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Energies, Volume 13, Issue 6 (March-2 2020) – 236 articles

Cover Story (view full-size image): 166 cm2 active area phosphoric-acid (PA)-doped polymer-based membrane electrode assembly (MEA) produced by Advent Technologies, Inc. during assembly in a high-temperature proton exchange membrane fuel cell (HT-PEMFC) stack at Georgia Southern University. View this paper.
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19 pages, 7513 KiB  
Article
Circuit Structure and Control Method to Reduce Size and Harmonic Distortion of Interleaved Dual Buck Inverter
by Min-Gi Cho, Sang-Hoon Lee, Hyeon-Seok Lee, Yoon-Geol Choi and Bongkoo Kang
Energies 2020, 13(6), 1531; https://doi.org/10.3390/en13061531 - 24 Mar 2020
Cited by 1 | Viewed by 2617
Abstract
A new circuit structure and control method for a high power interleaved dual-buck inverter are proposed. The proposed inverter consists of six switches, four diodes and two inductors, uses a dual-buck structure to eliminate zero-cross distortion, and operates in an interleaved mode to [...] Read more.
A new circuit structure and control method for a high power interleaved dual-buck inverter are proposed. The proposed inverter consists of six switches, four diodes and two inductors, uses a dual-buck structure to eliminate zero-cross distortion, and operates in an interleaved mode to reduce the current stress of switch. To reduce the total harmonic distortion at low output power, the inverter is controlled using discontinuous-current-mode control combined with continuous-current-mode control. The experimental inverter had a power-conversion efficiency of 98.5% at output power = 1300 W and 98.3% at output power = 2 kW, when the inverter was operated at an input voltage of 400 VDC, output voltage of 220 VAC/60 Hz, and switching frequency of 20 kHz. The total harmonic distortion was < 0.66%, which demonstrates that the inverter is suitable for high-power dc-ac power conversion. Full article
(This article belongs to the Section F: Electrical Engineering)
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Figure 1

Figure 1
<p>Circuit structure of dc-ac inverters: (<b>a</b>) full-bridge inverter (FBI) and (<b>b</b>) interleaved dual-buck inverter of [<a href="#B9-energies-13-01531" class="html-bibr">9</a>] (IDBI [<a href="#B9-energies-13-01531" class="html-bibr">9</a>]).</p>
Full article ">Figure 2
<p>Circuit structure of the proposed inverter.</p>
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<p>Simplified gate signals of the proposed inverter.</p>
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<p>Theoretical waveforms and circuit diagrams for leg 1: (<b>a</b>) <span class="html-italic">V<sub>L</sub></span><sub>1</sub> and <span class="html-italic">i<sub>L</sub></span><sub>1</sub> for DCM and CCM operation, (<b>b</b>) circuit diagrams for Mode 1, (<b>c</b>) circuit diagrams for Mode 2, and (<b>d</b>) circuit diagrams for Mode 3.</p>
Full article ">Figure 5
<p>Block diagram of the control circuit for the proposed inverter.</p>
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<p>Block diagram of the phase locked loop.</p>
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<p>Block diagram of the D-Q axis controller.</p>
Full article ">Figure 8
<p>Block diagram of the gate pulse generator.</p>
Full article ">Figure 9
<p>Photographs of the experimental inverters: (<b>a</b>) proposed inverter, (<b>b</b>) interleaved dual buck inverter (IDBI), and (<b>c</b>) full bridge inverter (FBI).</p>
Full article ">Figure 10
<p>Experimental waveforms of proposed inverter, measured at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub>/60 Hz, <span class="html-italic">f<sub>s</sub></span> = 20 kHz, and <span class="html-italic">P<sub>o</sub></span> = 2 kW: (<b>a</b>) gate input pulses, (<b>b</b>) <span class="html-italic">V<sub>GS_SU1</sub></span>, <span class="html-italic">V <sub>GS_SU2</sub></span>, <span class="html-italic">i<sub>L</sub></span><sub>1</sub>, and <span class="html-italic">i<sub>L</sub></span><sub>2</sub>, and (<b>c</b>) <span class="html-italic">i<sub>o</sub></span>, <span class="html-italic">V<sub>AN</sub></span>, and <span class="html-italic">V<sub>gri</sub></span><sub>d</sub>.</p>
Full article ">Figure 11
<p><span class="html-italic">η<sub>e</sub></span> vs. <span class="html-italic">P<sub>o</sub></span> for the experimental inverters operating at (<b>a</b>) <span class="html-italic">f<sub>s</sub></span> = 20 kHz and (<b>b</b>) <span class="html-italic">f<sub>s</sub></span> = 40 kHz: measured at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub>/60 Hz, and <span class="html-italic">Q<sub>o</sub></span> = 0 VAR. The power loss <span class="html-italic">P<sub>DSP</sub></span> in the control circuit was included in <span class="html-italic">η<sub>e</sub></span> measurement.</p>
Full article ">Figure 12
<p>Power losses in the experimental inverters at (<b>a</b>) <span class="html-italic">P<sub>o</sub></span> = 2 kW and (<b>b</b>) <span class="html-italic">P<sub>o</sub></span> = 150W: calculated using PSPICE at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub> / 60 Hz, <span class="html-italic">f<sub>s</sub></span> = 20 kHz, and <span class="html-italic">Q<sub>o</sub></span> = 0 VAR.</p>
Full article ">Figure 13
<p>Switch temperature <span class="html-italic">T<sub>SW</sub></span> vs. time of operation, measured at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub>/60 Hz, <span class="html-italic">f<sub>s</sub></span> = 20 kHz, <span class="html-italic">P<sub>o</sub></span> = 2 kW, and <span class="html-italic">Q<sub>o</sub></span> = 0 VAR.</p>
Full article ">Figure 14
<p>Total harmonic distortion (THD) of <span class="html-italic">i<sub>o</sub></span> vs. <span class="html-italic">P<sub>o</sub></span> for the experimental inverters operating at (<b>a</b>) <span class="html-italic">f<sub>s</sub></span> = 20 kHz and (<b>b</b>) <span class="html-italic">f<sub>s</sub></span> = 40 kHz: measured at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub> and <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub>/60 Hz.</p>
Full article ">Figure 15
<p>Waveforms of <span class="html-italic">i<sub>o</sub></span> and <span class="html-italic">i<sub>o_avg</sub></span> measured at at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 V<sub>AC</sub> / 60 Hz, fs = 20 kHz, <span class="html-italic">P<sub>o</sub></span> = 150 W, <span class="html-italic">Q<sub>o</sub></span> = 0 VAR, and <span class="html-italic">I<sub>o</sub></span> = 0.95 A: (a) <span class="html-italic">i<sub>o</sub></span> (Proposed, DCM+CCM), (b) <span class="html-italic">i<sub>o_avg</sub></span> (Proposed, DCM+CCM), (c) <span class="html-italic">i<sub>o</sub></span> (Proposed, CCM), (d) <span class="html-italic">i<sub>o_avg</sub></span> (Proposed, CCM), (e) <span class="html-italic">i<sub>o</sub></span> (FBI, CCM), (f) <span class="html-italic">i<sub>o_avg</sub></span> (FBI, CCM).</p>
Full article ">Figure 16
<p>Harmonic components of <span class="html-italic">i<sub>o</sub></span>; measured at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 VAC/60 Hz, <span class="html-italic">f<sub>s</sub></span> = 20 kHz, <span class="html-italic">P<sub>o</sub></span> = 150 W, <span class="html-italic">Q<sub>o</sub></span> = 0 VAR, and <span class="html-italic">I<sub>o</sub></span> = 0.95 A.</p>
Full article ">Figure 17
<p>Step responses of the proposed inverter at <span class="html-italic">V<sub>in</sub></span> = 400 V<sub>DC</sub>, <span class="html-italic">V<sub>grid</sub></span> = 220 VAC/60 Hz, <span class="html-italic">f<sub>s</sub></span> = 20 kHz, and <span class="html-italic">Q<sub>o</sub></span> = 0 VAR: (<b>a</b>) for a decrease of <span class="html-italic">P<sub>o</sub></span> from 2 kW to 1 kW and (<b>b</b>) for an increase of <span class="html-italic">P<sub>o</sub></span> from 1 kW to 2 kW.</p>
Full article ">
26 pages, 10812 KiB  
Article
Generalized Modeling of Soft-Capture Manipulator with Novel Soft-Contact Joints
by Xiaodong Zhang, Sheng Xu, Chen Jia, Gang Wang and Ming Chu
Energies 2020, 13(6), 1530; https://doi.org/10.3390/en13061530 - 24 Mar 2020
Cited by 4 | Viewed by 3140
Abstract
The space-borne manipulator has been playing an important part in docking tasks. Docking collision can easily lead to instability of both the manipulator and floating base. Aiming at the problem of soft capture, a novel soft-contact joint with dual working modes is developed, [...] Read more.
The space-borne manipulator has been playing an important part in docking tasks. Docking collision can easily lead to instability of both the manipulator and floating base. Aiming at the problem of soft capture, a novel soft-contact joint with dual working modes is developed, especially to buffer and unload the spatial collision momentum. Furthermore, considering a series-wound soft-capture manipulator with multi-joints, a generalized modeling method was established by using the Kane approach. Both the benefits of soft-contact joint and the effectiveness of dynamics equations are verified in MATLAB and Adams software by simulations of a two-joint manipulator with eight-DOF. The comparative simulation results showed the advantages of the proposed soft-contact joint in reducing instability from spatial impact. Full article
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Figure 1

Figure 1
<p>Three-dimensional model of the soft-contact joint.</p>
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<p>Structural block diagram of the soft-contact joint.</p>
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<p>Overall structure of the soft-contact joint.</p>
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<p>Block diagram of the dual working modes transmission.</p>
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<p>Block diagram of the soft-contact principle. (<b>a</b>) Block diagram of the buffer and unloading of spatial collision. (<b>b</b>) The representative force (moment) diagram of two consecutive joints.</p>
Full article ">Figure 5 Cont.
<p>Block diagram of the soft-contact principle. (<b>a</b>) Block diagram of the buffer and unloading of spatial collision. (<b>b</b>) The representative force (moment) diagram of two consecutive joints.</p>
Full article ">Figure 6
<p>Novel joint and generalized model of the manipulator. (<b>a</b>) Joint structure with six-dimensional damping. (<b>b</b>) Generalized model of the manipulator with multi-stage damping.</p>
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<p>Stress analysis for the <span class="html-italic">k</span>th segment.</p>
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<p>Conceptual model of simulation. (<b>a</b>) Structure diagram for MATLAB. (<b>b</b>) Adams model.</p>
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<p>Schematic diagram of six unidirectional collision forces.</p>
Full article ">Figure 10
<p>Vibration displacements of joints in a single-direction collision. (<b>a</b>) Vibration displacements in a single X-line collision. (<b>b</b>) Vibration displacements in a single X-angle collision. (<b>c</b>) Vibration displacements in a single Y-line collision. (<b>d</b>) Vibration displacements in a single Y-angle collision. (<b>e</b>) Vibration displacements in a single Z-line collision. (<b>f</b>) Vibration displacements in a single Z-angle collision.</p>
Full article ">Figure 10 Cont.
<p>Vibration displacements of joints in a single-direction collision. (<b>a</b>) Vibration displacements in a single X-line collision. (<b>b</b>) Vibration displacements in a single X-angle collision. (<b>c</b>) Vibration displacements in a single Y-line collision. (<b>d</b>) Vibration displacements in a single Y-angle collision. (<b>e</b>) Vibration displacements in a single Z-line collision. (<b>f</b>) Vibration displacements in a single Z-angle collision.</p>
Full article ">Figure 10 Cont.
<p>Vibration displacements of joints in a single-direction collision. (<b>a</b>) Vibration displacements in a single X-line collision. (<b>b</b>) Vibration displacements in a single X-angle collision. (<b>c</b>) Vibration displacements in a single Y-line collision. (<b>d</b>) Vibration displacements in a single Y-angle collision. (<b>e</b>) Vibration displacements in a single Z-line collision. (<b>f</b>) Vibration displacements in a single Z-angle collision.</p>
Full article ">Figure 10 Cont.
<p>Vibration displacements of joints in a single-direction collision. (<b>a</b>) Vibration displacements in a single X-line collision. (<b>b</b>) Vibration displacements in a single X-angle collision. (<b>c</b>) Vibration displacements in a single Y-line collision. (<b>d</b>) Vibration displacements in a single Y-angle collision. (<b>e</b>) Vibration displacements in a single Z-line collision. (<b>f</b>) Vibration displacements in a single Z-angle collision.</p>
Full article ">Figure 10 Cont.
<p>Vibration displacements of joints in a single-direction collision. (<b>a</b>) Vibration displacements in a single X-line collision. (<b>b</b>) Vibration displacements in a single X-angle collision. (<b>c</b>) Vibration displacements in a single Y-line collision. (<b>d</b>) Vibration displacements in a single Y-angle collision. (<b>e</b>) Vibration displacements in a single Z-line collision. (<b>f</b>) Vibration displacements in a single Z-angle collision.</p>
Full article ">Figure 11
<p>Schematic diagram of six unidirectional collision forces.</p>
Full article ">Figure 12
<p>The curves of linear and angular velocity of the base. (<b>a</b>) X-line velocity. (<b>b</b>) X-angle velocity. (<b>c</b>) Y-line velocity. (<b>d</b>) Y-angle velocity. (<b>e</b>) Z-line velocity. (<b>f</b>) Z-angle velocity.</p>
Full article ">Figure 12 Cont.
<p>The curves of linear and angular velocity of the base. (<b>a</b>) X-line velocity. (<b>b</b>) X-angle velocity. (<b>c</b>) Y-line velocity. (<b>d</b>) Y-angle velocity. (<b>e</b>) Z-line velocity. (<b>f</b>) Z-angle velocity.</p>
Full article ">Figure 12 Cont.
<p>The curves of linear and angular velocity of the base. (<b>a</b>) X-line velocity. (<b>b</b>) X-angle velocity. (<b>c</b>) Y-line velocity. (<b>d</b>) Y-angle velocity. (<b>e</b>) Z-line velocity. (<b>f</b>) Z-angle velocity.</p>
Full article ">Figure 12 Cont.
<p>The curves of linear and angular velocity of the base. (<b>a</b>) X-line velocity. (<b>b</b>) X-angle velocity. (<b>c</b>) Y-line velocity. (<b>d</b>) Y-angle velocity. (<b>e</b>) Z-line velocity. (<b>f</b>) Z-angle velocity.</p>
Full article ">Figure 12 Cont.
<p>The curves of linear and angular velocity of the base. (<b>a</b>) X-line velocity. (<b>b</b>) X-angle velocity. (<b>c</b>) Y-line velocity. (<b>d</b>) Y-angle velocity. (<b>e</b>) Z-line velocity. (<b>f</b>) Z-angle velocity.</p>
Full article ">Figure 13
<p>The curves of the torques and the force of the base. (<b>a</b>) X-angle torque. (<b>b</b>) Y-angle torque. (<b>c</b>) Z-angle torque. (<b>d</b>) Z-line force.</p>
Full article ">Figure 13 Cont.
<p>The curves of the torques and the force of the base. (<b>a</b>) X-angle torque. (<b>b</b>) Y-angle torque. (<b>c</b>) Z-angle torque. (<b>d</b>) Z-line force.</p>
Full article ">Figure 14
<p>Vibration displacements of joint 1. (<b>a</b>) X-angle vibration displacement. (<b>b</b>) Y-angle vibration displacement. (<b>c</b>) Z-angle vibration displacement. (<b>d</b>) Z-line vibration displacement.</p>
Full article ">Figure 15
<p>Vibration displacements of joint 2. (<b>a</b>) X-angle vibration displacement. (<b>b</b>) Y-angle vibration displacement. (<b>c</b>) Z-angle vibration displacement. (<b>d</b>) Z-line vibration displacement.</p>
Full article ">
18 pages, 3898 KiB  
Article
Application of the Feedback Linearization in Maximum Power Point Tracking Control for Hydraulic Wind Turbine
by Chao Ai, Wei Gao, Qinyu Hu, Yankang Zhang, Lijuan Chen, Jiawei Guo and Zengrui Han
Energies 2020, 13(6), 1529; https://doi.org/10.3390/en13061529 - 24 Mar 2020
Cited by 10 | Viewed by 3441
Abstract
Taking the hydraulic wind turbine as the research object, the method is studied to improve the utilization ratio of wind energy for hydraulic wind turbine, when the wind speed is lower than the rated wind speed. The hydraulic fixed displacement pump speed and [...] Read more.
Taking the hydraulic wind turbine as the research object, the method is studied to improve the utilization ratio of wind energy for hydraulic wind turbine, when the wind speed is lower than the rated wind speed. The hydraulic fixed displacement pump speed and generating power can be used as control output to realize the maximum power point tracking control. The characteristics of the maximum power point tracking control are analyzed for hydraulic wind turbine, and the hydraulic output power is taken as control output based on the comprehensive performance requirements. Because the hydraulic wind turbine is a strong multiplication nonlinear system, the system is globally linearized based the feedback linearization method, and the maximum power point tracking control law is obtained. The simulation and experiment results show that the system has good dynamic performance with the proposed control law. The control provides theoretical guidance for optimal power tracking control law application for hydraulic wind turbine. Full article
(This article belongs to the Collection Wind Turbines)
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Figure 1

Figure 1
<p>Schematic of the hydraulic wind turbine (HWT).</p>
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<p>Schematic of the maximum power point tracking (MPPT) method for direct generation power control.</p>
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<p>HWT MPPT control block diagram.</p>
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<p>Feedback linearization control law flowchart.</p>
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<p>Simulation model.</p>
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<p>The response curve under the condition of 7 m/s-8 m/s step wind speed at 10 s. (<b>a</b>) the wind speed; (<b>b</b>) the pump speed; (<b>c</b>) the high pressure; (<b>d</b>) the output power.</p>
Full article ">Figure 7
<p>the response curve under the condition of 8 m/s-7 m/s step wind speed at 10 s. (<b>a</b>) the wind speed; (<b>b</b>) the pump speed; (<b>c</b>) the high pressure; (<b>d</b>) the output power.</p>
Full article ">Figure 8
<p>The response curve under 8 ± 0.5 m/s wind speed. (<b>a</b>) the wind speed; (<b>b</b>) the pump speed; (<b>c</b>) the high pressure; (<b>d</b>) the output power.</p>
Full article ">Figure 9
<p>The structure diagram of 30 kVA semi-physical simulation platform.</p>
Full article ">Figure 10
<p>The system response curve under the step wind speed. (<b>a</b>) the wind speed; (<b>b</b>) the pump speed; (<b>c</b>) the high pressure; (<b>d</b>) the output power.</p>
Full article ">
22 pages, 3008 KiB  
Article
High-Temperature, Dry Scrubbing of Syngas with Use of Mineral Sorbents and Ceramic Rigid Filters
by Mateusz Szul, Tomasz Iluk and Aleksander Sobolewski
Energies 2020, 13(6), 1528; https://doi.org/10.3390/en13061528 - 24 Mar 2020
Cited by 9 | Viewed by 5944
Abstract
In this research, the idea of multicomponent, one-vessel cleaning of syngas through simultaneous dedusting and adsorption is described. Data presented were obtained with the use of a pilot-scale 60 kWth fixed-bed GazEla reactor, coupled with a dry gas cleaning unit where mineral [...] Read more.
In this research, the idea of multicomponent, one-vessel cleaning of syngas through simultaneous dedusting and adsorption is described. Data presented were obtained with the use of a pilot-scale 60 kWth fixed-bed GazEla reactor, coupled with a dry gas cleaning unit where mineral sorbents are injected into raw syngas at 500–650 °C, before dedusting at ceramic filters. The research primarily presents results of the application of four calcined sorbents, i.e., chalk (CaO), dolomite (MgO–CaO), halloysite (AlO–MgO–FeO), and kaolinite (AlO–MgO) for high-temperature (HT) adsorption of impurities contained in syngas from gasification of biomass. An emphasis on data regarding the stability of the filtration process is provided since the addition of coating and co-filtering materials is often necessary for keeping the filtration of syngas stable, in industrial applications. Full article
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Figure 1

Figure 1
<p>Comparison of the chemical composition of sorbents and biomass char. Content of main constituents (inorganic elements and carbon) that actively take part in the adsorption process.</p>
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<p>Schematic diagram of the pilot gasification installation with the fixed-bed GazEla reactor and the dry gas cleaning unit.</p>
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<p>Change in pressure drop on the high-temperature (HT) filter during filtration/adsorption tests. Pressure drop (dP) presented in a standardized form that takes into account the actual flow rate of syngas.</p>
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<p>Change in pressure drop on the HT filter during filtration/adsorption tests: comparison of three experiments with halloysite.</p>
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<p>Changes in the main process parameters regarding the halloysite-assisted filtration of syngas.</p>
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<p>Ratio of the major elements in filter cake calculated from the mixing law of sorbents and char vs. values measured analytically in recovered filter cake.</p>
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<p>Ratio of Cl recovered from syngas in filter cake and condensate from syngas cooling.</p>
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15 pages, 3989 KiB  
Article
Design and Implementation of a Low-Cost Real-Time Control Platform for Power Electronics Applications
by José Aravena, Dante Carrasco, Matias Diaz, Matias Uriarte, Felix Rojas, Roberto Cardenas and Juan Carlos Travieso
Energies 2020, 13(6), 1527; https://doi.org/10.3390/en13061527 - 24 Mar 2020
Cited by 23 | Viewed by 5533
Abstract
In recent years, different off-the-shelf solutions for the rapid control prototyping of power electronics converters have been commercialised. The main benefits of those systems are based on a fast and easy-to-use environment due to high-level programming. However, most of those systems are very [...] Read more.
In recent years, different off-the-shelf solutions for the rapid control prototyping of power electronics converters have been commercialised. The main benefits of those systems are based on a fast and easy-to-use environment due to high-level programming. However, most of those systems are very expensive and are closed software and hardware solutions. In this context, this paper presents the design and implementation of a control platform targeting at the segment in between expensive off-the-shelf control platforms and low-cost controllers. The control platform is based on the Launchpad TMS320F28379D from Texas Instruments, and it is equipped with an expansion board that provide analogue-to-digital measurements, switching signals and hardware protections. The performance of the control platform is experimentally tested on a 20 kVA power converter. Full article
(This article belongs to the Special Issue Control Strategies for Power Conversion Systems)
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Figure 1

Figure 1
<p>(<b>a</b>) Launchpad TMS320F28379D. (<b>b</b>) Overview of the proposed RTCP.</p>
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<p>Soldered Printed Circuit Board.</p>
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<p>Optical fibre driver circuit.</p>
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<p>Electrical protection based on comparators and logic gates.</p>
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<p>ADC Operation Diagram.</p>
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<p>(<b>a</b>) PWM block operation diagram. (<b>b</b>) PWM signal generation with Up-Down type counter.</p>
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<p>(<b>a</b>) Overview of the experimental setup. (<b>b</b>) AC-DC back-to-back converter topology.</p>
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<p>Proposed control algorithm.</p>
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<p>Experimental system implementation.</p>
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<p>Variable DC link voltage reference test.</p>
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<p>Steady-state operation.</p>
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<p>Variable DC current reference test.</p>
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<p>Experimental Results. (<b>a</b>) AC <span class="html-italic">d</span>-axis current. (<b>b</b>) AC <span class="html-italic">q</span>-axis current. (<b>c</b>) AC <math display="inline"><semantics> <mrow> <mi>a</mi> <mi>b</mi> <mi>c</mi> </mrow> </semantics></math> currents. (<b>d</b>) DC load current.</p>
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<p>Over-voltage protection.</p>
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21 pages, 10723 KiB  
Article
An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle
by Hanwu Liu, Yulong Lei, Yao Fu and Xingzhong Li
Energies 2020, 13(6), 1526; https://doi.org/10.3390/en13061526 - 24 Mar 2020
Cited by 22 | Viewed by 4280
Abstract
The energy recovered with regenerative braking system can greatly improve energy efficiency of range-extended electric vehicle (R-EEV). Nevertheless, maximizing braking energy recovery while maintaining braking performance remains a challenging issue, and it is also difficult to reduce the adverse effects of regenerative current [...] Read more.
The energy recovered with regenerative braking system can greatly improve energy efficiency of range-extended electric vehicle (R-EEV). Nevertheless, maximizing braking energy recovery while maintaining braking performance remains a challenging issue, and it is also difficult to reduce the adverse effects of regenerative current on battery capacity loss rate (Qloss,%) to extend its service life. To solve this problem, a revised regenerative braking control strategy (RRBCS) with the rate and shape of regenerative braking current considerations is proposed. Firstly, the initial regenerative braking control strategy (IRBCS) is researched in this paper. Then, the battery capacity loss model is established by using battery capacity test results. Eventually, RRBCS is obtained based on IRBCS to optimize and modify the allocation logic of braking work-point. The simulation results show that compared with IRBCS, the regenerative braking energy is slightly reduced by 16.6% and Qloss,% is reduced by 79.2%. It means that the RRBCS can reduce Qloss,% at the expense of small braking energy recovery loss. As expected, RRBCS has a positive effect on prolonging the battery service life while ensuring braking safety while maximizing recovery energy. This result can be used to develop regenerative braking control system to improve comprehensive performance levels. Full article
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Graphical abstract

Graphical abstract
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<p>Structure of range-extended electric vehicle with regenerative braking system.</p>
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<p>Process of regenerative braking control strategy based on optimal slip ratio.</p>
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<p>Slip ratio and friction adhesion coefficient relationship in Burckhardt tire model: (<b>a</b>) five typical roads; (<b>b</b>) identification of parameters used in tire–road adhesion coefficient recognition module.</p>
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<p>Coordinate system conversion: (<b>a</b>) braking force; (<b>b</b>) slip ratio.</p>
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<p>Allocation logic of braking work-point in initial regenerative braking control strategy: (<b>a</b>) area schematic; (<b>b</b>) logic schematic.</p>
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<p>Simulation platform of the R-EEV.</p>
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<p>Simulation result of TACRM: (<b>a</b>) μ<sub>max</sub> = 0.6; (<b>b</b>) μ<sub>max</sub> = 0.45; (<b>c</b>) μ<sub>max</sub> = 0.2; (d) μ<sub>max</sub> = 0.1.</p>
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<p>Diagram of optimal slip ratio control process: (<b>a</b>) fuzzy controller of front wheel; (<b>b</b>) fuzzy controller of rear wheel.</p>
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<p>Membership functions and output surface of the front wheel fuzzy controller: (<b>a</b>) <span class="html-italic">e<sub>f</sub> (t)</span>; (<b>b</b>) <span class="html-italic">s′<sub>f</sub> (t)</span>; (<b>c</b>) Δ<span class="html-italic">T<sub>f _e</sub>(t)</span>; (<b>d</b>) Δ<span class="html-italic">T<sub>f_m</sub> (t)</span>; (<b>e</b>) Δ<span class="html-italic">T<sub>f _e</sub>(t)</span>; (<b>f</b>) Δ<span class="html-italic">T<sub>f_m</sub> (t)</span>.</p>
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<p>Membership functions and output surface of the rear wheel fuzzy controller: (<b>a</b>) <span class="html-italic">e<sub>r</sub> (t)</span>; (<b>b</b>) <span class="html-italic">s′<sub>r</sub> (t)</span>; (<b>c</b>) Δ<span class="html-italic">T<sub>r _m</sub>(t)</span>; (<b>d</b>) Δ<span class="html-italic">T<sub>r_m</sub> (t)</span>.</p>
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<p>Simulation result of the RBCS under high tire–road adhesion condition (μ<sub>max</sub> = 0.8): (<b>a</b>) braking strength, TAC and SoC; (<b>b</b>) front wheel slip rate and speed; (<b>c</b>) rear wheel slip rate and speed; (<b>d</b>) braking torque.</p>
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<p>Simulation result of the RBCS under medium tire–road adhesion condition (μ<sub>max</sub> = 0.6): (<b>a</b>) braking strength, TAC and SoC; (<b>b</b>) front wheel slip rate and speed; (<b>c</b>) rear wheel slip rate and speed; (<b>d</b>) braking torque.</p>
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<p>Simulation result of the RBCS under low tire–road adhesion condition (μ<sub>max</sub> = 0.35): (<b>a</b>) braking strength, TAC and SoC; (<b>b</b>) front wheel slip rate and speed; (<b>c</b>) rear wheel slip rate and speed; (<b>d</b>) braking torque.</p>
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<p>Example of the current profiles of six cases: (<b>a</b>) case 1; (<b>b</b>) case 2 to case 6.</p>
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<p>Function relationship of correction factor <span class="html-italic">k<sub>s2</sub></span> and <span class="html-italic">S<sub>wc</sub><sup>2</sup></span>.</p>
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<p>Schematic diagram of braking work-point switching logic.</p>
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<p>Simulation results of control strategy 1 and control strategy 2.</p>
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<p>Work-point switching situation of strategy 3.</p>
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<p>Variation of the battery SoC with three control strategies.</p>
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<p>Variation of the current with three control strategies.</p>
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<p>Bar chart of regenerative braking performance with three control strategies.</p>
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27 pages, 9682 KiB  
Article
Pre-Drilling Production Forecasting of Parent and Child Wells Using a 2-Segment Decline Curve Analysis (DCA) Method Based on an Analytical Flow-Cell Model Scaled by a Single Type Well
by Ruud Weijermars and Kiran Nandlal
Energies 2020, 13(6), 1525; https://doi.org/10.3390/en13061525 - 24 Mar 2020
Cited by 7 | Viewed by 4560
Abstract
This paper advances a practical tool for production forecasting, using a 2-segment Decline Curve Analysis (DCA) method, based on an analytical flow-cell model for multi-stage fractured shale wells. The flow-cell model uses a type well and can forecast the production rate and estimated [...] Read more.
This paper advances a practical tool for production forecasting, using a 2-segment Decline Curve Analysis (DCA) method, based on an analytical flow-cell model for multi-stage fractured shale wells. The flow-cell model uses a type well and can forecast the production rate and estimated ultimate recovery (EUR) of newly planned wells, accounting for changes in completion design (fracture spacing, height, half-length), total well length, and well spacing. The basic equations for the flow-cell model have been derived in two earlier papers, the first one dedicated to well forecasts with fracture down-spacing, the second one to well performance forecasts when inter-well spacing changes (and for wells drilled at different times, to account for parent-child well interaction). The present paper provides a practical workflow, introduces correction parameters to account for acreage quality and fracture treatment quality. Further adjustments to the flow-cell model based 2-segment DCA method are made after history matching field data and numerical reservoir simulations, which indicate that terminal decline is not exponential (b = 0) but hyperbolic (with 0 < b< 1). The timing for the onset of boundary dominated flow was also better constrained, using inputs from a reservoir simulator. The new 2-segment DCA method is applied to real field data from the Eagle Ford Formation. Among the major insights of our analyses are: (1) fracture down-spacing does not increase the long-term EUR, and (2) fracture down-spacing of real wells does not result in the rate increases predicted by either the flow-cell model based 2-segment DCA (or its matching reservoir simulations) with the assumed perfect fractures in the down-spaced well models. Our conclusion is that real wells with down-spaced fracture clusters, involving up to 5000 perforations, are unlikely to develop successful hydraulic fractures from each cluster. The fracture treatment quality factor (TQF) or failure rate (1-TQF) can be estimated by comparing the actual well performance with the well forecast based on the ideal well model (albeit flow-cell model or reservoir model, both history-matched on the type curve). Full article
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Figure 1
<p>(<b>a</b>) Principle sketch of flow cells in single well. Blue lines are streamlines in flow cell between two sub-parallel hydraulic fractures. (<b>b</b>) Three flow cells in a partial section of a single horizontal well. Dashed lines are flow separation surface. Yellow dots represent stagnation points. <span class="html-italic">D</span> is fracture spacing, <span class="html-italic">H</span> pay height, and <span class="html-italic">L</span> hydraulic fracture length. After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>].</p>
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<p>(<b>a</b>) Principle sketch of flow separation surfaces between closely spaced, horizontal wells. Vertical fractures are transverse to the wellbore. Flow separation surfaces will also occur between individual fracture planes. Liquid migration from the matrix will approach bi-linear flow near the hydraulic factures. Flow in the fracture planes will be radial toward the perforation zones of the wellbore. (<b>b</b>) Flow separation surfaces can be distinguished into Interwell Drainage Boundaries (IDB), Transverse Flow Separation Boundaries (TFSB), and Longitudinal Flow Separation Boundaries (LFSB). After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>].</p>
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<p>Workflow for optimizing fracture spacing and well spacing given particular acreage dimensions. Process can be repeated for selected type wells to determine the optimized net present value (NPV) for the entire field. After [<a href="#B6-energies-13-01525" class="html-bibr">6</a>].</p>
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<p>Long-term production forecasts. (<b>a</b>) Monthly production rates for H1 well for 44 months data (H1production) with corresponding forecasts from a simple regression (H1_Regression), 3-segment decline curve (3-Seg_H1) and CMG reservoir simulation (H1_CMG). (<b>b</b>) Cumulative production for H1 well for 44 months data with corresponding cumulative forecasts from a simple regression, 3-segment decline curve and CMG reservoir simulation (same legends as in (<b>a</b>)).</p>
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<p>Short-term production forecasts. (<b>a</b>) Enhanced view of monthly production rates for H1 well for 44 months data (H1production) with corresponding forecasts from a simple regression (H1_Regression), 3-segment decline curve (3-Seg_H1) and CMG reservoir simulation (H1_CMG). (<b>b</b>) Enhanced view of cumulative production for H1 well for 44 months data with corresponding cumulative forecasts from a simple regression, 3-segment decline curve and CMG reservoir simulation (same legends as in (<b>a</b>)).</p>
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<p>Comparison of wells H1, H2, and H3 cumulative production data with long-term production forecasts from 1) Arps regressions (using both 6 months and 17 months production data), 2) Analytical flow-cell model, 3) 3-segment Decline Curve and 4) CMG model (for H1 and H3 wells only). Legend explained as follows (each box represents curves modeled for a single well): <b>H1production</b>—44 month H1 well production, <b>H1_Regression</b>—Arps regression on 44 months historical data, <b>3-Seg_H1</b>—3 segment decline curve based on 44 months production data, <b>H1_CMG</b> —production forecast for H1 from CMG reservoir simulation, <b>H2production</b>—H2 6 month production, <b>H2prod_17mth</b>—H2 production data updated to 17 months, <b>H2_Flowcell</b>—production forecast from flow cell model based on H1 44 month regression fit, <b>3-Seg_H2</b>—3 segment decline based on 17 month H2 production, <b>6mth_H2_Reg</b>—Arps regression on 6 month H2 production data, <b>17mth_H2_Reg</b>—Arps regression on 17 month H2 production data, <b>H3production</b>—H3 6 month production, <b>H3prod_17mth</b>—H3 production data updated to 17 months, <b>H3_Flowcell</b>—production forecast from flow cell model based on H1 44 month regression fit, <b>3-Seg_H3</b>—3 segment decline based on 17 month H3 production, <b>6mth_H3_Reg</b>—Arps regression on 6 month H3 production data, <b>17mth_H3_Reg</b>—Arps regression on 17 month H3 production data, <b>H3_CMG</b>—production forecast for H3 from CMG reservoir simulation.</p>
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<p>Enhanced view of the short-term comparisons of wells H1, H2, and H3 cumulative production data with short-term production forecasts from 1) Arps regressions (using both 6 months and 17 months production data), 2) Analytical flow-cell model, 3) 3-segment Decline Curve and 4) CMG model (for H1 and H3 wells only). Legend description follows from <a href="#energies-13-01525-f006" class="html-fig">Figure 6</a>.</p>
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<p>Long-term production forecasts for monthly production rates of Wells H1, H2, and H3, from (1) Arps regressions (using both 6 months and 17 months production data), (2) analytical flow-cell model, (3) 3-segment Decline Curve, and (4) CMG model (for H1 and H3 wells only).</p>
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<p>Enhanced view of short-term production rate forecasts for Wells H1, H2, and H3 monthly production data with forecasts from (1) Arps regressions (using both 6 months and 17 months production data), (2) analytical flow-cell model, (3) 3-segment Decline Curve, and (4) CMG model (for Wells H1 and H3 only).</p>
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<p>(<b>a</b>) Monthly production for H2 well with corresponding forecasts from different methods and the introduction of an adjusted flow-cell forecast making use of the fracture treatment quality factor (TQF) (H2_Flowcell adjusted). (<b>b</b>) Cumulative production for H2 well with corresponding forecasts from different methods and the introduction of an adjusted flow-cell forecast making use of the well TQF (H2_flow-cell adjusted).</p>
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<p>(<b>a</b>) Three-dimensional (3D) numerical model and the gridding built to generate rate data for well to type well (W/TW) cases. The model presents the pressure distribution at the final time step (30 years) of the constant pressure production simulation for W/TW = 1.0 case. (<b>b</b>) Map view of the numerical reservoir model used for the W/TW = 1.0 case. After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>].</p>
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<p>Numerical (KAPPA) model forecasts. (<b>a</b>) Cumulative production, and (<b>b</b>) log rate versus time plot, for W/TW = 1.0 to W/TW = 0.3 cases. After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>].</p>
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<p>Kick-off times due to onset of well interference for different well spacing using (1) depth of investigation (DOI) equation from wellbore, (2) DOI formula from hydraulic fracture tip, and (3) KAPPA simulation model.</p>
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<p>Flow-cell based forecasts. (<b>a</b>) Cumulative production, and (<b>b</b>) log rate versus time plot, for W/TW = 1.0 to W/TW = 0.3 cases.</p>
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<p>Flow-cell based forecasts. (<b>a</b>) Cumulative production, and (<b>b</b>) log rate versus time plot, for W/TW = 1.0 to W/TW = 0.3 cases.</p>
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<p>Log-log rate versus time plots for various well spacing with W/TW ranging between 1.0 to 0.3. After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>].</p>
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<p>Analysis of <span class="html-italic">b</span>-sigmoids in log-log rate versus time plots. (<b>a</b>) W/TW = 1.0, (<b>b</b>) W/TW = 0.7. After [<a href="#B7-energies-13-01525" class="html-bibr">7</a>]. For further details on <span class="html-italic">b</span>-sigmoids, see [<a href="#B17-energies-13-01525" class="html-bibr">17</a>].</p>
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<p>Screen capture of the “Introduction” tab.</p>
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<p>Screen capture of “Type well DCA fitting” tab to obtain forecast parameter to match type well production data.</p>
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<p>Screen capture of the cells requiring user input in the “Down-spacing ratio” tab with additional columns showing down-spacing well values alongside type well values.</p>
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<p>Input cells for reservoir properties in the “Down-spacing ratio” tab.</p>
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<p>(<b>a</b>) Daily production rate from different well spacing, (<b>b</b>) cumulative production from different well spacing. ‘TW’ represents type well production with no impact from well interference.</p>
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<p>Correlation of b-value match for secondary hyperbolic decline with corresponding kick-off times due to differing well spacing.</p>
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<p>Correlation of b-value match for secondary hyperbolic decline with corresponding well spacing ratio.</p>
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22 pages, 18280 KiB  
Article
Modeling and Analysis of a Deflection Type Permanent Magnet Synchronous Wind Generator System
by Weichao Dong, Zheng Li, Hexu Sun and Jingxuan Zhang
Energies 2020, 13(6), 1524; https://doi.org/10.3390/en13061524 - 24 Mar 2020
Cited by 4 | Viewed by 3289
Abstract
A novel type of multi-degree-of-freedom (multi-DOF) deflecting-type permanent-magnet synchronous wind generator (PMSWG) is constructed to improve the reliability and utilization of wind energy. The basic working principle of the multi-DOF deflecting-type permanent-magnet synchronous generator (PMSG) is introduced, and its structural size is experimentally [...] Read more.
A novel type of multi-degree-of-freedom (multi-DOF) deflecting-type permanent-magnet synchronous wind generator (PMSWG) is constructed to improve the reliability and utilization of wind energy. The basic working principle of the multi-DOF deflecting-type permanent-magnet synchronous generator (PMSG) is introduced, and its structural size is experimentally and theoretically determined. Subsequently, the multi-DOF deflecting-type PMSG was used to operate a complete wind turbine. A prototype and three-dimensional (3D) model of the wind turbine is simulated, allowing one to analyze the aerodynamics of the turbine and power generation performance. The electromagnetic field analysis is performed via analytical methods, followed by a 3D finite element and torque analyses. Furthermore, the wind turbine power generation characteristics curves are obtained through simulation software. Finally, transient analysis of post deflection is demonstrated. The before and after deflection values of the generator voltage, current, flux linkage, and induced voltage are compared and analyzed, relying on simulations and experiments. Additionally, the wind tunnel experiment is used to compare voltage variation with wind direction. The comparison reveals that the wind generator phase voltage remains maximized with wind direction variation. The results confirm that the proposed PMSWG has excellent performance and future research potential. Full article
(This article belongs to the Special Issue Electric Machines and Drive Systems for Emerging Applications)
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Graphical abstract
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<p>Basic structure of the generator.</p>
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<p>Region division of the permanent magnet.</p>
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<p>Three-dimensional distribution plot of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">B</mi> <mi mathvariant="normal">r</mi> </msub> </mrow> </semantics></math> obtained through the analytical method.</p>
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<p>Finite element solution model of the generator.</p>
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<p>Pole magnetic field distribution of the generator.</p>
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<p>Magnetic dense cloud plot of the rotor.</p>
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<p>Three-dimensional distribution plot of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">B</mi> <mi mathvariant="normal">r</mi> </msub> </mrow> </semantics></math> obtained through the finite element analysis.</p>
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<p>Air-gap flux density comparison diagram for θ = 90°.</p>
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<p>Transient rotation torque.</p>
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<p>Three-dimensional model of the wind turbine.</p>
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<p>Wind wheel model: (<b>a</b>) two-dimensional structural model of the double-layer wind wheel; (<b>b</b>) 3D structural model of the one-layer wind wheel.</p>
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<p>Boundary condition setting.</p>
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<p>Schematic diagram of meshing.</p>
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<p>Tip speed ratio–wind energy utilization coefficient curve.</p>
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<p>Torque coefficient comparison curves of the three blades.</p>
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<p>Torque coefficient comparison curves of single- and double-layer wind turbines.</p>
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<p>Stagger angles of two wind wheel layers. (<b>a</b>) Top view of different stagger angles. (<b>b</b>) Three-dimensional model of different stagger angles.</p>
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<p>Torque coefficient comparison curves at different stagger angles.</p>
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<p>Wind turbine deflection modes.</p>
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<p>Comparison results of wind energy utilization at different deflecting angles.</p>
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<p>Rotor eccentric motion diagram; (<b>a</b>) Without eccentricity; (<b>b</b>) With eccentricity.</p>
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<p>Projection at the center of the rotor in any plane.</p>
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<p>Voltage comparison diagram of phase A winding in three states.</p>
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<p>Current comparison diagram of phase A winding in three states.</p>
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<p>Magnetic linkage comparison diagram of phase A winding in three states.</p>
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<p>Induced voltage comparison diagram of phase A winding in three states.</p>
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<p>Experimental setup for the generator.</p>
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<p>Two-dimensional model of the stator winding scheme.</p>
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<p>Three-dimensional model of the rotor.</p>
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<p>Three-dimensional model of the stator.</p>
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<p>Phase voltage comparison diagram of phase A winding in three states, based on the experiment.</p>
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<p>Induced voltage comparison diagram of phase A winding in three states based on the experiment.</p>
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<p>Wind turbine prototype.</p>
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<p>Generator power generation characteristic curve based on the experiment; (<b>a</b>) Generator speed curve; (<b>b</b>) Generator current curve; (<b>c</b>) Generator torque curve.</p>
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<p>Voltage comparison curves post deflection.</p>
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<p>Comparison of power curves post deflection.</p>
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<p>Comparison of efficiency curves post deflection.</p>
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27 pages, 2622 KiB  
Article
Sargassum Inundations in Turks and Caicos: Methane Potential and Proximate, Ultimate, Lipid, Amino Acid, Metal and Metalloid Analyses
by John James Milledge, Supattra Maneein, Elena Arribas López and Debbie Bartlett
Energies 2020, 13(6), 1523; https://doi.org/10.3390/en13061523 - 23 Mar 2020
Cited by 80 | Viewed by 9517
Abstract
The Caribbean has been experiencing beach inundations of pelagic Sargassum, causing environmental, health and financial issues. This study showed variations in the composition and methane potential (MP) between the species of Sargassum. The MPs for S. natans VIII, S. natans I and [...] Read more.
The Caribbean has been experiencing beach inundations of pelagic Sargassum, causing environmental, health and financial issues. This study showed variations in the composition and methane potential (MP) between the species of Sargassum. The MPs for S. natans VIII, S. natans I and S. fluitans (145, 66 and 113 mL CH4 g−1 Volatile Solids) were considerably below theoretical potentials, possibly due to the high levels of indigestible fibre and inhibitors. The mixed mats Sargassum composition was substantially different from the individual species, being higher in ash, calcium, iron, arsenic and phenolics. The mixed mats produced no methane, perhaps due to the high levels of phenolics. There was a strong correlation between MP and phenolic content. Heavy metals and metalloids were at levels that should not cause concern, except for arsenic (21–124 mg kg−1 dry weight). Further work on the speciation of arsenic in Sargassum is required to fully determine the risk to health and agriculture. Both protein and lipid levels were low. The ‘indispensable amino acid’ profile compares favourably with that recommended by the World Health Organisation. Lipids had a high proportion of Polyunsaturated Fatty Acids. The use of Sargassum for biogas production could be challenging, and further work is required. Full article
(This article belongs to the Special Issue Algal Biotechnology and Biofuels)
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<p>Sample Identification sheet used to identify and separate the three dominant species of Sargassum (<span class="html-italic">S. natans VIII</span> (B), <span class="html-italic">S. natans I</span> (C) and <span class="html-italic">S. fluitans</span> (D)).</p>
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<p>CJC biomethane potential system (courtesy CJC labs).</p>
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<p>Calculated HHV based on the protein, lipid and carbohydrate (including fibre) content [<a href="#B56-energies-13-01523" class="html-bibr">56</a>] and average measured data.</p>
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<p>Net mean methane production from fresh and freeze-dried ‘Mixed Sargassum’ inundation samples from Turks and Caicos (<span class="html-italic">n</span> = 4, error bars are standard deviation).</p>
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<p>Net mean methane production from the three pelagic Sargassum species samples from Turks and Caicos (<span class="html-italic">n</span> = 4, error bars are standard deviation).</p>
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<p>The plot of the predicted yield of methane for the combined <span class="html-italic">S. natans VIII, S. natans I</span> and <span class="html-italic">S. fluitans</span> (based on the previous experimental results for each species) and the actual methane yield.</p>
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12 pages, 2386 KiB  
Article
Numerical Simulation of Sulfur Deposit with Particle Release
by Zhongyi Xu, Shaohua Gu, Daqian Zeng, Bing Sun and Liang Xue
Energies 2020, 13(6), 1522; https://doi.org/10.3390/en13061522 - 23 Mar 2020
Cited by 5 | Viewed by 2593
Abstract
Sulfur deposition commonly occurs during the development of a high-sulfur gas reservoirs. Due to the high gas flow velocity near the wellbore, some of the deposited sulfur particles re-enter the pores and continue to migrate driven by the high-speed gas flow. The current [...] Read more.
Sulfur deposition commonly occurs during the development of a high-sulfur gas reservoirs. Due to the high gas flow velocity near the wellbore, some of the deposited sulfur particles re-enter the pores and continue to migrate driven by the high-speed gas flow. The current mathematical model for sulfur deposition ignores the viscosity between particles, rising flow caused by turbulence, and the corresponding research on the release ratio of particles. In order to solve the above problems, firstly, the viscous force and rising force caused by turbulence disturbance are introduced, and the critical release velocity of sulfur particles is derived. Then, a release model of sulfur particles that consider the critical release velocity and release ratio is proposed by combining the probability theory with the hydrodynamics theory. Notably, based on the experimental data, the deposition ratio of sulfur particles and the damage coefficient in the sulfur damage model are determined. Finally, a comprehensive particle migration model considering the deposition and release of sulfur particles is established. The model is then applied to the actual gas wells with visible sulfur deposition that target the Da-wan gas reservoir, and the results show that the model correctly reflects flow transport during the process of sulfur deposition in porous media. In addition, through the numerical simulation experiments, it was found that considering the release of sulfur particles reduces the saturation of sulfur particles within a specific range around the well and improve the reservoir permeability in this range. From the perspective of gas production rate, the release of sulfur particles has a limited effect on the gas production rate, which is mainly due to the sulfur particle release being limited, having only a 5 m range near the wellbore area, and thus the amount of gas flow from the unaffected area is basically unchanged. Full article
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<p>Sulfur solubility curve.</p>
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<p>Sulfur release due to turbulence in a gas system.</p>
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<p>Deposition rate of sulfur particles with different diameters at different gas mass flow rates.</p>
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<p>Sulfur saturation compared to dimensionless permeability used to calculate the reservoir damage.</p>
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<p>Comparison of the calculated data and observed well data.</p>
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<p>Comparison of deposited sulfur saturation across the simulated reservoir.</p>
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<p>Comparison of permeability across the simulated reservoir.</p>
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<p>Gas production rate.</p>
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<p>Accumulated gas production.</p>
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24 pages, 4261 KiB  
Article
Coal Demand and Environmental Regulations: A Case Study of the Polish Power Sector
by Przemysław Kaszyński and Jacek Kamiński
Energies 2020, 13(6), 1521; https://doi.org/10.3390/en13061521 - 23 Mar 2020
Cited by 26 | Viewed by 3487
Abstract
The impact of environmental regulations implemented in the power industry that affect the consumption of solid fuels is of key importance to coal-based power generation systems, such as that in Poland. In this context, the main purpose of the paper was to determine [...] Read more.
The impact of environmental regulations implemented in the power industry that affect the consumption of solid fuels is of key importance to coal-based power generation systems, such as that in Poland. In this context, the main purpose of the paper was to determine the future demand for hard coal and brown coal in the Polish power sector by 2050 with reference to the environmental regulations implemented in the power sector. To achieve these goals, a mathematical model was developed using the linear programming approach, which reflected the key relationships between the hard and brown coal mining sector and the power sector in the context of the environmental regulations discussed. The environmental regulations selected had a great influence on the future demand for hard and brown coal in the power generation sector. The scope of this influence depended on particular regulations. The prices of CO2 emission allowances and stricter emissions standards stemming from the Industrial Emissions Directive and the BAT (Best Available Techniques) conclusions had the largest influence on the reduction of hard coal demand. In the case of brown coal, no new power generating units would be deployed; hence, brown coal consumption would drop practically to zero in 2050 under all the scenarios considered. Full article
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<p>Simplified structure of the optimization model.</p>
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<p>Gross electricity demand forecast until 2050.</p>
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<p>Peak power demand forecast until 2050 (Source: Own elaboration based on [<a href="#B92-energies-13-01521" class="html-bibr">92</a>,<a href="#B93-energies-13-01521" class="html-bibr">93</a>]).</p>
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<p>Energy carrier price forecasts until 2050 (Source: Own estimates based on [<a href="#B94-energies-13-01521" class="html-bibr">94</a>,<a href="#B95-energies-13-01521" class="html-bibr">95</a>]).</p>
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<p>Maximum brown coal supply forecast until 2050 (Source: Own estimates based on [<a href="#B96-energies-13-01521" class="html-bibr">96</a>]).</p>
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<p>Maximum renewable energy sources (RES) primary energy supply potential until 2050 (Source: Own estimates based on [<a href="#B97-energies-13-01521" class="html-bibr">97</a>,<a href="#B98-energies-13-01521" class="html-bibr">98</a>,<a href="#B99-energies-13-01521" class="html-bibr">99</a>]).</p>
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<p>CO<sub>2</sub> emission allowance price forecasts until 2050 (Source: Estimates based on [<a href="#B95-energies-13-01521" class="html-bibr">95</a>]).</p>
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<p>The total share of electricity production from coal (hard coal and brown coal) for the research scenarios until 2050, %.</p>
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<p>Fuel mix in electricity generation in 2017–2050 for the research scenarios analyzed: (<b>a</b>) REF, (<b>b</b>) RES-30%, (<b>c</b>) HighEUA, (<b>d</b>) Decom-BAT, (<b>e</b>) WindPot-100%, (<b>f</b>) HighEnEff, TWh.</p>
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<p>Fuel mix in electricity generation in 2017–2050 for the research scenarios analyzed: (<b>a</b>) REF, (<b>b</b>) RES-30%, (<b>c</b>) HighEUA, (<b>d</b>) Decom-BAT, (<b>e</b>) WindPot-100%, (<b>f</b>) HighEnEff, TWh.</p>
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<p>Hard coal demand for electricity generation in the public power sector until 2050 for research scenarios, mln Mg.</p>
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<p>Differences in total hard coal demand for electricity generation until 2050 in relation to the REF scenario, mln Mg.</p>
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<p>Percentage comparison of total hard coal demand until 2050 with the REF = 100% scenario.</p>
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<p>Brown coal demand for electricity generation in the public power sector until 2050 for research scenarios, mln Mg.</p>
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<p>Differences in relation to the REF scenario in total brown coal demand for electricity generation, mln Mg.</p>
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<p>Percentage comparison of total brown coal demand with the REF = 100% scenario.</p>
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15 pages, 7025 KiB  
Article
A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point
by Guo Xinming, Huo Qunhai, Wei Tongzhen and Yin Jingyuan
Energies 2020, 13(6), 1520; https://doi.org/10.3390/en13061520 - 23 Mar 2020
Cited by 12 | Viewed by 2662
Abstract
This paper proposes a local control strategy applied in the soft open point (SOP) to suppress voltage fluctuation when adding a renewable energy source into the system. The mathematic model of the grid connected to SOP is established based on the characteristics of [...] Read more.
This paper proposes a local control strategy applied in the soft open point (SOP) to suppress voltage fluctuation when adding a renewable energy source into the system. The mathematic model of the grid connected to SOP is established based on the characteristics of a low-voltage distribution network. Combined with the mathematic model and local voltage information, the local control strategy is proposed to optimize the active and reactive power distribution and consume the minimum apparent power of the converter. The local control strategy can effectively suppress the voltage fluctuation caused by renewable energy access, which was testified by MATLAB/Simulink simulation. In addition, the local control strategy can deduce the communication resource and increase the response speed compared to global optimization. This paper is meaningful for renewable energy source distribution and voltage balance in low-voltage distribution systems. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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<p>A typical application of soft open point.</p>
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<p>Three-terminal soft open point.</p>
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<p>Soft open point access network.</p>
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<p>Inner loop control strategy of soft open point.</p>
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<p>Outer-loop control strategy of SOP: (<b>a</b>) Active power regulation to DC voltage; (<b>b</b>) Reactive power regulation to AC voltage; (<b>c</b>) mixed regulation to AC voltage.</p>
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<p>Outer-loop control strategy of SOP: (<b>a</b>) Active power regulation to DC voltage; (<b>b</b>) Reactive power regulation to AC voltage; (<b>c</b>) mixed regulation to AC voltage.</p>
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<p>Principle of minimum apparent power.</p>
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<p>Power delivery based on minimum apparent power control.</p>
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<p>Transmission power boundary of SOP.</p>
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<p>Local control strategy of SOP.</p>
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<p>Photovoltaic active power fluctuation.</p>
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<p>Effect of different compensation modes for SOP: (<b>a</b>) Voltage at the end of feeder 1; (<b>b</b>) Voltage at the end of feeder 2; (<b>c</b>) Apparent power consumption.</p>
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<p>Effect of different compensation modes for SOP: (<b>a</b>) Voltage at the end of feeder 1; (<b>b</b>) Voltage at the end of feeder 2; (<b>c</b>) Apparent power consumption.</p>
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<p>Feeder node voltage in mode 1: (<b>a</b>) SOP is uncommitted into operation; (<b>b</b>) Soft open point is committed into operation.</p>
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<p>Feeder node voltage in mode 1: (<b>a</b>) SOP is uncommitted into operation; (<b>b</b>) Soft open point is committed into operation.</p>
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<p>Feeder node voltage in mode 2: (<b>a</b>) SOP is uncommitted to operation; (<b>b</b>) Soft open point is committed to operation.</p>
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15 pages, 2438 KiB  
Article
Mapping of the Temperature–Entropy Diagrams of van der Waals Fluids
by Attila R. Imre, Réka Kustán and Axel Groniewsky
Energies 2020, 13(6), 1519; https://doi.org/10.3390/en13061519 - 23 Mar 2020
Cited by 7 | Viewed by 3048
Abstract
The shape of the temperature vs. specific entropy diagram of a working fluid is very important to understanding the behavior of fluid during the expansion phase of the organic Rankine cycle or similar processes. Traditional wet-dry-isentropic classifications of these materials are not sufficient; [...] Read more.
The shape of the temperature vs. specific entropy diagram of a working fluid is very important to understanding the behavior of fluid during the expansion phase of the organic Rankine cycle or similar processes. Traditional wet-dry-isentropic classifications of these materials are not sufficient; several materials remain unclassified or misclassified, while materials listed in the same class might show crucial differences. A novel classification, based on the characteristic points of the T–s diagrams was introduced recently, listing eight different classes. In this paper, we present a map of these classes for a model material, namely, the van der Waals fluid in reduced temperature (i.e., reduced molecular degree of freedom) space; the latter quantity is related to the molar isochoric specific heat. Although van der Waals fluid cannot be used to predict material properties quantitatively, the model gives a very good and proper qualitative description. Using this map, some peculiarities related to Ts diagrams of working fluids can be understood. Full article
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<p>Schematic temperature–specific entropy (<span class="html-italic">T</span>–<span class="html-italic">s</span>) diagrams of two well-distinguishable dry working fluid subclasses (<b>a</b>,<b>b</b>) with a previously unclassifiable type (<b>c</b>). Some relevant expansion and compression routes are shown by arrows; the importance of these routes are described in the text.</p>
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<p>Temperature–specific entropy (<span class="html-italic">T</span>–<span class="html-italic">s</span>) diagram of butane, showing the classification in the full fluid-range (from the triple point to the critical point) as well as the classification (and related characteristic points) in various confined temperature ranges (colored arrows and letters). The blue dotted line shows ambient temperature (15 °C). <span class="html-italic">T</span>–<span class="html-italic">s</span> data taken from the NIST Webbook [<a href="#B13-energies-13-01519" class="html-bibr">13</a>].</p>
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<p>The potential changes in the classes by changing the lower end-point temperature. Color codes are identical with the colors of <a href="#energies-13-01519-f002" class="html-fig">Figure 2</a> and <a href="#energies-13-01519-f004" class="html-fig">Figure 4</a>.</p>
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<p>Reduced temperature vs. molecular degree of freedom for simple, one-component van der Waals fluids. Three-letter, four-letter, and five-letter parts show various wet, dry, and real isentropic subclasses.</p>
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<p>Magnified part of <a href="#energies-13-01519-f004" class="html-fig">Figure 4</a> showing the multiple points of the map.</p>
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<p>Temperature-dependent classification scheme for various working fluid classes.</p>
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<p>Temperature-dependent classification scheme for various working fluid classes.</p>
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<p>Correlation curves (<span class="html-italic">df</span> vs. <span class="html-italic">T</span><sub>r</sub>) for the different borders of the maps shown in <a href="#energies-13-01519-f004" class="html-fig">Figure 4</a>.</p>
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17 pages, 4706 KiB  
Article
Charging and Discharge Currents in Low-Density Polyethylene and its Nanocomposite
by Anh T. Hoang, Yuriy V. Serdyuk and Stanislaw M. Gubanski
Energies 2020, 13(6), 1518; https://doi.org/10.3390/en13061518 - 23 Mar 2020
Cited by 5 | Viewed by 3013
Abstract
Charging and discharge currents measured in low-density polyethylene (LDPE) and LDPE/Al2O3 nanocomposite are analyzed. The experiments were conducted at temperatures of 40–80 °C utilizing a consecutive charging–discharging procedure, with the charging step at electric fields varying between 20 and 60 [...] Read more.
Charging and discharge currents measured in low-density polyethylene (LDPE) and LDPE/Al2O3 nanocomposite are analyzed. The experiments were conducted at temperatures of 40–80 °C utilizing a consecutive charging–discharging procedure, with the charging step at electric fields varying between 20 and 60 kV/mm. A quasi-steady state of the charging currents was earlier observed for the nanofilled specimens and it was attributed to the enhanced trapping process at polymer–nanofiller interfaces. An anomalous behavior of the discharge currents was found at elevated temperatures for both the studied materials and its occurrence at lower temperatures in the nanofilled LDPE was due to the presence of deeply trapped charges at polymer–nanofiller interfaces. The field dependence of the quasi-steady charging currents is examined by testing for different conduction mechanisms. It is shown that the space-charge-limited process is dominant and the average trap site separation is estimated at less than 2 nm for the pristine LDPE and it is at about 5–7 nm for the LDPE/Al2O3 nanocomposite. Also, location of the trapping sites in the band gap structure of the nanofilled material is altered, which substantially weakens electrical transport as compared to the unfilled counterpart. Full article
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Graphical abstract

Graphical abstract
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<p>Time dependencies of charging current densities measured at various electric fields (in kV/mm) indicated in the legend; (<b>a</b>) low-density polyethylene (LDPE), 60 °C and (<b>b</b>) LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite, 80 °C. Arrows show the variation with the increasing field strength.</p>
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<p>Temperature dependencies of the isochronal current densities recorded at 3 h (1.08 × 10<sup>4</sup> s) for (<b>a</b>) LDPE and (<b>b</b>) LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite at different applied electric fields (in kV/mm) - markers are the measured points, whereas lines represent the fitting. Arrows show the variation with the increasing field strength.</p>
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<p>Characteristics of isochronal current densities versus applied electric field for LDPE and LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite (denoted respectively as PE and NC) at various temperatures. Markers are the measured points, whereas lines represent the fittings.</p>
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<p>Time dependencies of discharge current densities measured after charging at the indicated electric field levels (in kV/mm); (<b>a</b>) LDPE, 60 °C and (<b>b</b>) LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite, 80 °C. Arrows show the variation of the currents with the increasing field strength.</p>
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<p>Plots of <span class="html-italic">I</span> × <span class="html-italic">t</span> vs. log(<span class="html-italic">t</span>) for discharge currents measured in pure LDPE at 60 °C. The inset shows the field dependence of the peak values <span class="html-italic">I</span> × <span class="html-italic">t</span>.</p>
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<p>(<b>a</b>) Schottky plots (field-dependent currents) and (<b>b</b>) Poole–Frenkel plots (field-dependent conductivities) for reference LDPE and LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite at various temperatures. Markers are measured results and lines are introduced for fitting.</p>
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<p>Plots of <span class="html-italic">J</span> vs. <span class="html-italic">E</span> for pure LDPE and LDPE/Al<sub>2</sub>O<sub>3</sub> 3 wt% nanocomposite according to the Nath et al. model [<a href="#B26-energies-13-01518" class="html-bibr">26</a>]. Markers are measured results and lines show the fitting.</p>
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<p>Schematic representation of charge generation and transport of the model used in present study; (<b>a</b>) pure LDPE and (<b>b</b>) nanofilled LDPE. Symbols <span class="html-italic">T<sub>e,h</sub></span> denote trapping, <span class="html-italic">DT<sub>e,h</sub></span> — de-trapping, and <span class="html-italic">R<sub>eh,ehtr,etrh,etrhtr</sub></span> — recombination processes.</p>
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<p>Schematic representation of charge generation and transport of the model used in present study; (<b>a</b>) pure LDPE and (<b>b</b>) nanofilled LDPE. Symbols <span class="html-italic">T<sub>e,h</sub></span> denote trapping, <span class="html-italic">DT<sub>e,h</sub></span> — de-trapping, and <span class="html-italic">R<sub>eh,ehtr,etrh,etrhtr</sub></span> — recombination processes.</p>
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<p>(<b>a</b>) Simulated discharge currents in studied materials at different temperatures. The inset displays the magnification of the plot including discharge currents at 60 °C. (<b>b</b>) Magnitude of simulated positive and negative current components in studied materials at 60 °C. Arrows indicate the reduction in magnitude of the current components due to the suppression of electrical conduction in the nanocomposite.</p>
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<p>Temporal distributions of simulated electric field in pure LDPE during discharging stage at 80 °C: <span class="html-italic">z</span><sub>1</sub> and <span class="html-italic">z</span><sub>2</sub> are the zero-field points. The inset and its arrows show the temporal displacement of the left zero field point (<span class="html-italic">z</span><sub>1</sub>).</p>
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21 pages, 5926 KiB  
Article
Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems
by Ana Carolina do Amaral Burghi, Tobias Hirsch and Robert Pitz-Paal
Energies 2020, 13(6), 1517; https://doi.org/10.3390/en13061517 - 23 Mar 2020
Cited by 7 | Viewed by 4084
Abstract
Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential [...] Read more.
Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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<p>ALFRED (artificial learning flexible renewable energy system dispatch optimizer) dispatch planning tool scheme: partitioned strategy of optimization and uncertainty treatment.</p>
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<p>Electricity price profile for two days, with red labels of hourly prioritization.</p>
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<p>Uncertainty post-processing (UPP) development scheme.</p>
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<p>Classes’ definition for the concentrated solar power (CSP) plant example case. The classes of <span class="html-italic">deviation from persistence</span>, <span class="html-italic">hour priority</span> and <span class="html-italic">deviation from perfect</span> are determined by unsupervised learning, while the ones of <span class="html-italic">day of the year</span> are predetermined, based on the meteorological seasons.</p>
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<p>Final decision tree for the CSP plant application. In green, the branch used as an example for the pruning process, outlined in <a href="#energies-13-01517-f006" class="html-fig">Figure 6</a>. In blue, the branches used as example for the rules extraction explanation.</p>
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<p>Part of a pruned branch of the decision tree for the CSP plant application. The final branch is represented in green in <a href="#energies-13-01517-f005" class="html-fig">Figure 5</a>.</p>
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<p>Uncertainty post-processing (UPP) implementation scheme.</p>
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<p>Fuzzy classification and inference procedure for the given numerical example.</p>
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<p>Forecasted, adjusted and perfect schedule for some example days.</p>
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<p>Data setups used for the simulations, considering different training data (from 1 to 3 years in several combinations) for each one-year simulation, represented as the testing data.</p>
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<p>Mean hourly Spanish market price with standard deviation for the years 2014 to 2017 [<a href="#B32-energies-13-01517" class="html-bibr">32</a>].</p>
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<p>Annual financial income based on the optimized dispatching strategy without the UPP application, considering 0% (<b>a</b>), 50% (<b>b</b>) and 100% (<b>c</b>) penalty of the market price when scheduled electricity is not delivered. Persistence, perfect and product forecasts are considered as input. The rate of improvement with reference to the persistence forecast case is plotted.</p>
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<p>Annual financial income improvement rate in comparison to the persistence case for the simulated years with the forecast product as input. Red bars refer to the data presented also in red in <a href="#energies-13-01517-f012" class="html-fig">Figure 12</a>c. The optimized dispatching strategy is applied with and without the UPP, taking different training data sets into account, as explained in <a href="#energies-13-01517-f010" class="html-fig">Figure 10</a>.</p>
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<p>Total annual electricity delivered to the grid (<b>a</b>), scheduled but not delivered (<b>b</b>) and thermal dumped energy (<b>c</b>) for the simulated years with optimized approach.</p>
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14 pages, 1170 KiB  
Article
Energy Evaluation and Greenhouse Gas Emissions of Reed Plant Pelletizing and Utilization as Solid Biofuel
by Algirdas Jasinskas, Dionizas Streikus, Egidijus Šarauskis, Mečys Palšauskas and Kęstutis Venslauskas
Energies 2020, 13(6), 1516; https://doi.org/10.3390/en13061516 - 23 Mar 2020
Cited by 12 | Viewed by 3325
Abstract
This paper presents the results of research on the preparation and use for energy purposes of three reed herbaceous energy plants: reed (Phragmites australis) and bulrush (Typha); both grown in local vicinities on lakes and riverbanks and reed canary [...] Read more.
This paper presents the results of research on the preparation and use for energy purposes of three reed herbaceous energy plants: reed (Phragmites australis) and bulrush (Typha); both grown in local vicinities on lakes and riverbanks and reed canary grass (Phalaris arundinacea L.). The physical-mechanical characteristics (density, moisture, and ash content) of chopped and milled reeds were investigated. The investigation of mill fractional compositions determined the largest amount of mill—reed mill, collected on the sieves of 0.63 mm (40.0%). The pellet moisture ranged from 10.79% to 6.32%, while the density was 1178.9 kg m−3 for dry matter (DM) of reed. The ash content of reed, bulrush and reed canary grass pellets was 3.17%, 5.88%, and 7.99%, respectively. The ash melting temperature ranged from 865 to 1411 °C; these temperatures were high enough for ash melting. The determined pellet calorific value varied from 17.4 to 17.9 MJ kg−1 DM. The disintegration force, indicating pellet strength, ranged from 324.25 N for reed canary grass to 549.24 N for reed. The determined emissions of harmful pollutants—CO2, CO, NOx, and unburnt hydrocarbons (CxHy)—did not exceed the maximum permissible levels. The assessment of greenhouse gas emissions (GHG) from technology showed that the CO2 equivalents ranged from 7.3 to 10.1 kg CO2-eq. GJ−1 for reed and reed canary grass, respectively. Full article
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<p>Pellet mechanical strength.</p>
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<p>Density dependence on moisture.</p>
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<p>Dependence of pellet resistance to compression on moisture.</p>
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<p>Global warming potential of reed plant pellets, including biomass extraction, transportation, processing, and final product preparation.</p>
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11 pages, 818 KiB  
Article
Life Cycle Modelling of the Impact of Coal Quality on Emissions from Energy Generation
by Lukasz Lelek and Joanna Kulczycka
Energies 2020, 13(6), 1515; https://doi.org/10.3390/en13061515 - 23 Mar 2020
Cited by 10 | Viewed by 3187
Abstract
This paper presents a model combining the LCA (Life Cycle Assessment) of fossil fuel extraction with its quality parameters and related CO2, SO2 and dust emissions at the stage of the combustion process. The model which was developed aims to [...] Read more.
This paper presents a model combining the LCA (Life Cycle Assessment) of fossil fuel extraction with its quality parameters and related CO2, SO2 and dust emissions at the stage of the combustion process. The model which was developed aims to identify the environmental impact of the processes of electricity production from selected energy carriers over their whole life cycle. The model takes into account the full LCA of fossil fuel extraction (of both hard and brown coal), its enrichment and fuel production as well as the environmental impact associated with emissions introduced into the air at the stage of electricity generation based on the fuels evaluated. Such an approach allows one to determine the fuel quality parameters that affect the environmental impact of energy production based on an LCA of mining and assigns the degree of environmental impact involved in particular production processes. Overall, the results obtained based on the proposed model permit the identification and prioritisation of the individual processes in the electricity generation life cycle which contribute the highest share in the general environmental impact indicator, having taken into account the modelling of the quality of the fuels used (calorific value, ash and sulphur content). Full article
(This article belongs to the Section B: Energy and Environment)
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<p>The structure of the model of the environmental impact assessment of electricity generation processes from selected carriers in Polish conditions (Q<sub>i</sub>—calorific value, W<sub>t</sub>—total water, A—ash content, S<sub>t</sub>—total sulphur content, PC—pulverised coal).</p>
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<p>Algorithm of the procedure for the mining module in the model (LCIA—Life Cycle Impact Assessment, Q<sub>i</sub>—calorific value, A—ash content, S<sub>t</sub>—total sulphur content).</p>
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<p>Algorithm of the procedure for the model’s energy module.</p>
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19 pages, 5528 KiB  
Article
Investigation and Analysis of R463A as an Alternative Refrigerant to R404A with Lower Global Warming Potential
by Piyanut Saengsikhiao, Juntakan Taweekun, Kittinan Maliwan, Somchai Sae-ung and Thanansak Theppaya
Energies 2020, 13(6), 1514; https://doi.org/10.3390/en13061514 - 23 Mar 2020
Cited by 32 | Viewed by 5282
Abstract
This research presents the development of R463A refrigerant, a nonflammable refrigerant that was retrofitted to replace R404A. R463A is primarily composed of hydrofluorocarbons/hydrocarbons/carbon dioxide (HFCs/HCs/CO2), and has global-warming potential (GWP) of 1494. It is a nonazeotropic mixture of R32 (36%), R125 [...] Read more.
This research presents the development of R463A refrigerant, a nonflammable refrigerant that was retrofitted to replace R404A. R463A is primarily composed of hydrofluorocarbons/hydrocarbons/carbon dioxide (HFCs/HCs/CO2), and has global-warming potential (GWP) of 1494. It is a nonazeotropic mixture of R32 (36%), R125 (30%), R134a (14%), R1234yf (14%), and R744 (6%). R463A is composed of polyol ester oil (POE), and it is classified as a Class A1 incombustible and nontoxic refrigerant. R463A has a higher cooling capacity (Qe) than that of R404A, as it is composed of hydrofluorocarbons (HFCs) R32 and carbon dioxide (CO2) R744, and has lower GWP than that of R404A due to the use of hydrofluoroolefins (HFOs) from R1234yf. The results of this research showed that R463A can be retrofitted to replace R404A due to its composition of POE, Class A1 incombustibility, and lower toxicity. The properties of R463A and R404A, as analyzed using national institute of standards and technology (NIST) reference fluid thermodynamic and transport properties database (REFPROP) software and NIST vapor compression cycle model accounting for refrigerant thermodynamic and transport properties (CYCLE_D-HX) software, are in accordance with the CAN/ANSI/AHRI540 standards of the Air-Conditioning, Heating, and Refrigeration Institute (AHRI). The normal boiling point of R463A was found to be higher than that of R404A by 23%, with a higher cooling capacity and a 63% lower GWP value than that of R404A. The critical pressure and temperature of R463A were found to be higher than those of R404A; it can be used in a high-ambient-temperature environment, has higher refrigerant and heat-rejection effects, and has lower GWP than that of R404A by 52% due to the HFOs from the R1234yf component. The cooling coefficient of performance (COPc) of R463A was found to be higher than that of R404A by 10% under low-temperature applications. R463A is another refrigerant option that is composed of 7% carbon dioxide (CO2), and is consistent with the evolution of fourth-generation refrigerants that contain a mixture of HFCs, HFOs, HCs, and natural refrigerants, which are required to produce a low-GWP, zero-ozone-depletion-potential (ODP), high-capacity, low-operating-pressure, and nontoxic refrigerant. Full article
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<p>Proportions of energy use in Taiwanese convenience stores [<a href="#B5-energies-13-01514" class="html-bibr">5</a>].</p>
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<p>Examples of energy savings in refrigeration systems [<a href="#B6-energies-13-01514" class="html-bibr">6</a>].</p>
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<p>Evolution of refrigerants [<a href="#B7-energies-13-01514" class="html-bibr">7</a>].</p>
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<p>Hydrofluorocarbon (HFC) phase-down schedule (Co2e %) [<a href="#B9-energies-13-01514" class="html-bibr">9</a>].</p>
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<p>Refrigerant classification.</p>
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<p>Top refrigerants in food industry [<a href="#B9-energies-13-01514" class="html-bibr">9</a>].</p>
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<p>Properties of R463A obtained from REFPROP.</p>
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<p>Normal boiling point of all refrigerants.</p>
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<p>GWP of all refrigerants.</p>
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<p>Cp liquid/vapor (kJ/kg.K).</p>
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<p>Liquid/vapor conductivity (mW/m.K).</p>
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<p>Refrigerant effects of all refrigerants.</p>
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<p>Heat rejection of all refrigerants.</p>
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<p>Refrigerant work of all refrigerants.</p>
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<p>Evaporator pressure of all refrigerants.</p>
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<p>Condenser pressure of all refrigerants.</p>
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<p>Cooling coefficient of performance (COPc) for all refrigerants.</p>
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12 pages, 1278 KiB  
Article
Reutealis Trisperma Oil Esterification: Optimization and Kinetic Study
by Riky Lim, Deog-Keun Kim and Jin-Suk Lee
Energies 2020, 13(6), 1513; https://doi.org/10.3390/en13061513 - 23 Mar 2020
Cited by 4 | Viewed by 2468
Abstract
Reutealis trisperma, due to its high kernel-oil yield (±50%) and long productivity (±70 years), is considered to be a promising feedstock for biodiesel production. In addition, this plant, which can thrive on marginal lands, is classified as a non-edible oil since it [...] Read more.
Reutealis trisperma, due to its high kernel-oil yield (±50%) and long productivity (±70 years), is considered to be a promising feedstock for biodiesel production. In addition, this plant, which can thrive on marginal lands, is classified as a non-edible oil since it contains a toxin known as eleostearic acid. The present study aimed to optimize the esterification step in biodiesel production from R.trisperma oil catalyzed using sulfonic ion exchange resin Lewatit K2640. The optimization step was performed using a response surface methodology through the incorporation of a central composite design. A kinetic study was performed as well, based on the assumption of a pseudo-homogeneous second-order model. Catalyst loading was found to have the most significant impact on acid value, followed by temperature and methanol-to-oil molar ratio. The optimal conditions for the esterification step were 92 °C temperature, 5.34% catalyst loading, and 5.82:1 methanol-to-oil molar ratio. The acid value and FFA conversion of R.trisperma oil under these conditions were 2.49 mg KOH/g and 91.75%, respectively. The kinetics study revealed that the constructed model could fit the experimental data well with relatively high reliability. The activation energy required for the esterification of R.trisperma oil was 33.2 kJ/mol. Full article
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<p>Equipment used in study. (<b>a</b>) Schematic representation of 500 mL stainless steel reactor, (1) temperature controller, (2) chiller controller, (3) thermocouple, (4) agitator-speed controller, (5) motor, (6) agitator, (7) reactor vessel, (8) low-temperature sampling port, (9) high-temperature sampling port; (<b>b</b>) 4-blade propeller.</p>
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<p>(<b>a</b>) Correlation between predicted and actual acid values of esterified model oil; response contour plot of acid value as a function of (<b>b</b>) temperature and catalyst loading, (<b>c</b>) temperature and methanol-to-oil molar ratio, and (<b>d</b>) catalyst loading and methanol-to-oil molar ratio.</p>
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<p>(<b>a</b>) Acid value profile for model oil and <span class="html-italic">R.trisperma</span> oil esterification; (<b>b</b>) Free fatty acid (FFA) conversion profile for model oil and <span class="html-italic">R.trisperma</span> oil esterification.</p>
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<p>Acid value profiles of kinetics experiments for (<b>a</b>) model oil system and (<b>b</b>) <span class="html-italic">R.trisperma</span> oil; linearization of experimental data for (<b>c</b>) model oil system and (<b>d</b>) <span class="html-italic">R.trisperma</span> oil system.</p>
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<p>Arrhenius plots for (<b>a</b>) model oil system, (<b>b</b>) <span class="html-italic">R.trisperma</span> oil system.</p>
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15 pages, 2548 KiB  
Article
A Novel Risk-Based Prioritization Approach for Wireless Sensor Network Deployment in Pipeline Networks
by Xiaojian Yi, Peng Hou and Haiping Dong
Energies 2020, 13(6), 1512; https://doi.org/10.3390/en13061512 - 22 Mar 2020
Viewed by 2443
Abstract
In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions [...] Read more.
In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions rather than the available deployed ones. Unfortunately, the existing approaches face grave challenges due to the vulnerability of identification to human biases and errors. Here, we have proposed a novel approach to determine the criticality of different deployed regions by ranking them based on risk. The probability of occurrence of the failure event in each deployed region is estimated by spatial statistics to measure the uncertainty of risk. The severity of risk consequence is measured for each deployed region based on the total cost caused by failure events. At the same time, hypothesis testing is used before the application of the proposed approach. By validating the availability of the proposed approach, it provides a strong credible basis and the falsifiability for the analytical conclusion. Finally, a case study is used to validate the feasibility of our approach to identify the critical regions. The results of the case study have implications for understanding the spatial heterogeneity of the occurrence of failure in a pipeline network. Meanwhile, the spatial distribution of risk uncertainty is a useful priori knowledge on how to guide the random deployment of wireless sensors, rather than adopting the simple assumption that each sensor has an equal likelihood of being deployed at any location. Full article
(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)
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<p>Procedure of the proposed approach.</p>
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<p>Distribution of pipeline failure events in Kansas.</p>
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<p>The homogeneous Poisson point process (<b>left</b>) and the inhomogeneous Poisson point process (<b>right</b>).</p>
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<p>Illustration of the significance test based on Moran’s <span class="html-italic">I.</span></p>
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<p>The identification number and number of failure events that occurred in each sensor field.</p>
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<p>Spatial weights matrix.</p>
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<p>Illustration of the results.</p>
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19 pages, 8080 KiB  
Article
The Impact of Reconfiguration on the Energy Performance of the Distributed Maximum Power Point Tracking Approach in PV Plants
by Marco Balato and Carlo Petrarca
Energies 2020, 13(6), 1511; https://doi.org/10.3390/en13061511 - 22 Mar 2020
Cited by 9 | Viewed by 2498
Abstract
The following two approaches can address the drawbacks associated with mismatching phenomena in photovoltaic (PV) plants: distributed maximum power point tracking (DMPPT) architecture and reconfigurable PV array architecture. Until now, these two approaches have represented alternative solutions. In this paper, for the first [...] Read more.
The following two approaches can address the drawbacks associated with mismatching phenomena in photovoltaic (PV) plants: distributed maximum power point tracking (DMPPT) architecture and reconfigurable PV array architecture. Until now, these two approaches have represented alternative solutions. In this paper, for the first time, it is suggested that the two approaches can be used together. In particular, it will be shown how the joint adoption of the DMPPT and reconfiguration approaches can improve the performances of mismatched PV plants; here, performance is understood as the best compromise between the efficiency and reliability of the entire PV system. Numerical results confirm the above assumptions, providing the hints for the development of innovative reconfiguration techniques suitable for distributed applications. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Central Maximum Power Point Tracking (CMPPT) architecture in grid-connected photovoltaic (PV) applications.</p>
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<p>Distributed maximum power point tracking (DMPPT) architecture in grid-connected PV applications.</p>
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<p>PV module current versus voltage (I-V) characteristic and boost-based self-controlled PV module (SCPVM) I-V characteristic.</p>
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<p>Power versus current (P-I) characteristic of boost-based SCPVM.</p>
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<p>System under test.</p>
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<p>Constellation of all possible mismatching scenarios.</p>
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<p>I-V characteristics of individual SCPVMs (Case A).</p>
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<p>P-I characteristic of the series connection of four SCPVMs.</p>
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<p>Power-versus-voltage (P-V) characteristic of the series connection of four SCPVMs.</p>
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<p>I-V characteristics of individual SCPVMs (Case B).</p>
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<p>P-I characteristic of the series connection of four SCPVMs.</p>
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<p>P-V characteristic of the series connection of four SCPVMs.</p>
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<p>Block scheme of grid-connected PV system with the joint adoption of DMPPT and reconfiguration approaches.</p>
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<p>Optimal current intervals (Case I).</p>
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<p>P-V characteristics (CASE I).</p>
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<p>P-V characteristics (CASE II).</p>
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22 pages, 3764 KiB  
Article
Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs
by Anamarija Falkoni, Antun Pfeifer and Goran Krajačić
Energies 2020, 13(6), 1510; https://doi.org/10.3390/en13061510 - 22 Mar 2020
Cited by 12 | Viewed by 3544
Abstract
Croatia aims to achieve 10% of its energy production from the renewable energy sources in the total energy consumption in the transport sector. One of the ways to achieve this goal is by the use of electric vehicles. This work comparatively analyses the [...] Read more.
Croatia aims to achieve 10% of its energy production from the renewable energy sources in the total energy consumption in the transport sector. One of the ways to achieve this goal is by the use of electric vehicles. This work comparatively analyses the financial and social aspects of vehicle-to-grid charging in standard and fast charging mode, their impact on the renewable electricity production and the total electricity consumption regulated through variable electricity prices. Data were taken for the wider urban area of the Dubrovnik region. The assumption is that the Dubrovnik region will be self-sufficient by the year 2050 with 100% renewable electricity production and that all conventional vehicles will be replaced by electric vehicles. This work aims to show that the fast charging based on 10 min time steps offers more opportunities for flexibility and utilization of renewable generation in the energy system than the standard charging based on hourly time step. The results of this work showed the opposite, where in most of the scenarios standard charging provided better results. Replacement of the existing two tariff model in electricity prices with variable electricity prices contributes to the stability of the energy system, providing better regulation of charging and higher opportunities for renewable electricity utilization in standard and fast charging and reduction of charging costs. According to the financial aspects, fast charging is shown to be more expensive, but for the social aspects, it provides electric vehicles with more opportunities for better competition in the market. Full article
(This article belongs to the Special Issue Impact of Electric Vehicles on the Power System)
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<p>Flexibility in the power system.</p>
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<p>Results of the scenarios [A—2030, B—2050, period of the year (W—Winter, S—Summer), day (W—Wednesday, S—Sunday)]; RES utilization.</p>
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<p>Results of the scenarios [A—2030, B—2050, period of the year (W—Winter, S—Summer), day (W—Wednesday, S—Sunday)]; Charging cost.</p>
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<p>Results of the scenarios [A—2030, B—2050, period of the year (W—Winter, S—Summer), day (W—Wednesday, S—Sunday)]; Impact in maximum peak demand.</p>
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<p>Comparison of RC and URC for FC model of one specific Wednesday in the summer period for the year 2050.</p>
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<p>Comparison of RC and URC for SC model of one specific Sunday in the winter period for the year 2050.</p>
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<p>Comparison of SC and FC model with RC of EVs for one specific Wednesday in the winter period for the year 2050.</p>
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<p>EV charging cost in relation to the EV battery size.</p>
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<p>Import/Export in relation to the EV battery size for: (<b>a</b>) 2030 and (<b>b</b>) 2050.</p>
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<p>EV battery charge/discharge in relation to the battery size for: (<b>a</b>) 2030 and (<b>b</b>) 2050.</p>
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21 pages, 1347 KiB  
Article
Optimum Renewable Energy Investment Planning in Terms of Current Deficit: Turkey Model
by Sinem Yapar Saçık, Nihal Yokuş, Mehmet Alagöz and Turgut Yokuş
Energies 2020, 13(6), 1509; https://doi.org/10.3390/en13061509 - 22 Mar 2020
Cited by 4 | Viewed by 3615
Abstract
In this study, a methodology was suggested for wind and solar energy investment plans through linear optimization model for the countries with an energy-based current deficit problem. The originality of the study is that it is a renewable energy investment model based on [...] Read more.
In this study, a methodology was suggested for wind and solar energy investment plans through linear optimization model for the countries with an energy-based current deficit problem. The originality of the study is that it is a renewable energy investment model based on the functioning of the balance of payments for current deficit reduction, which has not previously been encountered in the literature. While creating the model, without causing external economic imbalance, certain parameters were taken into consideration such as profit transfers for the foreign direct investments, interest payments for the domestic investments, import rates for the wind and solar energy systems, energy electric power production values, electric power load balance, electricity transmission infrastructure, CO2 emission, future electric power demand projection, and import source rates in the electric power production. It was proven that the model, for the 2019–2030 period in Turkey, not only is an opportunity for decreasing the current deficit but also ensures reaching the CO2 emission reduction target. Additionally, through the investments in wind and solar energy, it was calculated that fossil-based electric power production will decrease by 80%, and a CO2 reduction will be provided, which is equivalent of 100 million tonnes GWh natural gas. As a more general result, an optimization model was created which provides a solution for countries coping with energy-based current deficit in economic terms, energy-based air pollution in environmental terms, and renewable energy technology insufficiency. Full article
(This article belongs to the Section A: Sustainable Energy)
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<p>Visual display of the optimization model. Source: Revised from the [<a href="#B55-energies-13-01509" class="html-bibr">55</a>].</p>
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23 pages, 1073 KiB  
Article
The Benefits of Local Cross-Sector Consumer Ownership Models for the Transition to a Renewable Smart Energy System in Denmark. An Exploratory Study
by Leire Gorroño-Albizu
Energies 2020, 13(6), 1508; https://doi.org/10.3390/en13061508 - 22 Mar 2020
Cited by 10 | Viewed by 5893
Abstract
Smart energy systems (SESs), with integrated energy sectors, provide several advantages over single-sector approaches for the development of renewable energy systems. However, cross-sector integration is at an early stage even in areas challenged by the existing high shares of variable renewable energy (VRE). [...] Read more.
Smart energy systems (SESs), with integrated energy sectors, provide several advantages over single-sector approaches for the development of renewable energy systems. However, cross-sector integration is at an early stage even in areas challenged by the existing high shares of variable renewable energy (VRE). The promotion of cross-sector integration requires institutional incentives and new forms of actor participation and interaction that are suitable to address the organisational challenges of implementing and operating SESs. Taking as the point of departure an empirical case and its institutional context, this article presents an exploratory study of the ability of cross-sector consumer ownership at different locations in the power distribution system to address those challenges in Denmark. The methods comprise interviews of relevant stakeholders and a literature review. The results indicate that distant and local cross-sector integration will be necessary to reduce overinvestments in the grid and that consumer co-ownership of wind turbines and power-to-heat (P2H) units in district heating (DH) systems may provide advantages over common separate ownership with regard to local acceptance and attractiveness of investments. Several possibilities are identified to improve the current institutional incentive system in Denmark. Finally, the results suggest the relevance of analysing the possibility for single-sector energy companies to transition to smart energy companies. Full article
(This article belongs to the Special Issue New Pathways for Community Energy and Storage)
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<p>The theoretical approach of the study, inspired by [<a href="#B12-energies-13-01508" class="html-bibr">12</a>] and [<a href="#B11-energies-13-01508" class="html-bibr">11</a>]. The white boxes in the diagram present the elements of SESs included in the scope of the study. The interactions between the actors are not drawn because questioning and analysing those interactions is one of the objectives of the study. DH: district heating.</p>
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<p>The location cases of cross-sector integration considered for the analysis. The location cases should not be regarded as either/or alternatives as they already co-exist and will probably still do so in the future. VRE: variable renewable energy; P2H: power-to-heat.</p>
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<p>Potential grid issues that could be caused by increasing shares of VRE in a scenario where no mitigation strategy (e.g., grid reinforcement and expansion or cross-sector integration) is implemented illustrated on a schematic representation of the electricity grid in Denmark. DK1 and DK2 are the two electricity market zones in Denmark. DS1 and DS3 represent the distribution system areas with high shares of VRE and DS2 and DS4 the distribution system areas with high electricity and heat demand. The dashed lines represent the transmission voltage connection to other market zones. For a more detailed representation of the Danish electricity grid, see [<a href="#B39-energies-13-01508" class="html-bibr">39</a>] and, e.g., [<a href="#B40-energies-13-01508" class="html-bibr">40</a>]. DS: Distribution system.</p>
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<p>Number of hours per year with spot market prices below the levelised cost of wind power in DK1. Based on data from [<a href="#B61-energies-13-01508" class="html-bibr">61</a>]. LCOE: Levelised cost of energy.</p>
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21 pages, 2549 KiB  
Article
Optimal Management of the Energy Flows of Interconnected Residential Users
by Lucrezia Manservigi, Mattia Cattozzo, Pier Ruggero Spina, Mauro Venturini and Hilal Bahlawan
Energies 2020, 13(6), 1507; https://doi.org/10.3390/en13061507 - 22 Mar 2020
Cited by 8 | Viewed by 2493
Abstract
In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and [...] Read more.
In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW. Full article
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<p>Reference scenario.</p>
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<p>CHP scenario with independent energy systems.</p>
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<p>CHP scenario with shared TES and EES.</p>
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<p>CHP scenario with shared TES, EES and PM.</p>
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<p>Daily power demand of two users: (<b>a</b>) thermal power, (<b>b</b>) electric power.</p>
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<p>Primary energy consumption with independent energy systems.</p>
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<p>Rate of PM working hours with independent energy systems.</p>
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<p>Thermal energy share.</p>
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<p>Electrical energy share.</p>
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<p>Best optimized strategy with “shared TES and EES”: (<b>a</b>) PM 1 switch-on time; (<b>b</b>) PM 2 switch-on time; (<b>c</b>) thermal power supplied by AB 1; (<b>d</b>) thermal power supplied by AB 2; (<b>e</b>) state of charge of the TES; (<b>f</b>) state of charge of the EES; (<b>g</b>) electric power taken from the electrical grid by user 1; (<b>h</b>) electric power taken from the electrical grid by user 2.</p>
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<p>Best optimized strategy with “shared TES, EES and PM”: (<b>a</b>) shared PM switch-on time; (<b>b</b>) thermal power supplied by AB 1; (<b>c</b>) thermal power supplied by AB 2; (<b>d</b>) state of charge of the TES; (<b>e</b>) state of charge of the EES; (<b>f</b>) electric power taken from the electrical grid by user 1; (<b>g</b>) electric power taken from the electrical grid by user 2.</p>
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22 pages, 6750 KiB  
Article
Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study
by Hongchun Shu, Yiming Han, Ran Huang, Yutao Tang, Pulin Cao, Bo Yang and Yu Zhang
Energies 2020, 13(6), 1506; https://doi.org/10.3390/en13061506 - 22 Mar 2020
Cited by 10 | Viewed by 3100
Abstract
Due to the complex structure of the T-connection transmission lines, it is extremely difficult to identify the reflected travelling wave from the fault point and that from the connection point by the measurement from only one terminal. According to the characteristics of the [...] Read more.
Due to the complex structure of the T-connection transmission lines, it is extremely difficult to identify the reflected travelling wave from the fault point and that from the connection point by the measurement from only one terminal. According to the characteristics of the structure of the T-connection transmission line, the reflection of the travelling wave within the line after the failure of different sections in T-connection transmission line are analyzed. Based on the lattice diagram of the travelling wave, the sequence of travelling waves detected at the measuring terminal varies with the fault distance and the faulty section. Moreover, the sequence of travelling waves detected in one terminal is unique at each faulty section. This article calculates the arrival time of travelling waves of fault points at different locations in different sections to form the collection of the travelling wave arrival time sequence. Then the sequence of travelling waves of the new added fault waveforms is extracted to compare with the sequences in the collection for the faulty section identification and fault location. This proposed method can accurately locate the fault with different fault types, fault resistances and system impedances by only single-terminal fault data. Both Power Systems Computer Aided Design/ Electromagnetic Transients including DC (PSCAD/EMTDC) and actual measurement data are implemented to verify the effectiveness of this method. Full article
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<p>T-connection transmission line topology structure diagram.</p>
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<p>Travelling wave propagation path within <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>l</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Travelling wave propagation path within <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>l</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Travelling wave propagation path within <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>l</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> <mo>&lt;</mo> <msub> <mi>x</mi> <mi mathvariant="normal">F</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Travelling wave propagation path in section TP.</p>
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<p>Travelling wave propagation path in section TN.</p>
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<p>Travelling wave propagation path in connection point T.</p>
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<p>Flow chart of full-line matching algorithm.</p>
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<p>Singular value calibration of <span class="html-italic">n</span>-division recursion SVD for section <span class="html-italic">l</span><sub>1</sub> fault travelling wave.</p>
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<p>Full-line matching algorithm for section <span class="html-italic">l</span><sub>1</sub> fault and matching results with <span class="html-italic">n</span>-division recursion SVD.</p>
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<p>Travelling wave arrive sequence.</p>
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<p>Singular value calibration of <span class="html-italic">n</span>-division recursion SVD for section <span class="html-italic">l</span><sub>3</sub> fault travelling wave.</p>
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<p>Full-line matching algorithm for section <span class="html-italic">l</span><sub>3</sub> fault and matching results with <span class="html-italic">n</span>-division recursion SVD.</p>
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<p>Travelling wave arrive sequence.</p>
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<p>Travelling wave analysis and locating device.</p>
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<p>Geographical location map of Yunnan and topological structure of Pu’er 110kV transmission lines.</p>
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<p>Geographical location map of Yunnan and topological structure of Pu’er 110kV transmission lines.</p>
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<p>Waveform diagram of measured data.</p>
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<p>Full-line matching algorithm for section <span class="html-italic">l</span><sub>1</sub> fault and matching results with <span class="html-italic">n</span>-division recursion SVD.</p>
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<p>Travelling wave arrive sequence.</p>
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15 pages, 6407 KiB  
Article
Numerical Investigation of Vertical Crossflow Jets with Various Orifice Shapes Discharged in Rectangular Open Channel
by Hao Yuan, Ruichang Hu, Xiaoming Xu, Liang Chen, Yongqin Peng and Jiawan Tan
Energies 2020, 13(6), 1505; https://doi.org/10.3390/en13061505 - 22 Mar 2020
Cited by 1 | Viewed by 2785
Abstract
Vertical jet in flowing water is a common phenomenon in daily life. To study the flow and turbulent characteristics of different jet orifice shapes and under different velocity ratios, the realizable k-ε turbulent model was adopted to analyze the three-dimensional (3D) [...] Read more.
Vertical jet in flowing water is a common phenomenon in daily life. To study the flow and turbulent characteristics of different jet orifice shapes and under different velocity ratios, the realizable k-ε turbulent model was adopted to analyze the three-dimensional (3D) flow, turbulence, and vortex characteristics using circular, square, and rectangular jet orifices and velocity ratios of 2, 5, 10, and 15. The following conclusions were drawn: The flow trajectory of the vertical jet in the channel exhibits remarkable 3D characteristics, and the jet orifice and velocity ratio have a significant influence on the flow characteristics of the channel. The heights at which the spiral deflection and maximum turbulent kinetic energy (TKE) occur for the circular jet are the smallest, while those for square jets are the largest. As the shape of the jet orifice changes from a circle to a square and then to a rectangle, the shape formed by the plane of the kidney vortices and the region above it gradually changes from a circle to a pentagon. With the increase in the velocity ratio, the 3D characteristics, maximum TKE, and kidney vortex coverage of the flow all gradually increase. Full article
(This article belongs to the Special Issue Engineering Fluid Dynamics 2019-2020)
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Graphical abstract
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<p>Three-dimensional (3D) view of the model layout.</p>
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<p>The grid of the numerical model.</p>
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<p>Mass flow rate error (MFRE) time-history curve.</p>
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<p>Vertical distribution of the grid convergence index (GCI) in jet orifice centerline.</p>
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<p>Distribution of the vertical flow velocity along the water depth at different locations.</p>
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<p>3D structure of the jet streamtrace.</p>
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<p>Variations of streamtraces under different cases: (<b>a</b>) case 1, (<b>b</b>) case 5, (<b>c</b>) case 9, (<b>d</b>) case 2, (<b>e</b>) case 6, (<b>f</b>) case 10, (<b>g</b>) case 3, (<b>h</b>) case 7, (<b>i</b>) case 11, (<b>j</b>) case 4, (<b>k</b>) case 8, (<b>l</b>) case 12.</p>
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<p>Variations of streamtraces under different cases: (<b>a</b>) case 1, (<b>b</b>) case 5, (<b>c</b>) case 9, (<b>d</b>) case 2, (<b>e</b>) case 6, (<b>f</b>) case 10, (<b>g</b>) case 3, (<b>h</b>) case 7, (<b>i</b>) case 11, (<b>j</b>) case 4, (<b>k</b>) case 8, (<b>l</b>) case 12.</p>
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<p>Distribution of the turbulent kinetic energy (<span class="html-italic">TKE</span>) values at different locations along the water depth: (<b>a</b>) P<sub>2</sub>, (<b>b</b>) P<sub>3</sub>, (<b>c</b>) P<sub>1</sub>, (<b>d</b>) P<sub>4</sub>, (<b>e</b>) P<sub>5</sub>.</p>
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<p>Distribution of the turbulent kinetic energy (<span class="html-italic">TKE</span>) values at different locations along the water depth: (<b>a</b>) P<sub>2</sub>, (<b>b</b>) P<sub>3</sub>, (<b>c</b>) P<sub>1</sub>, (<b>d</b>) P<sub>4</sub>, (<b>e</b>) P<sub>5</sub>.</p>
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<p>Vortex of the circular jet at cross-section Y = 0: (<b>a</b>) case 1, (<b>b</b>) case 2, (<b>c</b>) case 3, (<b>d</b>) case 4.</p>
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<p>Vortex of the square jet at cross-section Y = 0: (<b>a</b>) case 5, (<b>b</b>) case 6, (<b>c</b>) case 7, (<b>d</b>) case 8.</p>
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<p>Vortex of the rectangular jet at cross-section Y = 0: (<b>a</b>) case 9, (<b>b</b>) case 10, (<b>c</b>) case 11, (<b>d</b>) case 12.</p>
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<p>Vortex of the rectangular jet at cross-section Y = 0: (<b>a</b>) case 9, (<b>b</b>) case 10, (<b>c</b>) case 11, (<b>d</b>) case 12.</p>
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15 pages, 1886 KiB  
Article
Relative Contributions of Clouds and Aerosols to Surface Erythemal UV and Global Horizontal Irradiance in Korea
by Jaemin Kim, Yun Gon Lee, Ja-Ho Koo and Hanlim Lee
Energies 2020, 13(6), 1504; https://doi.org/10.3390/en13061504 - 22 Mar 2020
Cited by 3 | Viewed by 2690
Abstract
The attenuating effects of clouds and aerosols on global horizontal irradiance (GHI) and ultraviolet erythemal irradiance (UVER) were evaluated and compared using data from four sites in South Korea (Gangneung, Pohang, Mokpo, and Gosan) for the period 2005–2016. It was found that GHI [...] Read more.
The attenuating effects of clouds and aerosols on global horizontal irradiance (GHI) and ultraviolet erythemal irradiance (UVER) were evaluated and compared using data from four sites in South Korea (Gangneung, Pohang, Mokpo, and Gosan) for the period 2005–2016. It was found that GHI and UVER are affected differently by various attenuating factors, resulting in an increase in the ratio of UVER to GHI with a decrease in the clearness index of GHI. A comparative analysis of the clearness indices of GHI and UVER identified an almost linear relationship between two transmittances by applying UVER with fixed slant ozone ( UVER 300 ) and there was a latitudinal difference in the relationship. Some nonlinearity remained in this relationship, which suggests a contribution by other factors such as clouds and aerosols. Variations of the UVER 300 ratio to GHI with cloud cover and aerosol optical depth were analyzed. The ratio increased with cloud cover and decreased with aerosol optical depth, indicating that clouds attenuate GHI more efficiently than UVER and that the attenuation by aerosols is greater for UVER than for GHI. A multiple linear regression analysis of the clearness indices of GHI and UVER 300 quantitively demonstrates differences in the radiation-reducing effects of clouds and aerosols, with some regional differences by site that can be attributed to local climatic characteristics in South Korea. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Figure 1
<p>Spatial distribution of analysis sites (Gangneung, Pohang, Mokpo, and Gosan) in South Korea.</p>
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<p>Monthly mean values of daily accumulated global solar radiation (black filled circle) and erythemal UV radiation (red asterisk) at (<b>a</b>) Gangneung, (<b>b</b>) Pohang, (<b>c</b>) Mokpo, and (<b>d</b>) Gosan.</p>
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<p>Relationship between <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mi mathvariant="normal">T</mi> </msub> </mrow> </semantics></math> and ultraviolet erythemal irradiance/global horizontal irradiance (UVER/GHI) at the four sites.</p>
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<p>Relationship between <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mrow> <mi>TUVER</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mi mathvariant="normal">T</mi> </msub> </mrow> </semantics></math> at (<b>a</b>) Gangneung, (<b>b</b>) Pohang, (<b>c</b>) Mokpo, and (<b>d</b>) Gosan. The red line is the linear regression line.</p>
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<p>Relationship between <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mrow> <mi>TUVER</mi> <mn>300</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mi mathvariant="normal">T</mi> </msub> </mrow> </semantics></math> at (<b>a</b>) Gangneung, (<b>b</b>) Pohang, (<b>c</b>) Mokpo, and (<b>d</b>) Gosan. The red line is the linear regression line.</p>
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<p>Dependence of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>UVER</mi> </mrow> <mrow> <mn>300</mn> </mrow> </msub> <mo>/</mo> <mi mathvariant="normal">G</mi> </mrow> </semantics></math> (red filled circle) and two clearness indices, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mi mathvariant="normal">T</mi> </msub> </mrow> </semantics></math> (green asterisk) and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">K</mi> <mrow> <mi>TUVER</mi> <mn>300</mn> </mrow> </msub> </mrow> </semantics></math> (blue open circle), on (<b>a</b>) cloud cover and (<b>b</b>) aerosol optical depth.</p>
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8 pages, 524 KiB  
Communication
Camelina and Crambe Oil Crops for Bioeconomy—Straw Utilisation for Energy
by Michał Krzyżaniak, Mariusz J. Stolarski, Łukasz Graban, Waldemar Lajszner and Tomasz Kuriata
Energies 2020, 13(6), 1503; https://doi.org/10.3390/en13061503 - 22 Mar 2020
Cited by 20 | Viewed by 2904
Abstract
Agriculture can provide biomass for bioproducts, biofuels and as energy feedstock with a low environmental impact, derived from carbohydrate, protein and oil annual crops, as well from lignocellulosic crops. This paper presents the thermophysical and chemical features of camelina and crambe straw depending [...] Read more.
Agriculture can provide biomass for bioproducts, biofuels and as energy feedstock with a low environmental impact, derived from carbohydrate, protein and oil annual crops, as well from lignocellulosic crops. This paper presents the thermophysical and chemical features of camelina and crambe straw depending on nitrogen fertilisation rate with a view to their further use in a circular bioeconomy. A two-factorial field experiment was set up in 2016, with camelina and crambe as the first factor and the N fertilizer rate (0, 60 and 120 kg·ha−1·N) as the second factor. Ash content in crambe straw (6.97% d.m.) was significantly higher than in camelina straw (4.79% d.m.). The higher heating value was higher for the camelina (18.50 MJ·kg−1·d.m.) than for the crambe straw (17.94 MJ·kg−1·d.m.). Sulphur content was also significantly higher in camelina than in crambe straw. An increase in nitrogen content with increasing fertilisation rate was visible in the straw of both species (from 1.19 to 1.33% d.m., for no fertilisation and for a rate of 120 kg·ha−1·N, respectively). Crambe straw contained more than five times more chlorine than camelina straw. In conclusion, despite certain adverse properties, camelina and crambe straw can be an alternative to other types of biomass, both for direct combustion, gasification and in the production of second-generation biofuels. Full article
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<p>Higher heating value of camelina and crambe straw depending on fertilization rate; error bars-standard deviation; letters indicate that values are statistically different (Tukey’s test at <span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 6352 KiB  
Article
Effect of Axial In-Situ Stress in Deep Tunnel Analysis Considering Strain Softening and Dilatancy
by Kang Yi, Zhenghe Liu, Zhiguo Lu, Junwen Zhang and Shuangyong Dong
Energies 2020, 13(6), 1502; https://doi.org/10.3390/en13061502 - 22 Mar 2020
Cited by 6 | Viewed by 2390
Abstract
In many previous tunnel analyses, the axial in-situ stress was ignored. In this work, its effect on the deformation and failure of the surrounding rock of a deep tunnel was revealed, considering the objective strain softening and dilatancy behavior of the surrounding rock. [...] Read more.
In many previous tunnel analyses, the axial in-situ stress was ignored. In this work, its effect on the deformation and failure of the surrounding rock of a deep tunnel was revealed, considering the objective strain softening and dilatancy behavior of the surrounding rock. Analysis based on the incremental plastic flow theory was conducted, and C++ was used to write a constitutive model for numerical simulation to verify and further analyze this effect. Then, the results were validated by the field monitoring data of a coal mine gateway. Results show that the effect of the axial in-situ stress σa0 is more significant when strain softening is considered, compared with the results of a perfectly elastoplastic model. When the axial stress σa is σ1 or σ3 at the initial yield, an increase or decrease in σa0 intensifies the deformation and failure of the surrounding rock. When σa is σ2 at the initial yield, 3D plastic flow partly controlled by σa may occur, and an increase in σa0 intensifies the deformation and failure of the surrounding rock. The effect of σa0 will be amplified by considering dilatancy. Considering both strain softening and dilatancy, when σa0 is close to the tangential in-situ stress σt0 or significantly greater than σt0 (1.5 times), σa will be σ2 or σ1 at the initial yield, and then 3D plastic flow will occur. In the deformation prediction and support design of a deep tunnel, σa0 should not be ignored, and the strain softening and dilatancy behavior of the surrounding rock should be accurately considered. Full article
(This article belongs to the Section H: Geo-Energy)
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<p>A rectangular tunnel and the in-situ stresses.</p>
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<p>Stress adjustment and plastic flow of a microunit at the middle of the tunnel roof: (<b>a</b>) perfectly elastoplastic model and (<b>b</b>) considering strain softening.</p>
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<p>2D plastic flow and 3D plastic flow.</p>
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<p>Plastic principal strain increments of the microunit at the middle of the tunnel roof: (<b>a</b>) perfectly elastoplastic model, (<b>b</b>) considering only dilatancy, (<b>c</b>) considering only strain softening, and (<b>d</b>) considering both strain softening and dilatancy.</p>
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<p>Main calculation steps of the constitutive model.</p>
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<p>Model size and mesh.</p>
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<p>Results of the perfectly elastoplastic model: (<b>a</b>) roof plastic zone depth and (<b>b</b>) roof subsidence.</p>
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<p>Results considering only dilatancy: (<b>a</b>) roof plastic zone depth, and (<b>b</b>) roof subsidence.</p>
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<p>Results considering only strain softening: (<b>a</b>) roof plastic zone depth and (<b>b</b>) roof subsidence.</p>
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<p>Results considering both strain softening and dilatancy: (<b>a</b>) roof plastic zone depth and (<b>b</b>) roof subsidence.</p>
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<p>Monitoring results of the principal stresses: (<b>a</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, (<b>b</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, (<b>c</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.50, (<b>d</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, (<b>e</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, (<b>f</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.50, (<b>g</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 0.50, (<b>h</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.00, and (<b>i</b>) <span class="html-italic">σ</span><sub>h0</sub>/<span class="html-italic">σ</span><sub>v0</sub> = 1.50, <span class="html-italic">σ</span><sub>a0</sub>/<span class="html-italic">σ</span><sub>v0</sub>=1.50.</p>
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<p>Displacements of the 020202 rail gateway 60 d after excavation.</p>
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