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Search Results (7,826)

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12 pages, 5737 KiB  
Article
Modeling of 2-D Periodic Array of Dielectric Bars with a Low Reflection Angle for a Wind Tunnel High-Power Microwave Experiment
by Rong Bao, Yang Tao and Yongdong Li
Appl. Sci. 2024, 14(23), 10876; https://doi.org/10.3390/app142310876 (registering DOI) - 24 Nov 2024
Viewed by 83
Abstract
Two-dimensional periodic dielectric bars have potential applications in high-power microwave (HPM) radiation effect experiments performed in wind tunnels. Such a bar is designed to consist of two types of dielectric materials, and two lined-up blocks can be considered as a period along the [...] Read more.
Two-dimensional periodic dielectric bars have potential applications in high-power microwave (HPM) radiation effect experiments performed in wind tunnels. Such a bar is designed to consist of two types of dielectric materials, and two lined-up blocks can be considered as a period along the bar. Under plane excitation, the theoretical period length of the beat wave pattern fits well with the simulation result, which requires modifying the previously presented field-matching method. The phase distribution on the cross-section can be non-uniform when two different guiding modes are excited independently and propagate along different materials. Directional reflection with a low reflection angle can be obtained by reasonably choosing the parameters of the dielectric array. The designed array can decrease the returned-back microwave power toward the microwave source by 6 dB according to the numerical simulation, which included the wind tunnel, the input antenna, the test target, and the reflect array in one model. Full article
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Figure 1

Figure 1
<p>Illustration of HPM radiation effect experiment system [<a href="#B1-applsci-14-10876" class="html-bibr">1</a>].</p>
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<p>Illustration of the 2-D dielectric bar reflect array.</p>
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<p>Incident microwave after reflection: (<b>a</b>) <span class="html-italic">E<sub>y</sub></span> component on the <span class="html-italic">x</span>o<span class="html-italic">y</span> plane, (<b>b</b>) power flow on the <span class="html-italic">x</span>o<span class="html-italic">y</span> plane and (<b>c</b>) simulation model of the test target.</p>
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<p>Propagation in the dielectric bar waveguide along the −<span class="html-italic">x</span> direction: (<b>a</b>) illustration of geometry and (<b>b</b>) simulation model using periodic boundary. <span class="html-italic">w</span> is the width of the bars in the <span class="html-italic">y</span> direction; <span class="html-italic">l</span><sub>1</sub> and <span class="html-italic">l</span><sub>2</sub> are the sizes of the high-permittivity and low-permittivity materials, respectively; and <span class="html-italic">ε</span><sub>l</sub> and <span class="html-italic">ε</span><sub>h</sub> are the permittivities of the materials and <span class="html-italic">ε</span><sub>l</sub> &lt; <span class="html-italic">ε</span><sub>h</sub>.</p>
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<p>Electric field distribution under plane wave excitation at different frequencies: (<b>a</b>) electric field at 2 GHz, (<b>b</b>) electric field at 4 GHz, (<b>c</b>) electric field at 6 GHz, (<b>d</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 2 GHz, (<b>e</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 4 GHz, and (<b>f</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 6 GHz.</p>
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<p>Region division for the theoretical analysis.</p>
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<p>Simulated average norm of the electric field in the <span class="html-italic">x</span>o<span class="html-italic">z</span> plane (<b>a</b>) at 8.7 GHz, (<b>b</b>) at 9.2 GHz, and (<b>c</b>) at 9.7 GHz.</p>
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<p>Amplitude simulation model of (<b>a</b>) the array without metal wall and simulated power density distributions at (<b>b</b>) 7.7 GHz, (<b>c</b>) 8.7 GHz, and (<b>d</b>) 9.7 GHz.</p>
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<p>Simulated phase distribution using the model in <a href="#applsci-14-10876-f008" class="html-fig">Figure 8</a> at (<b>a</b>) 7.7 GHz, (<b>b</b>) 8.7 GHz, and (<b>c</b>) 9.7 GHz.</p>
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<p>Radiation pattern of structure in <a href="#applsci-14-10876-f008" class="html-fig">Figure 8</a> with (<b>a</b>) <span class="html-italic">h</span> = 40 mm, (<b>b</b>) <span class="html-italic">h</span> = 45 mm, and (<b>c</b>) <span class="html-italic">h</span> = 50 mm.</p>
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<p>Radiation pattern of the reflect-array structure: (<b>a</b>) simulation model of the reflect array, (<b>b</b>) radiation pattern of the electric field, (<b>c</b>) radiation pattern with ‘phi’ = 0°, (<b>d</b>) radiation pattern with ‘phi’ = 90°, (<b>e</b>) phase distribution on the interface when <span class="html-italic">g</span> is 13.5 mm, and (<b>f</b>) distribution of the amplitude of the <span class="html-italic">E<sub>y</sub></span> component on the interface when <span class="html-italic">g</span> is 17.5 mm.</p>
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<p>Simulation of the experimental setup in a wind tunnel: (<b>a</b>) simulation model, (<b>b</b>) outgoing power vs. frequency without reflect array, and (<b>c</b>) outgoing power vs. frequency with reflect array.</p>
Full article ">
16 pages, 3503 KiB  
Article
Wireless Remote-Monitoring Technology for Wind-Induced Galloping and Vibration of Transmission Lines
by Peng Wang, Yuanchang Zhong, Yu Chen and Dalin Li
Electronics 2024, 13(23), 4630; https://doi.org/10.3390/electronics13234630 (registering DOI) - 24 Nov 2024
Viewed by 132
Abstract
In order to achieve wireless remote monitoring of wind-induced vibrations in power-transmission lines based on MEMS sensors, it is necessary to devise a method for reconstructing the wind swing curve, enabling the device’s real-time performance to promptly acquire, restore, and analyze data. Based [...] Read more.
In order to achieve wireless remote monitoring of wind-induced vibrations in power-transmission lines based on MEMS sensors, it is necessary to devise a method for reconstructing the wind swing curve, enabling the device’s real-time performance to promptly acquire, restore, and analyze data. Based on existing single-axis vibration-sensitive components, a measurement array using self-powered MEMS sensors and spacers has been designed. The Orthogonal Matching Pursuit (OMP) algorithm is selected to obtain displacement data collected by sensors installed on the transmission-line spacers. Leveraging the inherent sparsity of the data, a Gaussian white noise regularization matrix is chosen to establish the observation matrix. Through the algorithm, wind data curve reconstruction is achieved, enabling the reconstruction of large-span wind-induced vibration information without distortion. The experimental results demonstrate that when applying the orthogonal tracking algorithm in transmission-line curve reconstruction, sparsity is selected based on the sampling length, that is, the number of sensors installed on the spacers is determined by the span length; a portion of the observation values are selected to generate the observation matrix; and the wind galloping data curve of the transmission line is well restored. Full article
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Figure 1
<p>Schematic diagram of the basic structure and principle of a single-axis micro inertial magnetoelectric velocity measurement element.</p>
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<p>Schematic of the vibration signal amplification circuit.</p>
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<p>The physical model of the spacer bar + self-powered MEMS three-axis inertial sensor.</p>
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<p>Force analysis diagram of the wire element.</p>
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<p>Theoretical framework of compressed sensing.</p>
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<p>Comparison graph and amplitude difference of reconstructed signal and original signal at different observation counts at t = 1 s.</p>
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<p>Comparison graph and amplitude difference of reconstructed signal and original signal at different observation counts at t = 1 s.</p>
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<p>Comparison graph and amplitude difference of reconstructed signal and original signal at different observation counts at t = 1 s.</p>
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<p>Comparison graph and amplitude difference of reconstructed signal and original signal at different observation counts at t = 1 s.</p>
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15 pages, 851 KiB  
Article
Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids
by Gustavo Adolfo Gómez-Ramírez, Luis García-Santander, José Rodrigo Rojas-Morales, Markel Lazkano-Zubiaga and Carlos Meza
Energies 2024, 17(23), 5878; https://doi.org/10.3390/en17235878 (registering DOI) - 23 Nov 2024
Viewed by 197
Abstract
The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of [...] Read more.
The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh. Full article
(This article belongs to the Special Issue Challenges and Opportunities for Renewable Energy)
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Figure 1
<p>Methodological framework for improving the flexibility of transfer capabilities among various areas.</p>
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<p>Interconnection voltage behaviour without electrochemical storage.</p>
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<p>Interconnection power behaviour without electrochemical storage.</p>
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<p>Seven states sequence of the collapse explained in case study.</p>
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<p>Interconnection frequency behaviour without electrochemical storage.</p>
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<p>Siting and sizing for electrochemical storage in Central American power system according to <a href="#energies-17-05878-t001" class="html-table">Table 1</a>.</p>
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<p>Interconnection frequency behaviour with electrochemical storage.</p>
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<p>Interconnection power behaviour with electrochemical storage.</p>
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<p>Sequence of power system states shown in case study and proposed solution.</p>
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26 pages, 834 KiB  
Article
Probabilistic Evaluation Method of Wind Resistance of Membrane Roofs Based on Aerodynamic Stability
by Weiju Song, Hongbo Liu and Heding Yu
Buildings 2024, 14(12), 3725; https://doi.org/10.3390/buildings14123725 - 22 Nov 2024
Viewed by 147
Abstract
The membrane structure or membrane roofing system is lightweight and flexible, with wind being the primary cause of structural and membrane material failure. To evaluate the disaster prevention and mitigation capacity of the membrane roofing system and enhance the wind disaster risk management [...] Read more.
The membrane structure or membrane roofing system is lightweight and flexible, with wind being the primary cause of structural and membrane material failure. To evaluate the disaster prevention and mitigation capacity of the membrane roofing system and enhance the wind disaster risk management capabilities, this paper studies the exceedance probability evaluation method for different wind resistance requirements of membrane roofs. Taking Hangzhou in China as an example, the design wind speed risk curve fitted by polynomial is obtained by referring to the PEER performance-based seismic design method and considering the randomness of the wind field. A polynomial fitting method is employed to obtain the design wind speed hazard curve. Considering the nonlinear characteristics of the membrane roof structure, the relationship between the roof’s wind resistance requirements (vertical displacement limits) and wind speed spectrum values is approximated using a power function. An annual average exceedance probability expression is derived for different normal deformation demand values of the membrane roofs under wind load. Based on this, a wind resistance probability evaluation method for membrane roofs considering aerodynamic stability is proposed, along with specific steps and related analytical formulas. The results indicate that polynomial fitting provides an effective simplification for deriving the annual average exceedance probability expression for the wind resistance demand of membrane roofs. The performance-based wind resistance probability evaluation method allows for obtaining exceedance probability values for different displacement requirements with minimal structural analysis, which enriches the wind resistance design theory of membrane roofs and further ensures the structural safety of tension membrane roofs under wind load. Full article
29 pages, 2899 KiB  
Article
Clean Energy and Carbon Emissions in Mexico’s Electric Power Sector: Past Performance and Current Trend
by Oliver Probst
Energies 2024, 17(23), 5859; https://doi.org/10.3390/en17235859 - 22 Nov 2024
Viewed by 247
Abstract
The concept of clean energy was introduced by the Mexican authorities as part of the wholesale electricity market with the objectives of both measuring the progress in decarbonization and fostering emission-free and low-emission technologies. In the present work, the evolution of clean energy [...] Read more.
The concept of clean energy was introduced by the Mexican authorities as part of the wholesale electricity market with the objectives of both measuring the progress in decarbonization and fostering emission-free and low-emission technologies. In the present work, the evolution of clean energy production for the period 2017–2023, corresponding to seven full years of operation of the electricity market, was analyzed and compared to official targets. Emission of greenhouse gases (GHGs) was calculated from fuel consumption statistics. The consistency between electricity generation and fuel consumption data has been assessed. The projected short-term evolution of electricity generation and GHG emissions through 2026, locked in by decisions in the recent past, was modeled and discussed. A reduction in carbon intensity from 0.56 gCO2,eq to 0.46 g CO2,eq was found for the 2017–2022 period, in qualitative agreement with official figures, mainly due to the large-scale introduction of wind and solar, as well as some displacement of coal- and fuel oil-fired generation. Total GHG emissions reached a minimum of about 150 Gt CO2,eq/a in 2020–2021; emissions are projected to rise to 190 Gt CO2,eq in 2026, due to a strong rise in natural gas-fired generation from combined-cycle plants and the largely stalled development of wind and solar plants. Clean energy figures were found to decouple from emissions and can therefore not be considered a good proxy for decarbonization. A recent roadmap presented by the incoming federal government does, however, indicate a change in policies which might bring Mexico back on track towards the decarbonization of the electric power sector. Full article
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Figure 1
<p>Generation from renewables and nuclear (<b>left graph</b>) and fossil fuels (<b>right graph</b>), based on technology-aggregated hourly data reported by CENACE for the Mexican power system (SEN) and the 2017–2023 period. These numbers do not include distributed generation from solar and biomass; see <a href="#energies-17-05859-t003" class="html-table">Table 3</a> for that information.</p>
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<p>(<b>Left graph</b>) Clean electricity generation in Mexico for the 2018–2022 period as reported by the authorities and regrouped into broad categories. Large-scale renewables and nuclear are reported on an hourly basis by CENACE; the other categories are only reported as annual figures (through the National Energy Balance (BNE)). (<b>Right graph</b>) Fossil and clean energy fractions. Clean fossil energy is defined in the regulatory document A/018/2023, which also defines new criteria for efficient cogeneration.</p>
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<p>Consolidated fuel consumption time series for the 2014–2022 for the four main fuels used in electricity generation in the Mexican power system. Continuous lines indicate the proposed consensus values, whereas the grey areas indicate the max/min error range. In the case of natural gas, an additional fuel consumption curve was included based on the sum rule <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∑</mo> <mrow> <mrow> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mrow> <mo stretchy="false">∑</mo> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mrow> </mrow> </semantics></math> and average efficiency values <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> for each technology class <math display="inline"><semantics> <mrow> <mi>i</mi> </mrow> </semantics></math>. See <a href="#secAdot3-energies-17-05859" class="html-sec">Appendix A.3</a> for further explanations.</p>
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<p>(<b>Left</b>): Cumulative installed wind power capacity in Mexico for the period 2008–2025; values for 2023–2025 have been estimated; see text for details. A logistic fit has been added, based on 3-year moving averages. (<b>Right</b>): Annual additions of wind power capacity and their 3-year moving averages. Source: Own elaboration based on data provided by the Mexican Wind Energy Association [<a href="#B35-energies-17-05859" class="html-bibr">35</a>].</p>
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<p>(<b>Left</b>): Cumulative installed large-scale solar PV power capacity in Mexico for the period 2008–2025; values for 2023–2025 have been estimated; see text for details. A logistic fit has been added, based on 3-year moving averages. (<b>Right</b>): Annual additions of large-scale solar PV power capacity and their 3-year moving averages. Source: Own elaboration, based on data from the Mexican Solar Energy Association [<a href="#B36-energies-17-05859" class="html-bibr">36</a>].</p>
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<p>(<b>Left</b>): Cumulative installed distributed solar PV power capacity in Mexico for the period 2008–2025; the value for 2023 has been estimated; see text for details. Both a logistic and an exponential fit have been added. (<b>Right</b>): Annual additions of distributed solar PV power capacity. Source: Own elaboration based on data from the Mexican Solar Energy Association [<a href="#B36-energies-17-05859" class="html-bibr">36</a>].</p>
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<p>(<b>Left</b>): Life-cycle CO<sub>2,eq</sub> emissions from fossil fuels for the Mexican power system (SEN) by fuel types, based on the reported fuel consumption values for electricity consumption. (<b>Right</b>): Life-cycle emissions of CO<sub>2,eq</sub> from renewable and nuclear energy. In the case of natural gas, an additional emission curve was included based on the sum rule <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∑</mo> <mrow> <mrow> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mrow> <mo stretchy="false">∑</mo> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mrow> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> is the average efficiency value for each technology class <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>.</mo> </mrow> </semantics></math> See <a href="#secAdot3-energies-17-05859" class="html-sec">Appendix A.3</a> for further explanations.</p>
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<p>Emission factors of the Mexican power system determined in this work and their comparison with the official numbers published by the Secretary of the Environment (SEMARNAT). See <a href="#secAdot3-energies-17-05859" class="html-sec">Appendix A.3</a> for an explanation of the sum rule value for natural gas consumption.</p>
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<p>Historical and projected electricity generation from fossil fuels (<b>left</b>) and renewables, including distributed solar, and nuclear (<b>right</b>) for the 2017–2026 period.</p>
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<p>Historical and projected GHG emissions (<b>left graph</b>) and clean energy fractions (<b>right graph</b>) for the 2017–2026 period. The average hydro scenario has been considered in the right graph, except for the curve identifying large-scale (LS) renewables and nuclear, for which the low–high hydro range has been indicated as well. Clean energy additions from items fuel-free fossil energy and auxiliary cooling for 2022 onwards have their regulatory standing in the disposition A/018/2023. Distributed biomass is only mentioned in recent reports of the National Energy Balance (BNE).</p>
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<p>Main elements of the fuel-technology matrix for the period 2017–2022 determined with the methodology described in the annex. The error margin corresponds to ± one standard deviation. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>CC</mi> <mo>,</mo> <mi>NG</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total natural gas (NG) consumption burned in combined-cycle (CC) plants. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>SC</mi> <mo>,</mo> <mi>NG</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total natural gas (NG) consumption burned in single-cycle (SC) gas turbines. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>CS</mi> <mo>,</mo> <mi>NG</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total natural gas (NG) consumption burned in conventional steam (CS) gas turbines. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>CC</mi> <mo>,</mo> <mi mathvariant="normal">D</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total Diesel (D) consumption burned in combined-cycle (CC) plants. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>CS</mi> <mo>,</mo> <mi>FO</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total fuel oil (FO) consumption burned in conventional steam (CS) plants. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mrow> <mi>CPP</mi> <mo>,</mo> <mi>FO</mi> </mrow> </mrow> </msub> </mrow> </semantics></math> = fraction of total fuel oil (FO) consumption burned in coal power plants (CPPs). Sum rule values were calculated from <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∑</mo> <mrow> <mrow> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mrow> <mo stretchy="false">∑</mo> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mrow> </mrow> </semantics></math> and average efficiency values <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> for each technology class <math display="inline"><semantics> <mrow> <mi>i</mi> </mrow> </semantics></math>. See <a href="#secAdot3-energies-17-05859" class="html-sec">Appendix A.3</a> for further explanations. Horizontal dotted lines delimit the range of the fuel-fraction factors (<math display="inline"><semantics> <mrow> <mn>0</mn> <mo>≤</mo> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>≤</mo> <mn>1</mn> </mrow> </semantics></math>). Note that all predicted <math display="inline"><semantics> <mrow> <mi>x</mi> </mrow> </semantics></math> values fall within the range limits within the margins of error.</p>
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16 pages, 2636 KiB  
Review
Suspended Particles in Water and Energetically Sustainable Solutions of Their Removal—A Review
by Štěpán Zezulka, Blahoslav Maršálek, Eliška Maršálková, Klára Odehnalová, Marcela Pavlíková and Adéla Lamaczová
Processes 2024, 12(12), 2627; https://doi.org/10.3390/pr12122627 - 22 Nov 2024
Viewed by 236
Abstract
Solid particles (SP) suspended in water represent a common contamination that degrades the water quality, not only in drinking water sources. Particles differ in size, nature, and related features like surface charge. Thus, various methods can be utilized for their removal—physical approaches including [...] Read more.
Solid particles (SP) suspended in water represent a common contamination that degrades the water quality, not only in drinking water sources. Particles differ in size, nature, and related features like surface charge. Thus, various methods can be utilized for their removal—physical approaches including settling or filtration, chemical coagulation/flocculation, biological microbial degradation, and others. This paper aims to summarize currently available methods for SP removal with special attention devoted to alternative, cost-effective, sustainable, and eco-friendly approaches with low energetic demands where the power of renewable energy sources can be utilized. Besides SP properties, the selection of the proper method (or a sequence of methods) for their separation also depends on the purpose of water treatment. Drinking water production demands technologies with immediate effect and high throughputs, like conventional filtration and coagulation/flocculation (electro- or chemical with alternative coagulant/flocculant agents) or some hybrid approaches to ensure quick and cost-effective decontamination. Such technologies usually imply heavy machinery with high electricity consumption, but current progress allows the construction of smaller facilities powered by solar or wind power plant systems. On the other hand, water decontamination in rivers or ponds can include slower processes based on phytoremediation, being long-term sustainable with minimal energy and cost investments. Full article
(This article belongs to the Special Issue Energy and Water Treatment Processes)
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Figure 1
<p>Illustration of the number of scientific works focused on suspended solid particles in water and related topics according to the Core Collection database (September 2024, Web of Science, Clarivate).</p>
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<p>Example of suspended solids from sediment; sand (S) and clay (C) particles on a scanning electron microscopy photograph.</p>
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<p>General scheme of coagulation and flocculation processes using coagulant and flocculant agents to interact with suspended particles and other impurities. In alternative approaches, chemical coagulants and flocculants can be replaced by natural products (starch, ash, etc.) or produced in situ in an electrochemical way from electrode material.</p>
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<p>Illustration of approaches based on phytoremediation, utilizing constructed wetlands, riparian vegetation stripes, and vegetated floating islands. Submersed parts of plants (especially roots or stems) can provide a place for microbial biofilm formation.</p>
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20 pages, 4043 KiB  
Article
Short-Term Wind Power Forecasting Based on OMNIC and Adaptive Fractional Order Generalized Pareto Motion Model
by Fan Cai, Dongdong Chen, Yuesong Jiang and Tongbo Zhu
Energies 2024, 17(23), 5848; https://doi.org/10.3390/en17235848 - 22 Nov 2024
Viewed by 272
Abstract
With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with [...] Read more.
With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R2 of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>PDF of GPD with Different Shape Parameters.</p>
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<p>PDF of GPD with Different Scale Parameters.</p>
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<p>Simulation sequences of the fGPm model under different conditions (<b>a</b>) <span class="html-italic">H</span> = 0.85, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math>; (<b>b</b>) <span class="html-italic">H</span> = 0.85, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>; (<b>c</b>) <span class="html-italic">H</span> = 0.85, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.7</mn> </mrow> </semantics></math>; (<b>d</b>) <span class="html-italic">H</span> = 0.85, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. Time representation in the figure is specified as time steps, where each time step represents the simulated sequence count.</p>
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<p>Original Wind Power Data.</p>
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<p>Wind farm power generation forecasting model framework.</p>
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<p>Winter Wind Power Forecasting Results for Wind Turbine Generators; (<b>a</b>) predicting 12 steps; (<b>b</b>) predicting 24 steps; (<b>c</b>) predicting 36 steps; (<b>d</b>) predicting 48 steps.</p>
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<p>Summer Wind Power Forecasting Results for Wind Turbine Generators; (<b>a</b>) predicting 12 steps; (<b>b</b>) predicting 24 steps; (<b>c</b>) predicting 36 steps; (<b>d</b>) predicting 48 steps.</p>
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<p>Comparison of Prediction Curves from Different Models in Winter. (<b>a</b>) 6 h (<b>b</b>) 12 h (<b>c</b>) 18 h (<b>d</b>) 24 h.</p>
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<p>Comparison of Prediction Curves from Different Models in Summer. (<b>a</b>) 6 h (<b>b</b>) 12 h (<b>c</b>) 18 h (<b>d</b>) 24 h.</p>
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20 pages, 10810 KiB  
Article
Design and Simulation of Portable Paving Vehicle for Straw Checkerboard Barriers
by Zuntao Peng, Mingrun Jia, Jingrong Fang and Feng Jiang
Machines 2024, 12(12), 835; https://doi.org/10.3390/machines12120835 - 22 Nov 2024
Viewed by 238
Abstract
Paving straw checkerboard barriers in the desert is an efficient measure of wind break and sand fixation. Generally, straw checkerboard barriers are paved manually. Focusing on the low automation level of straw checkerboard barrier paving, a portable paving vehicle for straw checkerboard barriers [...] Read more.
Paving straw checkerboard barriers in the desert is an efficient measure of wind break and sand fixation. Generally, straw checkerboard barriers are paved manually. Focusing on the low automation level of straw checkerboard barrier paving, a portable paving vehicle for straw checkerboard barriers was designed in this paper. First, the portable paving vehicle for straw checkerboard barriers was designed using SolidWorks, and the design contents include a grass insertion mechanism, an intermittent transmission mechanism, a metamorphic mechanism, and motor and power supply. Then, the load test of the grass insertion mechanism was carried out to determine the maximum force load of 25 N during the grass insertion process, and the strength of the rocker and the horizontal slide rod were checked. Among them, the safety factor of the rocker rod and the horizontal slide rod were 1 and 1.5, respectively, and the allowable stress of the rocker rod and the horizontal slide rod was 27.3 MPa and 205 MPa. The maximum stresses of 0.92 MPa and 67 MPa were less than the allowable stresses, which meet the strength requirements. In order to verify the design principle and the results of the strength check, the grass insertion mechanism, rocker, and horizontal slide rod were analyzed by using ABAQUS. The results show that the grass insertion mechanism has an obvious rapid return characteristic, which is in agreement with the design principle. At the same time, the maximum stress of the rocker rod and the horizontal slide rod was 1 MPa and 36 MPa, respectively, which meets the strength requirements. Finally, the physical prototype was manufactured and its running state was verified. The results show that the physical prototype can pave the straw checkerboard sand barrier on the sand normally, and the portable paving vehicle for straw checkerboard barriers can be a reference for other sand-control vehicles and provide an effective way of paving straw checkerboard barriers to control desertification. Full article
(This article belongs to the Section Vehicle Engineering)
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<p>Straw checkerboard barriers in the desert.</p>
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<p>Concept diagram of the vehicle, 1—grass insertion mechanism; 2—wheeled track; 3—feeding mechanism; 4—linkage mechanism.</p>
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<p>Diagram of the design process.</p>
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<p>Crank rocker and sinusoidal tandem mechanism and mechanical schematic diagram, 1—gear; 2—connect rod 1; 3—connect rod 2; 4—connect rod 3; 5—slide rod 1; 6—slide rod 2.</p>
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<p>Crank rocker movement track diagram.</p>
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<p>Intermittent gear and transmission mechanism (<b>left</b> for intermittent gear, <b>right</b> for transmission mechanism).</p>
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<p>The movement of grass blade and main frame changes under normal load (front view), 1—grass blade; 2—main frame.</p>
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<p>The motion of the blade and the main frame changes during overload (front view), 1—grass blade; 2—main frame.</p>
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<p>The photograph of test site, 1—Kistler 9257B dynamometer; 2—computer-installed DynoWare; 3—model of grass insertion mechanism.</p>
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<p>Load test signal.</p>
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<p>Force analysis diagram of rocker bar and horizontal slide bar.</p>
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<p>Force diagram of rocker.</p>
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<p>Shear diagram, bending moment diagram, and axial diagram of the rocker.</p>
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<p>Force diagram of horizontal slide rod.</p>
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<p>Shear diagram and bending moment diagram of horizontal slide bar.</p>
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<p>The diagram of multi-body kinematic finite element analysis.</p>
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<p>The motion state of each component at different times of the same cycle.</p>
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<p>Angular displacement <span class="html-italic">U</span><sub>R1</sub> and angular velocity <span class="html-italic">V</span><sub>R1</sub> of connecting rod 1.</p>
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<p>Angular displacement <span class="html-italic">U</span><sub>R2</sub> and angular velocity <span class="html-italic">V</span><sub>R2</sub> of connecting rod 2.</p>
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<p>Horizontal displacement <span class="html-italic">U</span><sub>1</sub> and linear velocity <span class="html-italic">V</span><sub>1</sub> of slide rod 1.</p>
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<p>Horizontal displacement <span class="html-italic">U</span><sub>2</sub> and linear speed <span class="html-italic">V</span><sub>2</sub> of grass blade (slide rod 2).</p>
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<p>Stress deformation and stress cloud diagram of the rocker.</p>
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<p>Stress deformation and stress cloud diagram of the horizontal slide.</p>
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<p>Physical prototype of the portable paving vehicle for straw checkerboard barriers.</p>
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<p>The process of paving straw checkerboard barriers.</p>
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17 pages, 3797 KiB  
Article
Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks
by Sonia Montecinos, Carlos Rodríguez, Andrea Torrejón, Jorge Cortez and Marcelo Jaque
Energies 2024, 17(23), 5844; https://doi.org/10.3390/en17235844 - 22 Nov 2024
Viewed by 259
Abstract
The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an [...] Read more.
The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an operational photovoltaic park in the semi-arid north of Chile using Artificial Neural Networks (ANNs). We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. The ANN model’s effectiveness was evaluated by comparing it to five different deterministic models: the Standard model, King’s model, Faiman’s model, Mattei’s model, and Skoplaki’s model. Additionally, we examined the sensitivity of panel temperature to changes in air temperature, TSR, and wind speed. Our findings show that the ANN model had the best prediction accuracy for panel temperature, with a Root Mean Squared Error (RMSE) of 1.59 °C, followed by Mattei’s model with a higher RMSE of 3.30 °C. We also determined that air temperature has the most significant impact on panel temperature, followed by TSR and wind speed. These results demonstrate that the ANN is a powerful tool for predicting panel temperature with high accuracy. Full article
(This article belongs to the Special Issue Photovoltaic Solar Cells and Systems: Fundamentals and Applications)
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<p>Location of the studied area and Luna PV park. The gray colors represent the altitude according to the scale shown on the right side of the figure. Contour lines are each 300 m.</p>
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<p>Artificial Neural Network scheme with a single hidden layer with 100 neurons, one input layer with neurons (TSR, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>v</mi> </mrow> </semantics></math>), and one output layer with one neuron.</p>
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<p>Meteorological characteristics of Luna. (<b>a</b>) Mean monthly evolution of GSR (black) and TSR (gray). The dashed line represents the minimum zenith angle. (<b>b</b>) Mean daily cycle of GSR (black) and TSR (gray). (<b>c</b>) Mean monthly evolution of air (black) and panel (gray) temperature. (<b>d</b>) Mean daily cycle of air (black) and panel (gray) temperature. (<b>e</b>) Wind direction distribution in Vicuña. (<b>f</b>) Mean daily cycle of wind speed.</p>
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<p>Relationships between variables. (<b>a</b>) Air temperature vs. tilted solar radiation; (<b>b</b>) panel temperature vs. air temperature; (<b>c</b>) panel temperature vs.; (<b>d</b>) wind speed vs. air temperature; (<b>e</b>) wind speed vs. panel temperature; (<b>f</b>) wind speed vs. tilted solar radiation.</p>
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<p>Mean daily cycle of the air and panel temperature as a function of TSR. Black: air temperature; gray: panel temperature.</p>
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<p>Panel temperature simulated (vertical axis) and observed (horizontal axis). The solid line represents the identity curve. (<b>a</b>) ANN; (<b>b</b>) SM; (<b>c</b>) KM; (<b>d</b>) FM; (<b>e</b>) MM; (<b>f</b>) SkM.</p>
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<p>Hourly MBE (<b>a</b>) and RMSE (<b>b</b>) of ANN and deterministic models.</p>
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<p>Panel temperature simulated (vertical axis) and observed (horizontal axis). The solid line represents the identity curve. (<b>a</b>) ANN<sub>TSR</sub>; (<b>b</b>) ANN<sub>T</sub>; (<b>c</b>) ANN<sub>v</sub>.</p>
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<p>(<b>a</b>) Daily cycle of MBE and (<b>b</b>) RMSE. In both graphics, solid line: ANN<sub>TSR</sub>, dotted line: ANN<sub>T</sub>, dashed line: ANN<sub>v</sub>.</p>
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26 pages, 2100 KiB  
Article
Energy–Economy–Carbon Emissions: Impacts of Energy Infrastructure Investments in Pakistan Under the China–Pakistan Economic Corridor
by Xiue Li, Zhirao Liu and Tariq Ali
Sustainability 2024, 16(23), 10191; https://doi.org/10.3390/su162310191 - 21 Nov 2024
Viewed by 418
Abstract
Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. [...] Read more.
Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. This study examines the energy, economic, and environmental effects of CPEC’s energy investments in Pakistan, covering a range of power sources, including coal, hydro, solar, wind, and nuclear energy. Utilizing data from 31 CPEC energy projects and employing the GTAP-E-Power model, this research assesses these impacts through seven scenarios, comprehensively analyzing the heterogeneity of different power sources. Our findings reveal that while all types of CPEC energy infrastructure investments contribute to increasing the share of zero-emissions electricity to 49.1% and reducing CO2 emissions by 18.61 million tons, the economic impacts vary significantly by energy source. The study suggests that it is crucial to prioritize renewable energy investments while addressing immediate power shortages to balance economic growth with environmental sustainability. Policymakers should also consider the potential inter-sectoral substitution effects when applying significant shocks to specific sectors. This analysis informs future energy investment decisions under CPEC and offers insights for other Belt and Road Initiative (BRI) countries aiming to optimize their energy strategies for sustainable development. Full article
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<p>The framework of the methodology, database, and procedure. The database is aggregated using GTAPagg2, while updates and simulations are performed using GEMPACK. Please refer to [<a href="#B28-sustainability-16-10191" class="html-bibr">28</a>] for the GTAP-E-Power Model.</p>
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<p>Nested electric power substitution in the GTAP-E-Power model and CO<sub>2</sub> releasing energy commodities. The sub-sectors of electricity are detailed in <a href="#app1-sustainability-16-10191" class="html-app">Appendix A</a> <a href="#sustainability-16-10191-t0A7" class="html-table">Table A7</a>. Source: Adapted from the GTAP-E-Model [<a href="#B28-sustainability-16-10191" class="html-bibr">28</a>].</p>
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<p>Overview of CPEC energy infrastructure investment. (<b>a</b>) Installed capacity (MW) added to different electricity generation sources. (<b>b</b>) Share of installed capacity (%) added to each province of Pakistan. (<b>c</b>) Estimated cost (USD million) for different electricity generation sources. (<b>d</b>) Share of estimated cost (%) for each province of Pakistan. Source: Calculated based on the project-level information in <a href="#app1-sustainability-16-10191" class="html-app">Appendix A</a> <a href="#sustainability-16-10191-t0A4" class="html-table">Table A4</a>, <a href="#sustainability-16-10191-t0A5" class="html-table">Table A5</a> and <a href="#sustainability-16-10191-t0A6" class="html-table">Table A6</a>.</p>
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<p>Change in energy output and price. (<b>a</b>) Percentage change in the output of electricity sub-sectors and non-electricity sectors (%). (<b>b</b>) Value change in the output of electricity sub-sectors and non-electricity sectors (USD million). (<b>c</b>) Percentage change in the price of electricity sub-sectors and non-electricity sectors (%). All changes are relative to the situation in the base year. Since HydroP and GasP in Pakistan are zero, there are no results for them. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in energy structure. (<b>a</b>) Output value share of electricity generated from zero-emissions power sources (NuclearBL, HydroBL, WindBL, and SolarP) and fuel-fired power sources (CoalBL, GasBL, OilBL, other BL, and OilP). (<b>b</b>) Output value share of electricity generated from each power source. “Pre” refers to the situation before shocks in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in CO<sub>2</sub> emissions from fuel energy commodities. (<b>a</b>) Percentage change in CO<sub>2</sub> emissions (%) from coal, oil, gas, p_c, and gas supply in Pakistan. (<b>b</b>) Absolute change in CO<sub>2</sub> emissions (Mts) from coal, oil, gas, p_c, and gas supply in Pakistan. The last two rows refer to the total absolute change in CO<sub>2</sub> emissions in Pakistan and the world, respectively. All changes are relative to the situation in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in CO<sub>2</sub> emissions from production across different sectors. (<b>a</b>) CO<sub>2</sub> emissions in the base year before shocks (Mts). (<b>b</b>) CO<sub>2</sub> emissions in scenario S7 (Mts). This figure represents CO<sub>2</sub> emissions from firm activities, covering 80% of Pakistan’s total emissions. The remaining 20% comes from consumption. Coal, oil, gas, p_c, and gas supply are the five fuel energy commodities that release CO<sub>2</sub>. Here, the nodes on the left represent different sectors (the sources) that use these energy commodities and thus emit CO<sub>2</sub>, while the nodes on the right correspond to the specific energy commodities (the target). The production of these energy commodities also emits (embodied) CO<sub>2</sub>, but the emissions are relatively very small. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in non-energy sectors. (<b>a</b>) Percentage change in the output of non-energy sectors (%). (<b>b</b>) Percentage change in the price of non-energy sectors (%). All changes are relative to the situation in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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16 pages, 1671 KiB  
Article
Combined Power Generating Complex and Energy Storage System
by Rollan Nussipali, Nikita V. Martyushev, Boris V. Malozyomov, Vladimir Yu. Konyukhov, Tatiana A. Oparina, Victoria V. Romanova and Roman V. Kononenko
Electricity 2024, 5(4), 931-946; https://doi.org/10.3390/electricity5040047 - 21 Nov 2024
Viewed by 175
Abstract
Combining wind and hydropower facilities makes it possible to solve the problems caused by power supply shortages in areas that are remote from the central energy system. Hydropower plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power outputs from wind [...] Read more.
Combining wind and hydropower facilities makes it possible to solve the problems caused by power supply shortages in areas that are remote from the central energy system. Hydropower plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power outputs from wind power plants, and the limitations associated with them are significantly reduced when they are integrated into the regional energy system. Such an integration contributes to increasing the efficiency of renewable energy sources, which in turn reduces our dependence on fossil resources and decreases their harmful impact on the environment, increasing the stability of the power supply to consumers. The results of optimisation calculations show that a consumer load security of 95% allows the set capacity of RESs to be used in the energy complex up to 700 MW. It is shown here that the joint operation of HPPs and WPPs as part of a power complex and hydraulic energy storage allows for the creation of a stable power supply system that can operate even in conditions of variable wind force or uneven water flow. The conclusions obtained allow us to say that the combination of hydro- and wind power facilities makes it possible to solve the problem of power supply deficits in the regions of Kazakhstan that are remote from the central power station. At the same time, hydroelectric power plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power output from wind power plants and significantly reduce the limitations associated with them during their integration into the regional energy system. Full article
(This article belongs to the Special Issue Recent Advances in Power and Smart Grids)
15 pages, 1847 KiB  
Article
Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method
by Dawei Zhao, Yujie Ning, Chuanzhi Zhang, Jin Ma, Minhui Qian and Yanzhang Liu
Energies 2024, 17(23), 5831; https://doi.org/10.3390/en17235831 - 21 Nov 2024
Viewed by 244
Abstract
The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts [...] Read more.
The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better. Full article
(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)
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<p>Research system with known (measurable) boundary conditions.</p>
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<p>Process of model validation based on hybrid dynamic simulation.</p>
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<p>Process of model error localization based on hybrid dynamic simulation.</p>
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<p>Implementation of fast-responding generator method: (<b>a</b>) Measured voltage is injected as reference of excitation system; (<b>b</b>) Measured frequency is injected as reference of speed governor.</p>
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<p>Structure of a power system with a practical PV power plant.</p>
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<p>Internal structure of YRHG PV power plant.</p>
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<p>Modeling of YRHG PV power plant based on fast-responding generator method: (<b>a</b>) Whole model in DIgSILENT/PowerFactory; (<b>b</b>) PV power generation model under a certain feeder.</p>
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<p>Positive, negative and zero sequence component of voltage.</p>
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<p>Synchronization of measured data and simulation step size.</p>
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<p>Comparison of voltage and frequency between simulation outputs and measured data (red line: simulation output; blue line: measured data): (<b>a</b>) Amplitude of voltage; (<b>b</b>) Amplitude of frequency.</p>
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<p>Comparison of voltage and frequency between simulation outputs and measured data (red line: simulation output; blue line: measured data): (<b>a</b>) Amplitude of voltage; (<b>b</b>) Amplitude of frequency.</p>
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<p>Comparison of real and reactive power between simulation outputs and measured data (red line: simulation output; blue line: measured data): (<b>a</b>) Active power; (<b>b</b>) Reactive power.</p>
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<p>Comparison of simulation output and measured data after parameter calibration (red line: simulation output of the fast-responding generator method; blue line: measured data; green line: simulation output of the conventional method): (<b>a</b>) Amplitude of voltage; (<b>b</b>) Active power.</p>
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14 pages, 2364 KiB  
Article
A Multi-Mode Recognition Method for Broadband Oscillation Based on CS-OMP and Adaptive VMD
by Jinggeng Gao, Honglei Xu, Yong Yang, Xujun Zhang, Xiangde Mao and Haiying Dong
Energies 2024, 17(23), 5821; https://doi.org/10.3390/en17235821 - 21 Nov 2024
Viewed by 220
Abstract
Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and [...] Read more.
Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of the system, and to detect and recognize the broadband oscillation information timely and accurately, this paper presents a multi-mode recognition method of broadband oscillation based on compressed sensing (CS) and the adaptive Variational Mode Decomposition (VMD) algorithm. Firstly, the high-dimensional oscillation signal data collected by the Phasor Measurement Unit (PMU) is compressed and sampled by a Gaussian random matrix, and the obtained low-dimensional data are uploaded to the main station. Secondly, the orthogonal matching pursuit (OMP) algorithm of the master station is used to reconstruct the low-dimension signal, and the original high-dimension signal data are recovered without losing the main features of the signal. Finally, an adaptive VMD algorithm with energy loss minimization as a threshold is used to decompose the reconstructed signal, and the Intrinsic Mode Function (IMF) components with broadband oscillation information are obtained. By constructing oscillating signals with different frequencies, Gaussian white noise with a signal-to-noise ratio of 10 dB to 30 dB is added successively. After the signal is compressed and reconstructed by the proposed method, the signal-to-noise ratio can reach 18.8221 dB to 40.0794 dB, etc., and the oscillation frequency and amplitude under each signal-to-noise ratio can be accurately identified. The results show that the proposed method not only has good robustness to noise, but also has good denoising effect to noise. By using the simulation measurement model, the original oscillation signal is compressed and reconstructed, and the reconstruction error is 0.1263. The basic characteristics of the signal are restored, and the frequency and amplitude of the oscillation mode are accurately identified, which proves that the method is feasible and accurate. Full article
(This article belongs to the Special Issue Clean and Efficient Use of Energy: 2nd Edition)
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<p>The process of broadband oscillation signal recognition.</p>
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<p>Reconstructed signal and original signal.</p>
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<p>VMD adaptive decomposition results and FFT analysis.</p>
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<p>Comparison between the original and reconstructed signal (SNR = 10 dB, SNR = 30 dB).</p>
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<p>Simplified model for power generation system composed of six wind turbines.</p>
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<p>Broadband oscillation signal and FFT analysis.</p>
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<p>Comparison of a broadband oscillation signal with a reconstructed signal.</p>
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<p>VMD and information recognition of oscillation signal.</p>
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34 pages, 12326 KiB  
Article
A Study on the Influence of Different Inflow Conditions on the Output Power and Dynamic Response Characteristics of a Variable Pitch Wind Turbine Structure
by Daorina Bao, Zhongyu Shi, Chengze Li, Aoxiang Jiang, Qingsong Han, Yongshui Luo and Shaohua Zhang
Energies 2024, 17(23), 5818; https://doi.org/10.3390/en17235818 - 21 Nov 2024
Viewed by 246
Abstract
This paper introduces a novel pitch adjustment device applicable to small wind turbines. To validate its feasibility under high wind speeds and analyze the impact of pitch angle on the power output characteristics of small wind turbines, a prototype model was manufactured for [...] Read more.
This paper introduces a novel pitch adjustment device applicable to small wind turbines. To validate its feasibility under high wind speeds and analyze the impact of pitch angle on the power output characteristics of small wind turbines, a prototype model was manufactured for wind tunnel experiments. Additionally, we conducted simulations to analyze the stress and displacement responses of key components under uniform airflow, shear airflow, and Extreme Operated Gust conditions. The numerical simulation results were compared with experimental results based on actual measurement points in the wind tunnel experiment, demonstrating that the simulation data accurately reflect the experimental test results, with an overall discrepancy of around 10%, thereby validating the accuracy of the load and constraint settings in the transient dynamics analysis. This study found that, as the pitch angle increased, the structural dynamic response of key wind turbine components under uniform airflow conditions exhibited a decreasing trend, which was proportional to wind speed. Under shear airflow conditions, the response of key components was positively correlated with the shear index, while Extreme Operated Gust significantly increased the amplitude of the response fluctuations. Furthermore, this research revealed that, with an increase in pitch angle, the maximum stress value of the gear under uniform airflow conditions decreased from 27.42 MPa to 7.64 MPa, a reduction of 72.1%. Under shear airflow conditions, the root stress of the gear decreased from 14.441 MPa to 8.879 MPa, a reduction of 49.60%. Under Extreme Operated Gust conditions, the maximum stress of the gear decreased from 17.82 MPa to 15.18 MPa, a reduction of 22.99%. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Examples of research related to small pitch wind turbines. Xie, W., 2015 [<a href="#B2-energies-17-05818" class="html-bibr">2</a>]. Sung, C.M., 2016 [<a href="#B3-energies-17-05818" class="html-bibr">3</a>]. Chen, Y.J., 2016 [<a href="#B4-energies-17-05818" class="html-bibr">4</a>].</p>
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<p>Basic structure of variable pitch wind turbine. (<b>a</b>) Sketch of the overall structure of the new wind turbine; (<b>b</b>) sketch of pitch adjustment mechanism; (<b>c</b>) schematic diagram of the overall structure of the new wind turbine; (<b>d</b>) schematic diagram of pitch adjustment mechanism. 1—Blade. 2—Rack connection plate. 3—Pitch connectors. 4—Hub. 5—Generator. 6—Thrust bearing bracket. 7—Thrust bearing. 8—Worm gear reducer. 9—Reducer support. 10—Drive rod. 11—Rack.</p>
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<p>Wind tunnel laboratory.</p>
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<p>Test prototype of variable pitch wind turbine.</p>
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<p>The test system used for load measurement.</p>
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<p>Curves of output power with wind speed (56 V).</p>
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<p>Constant power output control curves.</p>
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<p>The measurement positions for the rack and rack connection plate. (<b>a</b>) Schematic diagram of variable pitch structure; (<b>b</b>) schematic diagram of the test; (<b>c</b>) sketch of the measurement positions for the rack; (<b>d</b>) sketch of the measurement positions for the rack connection plate.</p>
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<p>Stress signal of regulating mechanism: (<b>a</b>) 6° pitch angle; (<b>b</b>) 9° pitch angle; (<b>c</b>) 12° pitch angle; (<b>d</b>) 15° pitch angle; (<b>e</b>) 25° pitch angle.</p>
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<p>Geometric model.</p>
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<p>Three-dimensional model of wind turbine. (<b>a</b>) Blade. (<b>b</b>) Gear. (<b>c</b>) pitch adjustment mechanism. (<b>d</b>) wind turbine.</p>
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<p>Setup of computational domain.</p>
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<p>Mesh independence verification.</p>
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<p>The meshing results of the structural field.</p>
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<p>Constraint conditions.</p>
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<p>Comparison of simulation and test value of rack stress response. (<b>a</b>) wind speed of 12m/s. (<b>b</b>) wind speed of 14 m/s. (<b>c</b>) wind speed of 16 m/s.</p>
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<p>Flow characteristics. (<b>a</b>) the airfoil exhibits a smaller angle of attack. (<b>b</b>) the airfoil exhibits a larger angle of attack.</p>
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<p>The streamlines on the leeward side of the blade at a 3° pitch angle under varying wind speeds. (<b>a</b>) 7 m/s; (<b>b</b>) 9 m/s; (<b>c</b>) 11 m/s; (<b>d</b>) 14 m/s.</p>
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<p>Maximum stress contour of the hub.</p>
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<p>Maximum stress contour of the rack connection plate.</p>
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<p>Maximum stress contour of the blade connector.</p>
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<p>Maximum stress contour of the gear-rack mechanism.</p>
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<p>Contact stress.</p>
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<p>Bending stress.</p>
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<p>Maximum stress values of each component.</p>
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<p>Schematic diagram of wind profiles with different shear indexes.</p>
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<p>Comparison between theoretical and monitoring values of shear flow model.</p>
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<p>Time–history curves of the stress response at the gear root under different shear indexes.(<b>a</b>) 6° pitch angle. (<b>b</b>) 15° pitch angle.</p>
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<p>Stress response characteristics of gear root. (<b>a</b>) Average stress response of gears with different shear indexes. (<b>b</b>) Standard deviation of stress response of gears with different shear indexes.</p>
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<p>Comparison between theoretical and monitoring values of EOG model. (<b>a</b>) EOG 1-year recurrence period; (<b>b</b>) EOG 50-year recurrence period.</p>
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<p>Stress response time history curve of blade and gear root.</p>
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<p>Comparison of gear root stress responses under EOG and uniform inflow conditions. (a) wind speeds of 12 m/s at moments a. (b) wind speeds of 14 m/s at moments b. (c) wind speeds of 12 m/s at moments c. (d) wind speed corresponding to the 1-year recurrence period EOG speed of 14.8 m/s at moment d. (e) wind speeds of 14 m/s at moments e.</p>
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21 pages, 9630 KiB  
Article
Parameter Tuning Method for a Lattice Compensated Wireless Power Transfer System
by Ebrahim Nasr Esfahani and Indranil Bhattacharya
Electricity 2024, 5(4), 895-915; https://doi.org/10.3390/electricity5040045 - 21 Nov 2024
Viewed by 284
Abstract
This study presents a new charging system with lattice compensation for wireless power transfer (WPT) applications. A mathematical model is developed for the proposed system to accurately estimate power transfer capabilities. Furthermore, a linear programming algorithm is used to find the proper values [...] Read more.
This study presents a new charging system with lattice compensation for wireless power transfer (WPT) applications. A mathematical model is developed for the proposed system to accurately estimate power transfer capabilities. Furthermore, a linear programming algorithm is used to find the proper values for lattice compensation, which helps achieve high efficiency over a wide range of loads and zero voltage switching (ZVS) for the proposed system. The approach is validated through analysis, modeling, and simulation of a 3-kilowatt WPT system. Additionally, a 200-watt prototype with a 100 mm air gap was built and tested, showing an efficiency of 86.3% during charging. This method eliminates the need for an auxiliary DC–DC converter, ensuring efficient charging across various load conditions. The prototype’s performance closely matches the simulation results, indicating its potential for scaling up to electric vehicle (EV) battery charging applications. Full article
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<p>The concept of wireless charging and components of WPT system.</p>
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<p>General classification of resonant network topologies for WPT systems.</p>
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<p>(<b>a</b>) Symmetrical lattice network; (<b>b</b>) the G-parameter description of the lattice network.</p>
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<p>(<b>a</b>) Equivalent circuit model of the lattice network: (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>Z</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> <mo>(</mo> <mo>=</mo> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math>.</p>
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<p>The proposed topology with double-sided lattice compensation network.</p>
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<p>Schematic of the cascaded T model of the proposed double-sided lattice network-impedance as a two-port network.</p>
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<p>The proposed topology with a double-sided lattice compensation network by using a winding-cross-coupled inductor.</p>
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<p>(<b>a</b>) Output voltages for different switching frequencies f = [60, 65, 70, 75, 80, 85, 90] kHz for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>; (<b>b</b>) Output powers for different switching frequencies f = [60, 65, 70, 75, 80, 85, 90] kHz for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Output voltages for different capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> <mo>=</mo> <mo>[</mo> <mn>60</mn> <mo>,</mo> <mo> </mo> <mn>65</mn> <mo>,</mo> <mo> </mo> <mn>70</mn> <mo>,</mo> <mo> </mo> <mn>75</mn> <mo>,</mo> <mo> </mo> <mn>80</mn> <mo>,</mo> <mo> </mo> <mn>85</mn> <mo>,</mo> <mo> </mo> <mn>90</mn> <mo>]</mo> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> </mrow> </semantics></math> for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>; (<b>b</b>) Output powers for different capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mn>60</mn> <mo>,</mo> <mo> </mo> <mn>65</mn> <mo>,</mo> <mo> </mo> <mn>70</mn> <mo>,</mo> <mo> </mo> <mn>75</mn> <mo>,</mo> <mo> </mo> <mn>80</mn> <mo>,</mo> <mo> </mo> <mn>85</mn> <mo>,</mo> <mo> </mo> <mn>90</mn> </mrow> </mfenced> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> </mrow> </semantics></math> for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Output voltages for different capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> <mo>=</mo> <mo>[</mo> <mn>60</mn> <mo>,</mo> <mo> </mo> <mn>65</mn> <mo>,</mo> <mo> </mo> <mn>70</mn> <mo>,</mo> <mo> </mo> <mn>75</mn> <mo>,</mo> <mo> </mo> <mn>80</mn> <mo>,</mo> <mo> </mo> <mn>85</mn> <mo>,</mo> <mo> </mo> <mn>90</mn> <mo>]</mo> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> </mrow> </semantics></math> for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>; (<b>b</b>) Output powers for different capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> <mo>=</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mn>60</mn> <mo>,</mo> <mo> </mo> <mn>65</mn> <mo>,</mo> <mo> </mo> <mn>70</mn> <mo>,</mo> <mo> </mo> <mn>75</mn> <mo>,</mo> <mo> </mo> <mn>80</mn> <mo>,</mo> <mo> </mo> <mn>85</mn> <mo>,</mo> <mo> </mo> <mn>90</mn> </mrow> </mfenced> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">F</mi> </mrow> </semantics></math> for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Primary side compensation network voltages and currents for varying loads for WPT system with double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Secondary side compensation network voltages and currents for varying loads for WPT system with double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Output power and efficiency vs. load resistance for the WPT system with double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Procedure of the proposed optimized resonant network design.</p>
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<p>Typical charging profile of Li-ion battery.</p>
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<p>The projections of the obtained Pareto-optimal front for the WPT system with double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Experimental setup illustrated by a block diagram.</p>
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<p>Experimental test setup used to validate the presented algorithm for the WPT system with a double-sided lattice compensation network at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Prototype of a dual-coupled magnetic coupler. (<b>a</b>) Receiver magnetic coil pad in 3D printed coil former (R = 2.5”); (<b>b</b>) winding-cross-coupled inductor (h = 5”); (<b>c</b>) top view of a coil former for a winding-cross-coupled inductor with 11 sections.</p>
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<p>Comparison of the coupling factor at perfect alignment and 50 mm misalignment using FEM and measurements.</p>
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<p>Experimental capture of the transient response at full load aligned case at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>D</mi> <mi>C</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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<p>Experimental capture of output voltage and current the at full load aligned case at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> <mo> </mo> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>.</p>
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