>a</b>) energy purchased from grid, (<b>b</b>) energy sold to grid, (<b>c</b>) CHP output power, (<b>d</b>) PV output power, (<b>e</b>) wind power output, and (<b>f</b>) state of charge.</p>
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<p>System 5: grid, CHP, PV, ESS, and wind (<b>a</b>) energy purchased from grid, (<b>b</b>) energy sold to grid, (<b>c</b>) CHP output power, (<b>d</b>) PV output power, (<b>e</b>) wind power output, and (<b>f</b>) state of charge.</p>
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<p>System 6: CHP, WT, PV, and ESS design.</p>
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<p>Output power (System 6: CHP, PV, ESS, and WT).</p>
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<p>System 6: CHP, PV, ESS, and wind (<b>a</b>) CHP output power, (<b>d</b>) PV power output, (<b>c</b>) wind output power, and (<b>d</b>) state of charge.</p>
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<p>Monthly electric production for all proposed systems.</p>
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<p>Monthly electric production for all proposed systems.</p>
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<p>Annual renewable electric production for all proposed systems.</p>
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<p>Unmet load for the proposed systems.</p>
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<p>Unmet load for the proposed systems.</p>
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20 pages, 3371 KiB  
Article
The Impact of Bend–Twist Coupling on Structural Characteristics and Flutter Limit of Ultra-Long Flexible Wind Turbine Composite Blades
by Bei Li, De Tian, Xiaoxuan Wu, Huiwen Meng and Yi Su
Energies 2023, 16(15), 5829; https://doi.org/10.3390/en16155829 - 6 Aug 2023
Viewed by 1436
Abstract
Flutter is an instability phenomenon that can occur in wind turbine blades due to fluid–structure interaction, particularly for longer and more flexible blades. Aeroelastic tailoring through bend–twist coupling is an effective method to enhance the aeroelastic performance of blades. In this study, we [...] Read more.
Flutter is an instability phenomenon that can occur in wind turbine blades due to fluid–structure interaction, particularly for longer and more flexible blades. Aeroelastic tailoring through bend–twist coupling is an effective method to enhance the aeroelastic performance of blades. In this study, we investigate the impact of bend–twist coupling on the structural performance and flutter limit of the IEA 15 MW blade, which is currently the longest reference wind turbine blade, and determine the optimal layup configuration that maximizes the flutter speed. The blade is modeled by NuMAD and iVABS, and the cross-section properties are obtained by PreComb and VABS. The accuracy of the blade model is verified in terms of stiffness and frequency. The bend–twist coupling is implemented by changing the fiber angle of the skin and spar cap considering symmetric and asymmetric layups. The flutter limits of both the baseline and the bend–twist coupled blade are evaluated based on HAWC2. The results show that the angle of spar cap carbon fiber has a greater effect on the blade’s structural properties and flutter speed than the skin fiber. Varying the spar cap carbon fiber angle increases the flutter speed, with the effect being more significant for the symmetric layup, up to 9.66% at a fiber angle of 25 degrees. In contrast, the variation in skin fiber angle has a relatively small impact on flutter speed—within ±3%. Full article
(This article belongs to the Special Issue Wind Turbine 2023)
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<p>Schematic diagram of the polar grid BEM.</p>
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<p>Schematic diagram of the co-ordinate system in HAWC2.</p>
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<p>Illustration of the cross-section geometry.</p>
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<p>Blade layup: (<b>a</b>) thickness of layers in the LE reinforcement; (<b>b</b>) thickness of layers in the LE and TE panel; (<b>c</b>) thickness of layers in the spar; (<b>d</b>) thickness of layers in the TE reinforcement; and (<b>e</b>) thickness of layers in the web.</p>
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<p>Blade model established by NuMAD.</p>
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<p>Cross-sectional layout at different spans locations: (<b>a</b>) s = 0; (<b>b</b>) s = 0.1; (<b>c</b>) s = 0.47; (<b>d</b>) s = 0.65; (<b>e</b>) s = 0.78; and (<b>f</b>) s = 1.</p>
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<p>Comparison of blade stiffness properties: (<b>a</b>) tension stiffness; (<b>b</b>) edgewise stiffness; (<b>c</b>) flapwise stiffness; and (<b>d</b>) torsional stiffness.</p>
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<p>(<b>a</b>) Rotor speed and wind speed; (<b>b</b>) blade tip flapwise displacement and AOA near the instability phenomenon.</p>
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<p>Variation of blade structural properties at different span locations with respect to skin fiber angle: (<b>a</b>) edge-twist coupling factor; (<b>b</b>) flap-twist coupling factor; (<b>c</b>) ROC for tension stiffness; (<b>d</b>) ROC for edgewise stiffness; (<b>e</b>) ROC for flapwise stiffness; and (<b>f</b>) ROC for torsional stiffness.</p>
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<p>Flutter speed at different ply angles for wind turbine blades with symmetric or asymmetric skin.</p>
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<p>Variation of blade structural properties at different span locations with respect to symmetric spar cap fiber angle: (<b>a</b>) edge-twist coupling factor; (<b>b</b>) flap-twist coupling factor; (<b>c</b>) ROC for tension stiffness; (<b>d</b>) ROC for edgewise stiffness; (<b>e</b>) ROC for flapwise stiffness; and (<b>f</b>) ROC for torsional stiffness.</p>
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<p>Variation of blade structural properties at different span locations with respect to asymmetric spar cap fiber angle: (<b>a</b>) edge-twist coupling factor; (<b>b</b>) flap-twist coupling factor; (<b>c</b>) ROC for tension stiffness; (<b>d</b>) ROC for edgewise stiffness; (<b>e</b>) ROC for flapwise stiffness; and (<b>f</b>) ROC for torsional stiffness.</p>
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<p>Flutter speed at different angles for wind turbine blades with symmetric or asymmetric spar cap.</p>
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22 pages, 2315 KiB  
Review
Alternative Methods of Replacing Electric Batteries in Public Transport Vehicles
by Dariusz Masłowski, Ewa Kulińska and Łukasz Krzewicki
Energies 2023, 16(15), 5828; https://doi.org/10.3390/en16155828 - 6 Aug 2023
Cited by 1 | Viewed by 1640
Abstract
Current electric vehicle solutions offer the possibility of a fully electrified bus fleet, although due to financial constraints, most cities cannot afford it. Therefore, the possibility of battery replacement is a needed alternative to the electrification process of a city’s bus fleet. The [...] Read more.
Current electric vehicle solutions offer the possibility of a fully electrified bus fleet, although due to financial constraints, most cities cannot afford it. Therefore, the possibility of battery replacement is a needed alternative to the electrification process of a city’s bus fleet. The aim of this study is to investigate the needs of cities and present the concept of battery replacement in an electric bus. The research was based on two groups of selected Polish cities: (1) up to 150,000 inhabitants, and (2) up to 1 million inhabitants. The research part includes an analysis of the means of transport in provincial cities in Poland, an analysis of the kilometers covered by the city fleet, the average distances covered by buses per day, and an estimate of the number of battery replacements. The concept is based on current technological solutions. The description of the concept includes the proposed battery and the technology used, the placement of the battery in the vehicle, and the replacement scheme. Research indicates that the concept can be used with existing technology but will be more justifiable for a larger city due to the higher fleet load. The paper shows the importance of researching bus electrification solutions and that modern solutions can improve existing urban networks in cities. Full article
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<p>Diagram showing the type of propulsion system of buses used in Poland’s provincial cities.</p>
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<p>Graph showing the share of bus manufacturers used in Poland’s provincial cities.</p>
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<p>Share of electric bus models used in Poland’s provincial cities.</p>
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<p>Procedure for replacing the battery in the following steps—top view.</p>
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<p>Diagram of the underground NEP exchange system. Green color—battery charged, red color—battery discharged.</p>
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<p>Battery replacement procedure using NEP technology.</p>
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24 pages, 28920 KiB  
Article
Cogging Force Reduction and Profile Smoothening Methods for a Slot-Spaced Permanent Magnet Linear Alternator
by Chin-Hsiang Cheng and Surender Dhanasekaran
Energies 2023, 16(15), 5827; https://doi.org/10.3390/en16155827 - 6 Aug 2023
Viewed by 1123
Abstract
A Permanent Magnet Linear Alternator (PMLA) works seamlessly with a Free Piston Stirling Engine (FPSE) to convert short-stroke and high-frequency linear motion to electric power. Cogging force is an unavoidable opposition force acting on the translator, limiting the linear motion from the driving [...] Read more.
A Permanent Magnet Linear Alternator (PMLA) works seamlessly with a Free Piston Stirling Engine (FPSE) to convert short-stroke and high-frequency linear motion to electric power. Cogging force is an unavoidable opposition force acting on the translator, limiting the linear motion from the driving force, which shortens the lifespan of the machine, causing oscillatory power output and increased maintenance costs. This research focuses on the methods to reduce the cogging force acting on the translator of a slot-spaced PMLA by making geometrical changes to the structure of the machine. The profile of the cogging force is made to be in line with the displacement profile of the translator to avoid unnecessary vibrations and damaging the piston of the FPSE. The changes made also influence the induced voltage. Bringing a balance between reduced voltage and cogging force with minor geometrical changes and a sinusoidal cogging force profile is the outcome of this work. Full article
(This article belongs to the Special Issue Distributed Energy Systems for Combined Heat and Power Production)
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<p>Cut-section view of the slot-spaced linear alternator.</p>
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<p>Grid independence check.</p>
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<p>Contour plot of magnetic flux density in a cut-section view of the PMLA for the baseline case.</p>
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<p>Vector plot of magnetic flux density in a cut-section view of the PMLA for the baseline case.</p>
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<p>Performance of the PMLA in the baseline condition.</p>
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<p>Chamfering radius on stator tooth (<b>a</b>) 0.25 mm and (<b>b</b>) 2 mm.</p>
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<p>Influence of stator tooth chamfering on cogging force and induced voltage.</p>
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<p>Comparison of cogging force profiles with the chamfered stator tooth.</p>
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<p>Chamfering radius on magnets (<b>a</b>) 0.25 mm and (<b>b</b>) 2 mm.</p>
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<p>Influence of chamfered magnets on cogging force and induced voltage.</p>
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<p>Comparison of cogging force profile between 0.5, 0.75, and 1 mm chamfering radius of curvature.</p>
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<p>Cut-section view of the four-tooth model.</p>
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<p>Comparison of induced voltage and cogging force for four- and six-tooth models.</p>
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<p>Comparison of cogging force profile between four- and six-tooth models.</p>
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<p>Axial stator notching with a radius of curvature (<b>a</b>) 27.5 mm and (<b>b</b>) 15 mm.</p>
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<p>Influence of axial stator notching over induced voltage and cogging force.</p>
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<p>Comparison of the cogging force profile between 20, 22.5, and 25 mm models.</p>
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<p>Axial magnet notching radius of curvature (<b>a</b>) 15 mm and (<b>b</b>) 60 mm.</p>
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<p>Influence of axial magnet notching over induced voltage and cogging force.</p>
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<p>Comparison of cogging force profile with 30, 35, and 40 mm axial magnet notching.</p>
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<p>Radial stator notching with a radius of curvature of (<b>a</b>) 9.5 mm and (<b>b</b>) 10 mm.</p>
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<p>Influence of radial stator notching on induced voltage and cogging force.</p>
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<p>Comparison of cogging force profile for 9.4, 9.5, and 9.6 mm radial notching.</p>
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<p>Type of notching: (<b>a</b>) flat end tooth, (<b>b</b>) center tooth notched.</p>
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<p>Influence of type of stator notching over induced voltage and cogging force.</p>
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<p>A ridged tooth with a 15 mm radius of curvature.</p>
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<p>Magnetic flux density in a ridged tooth.</p>
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<p>Comparison of cogging force and induced voltage of a ridged model with the original model.</p>
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<p>Split magnets (<b>a</b>) two pieces, (<b>b</b>) three pieces, and (<b>c</b>) four pieces.</p>
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<p>Influence of magnet splitting over induced voltage and cogging force.</p>
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<p>Cogging force profile of 1–4-piece magnets.</p>
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<p>Cut-section schematic of the (<b>a</b>) original and (<b>b</b>) modified model.</p>
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<p>Comparison of induced voltage, cogging force, and displacement of the modified model.</p>
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<p>Comparison of original model cogging force with the modified model.</p>
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<p>Comparison of performance with the original and the modified model.</p>
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16 pages, 5382 KiB  
Article
Eddy Current Braking Force Analysis of a Water-Cooled Ironless Linear Permanent Magnet Synchronous Motor
by Mengyao Wang, Lu Zhang, Kai Yang, Junjie Xu and Chunyu Du
Energies 2023, 16(15), 5826; https://doi.org/10.3390/en16155826 - 6 Aug 2023
Viewed by 934
Abstract
The ironless linear permanent magnet synchronous motor (ILPMSM) is highly compact, easy to control, and exhibits minimal thrust fluctuations, making it an ideal choice for direct loading applications requiring precise positioning accuracy in linear motor test rigs. To address the issue of temperature [...] Read more.
The ironless linear permanent magnet synchronous motor (ILPMSM) is highly compact, easy to control, and exhibits minimal thrust fluctuations, making it an ideal choice for direct loading applications requiring precise positioning accuracy in linear motor test rigs. To address the issue of temperature rise resulting from increased primary winding current and to simultaneously enhance thrust density while minimizing thrust fluctuations, this paper introduces a bilateral-type ILPMSM with a cooling water jacket integrated between the dual-layer windings of the primary movers. The primary winding of the motor adopts a dual-layer coreless structure where the upper and lower windings are closely spaced and cooled by a non-conductive metal cooling water jacket, while the dual-sided secondary employs a Halbach permanent magnet array. The motor’s overall braking force is a combination of the electromagnetic braking force generated by the energized windings and the eddy current braking force induced on the cooling water jacket. This paper specifically focuses on the analysis of the eddy current braking force. Initially, the motor’s geometry and working principle are presented. Subsequently, the equivalent magnetization intensity method is employed to determine the no-load air gap magnetic density resulting from the Halbach array. An analytical model is then developed to derive expressions for the eddy current density and braking force induced in the water-cooling jacket. The accuracy of the analytical method is validated through finite element analysis. Then, a comparative analysis of the braking forces in two primary cooling structures, namely the inter-cooled type and the two-side cooled type ILPMSM, is conducted. Moreover, the characteristics of the eddy current braking force are thoroughly examined concerning motor size parameters and operating conditions. This paper provides a solid theoretical foundation for the subsequent optimization design of the proposed motor. Full article
(This article belongs to the Special Issue Advanced Permanent-Magnet Machines for Electric Vehicles)
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<p>The geometry of the proposed ILPMSM: (<b>a</b>) 3D assembly drawing; (<b>b</b>) 2D electromagnetic structure.</p>
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<p>The geometry of the Primary: (<b>a</b>) partial cross-section; (<b>b</b>) cooling water paths.</p>
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<p>The analytical model of ILPMSM.</p>
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<p>The analytical model of braking force.</p>
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<p>The FEA-simulation model of the ILPMSM with an intermediate water-cooled jacket: (<b>a</b>) 2D FEA-simulation model; (<b>b</b>) sectional views of the topology.</p>
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<p>Prototype photos for the ILPMSM with an intermediate water-cooled jacket: (<b>a</b>) the water path side of the cooling water jacket; (<b>b</b>) the primary coil side of the cooling water jacket; (<b>c</b>) the primary prototype before potting; (<b>d</b>) the secondary prototype.</p>
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<p>FEA and analytical comparison of eddy current braking.</p>
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<p>The influence of cooling water slotting on eddy current braking force by FEA.</p>
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<p>The FEA model of the ILPMSM with two-side water-cooling jacket.</p>
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<p>Comparison of eddy current braking force between analytical and FEA: (<b>a</b>) two-side water cooling ILPMSM; (<b>b</b>) comparison between two cooling structures of ILPMSM.</p>
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<p>The curve of cooling water jacket thickness <span class="html-italic">h</span><sub>p</sub> with braking force magnitude <span class="html-italic">f</span><sub>x1</sub> and fluctuation <span class="html-italic">f</span><sub>x2</sub>: (<b>a</b>) the curve of <span class="html-italic">f</span><sub>x1</sub> with <span class="html-italic">h</span><sub>p</sub>; (<b>b</b>) the curve of <span class="html-italic">f</span><sub>x2</sub> with <span class="html-italic">h</span><sub>p</sub>.</p>
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<p>The curve of <span class="html-italic">L</span><sub>p</sub>/<span class="html-italic">τ</span> with braking force magnitude <span class="html-italic">f</span><sub>x1</sub> and fluctuation <span class="html-italic">f</span><sub>x2</sub>: (<b>a</b>) the curve of <span class="html-italic">f</span><sub>x1</sub> with <span class="html-italic">L</span><sub>p</sub>/<span class="html-italic">τ</span>; (<b>b</b>) the curve of <span class="html-italic">f</span><sub>x2</sub> with <span class="html-italic">L</span><sub>p</sub>/<span class="html-italic">τ</span>.</p>
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<p>The curve of pole pitch τ and major PM pole pitch ratio <span class="html-italic">α</span><sub>p</sub> with braking force magnitude <span class="html-italic">f</span><sub>x1</sub>: (<b>a</b>) the curve of <span class="html-italic">f</span><sub>x1</sub> with <span class="html-italic">τ</span>; (<b>b</b>) the curve of <span class="html-italic">f</span><sub>x1</sub> with <span class="html-italic">α</span><sub>p</sub>.</p>
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<p>The effect of velocity and materials on eddy current braking force: (<b>a</b>) working speed; (<b>b</b>) material of cooling water jacket.</p>
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22 pages, 3857 KiB  
Article
TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction
by Songjiang Li, Wenxin Zhang and Peng Wang
Energies 2023, 16(15), 5825; https://doi.org/10.3390/en16155825 - 5 Aug 2023
Cited by 1 | Viewed by 2184
Abstract
Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate [...] Read more.
Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate features in multivariate time series data. To address these limitations, we propose a multi-dimensional time series forecasting framework called TS2ARCformer. The TS2ARCformer framework incorporates the TS2Vec layer for contextual encoding and utilizes the Transformer model for prediction. This combination effectively captures the multi-dimensional features of the data. Additionally, TS2ARCformer introduces a Cross-Dimensional-Self-Attention module, which leverages interactions across channels and temporal dimensions to enhance the extraction of multivariate features. Furthermore, TS2ARCformer leverage a traditional autoregressive component to overcome the issue of deep learning models being insensitive to input scale. This also enhances the model’s ability to extract linear features. Experimental results on two publicly available power load datasets demonstrate significant improvements in prediction accuracy compared to baseline models, with reductions of 43.2% and 37.8% in the aspect of mean absolute percentage error (MAPE) for dataset area1 and area2, respectively. These findings have important implications for the accurate prediction of power load and the optimization of power system operation and analysis. Full article
(This article belongs to the Topic Short-Term Load Forecasting)
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<p>Impact of Meteorological Data on Load.</p>
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<p>The flowchart of TS2ARCformer.</p>
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<p>The structure of the TS2Vec Layer.</p>
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<p>Multi-Head Self-Attention Component.</p>
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<p>Comparing Various Attention Mechanisms.</p>
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<p>The structure of the TS2ARCformer Model.</p>
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<p>The analysis of Electric Power Load Data. Explanation: (<b>A</b>) represents the analysis of load data stationarity. (<b>B</b>) illustrates the analysis of load data autocorrelation. (<b>C</b>) depicts the analysis of feature correlations. (<b>D</b>) displays the three-dimensional visualization of the load data.</p>
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<p>The plot of the forecasting results of all models on the public dataset area1.</p>
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<p>Comparison experiment results of 10 models on dataset area1.</p>
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<p>The plot of the forecasting results of all models on the public dataset area2.</p>
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<p>Comparison experiment results of 10 models on dataset area2.</p>
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<p>Ablation experiment results of 6 models on dataset area1.</p>
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<p>Ablation experiment results of 6 models on dataset area2.</p>
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17 pages, 4164 KiB  
Article
Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules
by Naveen Venkatesh Sridharan, Jerome Vasanth Joseph, Sugumaran Vaithiyanathan and Mohammadreza Aghaei
Energies 2023, 16(15), 5824; https://doi.org/10.3390/en16155824 - 5 Aug 2023
Cited by 6 | Viewed by 1188
Abstract
The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a [...] Read more.
The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN’s performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems. Full article
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<p>An overview of the proposed PV module data acquisition process.</p>
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<p>The complete methodology of the proposed WiSARD classifier.</p>
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<p>Feature selection process using DenseNet-201.</p>
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<p>(<b>a</b>) Simple difference among weightless and weighted neural networks. (<b>b</b>) A simplified view of RAM network and neuron (RAM devices).</p>
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<p>WiSARD classifier with discriminator module.</p>
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<p>Confusion matrix of WiSARD classifier with optimal hyperparameters.</p>
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15 pages, 3806 KiB  
Article
A Multiphysics-Multiscale Model for Particle–Binder Interactions in Electrode of Lithium-Ion Batteries
by Yasir Ali, Imran Shah, Tariq Amin Khan and Noman Iqbal
Energies 2023, 16(15), 5823; https://doi.org/10.3390/en16155823 - 5 Aug 2023
Cited by 1 | Viewed by 1332
Abstract
Understanding the electrochemical and mechanical degradations inside the electrodes of lithium-ion battery is crucial for the design of robust electrodes. A typical lithium-ion battery electrode consists of active particles enclosed with conductive binder and an electrolyte. During the charging and discharging process, these [...] Read more.
Understanding the electrochemical and mechanical degradations inside the electrodes of lithium-ion battery is crucial for the design of robust electrodes. A typical lithium-ion battery electrode consists of active particles enclosed with conductive binder and an electrolyte. During the charging and discharging process, these adjacent materials create a mechanical confinement which suppresses the expansion and contraction of the particles and affects overall performance. The electrochemical and mechanical response mutually affect each other. The particle level expansion/contraction alters the electrochemical response at the electrode level. In return, the electrode level kinetics affect the stress at the particle level. In this paper, we developed a multiphysics–multiscale model to analyze the electrochemical and mechanical responses at both the particle and cell level. The 1D Li-ion battery model is fully coupled with 2D representative volume element (RVE) model, where the particles are covered in binder layers and bridged through the binder. The simulation results show that when the binder constraint is incorporated, the particles achieve a lower surface state of charge during charging. Further, the cell charging time increases by 7.4% and the discharge capacity reduces by 1.4% for 1 C-rate charge/discharge. In addition, mechanical interaction creates inhomogeneous stress inside the particle, which results in particle fracture and particle–binder debonding. The developed model will provide insights into the mechanisms of battery degradation for improving the performance of Li-ion batteries. Full article
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<p>Schematic of the simulation model showing the coupling between the 1D electrochemical model and 2D RVE model. Inside the 1D model, the whole anode is divided into five equal parts, and each part is two-way coupled with its corresponding 2D RVE. RVE1 and RVE5 are near to current collector and separator respectively. Point 1 and 2 refer to the center of particle and particle-binder interface, respectively.</p>
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<p>(<b>a</b>) The total stress inside the particle is the superposition of concentration gradient induced stress <math display="inline"><semantics><mrow><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mi mathvariant="normal">c</mi></mrow></msubsup></mrow></semantics></math> and stress coming from P/P and P/B interaction <math display="inline"><semantics><mrow><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mi>in</mi></mrow></msubsup></mrow></semantics></math>. (<b>b</b>) Coupling between electrochemistry and mechanics in both 1D electrochemical model and 2D RVE model.</p>
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<p>Evolution of (<b>a</b>) cell voltage and (<b>b</b>) surface SOC for the isolated and binder-constrained particles throughout the whole charge–discharge cycle. The inset in (<b>a</b>) shows the profile of applied current; (<b>c</b>) comparison of the surface SOC across the electrode thickness for the case isolated and binder-constrained particles; (<b>d</b>) bar graphs comparing the charging time and discharge capacity for isolated and binder-constrained particles.</p>
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<p>Contour plots of hydrostatic stress inside the RVE1 and RVE5 at the end of (<b>a</b>) charging, (<b>b</b>) discharging; (<b>c</b>) maximum compressive hydrostatic stress distribution inside the binder-constrained particles across the electrode thickness; (<b>d</b>) evolution of stress-induced overpotential inside RVE1 and RVE5 during the complete charge–discharge cycle.</p>
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<p>Temporal evolution of von Mises stress at Point 1 and Point 2 and the maximum von Mises stress inside the particle of (<b>a</b>) RVE1 and (<b>b</b>) RVE5. The von Mises stress variation at Point 1 and Point 2 and the maximum von Mises stress values across the electrode thickness at the end of (<b>c</b>) cc_ch, (<b>d</b>) cv_ch, and (<b>e</b>) cc_dch.</p>
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<p>(<b>a</b>) Maximum compressive hydrostatic stress inside the binder-constrained particles across the electrode thickness for the case of soft binder; (<b>b</b>) temporal evolution of stress-induced overpotential inside RVE1 and RVE5 during the whole charge/discharge cycle for the soft binder’s case; (<b>c</b>) comparison of surface SOC for the soft and hard binder. The von Mises stress variation at Point 1 and Point 2, and the maximum von Mises stress across the electrode thickness at the end of (<b>d</b>) cc_ch, (<b>e</b>) cv_ch, and (<b>f</b>) cc_dch, when the soft binder is considered.</p>
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<p>Temporal evolution of (<b>a</b>) surface SOC and (<b>b</b>) maximum von Mises stress as a function of C-rates inside RVE5. Comparison of (<b>c</b>) total charging time, (<b>d</b>) cc_ch and cv_ch periods.</p>
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<p>Von Mises stress profile inside the binder-constrained particles across the electrode thickness for different C-rates at (<b>a</b>) Point 1, end of cc_ch; (<b>b</b>) Point 2, end of cc_ch; (<b>c</b>) maximum von Mises stress, end of cc_ch; (<b>d</b>) Point 1, end of cv_ch; (<b>e</b>) Point 2, end of cv_ch; (<b>f</b>) maximum von Mises stress, end of cv_ch; (<b>g</b>) Point 1, end of cc_dch; (<b>h</b>) Point 2, end of cc_dch; and (<b>i</b>) maximum von Mises stress, end of cc_dch.</p>
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