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Energies, Volume 12, Issue 15 (August-1 2019) – 197 articles

Cover Story (view full-size image): Amorphous Si multijunction PV modules are subjected to instability phenomena due to the Staebler-Wronski effect. We performed outdoor tests to study the effect of operating temperature and incident solar spectrum. Experimental results show clear correlation between performance improvements and PV module thermal history. View this paper.
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28 pages, 5159 KiB  
Article
Thermodynamic Evaluation of LiCl-H2O and LiBr-H2O Absorption Refrigeration Systems Based on a Novel Model and Algorithm
by Jie Ren, Zuoqin Qian, Zhimin Yao, Nianzhong Gan and Yujia Zhang
Energies 2019, 12(15), 3037; https://doi.org/10.3390/en12153037 - 6 Aug 2019
Cited by 31 | Viewed by 5591
Abstract
An absorption refrigeration system (ARS) is an alternative to the conventional mechanical compression system for cold production. This study developed a novel calculation model using the Matlab language for the thermodynamic analysis of ARS. It was found to be reliable in LiCl-H2 [...] Read more.
An absorption refrigeration system (ARS) is an alternative to the conventional mechanical compression system for cold production. This study developed a novel calculation model using the Matlab language for the thermodynamic analysis of ARS. It was found to be reliable in LiCl-H2O and LiBr-H2O ARS simulations and the parametric study was performed in detail. Moreover, two 50 kW water-cooled single effect absorption chillers were simply designed to analyze their off-design behaviors. The results indicate that LiCl-H2O ARS had a higher coefficient of performance (COP) and exergetic efficiency, particularly in the lower generator or higher condenser temperature conditions, but it operated more restrictively due to crystallization. The off-design analyses revealed that the preponderant performance of LiCl-H2O ARS was mainly due to its better solution properties because the temperature of each component was almost the same for both chillers in the operation. The optimum inlet temperature of hot water for LiCl-H2O (83 °C) was lower than that of LiBr-H2O (98 °C). The cooling water inlet temperature should be controlled within 41 °C, otherwise the performances are discounted heavily. The COP and cooling capacity could be improved by increasing the temperature of hot water or chilled water properly, contrary to the exergetic efficiency. Full article
(This article belongs to the Special Issue Refrigeration Systems and Applications 2019)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Schematic representation of the single effect absorption refrigeration system, and (<b>b</b>) the pressure–temperature (P–T) diagram of the cycle.</p> Full article ">Figure 2
<p>Flow chart of the calculation procedure.</p> Full article ">Figure 3
<p>Generator temperature impact on the <span class="html-italic">COP</span> of the absorption system under various condenser and absorber temperature levels (32 °C, 36 °C, and 40 °C) for the two working pairs (<span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 4
<p>Generator temperature impact on the efficiency ratio of absorption system under various condenser and absorber temperature levels (32 °C, 36 °C, and 40 °C) for the two working pairs (<span class="html-italic">t</span><sub>10</sub> = 7 °C, ε = 0.7).</p> Full article ">Figure 5
<p>Condenser and absorber temperatures impact on the <span class="html-italic">COP</span> of the absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 6
<p>Condenser and absorber temperatures impact on the efficiency ratio of the absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 7
<p>Absorber temperature impact on the <span class="html-italic">COP</span> and efficiency ratio of the absorption system under the examined operating condition for the two working pairs (<span class="html-italic">t</span><sub>4</sub> = 80 °C, <span class="html-italic">t</span><sub>8</sub> = 40 °C, <span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 8
<p>Condenser temperature impact on the <span class="html-italic">COP</span> and efficiency ratio of absorption system under the examined operating condition for the two working pairs (<span class="html-italic">t</span><sub>1</sub> = 40 °C, <span class="html-italic">t</span><sub>4</sub> = 80 °C, <span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 9
<p>Evaporator temperature impact on the <span class="html-italic">COP</span> of the absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>1</sub> = <span class="html-italic">t</span><sub>8</sub> = 40 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 10
<p>Evaporator temperature impact on the efficiency ratio of absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>1</sub> = <span class="html-italic">t</span><sub>8</sub> = 40 °C, ε = 0.7).</p> Full article ">Figure 11
<p>Heat exchanger effectiveness impact on the <span class="html-italic">COP</span> of the absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>1</sub> = <span class="html-italic">t</span><sub>8</sub> = 40 °C, <span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 12
<p>Heat exchanger effectiveness impact on the efficiency ratio of the absorption system under various generator temperature levels (78 °C, 80 °C, and 82 °C) for the two working pairs (<span class="html-italic">t</span><sub>1</sub> = <span class="html-italic">t</span><sub>8</sub> = 40 °C, <span class="html-italic">t</span><sub>10</sub> = 7 °C, <span class="html-italic">ε</span> = 0.7).</p> Full article ">Figure 13
<p>Variation of <span class="html-italic">COP</span> and exergy efficiency with the hot water inlet temperature for the two absorption chillers.</p> Full article ">Figure 14
<p>Variations of the exit temperatures of the internal and external fluids with hot water inlet temperature for the LiBr-H<sub>2</sub>O absorption chiller.</p> Full article ">Figure 15
<p>Variations of the exit temperatures of the internal and external fluids with hot water inlet temperature for the LiCl-H<sub>2</sub>O absorption chiller.</p> Full article ">Figure 16
<p>Variation of <span class="html-italic">COP</span> and the exergy efficiency with the cooling water inlet temperature for the two absorption chillers.</p> Full article ">Figure 17
<p>Variation of the exit temperature and heat capacity of each component with the cooling water inlet temperature for the LiBr-H<sub>2</sub>O absorption chiller.</p> Full article ">Figure 18
<p>Variation of the exit temperature and heat capacity of each component with the cooling water inlet temperature for LiCl-H<sub>2</sub>O absorption chiller.</p> Full article ">Figure 19
<p>Variation of <span class="html-italic">COP</span> and exergy efficiency with the chilled water inlet temperature for the two absorption chillers.</p> Full article ">Figure 20
<p>Variation of the exit temperature and heat capacity of each component with the chilled water inlet temperature for the LiBr-H<sub>2</sub>O absorption chiller.</p> Full article ">Figure 21
<p>Variation of the exit temperature and heat capacity of each component with the cooling water inlet temperature for the LiCl-H<sub>2</sub>O absorption chiller.</p> Full article ">
15 pages, 3194 KiB  
Article
Experimental Testing of Hydrophobic Microchannels, with and without Nanofluids, for Solar PV/T Collectors
by Mahdi Motamedi, Chia-Yang Chung, Mehdi Rafeie, Natasha Hjerrild, Fan Jiang, Haoran Qu and Robert A. Taylor
Energies 2019, 12(15), 3036; https://doi.org/10.3390/en12153036 - 6 Aug 2019
Cited by 14 | Viewed by 4030
Abstract
Solar energy can be converted into useful energy via photovoltaic cells or with a photothermal absorber. While these technologies are well-developed and commercially viable, significant benefits can be realised by pulling these two technologies together in photovoltaic/thermal (PV/T) systems which can provide both [...] Read more.
Solar energy can be converted into useful energy via photovoltaic cells or with a photothermal absorber. While these technologies are well-developed and commercially viable, significant benefits can be realised by pulling these two technologies together in photovoltaic/thermal (PV/T) systems which can provide both heat and electricity from a single collector. Emerging configurations in the PV/T field aim to incorporate micro and/or nanotechnology to boost total solar utilisation even further. One example of this is the nanofluid-based PV/T collector. This type of solar collector utilises nanofluids—suspensions of nanoparticles in traditional heat transfer fluids—as both an optical filter and as a thermal absorber. This concept seeks to harvest the whole solar spectrum at its highest thermodynamic potential through specially engineered nanofluids which transmit the portion of solar spectrum corresponding to the PV response curve while absorbing the rest as heat. Depending on the nanoparticle concentration, employing nanofluids in a flowing system may come with a price—an efficiency penalty in the form of increased pumping power (due to increased viscosity). Similarly, microchannel-based heat exchangers have been shown to increase heat transfer, but they may also pay the price of high pumping power due to additional wall-shear-related pressure drop (i.e., more no-slip boundary area). To develop a novel PV/T configuration which pulls together the advantages of these micro and nanotechnologies with minimal pumping power requirements, the present study experimentally investigated the use of nanofluids in patterned hydrophobic microchannels. It was found that slip with the walls reduced the impact of the increased viscosity of nanofluids by reducing the pressure drop on average 17% relative to a smooth channel. In addition, flowing a selective Ag/SiO2 core–shell nanofluid over a silicon surface (simulating a PV cell underneath the fluid) provided a 20% increase in solar thermal conversion efficiency and ~3% higher stagnation temperature than using pure water. This demonstrates the potential of this proposed system for extracting more useful energy from the same incident flux. Although no electrical energy was extracted from the underlying patterned silicon, this study highlights potential a new development path for micro and nanotechnology to be integrated into next-generation PV/T solar collectors. Full article
(This article belongs to the Special Issue Hybrid Solar Photovoltaic / Thermal (PVT) Collectors)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The experimental set-up; (<b>b</b>) the assembled microchannel mounted on its 3D-printed holder; (<b>c</b>) a close-up view of the assembled microchannel (with dimensions); (<b>d</b>) schematic diagrams of the holder and the locations of thermocouples and pressure reading ports; (<b>e</b>) schematic diagram of the microchannel assembly; and (<b>f</b>) schematic diagram of the longitudinal microstructural configuration.</p> Full article ">Figure 2
<p>Water static contact angle measurement images of the longitudinal (0.6) microchannels (<b>a</b>) before silanisation and (<b>b</b>) after silanisation.</p> Full article ">Figure 3
<p>(<b>a</b>) Microscopic image of the smooth microchannel prior to the nanofluid test, (<b>b</b>) microscopic image after the nanofluid test and (<b>c</b>) TEM image of suspended nanoparticles in Ag-SiO<sub>2</sub> nanodisc solution (reproduced with permission from Hjerrild et. al. [<a href="#B29-energies-12-03036" class="html-bibr">29</a>] Copyright 2016, Elsevier).</p> Full article ">Figure 4
<p>Optical characterisation of the solar spectrum as compared to the lamp and the nanofluid transmission as compared to the external quantum efficiency (EQE) of a silicon PV cell (taken from [<a href="#B40-energies-12-03036" class="html-bibr">40</a>]). The transmission of the glass cover is also shown.</p> Full article ">Figure 5
<p>Experimental Poiseuille number (Po) as a function of Reynolds number (Re) for the three longitudinal microchannels as well as the smooth microchannel (compared with theory).</p> Full article ">Figure 6
<p>Results of Po for four different microchannels plotted as a function of cavity fraction (F<sub>c</sub>) against the analytical results for comparison.</p> Full article ">Figure 7
<p>Comparison of the Poiseuille numbers of the smooth and superhydrophobic microchannels for water and the nanofluid. Note that pure water and the nanofluid were at ~20 °C and ~19 °C, respectively, which shifted the nanofluid results to higher Po numbers due to the temperature dependence of viscosity).</p> Full article ">Figure 8
<p>Experimental thermal efficiency curves of the proposed receiver, comparing water (blue diamonds) and a nanofluid (red squares). The dashed lines connect the bounds of experimental uncertainty for water (blue) and the nanofluid (red). Note that the ambient temperature (T<sub>a</sub>) was approximately 20 °C during the test.</p> Full article ">
19 pages, 7398 KiB  
Article
Effects of Blade Fillet Structures on Flow Field and Surface Heat Transfer in a Large Meridional Expansion Turbine
by Fusheng Meng, Qun Zheng and Jian Zhang
Energies 2019, 12(15), 3035; https://doi.org/10.3390/en12153035 - 6 Aug 2019
Cited by 4 | Viewed by 3591
Abstract
This paper is a continuation of the previous work, aiming to explore the influence of fillet configurations on flow and heat transfer in a large meridional expansion turbine. The endwall of large meridional expansion turbine stator has a large expansion angle, which leads [...] Read more.
This paper is a continuation of the previous work, aiming to explore the influence of fillet configurations on flow and heat transfer in a large meridional expansion turbine. The endwall of large meridional expansion turbine stator has a large expansion angle, which leads to early separation of the endwall boundary layer, resulting in excessive aerodynamic loss and local thermal load. In order to improve the flow state and reduce the local high thermal load, five typical fillet distribution rules are designed. The three-dimensional Reynolds-Averaged Navier-Stokes (RANS) solver for viscous turbulent flows was used to investigate the different fillet configurations of the second stage stator blades of a 1.5-stage turbine, and which fillet distribution is suitable for large meridional expansion turbines. The influence of fillet structures on the vortex system and loss characteristics was analyzed, and its impact on wall thermal load was studied in detail. The fillet structure mainly affects the formation of horseshoe vortexes at the leading edge of the blade so as to reduce the loss caused by horseshoe vortexes and passage vortexes. The fillet structure suitable for the large meridional expansion turbine was obtained through the research. Reasonable fillet structure distribution can not only improve the flow state but also reduce the high thermal load on the wall surface of the meridional expansion turbine. It has a positive engineering guiding value. Full article
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Figure 1

Figure 1
<p>Meridional passage of the 1.5-stage turbine.</p> Full article ">Figure 2
<p>Schematic of computational domain of the 1.5-stage turbine.</p> Full article ">Figure 3
<p>Blade-to-blade grid topology of stator blades (S2).</p> Full article ">Figure 4
<p>Isentropic Mach number of S2 at 90% span.</p> Full article ">Figure 5
<p>Nu distribution of S2 shroud.</p> Full article ">Figure 6
<p>Design parameters: fillet radius and minimum angle.</p> Full article ">Figure 7
<p>Radius distribution of six different fillet structures.</p> Full article ">Figure 8
<p>Three-dimensional (3D) schematic diagram of S2 with fillet.</p> Full article ">Figure 9
<p>Horseshoe vortex structure at the leading edge of S2.</p> Full article ">Figure 10
<p><span class="html-italic">C<sub>ps</sub></span> distribution for S2 at 1% span (<b>a</b>), 50% span (<b>b</b>), and 99% span (<b>c</b>) blade and characteristics of adverse-pressure region (<b>d</b>).</p> Full article ">Figure 11
<p>Horseshoe vortex imposed with relative total pressure loss coefficient.</p> Full article ">Figure 12
<p>Entropy-increase isoline at 110% axial chord length of S2.</p> Full article ">Figure 13
<p>Radial distribution of entropy-increase at 110% axial chord length of S2.</p> Full article ">Figure 14
<p>Distribution of <span class="html-italic">Nu</span> of shroud endwall of S2.</p> Full article ">Figure 15
<p><span class="html-italic">Nu</span> distribution of the S2 blade suction surface.</p> Full article ">Figure 16
<p><span class="html-italic">Nu</span> distribution for S2 at 1% span (<b>a</b>), 50% span (<b>b</b>), and 99% span (<b>c</b>) blade.</p> Full article ">
26 pages, 33473 KiB  
Article
A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement
by Xiaoming Zhang and Hui Yin
Energies 2019, 12(15), 3034; https://doi.org/10.3390/en12153034 - 6 Aug 2019
Cited by 2 | Viewed by 3875
Abstract
The measurements of the diameter of different layers, the thickness of different layers and the eccentricity of insulation layer in the cross-section of power cables are important items of power cable test, which currently depend on labor-intensive manual operations. To improve efficiency, automatic [...] Read more.
The measurements of the diameter of different layers, the thickness of different layers and the eccentricity of insulation layer in the cross-section of power cables are important items of power cable test, which currently depend on labor-intensive manual operations. To improve efficiency, automatic measurement methods are in urgent need. In this paper, a monocular vision-based framework for automatic measurement of the diameter, thickness, and eccentricity of interest in the cross-section of power cables is proposed. The proposed framework mainly consists of three steps. In the first step, the images of cable cross-section are captured and undistorted with the camera calibration parameters. In the second step, the contours of each layer are detected in the cable cross-section image. In order to detect the complete and accurate contours of each layer, the structural edges in the cross-section image are firstly detected and divided into individual layers, then unconnected edges are connected by arc-based method, and finally contours are refined by the proposed break detection and grouping (BDG) and linear trend-based correction (LTBC) algorithm. In the third step, the monocular vision-based cross-section dimension measurement is accomplished by placing a chessboard coplanar with the power cable cross-section plane. The homography matrix mapping pixel coordinates to chessboard world coordinates is estimated, and the diameter, thickness and eccentricity of specific layers are calculated by homography matrix-based measurement method. Simulated data and actual cable data are both used to validate the proposed method. The experimental results show that diameter, minimum thickness, mean thickness and insulation eccentricity of simulated image without additive noise are measured with root mean squared error (RMSE) of 0.424, 0.103 and 0.063 mm, and 0.002, respectively, those of simulated image with additive Gaussian noise and salt and pepper noise are measured with RMSE of 0.502, 0.243 and 0.058 mm and 0.001. Diameter, minimum thickness and mean thickness of actual cable images are measured with average RMSE of 0.768, 0.308 and 0.327 mm. The measurement error of insulation eccentricity of actual cable image is comparatively large, and the measurement accuracy should be improved. Full article
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Cross-section of the structure of typical single-core cable.</p> Full article ">Figure 2
<p>Flow diagram of the proposed vision-based framework for power cable cross-section measurement.</p> Full article ">Figure 3
<p>Coordinate systems.</p> Full article ">Figure 4
<p>Edge filtering based on length and linearity. (<b>a</b>) Long structural edges; (<b>b</b>) Very short interference details; (<b>c</b>) Pending short edges; (<b>d</b>) Short structural edges with fairly good linearity.</p> Full article ">Figure 5
<p>Arc-based edge connection, in which red dots are clockwise organized edge points to be connected, red asterisks denote arc endpoints, and green line denotes the fitted circular contour <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>a</b>) Direct connection with connecting line <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mn>1</mn> <mi mathvariant="normal">E</mi> </mrow> </msub> <msub> <mi>P</mi> <mrow> <mn>2</mn> <mi mathvariant="normal">S</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) PSC method, in which red arrow shows the tangential direction of <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mn>1</mn> <mi mathvariant="normal">E</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <msup> <mi>P</mi> <mo>′</mo> </msup> </semantics></math> denotes the prediction point, magenta arrow shows the normal direction of <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <msup> <mi>P</mi> <mo>′</mo> </msup> </semantics></math>, and blue boxes indicate the search range for gradient calculation.</p> Full article ">Figure 6
<p>Fluctuations in initial contour <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>k</mi> </msub> </mrow> </semantics></math>, in which red dots are contour points, green line denotes the fitted circular contour <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math>, and fluctuations exist in blue ellipse. (<b>a</b>) Structure-related gentle changes; (<b>b</b>) Mutations with steep changes.</p> Full article ">Figure 7
<p>Breaks detection and mutation grouping, in which horizontal axis is polar angle <math display="inline"><semantics> <mi>θ</mi> </semantics></math>, vertical axis is radial distance to the fitted circle, green solid line denotes original <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>ρ</mi> </msub> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> sequence, red dashed line denotes the simplified <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>P</mi> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> sequence, blue line is the absolute difference of <math display="inline"><semantics> <mrow> <mi>D</mi> <mi>P</mi> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and the segments divided by break groups are labeled. (<b>a</b>) I-Break in magenta lines with detected major peaks in circles; (<b>b</b>) P-Break in orange lines with a detected major peak in circle.</p> Full article ">Figure 8
<p>Break between point <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>j</mi> </msub> </mrow> </semantics></math> in polar coordinate system, in which <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> denotes the pole, <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> and <math display="inline"><semantics> <mi>θ</mi> </semantics></math> denote polar coordinates, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> is radius of the fitted circle, <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>ρ</mi> </msub> </mrow> </semantics></math> is radial distance between contour point and the fitted circle, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">d</mi> <mi>D</mi> <mi>P</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>j</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="normal">d</mi> <msub> <mi>θ</mi> <mi>j</mi> </msub> </mrow> </semantics></math> are radial and angular difference between the two points, and <math display="inline"><semantics> <mi mathvariant="sans-serif">α</mi> </semantics></math> is the angle between line <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>P</mi> <mi>j</mi> </msub> </mrow> </semantics></math> and radial direction at point <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>j</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>LTBC to <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>ρ</mi> </msub> </mrow> </semantics></math> of I-Mutation, in which vertical axis is radial distance to the fitted circle, and horizontal axis is the dynamic number rather than polar angle <math display="inline"><semantics> <mi>θ</mi> </semantics></math>, to avoid the discontinuity of clockwise <math display="inline"><semantics> <mi>θ</mi> </semantics></math> ranging from <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>180</mn> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mo>−</mo> <msup> <mrow> <mn>180</mn> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math>. The blue lines are <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>t</mi> <mi>e</mi> <msub> <mi>p</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, red lines are corresponding fitted lines, the magenta and green solid lines are detected I-Mutation, black arrow is the translation for <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>t</mi> <mi>e</mi> <msub> <mi>p</mi> <mi mathvariant="normal">b</mi> </msub> </mrow> </semantics></math> correction, and the magenta and green dashed lines are corrected sequence.</p> Full article ">Figure 10
<p>Cross-section measurement. (<b>a</b>) Perimeter measurement, in which red dots are contour <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>k</mi> </msub> </mrow> </semantics></math> and blue asterisks are vertexes of convex hull; (<b>b</b>) Thickness measurement, in which magenta line is minimum thickness and green lines are the other five thickness measurements clockwise.</p> Full article ">Figure 11
<p>Simulated images. (<b>a</b>) Original image <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) Image <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math> with additive Gaussian noise and salt and pepper noise.</p> Full article ">Figure 12
<p>Edge detection and layering of <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math> with mixed noise. (<b>a</b>) Candidate edges by Canny detection; (<b>b</b>) Structural edges by filtering based on length and linearity; (<b>c</b>) Layering result of <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, in which edges in different layers are rendered with different colors.</p> Full article ">Figure 13
<p>Arc-based edge connection, in which yellow dots are previous edges and magenta dots are arc-based connections. (<b>a</b>) Edge connection of <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) Edge connection of <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p><math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> connection and <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> refinement. (<b>a</b>) Arc-based connection of <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> with the enhanced grayscale image as base map, in which yellow dots are <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math>, blue dots denote search range and magenta asterisks are accepted search points; (<b>b</b>) Partial enlargement of (<b>a</b>); (<b>c</b>) LTBC to a mutation in edge connection result, in which yellow dots are initial closed <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> and red dots are refined results after corrections to detected mutation.</p> Full article ">Figure 15
<p>General view of cross-section measurement. (<b>a</b>) Perimeter and thickness for <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, in which red dots are extracted contour points, blue asterisks are vertexes of contour convex hull, magenta lines denote minimum thickness <math display="inline"><semantics> <mrow> <msub> <mrow> <mo stretchy="false">(</mo> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo stretchy="false">)</mo> </mrow> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math>, and green lines are the other five thickness measurements clockwise; (<b>b</b>) Perimeter and thickness for <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Image of actual specimen cross-section.</p> Full article ">Figure 17
<p>Coarse contour extraction of metal layers. (<b>a</b>) Extracted <math display="inline"><semantics> <mrow> <mi>r</mi> <msub> <mi>C</mi> <mrow> <mn>12</mn> </mrow> </msub> </mrow> </semantics></math> plotted with red dots on <math display="inline"><semantics> <msup> <mi>U</mi> <mo>′</mo> </msup> </semantics></math> component; (<b>b</b>) Extracted <math display="inline"><semantics> <mrow> <mi>r</mi> <msub> <mi>C</mi> <mrow> <mn>67</mn> </mrow> </msub> </mrow> </semantics></math> in blue dots and <math display="inline"><semantics> <mrow> <mi>r</mi> <msub> <mi>C</mi> <mrow> <mn>78</mn> </mrow> </msub> </mrow> </semantics></math> in green dots; (<b>c</b>) Partial enlargement of (<b>b</b>), in which deviations are circled.</p> Full article ">Figure 18
<p>Edge detection. (<b>a</b>) Candidate edges by Canny detection. (<b>b</b>) Structural edges by filtering based on length and linearity features.</p> Full article ">Figure 19
<p>Edge layering. (<b>a</b>) Extraction based on previous coarse contours, in which edges in different layers are rendered with different colors; (<b>b</b>) Annulus division of the left structural edges, in which different annuli are rendered with different colors; (<b>c</b>) Annuli with structural edges.</p> Full article ">Figure 20
<p>Layering result of edge points. (<b>a</b>) Layered edges plotted in red dots; (<b>b</b>) Partial enlargement of (<b>a</b>), in which red dots are layered edges, green lines are the fitted circular contour, and interferences and discontinuity exist in blue and magenta ellipses.</p> Full article ">Figure 21
<p>Arc-based edge connection result. (<b>a</b>) All connection locations, in which yellow dots are previous result and magenta dots are connections; (<b>b</b>) Closed initial <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>k</mi> </msub> </mrow> </semantics></math> after arc connection.</p> Full article ">Figure 22
<p><math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> connection plotted on enhanced image. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> connection, in which yellow dots are previous <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> and magenta dots are connections; (<b>b</b>) Partial enlargement of (<b>a</b>) with accurate connections; (<b>c</b>,<b>d</b>) are partial enlargements of (a) with inaccurate <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>d</mi> <mi>g</mi> <msub> <mi>e</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math> in yellow.</p> Full article ">Figure 23
<p>LTBC to detected mutations, in which yellow dots are initial closed contours and red dots are corrections. (<b>a</b>) LTBC to a detected P-Mutation in <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mn>45</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) LTBC to detected P-Mutations in <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mn>56</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 24
<p>General view of cross-section measurement. (<b>a</b>) Perimeter measurement, in which red dots are extracted contour and blue asterisks are vertexes of contour convex hull; (<b>b</b>) Thickness measurement, in which magenta line denotes <math display="inline"><semantics> <mrow> <msub> <mrow> <mo stretchy="false">(</mo> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo stretchy="false">)</mo> </mrow> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math> and green lines are the other five measurements clockwise.</p> Full article ">Figure 25
<p>Cutting burrs of outermost contour.</p> Full article ">Figure 26
<p>Coordinates differences between mapped and actual chessboard world coordinates of chessboard corners. <b>(</b><b>a)</b> The coordinates differences in two experiments of simulated cable images; (<b>b</b>) The coordinates differences in four experiments of actual cable images.</p> Full article ">
14 pages, 2681 KiB  
Article
Improved Performance of a PV Integrated Ventilated Façade at an Existing nZEB
by Ana Tejero-González, Dorota Anna Krawczyk, José Ramón Martín-Sanz García, Francisco Javier Rey-Martínez and Eloy Velasco-Gómez
Energies 2019, 12(15), 3033; https://doi.org/10.3390/en12153033 - 6 Aug 2019
Cited by 5 | Viewed by 3367
Abstract
Ventilated façades are among the existing measures to reduce the energy demand in buildings. The combination of this passive heating and cooling strategy with photovoltaics (PV) can drive new buildings towards the current European targets near or even to net zero energy Buildings [...] Read more.
Ventilated façades are among the existing measures to reduce the energy demand in buildings. The combination of this passive heating and cooling strategy with photovoltaics (PV) can drive new buildings towards the current European targets near or even to net zero energy Buildings (nZEB). The present work studies the thermal behavior of the PV integrated ventilated façade applied in the nZEB known as “LUCIA” (acronym in Spanish for “University Centre to Launch Applied Research”) at the University of Valladolid, Spain. The aim is to evaluate the interest of recirculating indoor air within the façade during winter, as an alternative to the present preferred operating mode during the target season, in which the façade acts as further insulation. First, the radiant properties of the PV façade are measured to use the values in a mathematical model that describes the behavior of the ventilated façade in its current operating mode in winter. Then, the solar radiation available, the air-dry bulb temperatures indoors, outdoors and inside the ventilated façade are monitored to obtain experimental data to validate the model. The results show that air recirculation can entail favorable heat gains during 10% of winter, being this alternative preferable to the present operating mode when outdoor temperatures are over 18.4 °C. Full article
(This article belongs to the Special Issue Innovations-Sustainability-Modernity-Openness in Energy Research 2019)
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Graphical abstract
Full article ">Figure 1
<p>Different views of the ventilated façade with photovoltaics (PV) modules and zones studied in the model.</p> Full article ">Figure 2
<p>Schemes of the façade current possible operating modes; (<b>a</b>) air circulation, and (<b>b</b>) additional insulation; and proposed operating mode (<b>c</b>) air recirculation.</p> Full article ">Figure 3
<p>Position of DBT/RH sensors within the façade (positions for an observer inside the building).</p> Full article ">Figure 4
<p>Outdoor, indoor and façade temperatures registered from 5 November to 5 December.</p> Full article ">Figure 5
<p>Outdoor, indoor and façade temperatures registered from 18–20 February.</p> Full article ">Figure 6
<p>Absolute error of the predicted temperature inside the façade to the measured value from 5–15 November.</p> Full article ">Figure 7
<p>Absolute error of the predicted temperature inside the façade to the standard deviation of the measured value from 5–15 November.</p> Full article ">Figure 8
<p>Periods for positive heat gains through air recirculation inside the façade.</p> Full article ">
13 pages, 3740 KiB  
Article
High Step-up Coupled Inductor Inverters Based on qSBIs
by Hongchen Liu, Xi Su and Junxiong Wang
Energies 2019, 12(15), 3032; https://doi.org/10.3390/en12153032 - 6 Aug 2019
Cited by 4 | Viewed by 3069
Abstract
In this paper, two types of high step-up coupled inductor inverters based on qSBIs (quasi- switched boost inverters) are proposed. By applying the coupled inductor to the qSBIs, the voltage gain of the proposed inverter is regulated by turn ratio and duty ratio. [...] Read more.
In this paper, two types of high step-up coupled inductor inverters based on qSBIs (quasi- switched boost inverters) are proposed. By applying the coupled inductor to the qSBIs, the voltage gain of the proposed inverter is regulated by turn ratio and duty ratio. Thus, a high voltage gain can be achieved without the circuits operating at the extreme duty cycle by choosing a suitable turn ratio of the coupled inductor. In addition, the proposed circuits have the characteristics of continuous input current and low voltage stress across the passive components. A boost unit can be added to the proposed inverters for further improvement of the voltage gain. In this paper, the working principle, steady state analysis, and the comparisons of the proposed inverter with other impedance-source inverters are described. A 200 W prototype was created and the experimental results confirm the correctness of the analysis in this paper. Full article
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<p>(<b>a</b>) Type I qSBI and (<b>b</b>) type II qSBI (qSBIs: quasi-switched boost inverters).</p> Full article ">Figure 2
<p>The topology of the proposed inverters (<b>a</b>) Type 1; (<b>b</b>) Type 2.</p> Full article ">Figure 3
<p>Equivalent circuit of the proposed type 1 inverter.</p> Full article ">Figure 4
<p>Typical waveforms of the proposed converter during one switching period.</p> Full article ">Figure 5
<p>Operating modes of the proposed inverter. (<b>a</b>) stage I; (<b>b</b>). stage II; (<b>c</b>). stage III; (<b>d</b>). stage IV.</p> Full article ">Figure 6
<p>(<b>a</b>) Space vector distribution; (<b>b</b>) vector synthesis.</p> Full article ">Figure 7
<p>The vectors sequence of seven-part SVPWM scheme. (SVPWM: Space Vector Pulse Width Modulation).</p> Full article ">Figure 8
<p>Extended circuit of the proposed inverter.</p> Full article ">Figure 9
<p>Boost factor comparisons.</p> Full article ">Figure 10
<p>The voltage stress of the switch comparisons.</p> Full article ">Figure 11
<p>The voltage stress of the capacitors comparisons.</p> Full article ">Figure 12
<p>The prototype of the experimental setup.</p> Full article ">Figure 13
<p>Experimental waveforms for the proposed inverter: (<b>a</b>) The input voltage <span class="html-italic">V<sub>in</sub></span> and the peak DC-link voltages <span class="html-italic">V<sub>PN</sub></span>; (<b>b</b>) The voltage of the capacitor <span class="html-italic">C</span><sub>1</sub> and <span class="html-italic">C</span><sub>2</sub>; (<b>c</b>) The current of <span class="html-italic">N</span><sub>1</sub> and <span class="html-italic">N</span><sub>2</sub>; (<b>d</b>) The voltage stresses of diode <span class="html-italic">D</span><sub>1</sub> and <span class="html-italic">D</span><sub>2</sub>; (<b>e</b>) The voltage and current of the switch <span class="html-italic">S</span>; (<b>f</b>) The output voltage <span class="html-italic">V<sub>ac</sub></span> and the output current <span class="html-italic">i<sub>a</sub></span>.</p> Full article ">Figure 14
<p>Measured efficiency of the converter.</p> Full article ">
24 pages, 5463 KiB  
Article
Life Cycle Assessment of Fuel Cell Vehicles Considering the Detailed Vehicle Components: Comparison and Scenario Analysis in China Based on Different Hydrogen Production Schemes
by Yisong Chen, Xu Hu and Jiahui Liu
Energies 2019, 12(15), 3031; https://doi.org/10.3390/en12153031 - 6 Aug 2019
Cited by 32 | Viewed by 9069
Abstract
Numerous studies concerning the life cycle assessment of fuel cell vehicles (FCVs) have been conducted. However, little attention has been paid to the life cycle assessment of an FCV from the perspective of the detailed vehicle components. This work conducts the life cycle [...] Read more.
Numerous studies concerning the life cycle assessment of fuel cell vehicles (FCVs) have been conducted. However, little attention has been paid to the life cycle assessment of an FCV from the perspective of the detailed vehicle components. This work conducts the life cycle assessment of Toyota Mirai with all major components considered in a Chinese context. Both the vehicle cycle and the fuel cycle are included. Both comprehensive resources and energy consumption and comprehensive environmental emissions of the life cycles are investigated. Potential environmental impacts are further explored based on CML 2001 method. Then different hydrogen production schemes are compared to obtain the most favorable solution. To explore the potential of the electrolysis, the scenario analysis of the power structure is conducted. The results show that the most mineral resources are consumed in the raw material acquisition stage, the most fossil energy is consumed in the use stage and global warming potential (GWP) value is fairly high in all life cycle stages of Toyota Mirai using electrolyzed hydrogen. For hydrogen production schemes, the scenario analysis indicates that simply by optimizing the power structure, the environmental impact of the electrolysis remains higher than other schemes. When using the electricity from hydropower or wind power, the best choice will be the electrolysis. Full article
(This article belongs to the Section B: Energy and Environment)
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Graphical abstract
Full article ">Figure 1
<p>System boundaries.</p> Full article ">Figure 2
<p>Energy consumption at all stages of the life cycle.</p> Full article ">Figure 3
<p>Component-wise energy consumption in the raw material acquisition stage for the FCV.</p> Full article ">Figure 4
<p>Component-wise energy consumption during the parts manufacturing stage for the FCV.</p> Full article ">Figure 5
<p>Emissions of various stages in the life cycle.</p> Full article ">Figure 6
<p>Statistics of environmental emissions for various parts during raw material acquisition.</p> Full article ">Figure 7
<p>Environmental emissions from the manufacturing stage of major parts of the FCV.</p> Full article ">Figure 8
<p>Results of the mineral resource consumption for the life cycle of Toyota Mirai.</p> Full article ">Figure 9
<p>Results of the fossil fuel energy consumption for the life cycle of Toyota Mirai.</p> Full article ">Figure 10
<p>Environmental emissions for the life cycle of Toyota Mirai.</p> Full article ">Figure 11
<p>Normalized and quantitative results of five types of environmental impacts of the four hydrogen production schemes.</p> Full article ">Figure 12
<p>Comprehensive environmental impact of four hydrogen production schemes and the three improved scenarios.</p> Full article ">Figure 13
<p>Environmental impact comparison between the electrolysis from single clean electrical energy and other hydrogen production schemes.</p> Full article ">
26 pages, 1438 KiB  
Article
Load Areas in Radial Unbalanced Distribution Systems
by Giovanni M. Casolino and Arturo Losi
Energies 2019, 12(15), 3030; https://doi.org/10.3390/en12153030 - 6 Aug 2019
Cited by 7 | Viewed by 2932
Abstract
The demand becoming flexible is a requirement for the full exploitation of renewable energy sources. Aggregation may foster the provision of flexibility by small-scale providers connected to distribution grids, since it allows offering significant flexibility volumes to the market. The aggregation of flexibility [...] Read more.
The demand becoming flexible is a requirement for the full exploitation of renewable energy sources. Aggregation may foster the provision of flexibility by small-scale providers connected to distribution grids, since it allows offering significant flexibility volumes to the market. The aggregation of flexibility providers is carried out by the aggregator, a new market role and possibly a new market player. Location information of individual flexibility providers is necessary for both the aggregator and the system operators, in particular, the Distribution System Operator (DSO). For the former, information should allow treating a high number of individual flexibility providers as a single provider to offer significant flexibility volumes to the markets; for the latter, the information should ensure an adequate visibility of the connection of the individual providers to the grid. In the paper, the concept of Load Area (LA) is recalled, which combines the needs of location information of the aggregator and of the DSO. A method for the identification and modeling of LAs for the general case of unbalanced radial systems is proposed. The results of the methods’ application to two studied unbalanced networks are presented, showing the effectiveness and viability of the proposed approach. Full article
(This article belongs to the Special Issue Distribution System Optimization)
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<p>Possible Load Areas (LAs) in a distribution system [<a href="#B19-energies-12-03030" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Functional block diagram to obtain b-LAs.</p> Full article ">Figure 3
<p>Network example.</p> Full article ">Figure 4
<p>Phase Voltage Load Areas (ph-VLAs) with coloured bold stripes (similar results for phase Overload Load Areas (ph-OLAs)).</p> Full article ">Figure 5
<p>From phase to bus clusters (either OLAs or VLAs).</p> Full article ">Figure 6
<p>b-VLAs resulting from ph-VLAs (similar results for b-OLAs).</p> Full article ">Figure 7
<p>Example of b-LAs.</p> Full article ">Figure 8
<p>Small-size grid.</p> Full article ">Figure 9
<p>Small-size grid—(<b>a</b>) b-OLAs; (<b>b</b>) b-VLAs; (<b>c</b>) b-LAs; and (<b>d</b>) compact representation by b-LAs.</p> Full article ">Figure 10
<p>Voltage sensitivity analysis for the small-size grid—(<b>a</b>) phase 1; (<b>b</b>) phase 2; and (<b>c</b>) phase 3.</p> Full article ">Figure 11
<p>Small grid voltage error, for both the models of Equation (<a href="#FD21-energies-12-03030" class="html-disp-formula">21</a>) and of Equation (<a href="#FD18-energies-12-03030" class="html-disp-formula">18</a>), with <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>l</mi> </msub> <mo>=</mo> <mn>0.008</mn> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>The 123-bus grid.</p> Full article ">Figure 13
<p>The 123-bus grid—(<b>a</b>) b-OLAs; (<b>b</b>) b-VLAs; (<b>c</b>) b-LAs; and (<b>d</b>) compact representation by b-LAs.</p> Full article ">Figure 14
<p>The 123-bus grid voltage error for both the models in Equation (<a href="#FD21-energies-12-03030" class="html-disp-formula">21</a>) and Equation (<a href="#FD18-energies-12-03030" class="html-disp-formula">18</a>), with <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>l</mi> </msub> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math>.</p> Full article ">Figure A1
<p>Power factor characteristic for medium/large DG plants [<a href="#B20-energies-12-03030" class="html-bibr">20</a>].</p> Full article ">
20 pages, 9481 KiB  
Article
Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control
by Shuang Feng, Chaofan Wei and Jiaxing Lei
Energies 2019, 12(15), 3029; https://doi.org/10.3390/en12153029 - 6 Aug 2019
Cited by 10 | Viewed by 2393
Abstract
In this paper, an improved model predictive control (MPC) is proposed for the matrix converter (MC). First, the conventional MPC which adopts the separately discretized prediction models is discussed. It shows that the conventional MPC ignores the input–output interaction in every sampling period. [...] Read more.
In this paper, an improved model predictive control (MPC) is proposed for the matrix converter (MC). First, the conventional MPC which adopts the separately discretized prediction models is discussed. It shows that the conventional MPC ignores the input–output interaction in every sampling period. Consequently, additional prediction errors arise, resulting in more current harmonics. Second, the principle of the improved MPC is presented. With the interaction considered, the integral state-space equation of the whole MC system is constructed and discretized to obtain the precise model. The eigenvalue analysis shows that the proposed prediction model has the same eigenvalues with the continuous model, and thus is more accurate than the conventional one to describe the MC’s behavior in every sampling period. Finally, experimental results under various working conditions prove that the proposed approach can always increase the control accuracy and reduce the harmonic distortions, which in turn requires smaller filter components. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Basic schematic of the matrix converter (MC).</p> Full article ">Figure 2
<p>Control flowchart of the conventional MPC scheme.</p> Full article ">Figure 3
<p>Prediction performance with the conventional and improved model predictive control (MPC) schemes: (<b>a</b>) the α-axis input voltage <span class="html-italic">u</span><sub>iα</sub>; (<b>b</b>) the α-axis output current <span class="html-italic">i</span><sub>oα</sub>.</p> Full article ">Figure 4
<p>Control flowchart of the improved MPC with the precise prediction model.</p> Full article ">Figure 5
<p>Picture of the experimental prototype.</p> Full article ">Figure 6
<p>Experimental results of Case 1: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. Experimental conditions are normal.</p> Full article ">Figure 7
<p>Experimental results of Case 2: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. The sampling time is increased to 40 μs.</p> Full article ">Figure 8
<p>Experimental results of Case 3: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. Parameters of the prediction models are inaccurate.</p> Full article ">Figure 9
<p>Experimental results of Case 4: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. Output filter inductor is changed to 2.51 mH.</p> Full article ">Figure 10
<p>Experimental results of Case 5: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. Source voltages are unbalanced and distorted.</p> Full article ">Figure 11
<p>Experimental results of Case 6: (<b>a</b>) with the conventional MPC and (<b>b</b>) with the improved MPC. The reference amplitude of the output current steps between 10A and 5A.</p> Full article ">
17 pages, 1586 KiB  
Article
Using Biofuels for Highly Renewable Electricity Systems: A Case Study of the Jatropha curcas
by Petr Procházka, Luboš Smutka and Vladimír Hönig
Energies 2019, 12(15), 3028; https://doi.org/10.3390/en12153028 - 6 Aug 2019
Cited by 3 | Viewed by 3716
Abstract
Recent movements for the decarbonization of the electricity sector have become a priority for many countries around the world and will inevitably lead to the sharp decline of fossil-fuel-based energy. Energy from fossil fuels is to be replaced by renewable energy sources (RES), [...] Read more.
Recent movements for the decarbonization of the electricity sector have become a priority for many countries around the world and will inevitably lead to the sharp decline of fossil-fuel-based energy. Energy from fossil fuels is to be replaced by renewable energy sources (RES), although the transition will neither be cheap nor smooth. One sustainable and environmentally friendly alternative to fossil fuels and which will take a considerable share in the increasing supply of renewable energy resources is biofuels. There are various types of biofuels used in practice; however, biodiesels represent one of the most popular and widespread ones. This paper focuses as a case study on the byproducts of Jatropha curcas, a crop and a plant that is already used for biofuel production and which is subsequently employed in electricity generation in Jatropha curcas producing regions. This paper identifies the limitations and prospects of Jatropha curcas utilization. Also, Jatropha curcas is compared to other materials suitable for biomass generation. An economic analysis for a 2 MW biofuel powerplant was conducted incorporating various market-related risks. The study shows that at current prices, net profitability can be achieved using Jatropha curcas byproducts for producing electricity. Full article
(This article belongs to the Special Issue Market Design for a High-Renewables Electricity System)
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<p>Contribution of this article to Sustainable Development Goals [<a href="#B1-energies-12-03028" class="html-bibr">1</a>]. Source: Own results based on Reference [<a href="#B1-energies-12-03028" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Usage of <span class="html-italic">Jatropha curcas</span>. Source: Own results.</p> Full article ">Figure 3
<p><span class="html-italic">Jatropha curcas</span> biogas power plant. Source: Own results.</p> Full article ">Figure 4
<p>Evolution of <span class="html-italic">Jatropha curcas</span> price since 1999. Source: Own results.</p> Full article ">Figure 5
<p>Prediction of future price for palm oil. Source: Own results.</p> Full article ">Figure 6
<p>Net present value calculations. Source: Own results.</p> Full article ">
13 pages, 2598 KiB  
Article
Potential of District Cooling Systems: A Case Study on Recovering Cold Energy from Liquefied Natural Gas Vaporization
by Alice Mugnini, Gianluca Coccia, Fabio Polonara and Alessia Arteconi
Energies 2019, 12(15), 3027; https://doi.org/10.3390/en12153027 - 6 Aug 2019
Cited by 11 | Viewed by 5244
Abstract
District cooling systems (DCSs) are networks able to distribute thermal energy, usually as chilled water, from a central source to industrial, commercial, and residential consumers, to be used for space cooling/dehumidification. As cooling demand will increase significantly in the next decades, DCSs can [...] Read more.
District cooling systems (DCSs) are networks able to distribute thermal energy, usually as chilled water, from a central source to industrial, commercial, and residential consumers, to be used for space cooling/dehumidification. As cooling demand will increase significantly in the next decades, DCSs can be seen as efficient solutions to improve sustainability. Although DCSs are considered so relevant for new city developments, there are still many technical, economic, and social issues to be overcome to let such systems to spread out. Thus, this paper aims to highlight the advantages and issues linked to the adoption of DCSs for building cooling when cold is recovered from a specific application. A case study based on liquified natural gas (LNG) cold energy recovery from the transport sector is presented. Starting from the estimation of the free cooling availability, a DCS design method is proposed and the potential energy saving is investigated. Results show that a DCS using the cold waste derived from LNG can provide a relevant amount of electricity saving (about 60%) for space cooling compared to traditional solutions, in which standard air conditioning systems are installed in every building. Full article
(This article belongs to the Special Issue Refrigeration, Air Conditioning and Heat Pumps: Energy and Environmental Issues)
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Full article ">Figure 1
<p>Plant scheme: cooling energy recovery from liquid to compressed natural gas (L–CNG) vaporizer to fulfil a residential district cooling systems (DCS) (focus on the cooling supply side).</p> Full article ">Figure 2
<p>Daily cooling power recovery from a L–CNG fuel station. (<b>a</b>): constant profile for a station working every day of the year (P1); (<b>b</b>): constant profile for a station not working during weekends and stopping during lunch time (P2); (<b>c</b>): variable profile for a station not working during weekends and stopping during lunch time (P3).</p> Full article ">Figure 3
<p>District daily cooling energy demand (July and August).</p> Full article ">Figure 4
<p>Residential district cooling network scheme (see <a href="#energies-12-03027-f001" class="html-fig">Figure 1</a> for the details of the supply side). Focus on a single user configuration.</p> Full article ">Figure 5
<p>Total district electric consumption and users’ cold energy demand divided into the share provided by DCS and by the backup systems (i.e., HP) with different free cooling power profile (months of July and August).</p> Full article ">Figure 6
<p>Total daily district cooling power demand in the reference case.</p> Full article ">Figure 7
<p>Total district electric consumption and users’ cold energy demand divided into the share provided by DCS and by the backup systems (i.e., HP) with different free cooling power profile (months of July and August) in presence of a cold-water tank of 60 m<sup>3</sup>.</p> Full article ">Figure 8
<p>Daily cooling demand breakdown into the DCS and HP share in case of cooling supply profile P3: (<b>a</b>) without a TES; (<b>b</b>) in presence of a cold-water tank of 60 m<sup>3</sup>.</p> Full article ">Figure 9
<p>Percentage share of cooling from DCS to meet the total users’ demand and electricity saving for different thermal energy storage (TES) sizes.</p> Full article ">
25 pages, 6872 KiB  
Article
Multi-Objective Optimization of Off-Grid Hybrid Renewable Energy Systems in Buildings with Prior Design-Variable Screening
by Paolo Conti, Giovanni Lutzemberger, Eva Schito, Davide Poli and Daniele Testi
Energies 2019, 12(15), 3026; https://doi.org/10.3390/en12153026 - 6 Aug 2019
Cited by 17 | Viewed by 3694
Abstract
This work presents an optimization strategy and the cost-optimal design of an off-grid building served by an energy system involving solar technologies, thermal and electrochemical storages. Independently from the multi-objective method (e.g., utility function) and algorithm used (e.g., genetic algorithms), the optimization of [...] Read more.
This work presents an optimization strategy and the cost-optimal design of an off-grid building served by an energy system involving solar technologies, thermal and electrochemical storages. Independently from the multi-objective method (e.g., utility function) and algorithm used (e.g., genetic algorithms), the optimization of this kind of systems is typically characterized by a high-dimensional variables space, computational effort and results uncertainty (e.g., local minimum solutions). Instead of focusing on advanced optimization tools to handle the design problem, the dimension of the full problem has been reduced, only considering the design variables with a high “effect” on the objective functions. An off-grid accommodation building is presented as test case: the original six-variable design problem consisting of about 300,000 possible configurations is reduced to a two-variable problem, after the analysis of 870 Monte Carlo simulations. The new problem includes only 220 possible design alternatives with a clear benefit for the multi-objective optimization algorithm. The energy-economy Pareto frontiers obtained by the original and the reduced problems overlap, showing the validity of the proposed methodology. The No-RES (no renewable energy sources) primary energy consumption can be reduced up to almost 0 kWh/(m2yr) and the net present value (NPV) after 20 years can reach 70 k€ depending on the number of photovoltaic panels and electrochemical storage size. The reduction of the problem also allows for a plain analysis of the results and the drawing of handy decision charts to help the investor/designer in finding the best design according to the specific investment availability and target performances. The configurations on the Pareto frontier are characterized by a notable electrical overproduction and a ratio between the two main design variables that goes from 4 to 8 h. A sensitivity analysis to the unitary price of the electrochemical storage reveals the robustness of the sizing criterion. Full article
(This article belongs to the Special Issue Solar Thermal Energy Storage and Conversion)
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Graphical abstract
Full article ">Figure 1
<p>Schematization of the overall building system.</p> Full article ">Figure 2
<p>Number of allowed charging-discharging cycles vs. depth of discharge.</p> Full article ">Figure 3
<p>Pareto frontiers of the full (477 points) and reduced (15 points) optimization problems. The <span class="html-italic">No-RES</span> configuration is shown with a green marker.</p> Full article ">Figure 4
<p>Number of photovoltaic (<span class="html-italic">PV</span>) modules for the configurations on the Pareto Frontier.</p> Full article ">Figure 5
<p>Nominal electrochemical storage (<span class="html-italic">ES</span>) energy for the configurations on the Pareto Frontier.</p> Full article ">Figure 6
<p>Energy consumption and net present value as a function of the number of <span class="html-italic">PV</span> modules on the Pareto frontier.</p> Full article ">Figure 7
<p>Energy consumption and net present value as a function of the nominal <span class="html-italic">ES</span> energy on the Pareto frontier.</p> Full article ">Figure 8
<p>Internal rate of return (<span class="html-italic">IRR</span>) and initial cost as a function of the number of <span class="html-italic">PV</span> modules number.</p> Full article ">Figure 9
<p><span class="html-italic">IRR</span> and initial cost as a function of the nominal <span class="html-italic">ES</span> capacity.</p> Full article ">Figure 10
<p>Box and whisker plot of the heating demand percentage directly delivered by the heat pump (<span class="html-italic">HP</span>) and thermal storage (<span class="html-italic">TS</span>).</p> Full article ">Figure 11
<p>Box and whisker plot of the energy percentage delivered to the thermal storage by the different sources.</p> Full article ">Figure 12
<p>Box and whisker plot of the percentage of the thermal storage losses, overproduction, domestic hot water (<span class="html-italic">DHW</span>), and heating output with respect to the total energy input.</p> Full article ">Figure 13
<p>Box and whisker plot of the electrical production percentage by the <span class="html-italic">PV</span> modules and <span class="html-italic">CHP</span> unit.</p> Full article ">Figure 14
<p>Box and whisker plot of the mean and median values of the <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>o</mi> <msub> <mi>C</mi> <mrow> <mi>E</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Box and whisker plot of the percentage of the electrical uses with respect to the total electricity production.</p> Full article ">Figure 16
<p>Correlation coefficient between the design variables and the <span class="html-italic">No-RES</span> (renewable energy sources) primary energy consumption, depending on <span class="html-italic">ES</span> unitary price.</p> Full article ">Figure 17
<p>Correlation coefficient between the design variables and the net present value, depending on <span class="html-italic">ES</span> unitary price.</p> Full article ">Figure 18
<p>Sensitivity analysis of the Pareto frontier depending on the <span class="html-italic">ES</span> unitary cost.</p> Full article ">Figure 19
<p>Ratio between the nominal electrochemical storage capacity and the <span class="html-italic">PV</span> capacity depending on <span class="html-italic">ES</span> unitary price.</p> Full article ">Figure 20
<p>Box and whisker plot of the percentage of the electrical overproduction with respect to the total electricity production, depending on the <span class="html-italic">ES</span> unitary cost.</p> Full article ">Figure 21
<p>Box and whisker plot of the median value of the electrochemical storage <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>o</mi> <msub> <mi>C</mi> <mrow> <mi>E</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math>, depending on the <span class="html-italic">ES</span> unitary cost.</p> Full article ">
17 pages, 10414 KiB  
Article
Electric Vehicle Battery Simulation System for Mobile Field Test of Off-Board Charger
by Xiangwu Yan, Ling Wang, Zhichao Chai, Shuaishuai Zhao, Zisheng Liu and Xuewei Sun
Energies 2019, 12(15), 3025; https://doi.org/10.3390/en12153025 - 6 Aug 2019
Cited by 3 | Viewed by 3174
Abstract
An electric vehicle power battery simulation system simulating different power battery packs for the field test of the off-board charger is designed, which can be used to test the performance of an off-board charger. Specifically, the improved power battery model is combined with [...] Read more.
An electric vehicle power battery simulation system simulating different power battery packs for the field test of the off-board charger is designed, which can be used to test the performance of an off-board charger. Specifically, the improved power battery model is combined with the improved lightweight charging load and the online estimation of the state of charge as well as the electromotive force of the battery model are used to adjust charging load parameters in real time to simulate the charging response. An acceleration coefficient is introduced into the traditional battery model to improve test efficiency, and the type, specification, temperature and voltage parameters of the battery can be set online according to the test requirements. An improved charging load scheme is proposed, in which a DC converter cascaded power battery pack of the mobile test vehicle is used to form a lightweight charging load with the mode of constant voltage, constant current, constant power and constant resistance and the ability to be adjusted continuously within the rated range. As a result, the size and weight of the charging load are reduced and the autonomous test of the off-board charger is realized. The performances of the proposed battery simulation system are validated through the various experimental results. Full article
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<p>Function chart of the battery simulation system.</p> Full article ">Figure 2
<p>Dynamic circuit model of power cell.</p> Full article ">Figure 3
<p>Calculation flow chart of simulation battery system.</p> Full article ">Figure 4
<p>Topology of the lightweight adjustable charging load.</p> Full article ">Figure 5
<p>Control diagram of Boost converter.</p> Full article ">Figure 6
<p>Control diagram of Buck converter.</p> Full article ">Figure 7
<p>Structure diagram of control system with single-phase adjustable charging load working in constant voltage mode.</p> Full article ">Figure 8
<p>Waveform of input voltage in the constant voltage mode.</p> Full article ">Figure 9
<p>Waveform of cascading-side voltage in the constant voltage mode.</p> Full article ">Figure 10
<p>Waveform of output current in the constant voltage mode.</p> Full article ">Figure 11
<p>Waveform of input current in the constant current mode.</p> Full article ">Figure 12
<p>Waveform of cascading-side voltage in the constant current mode.</p> Full article ">Figure 13
<p>Waveform of output current in the constant current mode.</p> Full article ">Figure 14
<p>Waveform of input power in the constant power mode.</p> Full article ">Figure 15
<p>Waveform of cascading-side voltage in the constant power mode.</p> Full article ">Figure 16
<p>Waveform of output current in the constant power mode.</p> Full article ">Figure 17
<p>Waveform of load resistance in the constant resistance mode.</p> Full article ">Figure 18
<p>Waveform of cascading-side voltage in the constant resistance mode.</p> Full article ">Figure 19
<p>Waveform of output current in the constant resistance mode.</p> Full article ">Figure 20
<p>Real-time state interface of single cell.</p> Full article ">Figure 21
<p>Current waveform of battery pack in the constant current and constant voltage charging mode.</p> Full article ">Figure 22
<p>Voltage waveform of battery pack in the constant current and constant voltage charging mode.</p> Full article ">Figure 23
<p>SOC waveform of battery pack in the constant current and constant voltage charging mode.</p> Full article ">Figure 24
<p>Voltage waveform of cells in the constant current and constant voltage charging mode.</p> Full article ">Figure 25
<p>Temperature waveform of cells in the constant current and constant voltage charging mode.</p> Full article ">Figure 26
<p>Voltage waveform of normal cell and cell No.1 in the constant current charging mode.</p> Full article ">Figure 27
<p>Temperature waveform of normal cell and cell No.1 in the constant current charging mode.</p> Full article ">Figure 28
<p>Comparison of experiment results and simulation results of charging in 5 A intermittent current.</p> Full article ">Figure 29
<p>Comparison of experiment results and simulation results of charging in 6 A intermittent current.</p> Full article ">Figure 30
<p>Comparison of experiment results and simulation results of charging in 10 A intermittent current.</p> Full article ">Figure 31
<p>Comparison of experiment results and simulation results of charging in 20 A intermittent current.</p> Full article ">Figure 32
<p>Comparison of experiment results and simulation results of charging in “constant current 5 A- constant voltage 350 V”.</p> Full article ">
24 pages, 8807 KiB  
Article
A Control Strategy for Smooth Power Tracking of a Grid-Connected Virtual Synchronous Generator Based on Linear Active Disturbance Rejection Control
by Yaya Zhang, Jianzhong Zhu, Xueyu Dong, Pinchao Zhao, Peng Ge and Xiaolian Zhang
Energies 2019, 12(15), 3024; https://doi.org/10.3390/en12153024 - 6 Aug 2019
Cited by 6 | Viewed by 3340
Abstract
The power quality of new energy resources has received tremendous attention recently. The control approach for the inverter, an interface between the new energy resources, and the infinite bus system is of vital importance. For the virtual synchronous generator (VSG), one of the [...] Read more.
The power quality of new energy resources has received tremendous attention recently. The control approach for the inverter, an interface between the new energy resources, and the infinite bus system is of vital importance. For the virtual synchronous generator (VSG), one of the research hotspots in the inverter control field, there are some challenges remaining to be dealt with. First is the contradiction between the rapid response and overshoot of active power output if VSG is connected to the grid. Secondly, the active power is deeply influenced by the fluctuation of gird frequency and this may bring power oscillation to VSG in weak grids. In this article, an active power controller for power tracking of grid-connected VSG is designed based on linear active disturbance rejection control (LADRC) by compensating for the lumped disturbance in a feedforward fashion. The parameters of the controller are analyzed and tuned in the frequency domain to acquire a desirable control performance. Moreover, the robustness of the control system is also considered. Simulation results show that the designed control system can transmit active power to the grid in a timely manner with no overshoot, as demanded. Additionally, it can output active power steadily according to the power reference without using a phase-locked loop (PLL) when the grid frequency has different features of fluctuation. In addition, the simulation results demonstrate that the improved VSG has strong robustness to the model parameter perturbation and mismatch. Full article
(This article belongs to the Special Issue Control Strategies for Power Conversion Systems)
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<p>Topology and general control structure of grid-connected inverter.</p> Full article ">Figure 2
<p>Overall schematic control diagram of grid-connected virtual synchronous generator (VSG).</p> Full article ">Figure 3
<p>Equivalent diagram of grid-connected VSG.</p> Full article ">Figure 4
<p>Power control diagram of grid-connected VSG.</p> Full article ">Figure 5
<p>Structure of second-order linear active disturbance rejection control (LADRC).</p> Full article ">Figure 6
<p>Simplified structure of LADRC.</p> Full article ">Figure 7
<p>Control structure for power of VSG based on LADRC.</p> Full article ">Figure 8
<p>Pole-zero map of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>N</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> with changing <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> (the “<math display="inline"><semantics> <mo>×</mo> </semantics></math>” represents the pole points and “<math display="inline"><semantics> <mo>∘</mo> </semantics></math>” represents zero points; the same below).</p> Full article ">Figure 9
<p>Bode diagram of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>M</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> with changing <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> (different colors represent different gains; the same below).</p> Full article ">Figure 10
<p>Pole-zero map of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>N</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> with changing <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">o</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Bode diagram of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>M</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> with changing <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">o</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Pole-zero map of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>N</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics></math> changes.</p> Full article ">Figure 13
<p>Bode diagram of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mi>M</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mrow> <mi>Q</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mfrac> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics></math> changes.</p> Full article ">Figure 14
<p>The procedure of the operation of linear active disturbance rejection control-virtual synchronous generator (LADRC-VSG).</p> Full article ">Figure 15
<p>Frequencies of LADRC-VSG and grid measured by phase-locked loop (PLL).</p> Full article ">Figure 16
<p>Output active power of VSG under different control strategies.</p> Full article ">Figure 17
<p>Corresponding output reactive power of VSG under different control strategies.</p> Full article ">Figure 18
<p>Output current (Phase A) of LADRC-VSG.</p> Full article ">Figure 19
<p>Output current (Phase A) of conventional VSG.</p> Full article ">Figure 20
<p>Output current (Phase A) of PLL-VSG.</p> Full article ">Figure 21
<p>Output power of VSG under different control strategies.</p> Full article ">Figure 22
<p>Output power of VSG under different control strategies.</p> Full article ">Figure 23
<p>Output power of VSG under different control strategies.</p> Full article ">Figure 24
<p>Corresponding output reactive power of VSG under different control strategies.</p> Full article ">Figure 25
<p>Output angular frequencies of inverter under different control strategies.</p> Full article ">Figure 26
<p>Output power observed by linear extended state observer (LESO).</p> Full article ">Figure 27
<p>Derivative value of active power observed by LESO.</p> Full article ">Figure 28
<p>The lumped disturbance observed by LESO.</p> Full article ">Figure A1
<p>Control structure for the power of the VSG based on LADRC when the measurement noise is considered.</p> Full article ">Figure A2
<p>Bode diagram of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">e</mi> </msub> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> <mrow> <msub> <mi>P</mi> <mrow> <mi>noise</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> </mfrac> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> increases.</p> Full article ">Figure A3
<p>Bode diagram of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">e</mi> </msub> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> <mrow> <msub> <mi>P</mi> <mrow> <mi>noise</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> </mfrac> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi mathvariant="normal">o</mi> </msub> </mrow> </semantics></math> increases.</p> Full article ">
11 pages, 3774 KiB  
Article
Analysis of the Effect of the Variable Charging Current Control Method on Cycle Life of Li-ion Batteries
by In-Ho Cho, Pyeong-Yeon Lee and Jong-Hoon Kim
Energies 2019, 12(15), 3023; https://doi.org/10.3390/en12153023 - 6 Aug 2019
Cited by 39 | Viewed by 7507
Abstract
Applications of rechargeable batteries have recently expanded from small information technology (IT) devices to a wide range of other industrial sectors, including vehicles, rolling stocks, and energy storage system (ESS), as a part of efforts to reduce greenhouse gas emissions and enhance convenience. [...] Read more.
Applications of rechargeable batteries have recently expanded from small information technology (IT) devices to a wide range of other industrial sectors, including vehicles, rolling stocks, and energy storage system (ESS), as a part of efforts to reduce greenhouse gas emissions and enhance convenience. The capacity of rechargeable batteries adopted in individual products is meanwhile increasing and the price of the batteries in such products has become an important factor in determining the product price. In the case of electric vehicles, the price of batteries has increased to more than 40% of the total product cost. In response, various battery management technologies are being studied to increase the service life of products with large-capacity batteries and reduce maintenance costs. In this paper, a charging algorithm to increase the service life of batteries is proposed. The proposed charging algorithm controls charging current in anticipation of heating inside the battery while the battery is being charged. The validity of the proposed charging algorithm is verified through an experiment to compare charging cycles using high-capacity type lithium-ion cells and high-power type lithium-ion cells. Full article
(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)
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Graphical abstract
Full article ">Figure 1
<p>Battery charging profiles: (<b>a</b>) Constant current-constant voltage (CC-CV) charging method; (<b>b</b>) Multi-stage constant current (MCC) charging method; (<b>c</b>) Constant power (CP) charging method; (<b>d</b>) Boost charging method.</p> Full article ">Figure 2
<p>Configuration of the equivalent circuit model.</p> Full article ">Figure 3
<p>Hybrid pulse power characterization (HPPC) parameter calculation method: (<b>a</b>) Charging-discharging profile; (<b>b</b>) Parameters estimation method.</p> Full article ">Figure 4
<p>Measured internal resistance of INR 25R battery as it aged: (<b>a</b>) Internal ohmic resistance, <span class="html-italic">R<sub>i</sub></span>; (<b>b</b>) Internal diffusion resistance, <span class="html-italic">R<sub>diff</sub>.</span></p> Full article ">Figure 5
<p>Experimental procedure used to measure the internal resistance.</p> Full article ">Figure 6
<p>Hybrid pulse power characterization (HPPC) parameter calculation method: (<b>a</b>) DCIR of cell with NCA material (CC-CV); (<b>b</b>) DCIR of cell with NMC material (CC-CV); (<b>c</b>) DCIR of cell with NCA material (VCC); (<b>d</b>) DCIR of cell with NMC material (VCC); (<b>e</b>) comparison of internal resistance (NCA); (<b>f</b>) comparison of internal resistance (NMC).</p> Full article ">Figure 6 Cont.
<p>Hybrid pulse power characterization (HPPC) parameter calculation method: (<b>a</b>) DCIR of cell with NCA material (CC-CV); (<b>b</b>) DCIR of cell with NMC material (CC-CV); (<b>c</b>) DCIR of cell with NCA material (VCC); (<b>d</b>) DCIR of cell with NMC material (VCC); (<b>e</b>) comparison of internal resistance (NCA); (<b>f</b>) comparison of internal resistance (NMC).</p> Full article ">Figure 7
<p>The discharging capacity by cycle number: (<b>a</b>) cell with NCA material; (<b>b</b>) cell with NMC material.</p> Full article ">Figure 8
<p>The charge time by cycle number: (<b>a</b>) cell with NCA material; (<b>b</b>) cell with NMC material.</p> Full article ">
16 pages, 2875 KiB  
Article
A Voltage-Based Approach for Series High Impedance Fault Detection and Location in Distribution Systems Using Smart Meters
by Francinei L. Vieira, Pedro H. M. Santos, José M. Carvalho Filho, Roberto C. Leborgne and Marino P. Leite
Energies 2019, 12(15), 3022; https://doi.org/10.3390/en12153022 - 6 Aug 2019
Cited by 14 | Viewed by 4907
Abstract
High impedance faults (HIFs) have been a major concern for protecting distribution systems and public safety hazards when involving downed conductors. The deployment of smarter grids brings new technologies for smart monitoring, automation, and protection of distribution networks. This paper presents a new [...] Read more.
High impedance faults (HIFs) have been a major concern for protecting distribution systems and public safety hazards when involving downed conductors. The deployment of smarter grids brings new technologies for smart monitoring, automation, and protection of distribution networks. This paper presents a new method for a series of HIF detection and location in primary distribution feeders, using voltage unbalance measurements collected from smart meters (SMs) installed at low-voltage end-users. The methodology was tested in MATLAB and Simulink through steady-state simulations of a typical 13.8 kV distribution system, under load unbalance and different fault scenarios. Results show that the proposed method is robust and accurate for the detection of blown fuses and broken conductors, with or without ground faults, located either at the source or the load-side. The ease of implementation in SM design, formulation of parameters, and reliable simulation results show potential real-life applications. Full article
(This article belongs to the Special Issue Fault Diagnosis on MV and HV Transmission Lines)
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Figure 1
<p>High impedance faults (HIF) types: (<b>a</b>) Passive series fault; (<b>b</b>) downed conductor, contact to the source-side; (<b>c</b>) downed conductor, contact to the load-side; and (<b>d</b>) shunt fault.</p> Full article ">Figure 2
<p>Smart meter (SM) diagram with new functionality for HIF detection.</p> Full article ">Figure 3
<p>Three-phase feeder diagram with Y-connected loads and an opening at phase A.</p> Full article ">Figure 4
<p>Flowchart of the proposed HIF location algorithm.</p> Full article ">Figure 5
<p>One-line diagram of the test distribution feeder (adapted from [<a href="#B28-energies-12-03022" class="html-bibr">28</a>]).</p> Full article ">Figure 6
<p>Faults on section DG.</p> Full article ">Figure 7
<p>Faults on section DE.</p> Full article ">Figure 8
<p>Faults on section CD.</p> Full article ">Figure 9
<p>Faults on section BC.</p> Full article ">
18 pages, 2283 KiB  
Article
Resource Security Strategies and Their Environmental and Economic Implications: A Case Study of Copper Production in Japan
by Ran Motoori, Benjamin McLellan, Andrew Chapman and Tetsuo Tezuka
Energies 2019, 12(15), 3021; https://doi.org/10.3390/en12153021 - 6 Aug 2019
Cited by 7 | Viewed by 3941
Abstract
Japan is a nation which is highly dependent on the import of raw materials to supply its manufacturing industry, notable among them copper. When extracting copper from ore, a large amount of energy is required, typically leading to high levels of CO2 [...] Read more.
Japan is a nation which is highly dependent on the import of raw materials to supply its manufacturing industry, notable among them copper. When extracting copper from ore, a large amount of energy is required, typically leading to high levels of CO2 emissions due to the fossil fuel-dominated energy mix. Moreover, maintaining security of raw material supply is difficult if imports are the only source utilized. This study examines the environmental and economic impacts of domestic mineral production from the recycling of end-of-life products and deep ocean mining as strategies to reduce CO2 emissions and enhance security of raw material supplies. The results indicate that under the given assumptions, recycling, which is typically considered to be less CO2 intensive, produces higher domestic emissions than current copper processing, although across the whole supply chain shows promise. As the total quantity of domestic resources from deep ocean ores are much smaller than the potential from recycling, it is possible that recycling could become a mainstream supply alternative, while deep ocean mining is more likely to be a niche supply source. Implications of a progressively aging society and flow-on impacts for the recycling sector are discussed. Full article
(This article belongs to the Special Issue Advances in Low Carbon Technologies and Transition)
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<p>Copper material flow of domestic copper production system (bold box indicates the system boundary).</p> Full article ">Figure 2
<p>Material flows between sectors related to copper production in this study (boxes indicate sectors in the I-O table).</p> Full article ">Figure 3
<p>General conceptualized input-output table.</p> Full article ">Figure 4
<p>Methodology to extend the I-O table.</p> Full article ">Figure 5
<p>Employment table.</p> Full article ">Figure 6
<p>Final energy consumption in a recycling society.</p> Full article ">Figure 7
<p>Final energy consumption for deep ocean mining.</p> Full article ">Figure 8
<p>CO<sub>2</sub> emissions in copper production.</p> Full article ">Figure 9
<p>CO<sub>2</sub> emissions under the assumption that all emissions for deep ocean mining and recycling are allocated to copper.</p> Full article ">
22 pages, 1285 KiB  
Review
Green, Yellow, and Woody Biomass Supply-Chain Management: A Review
by Efthymios Rodias, Remigio Berruto, Dionysis Bochtis, Alessandro Sopegno and Patrizia Busato
Energies 2019, 12(15), 3020; https://doi.org/10.3390/en12153020 - 6 Aug 2019
Cited by 13 | Viewed by 3744
Abstract
Various sources of biomass contribute significantly in energy production globally given a series of constraints in its primary production. Green biomass sources (such as perennial grasses), yellow biomass sources (such as crop residues), and woody biomass sources (such as willow) represent the three [...] Read more.
Various sources of biomass contribute significantly in energy production globally given a series of constraints in its primary production. Green biomass sources (such as perennial grasses), yellow biomass sources (such as crop residues), and woody biomass sources (such as willow) represent the three pillars in biomass production by crops. In this paper, we conducted a comprehensive review on research studies targeted to advancements at biomass supply-chain management in connection to these three types of biomass sources. A framework that classifies the works in problem-based and methodology-based approaches was followed. Results show the use of modern technological means and tools in current management-related problems. From the review, it is evident that the presented up-to-date trends on biomass supply-chain management and the potential for future advanced approach applications play a crucial role on business and sustainability efficiency of biomass supply chain. Full article
(This article belongs to the Special Issue Supply Chain Management for Bioenergy and Bioresources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The summary of the review process.</p> Full article ">Figure 2
<p>Processes included in this review’s boundary.</p> Full article ">Figure 3
<p>Number of papers extracted by each journal.</p> Full article ">Figure 4
<p>Biomass type in relation to the final product.</p> Full article ">Figure 5
<p>Biomass type and methodological approaches correlation.</p> Full article ">Figure 6
<p>Number of works allocated to various parts of supply chain: (<b>1</b>) Harvest and transport; (<b>2</b>) production; (<b>3</b>) production and transport; (<b>4</b>) collection and quantification; (<b>5</b>) collection and transport; (<b>6</b>) handling and transport; (<b>7</b>) harvesting, collection, transport, and handling; (<b>8</b>) transport; (<b>9</b>) harvesting, collection, and transportation; (<b>10</b>) harvesting and handling; (<b>11</b>) harvesting, handling, and transport.</p> Full article ">
27 pages, 54641 KiB  
Article
A Multi-Agent Social Gamification Model to Guide Sustainable Urban Photovoltaic Panels Installation Policies
by Robert Olszewski, Piotr Pałka, Agnieszka Wendland and Jacek Kamiński
Energies 2019, 12(15), 3019; https://doi.org/10.3390/en12153019 - 6 Aug 2019
Cited by 8 | Viewed by 3582
Abstract
The paper presents a holistic and quantitative model of social gamification in a smart city, which is likely to stimulate the photovoltaic panels installation. The coupling of multi-agent systems, GIS tools, demographic data, and a spatial knowledge base made it possible to develop [...] Read more.
The paper presents a holistic and quantitative model of social gamification in a smart city, which is likely to stimulate the photovoltaic panels installation. The coupling of multi-agent systems, GIS tools, demographic data, and a spatial knowledge base made it possible to develop and calibrate a computable model of social interaction in a “model smart city,” as well as to quantitatively evaluate the deployment of photovoltaic panels. It also enabled the analysis of factors affecting the efficiency of this process, e.g., the photovoltaic potential of solar roofs, the ownership of buildings, the type of building development, the level of social trust, institutional and social incentives, and the development of an information society. The devised model is tested on the city of Warsaw, utilizing spatial and descriptive data provided by city authorities. Full article
(This article belongs to the Special Issue Approaches, Advances and Applications in Sustainable Development of Smart Cities )
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<p>Flowchart of methodology used (source: Authors’ own work).</p> Full article ">Figure 2
<p>Model city (source: Authors’ own work).</p> Full article ">Figure 3
<p>Dot distribution map (each dot represents 1000 inhabitants of the model city) (source: Authors’ own work).</p> Full article ">Figure 4
<p>The increase in the number of PVs in the model city: The base case. The number of PVs installed on specific building types.</p> Full article ">Figure 5
<p>The increase in the number of PVs in the model city: Direct subsidy and the number of PVs installed on specific building types.</p> Full article ">Figure 6
<p>Simulation results for selected scenarios for the model city.</p> Full article ">Figure 7
<p>The trust-based gamification scenario for a model city.</p> Full article ">Figure 8
<p>Currently existing PVs in Warsaw and panels forecasted in the trust-based gamification scenario.</p> Full article ">Figure 9
<p>Dot density map of the population distribution in Warsaw (each agent (red dot) represents 1000 inhabitants).</p> Full article ">Figure 10
<p>Simulation results for selected scenarios for Warsaw.</p> Full article ">Figure 11
<p>The increase in the number of PVs in Warsaw: Trust-based gamification, number of PVs installed on specific building types.</p> Full article ">Figure 12
<p>Base model simulation for Warsaw (over 60% of PV density—3% of the area of Warsaw, over 40% of PV density—31% of the area of Warsaw).</p> Full article ">Figure 13
<p>Trust-based gamification model simulation for Warsaw (over 60% of PV density—32% of the area of Warsaw, over 40% of PV density—56% of the area of Warsaw).</p> Full article ">Figure 14
<p>Currently existing PVs in Wawer district and panels forecast in the trust-based gamification scenario.</p> Full article ">Figure 15
<p>Simulation results for selected scenarios for Wawer district.</p> Full article ">Figure 16
<p>The increase in the number of PVs in Wawer district: Trust-based gamification and the number of PVs installed on specific building types.</p> Full article ">Figure 17
<p>The base model simulation for Wawer and dot density map of the population distribution in Wawer (each agent represents 10 inhabitants).</p> Full article ">Figure 18
<p>The trust-based gamification model simulation for Wawer and dot density map of the population distribution in Wawer (each agent represents 10 inhabitants).</p> Full article ">
14 pages, 2954 KiB  
Article
Empirical Conductivity Equation for the Simulation of the Stationary Space Charge Distribution in Polymeric HVDC Cable Insulations
by Christoph Jörgens and Markus Clemens
Energies 2019, 12(15), 3018; https://doi.org/10.3390/en12153018 - 5 Aug 2019
Cited by 2 | Viewed by 3411
Abstract
Many processes are involved in the accumulation of space charges within the insulation materials of high voltage direct current (HVDC) cables, e.g., the local electric field, a conductivity gradient inside the insulation, and the injection of charges at both electrodes. An accurate description [...] Read more.
Many processes are involved in the accumulation of space charges within the insulation materials of high voltage direct current (HVDC) cables, e.g., the local electric field, a conductivity gradient inside the insulation, and the injection of charges at both electrodes. An accurate description of the time dependent charge distribution needs to include these effects. Furthermore, using an explicit Euler method for the time integration of a suitably formulated transient model, low time steps are used to resolve fast charge dynamics and to satisfy the Courant–Friedrichs–Lewy (CFL) stability condition. The long lifetime of power cables makes the use of a final stationary charge distribution necessary to assess the reliability of the cable insulations. For an accurate description of the stationary space charge and electric field distribution, an empirical conductivity equation is developed. The bulk conductivity, found in literature, is extended with two sigmoid functions to represent a conductivity gradient near the electrodes. With this extended conductivity equation, accumulated bulk space charges and hetero charges are simulated. New introduced constants to specify the sigmoid functions are determined by space charge measurements, taken from the literature. The measurements indicate accumulated hetero charges in about one quarter of the insulation thickness in the vicinity of both electrodes. The simulation results conform well to published measurements and show an improvement to previously published models, i.e., the developed model shows a good approximation to simulate the stationary bulk and hetero charge distribution. Full article
(This article belongs to the Special Issue Selected Papers from 2018 IEEE International Conference on High Voltage Engineering (ICHVE 2018))
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Figure 1
<p>(<b>a</b>) geometry of a planplanar insulation; (<b>b</b>) geometry of a cylindrical insulation [<a href="#B10-energies-12-03018" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Influence of distance constant <span class="html-italic">χ</span> on conductivity gradient and resulting space charge distribution <span class="html-italic">ρ</span>. (<b>a</b>) normalized conductivity <span class="html-italic">σ</span>/<span class="html-italic">σ</span><sub>B</sub>; (<b>b</b>) normalized stationary space charge distribution [<a href="#B10-energies-12-03018" class="html-bibr">10</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) Gradient region Δ, where the conductivity gradient is present. The position <span class="html-italic">r</span> = <span class="html-italic">r</span><sub>x</sub> = Δ/2 has the highest gradient; (<b>b</b>) to determine <span class="html-italic">χ</span> by Δ, a straight line <span class="html-italic">f</span>(<span class="html-italic">x</span>) = <span class="html-italic">a</span>(<span class="html-italic">x</span> − <span class="html-italic">r</span><sub>x</sub>) + 0.5 = (1/Δ)(<span class="html-italic">x</span> − <span class="html-italic">r</span><sub>x</sub>) + 0.5 is used, where the best approximation is shown by Δ = 10<span class="html-italic">χ</span> [<a href="#B10-energies-12-03018" class="html-bibr">10</a>].</p> Full article ">Figure 4
<p>(<b>a</b>) Measured and simulated charge distribution in a planplanar cross-linked polyethylene (XLPE) insulation [<a href="#B20-energies-12-03018" class="html-bibr">20</a>]; (<b>b</b>) Measured and simulated charge distribution in a cylindrical XLPE insulation. Three different XLPE cable measurements, labeled with numbers “1”–“3”, are seen [<a href="#B10-energies-12-03018" class="html-bibr">10</a>,<a href="#B19-energies-12-03018" class="html-bibr">19</a>].</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Measured and simulated charge distribution in a planplanar cross-linked polyethylene (XLPE) insulation [<a href="#B20-energies-12-03018" class="html-bibr">20</a>]; (<b>b</b>) Measured and simulated charge distribution in a cylindrical XLPE insulation. Three different XLPE cable measurements, labeled with numbers “1”–“3”, are seen [<a href="#B10-energies-12-03018" class="html-bibr">10</a>,<a href="#B19-energies-12-03018" class="html-bibr">19</a>].</p> Full article ">Figure 5
<p>Measured and simulated charge distribution in a planplanar low-density polyethylene (LDPE) insulation [<a href="#B10-energies-12-03018" class="html-bibr">10</a>,<a href="#B21-energies-12-03018" class="html-bibr">21</a>].</p> Full article ">Figure 6
<p>Measured and simulated charge distribution in a XLPE and LDPE insulation [<a href="#B6-energies-12-03018" class="html-bibr">6</a>]. (<b>a</b>) XLPE, planplanar, +<span class="html-italic">U</span>; (<b>b</b>) XLPE, planplanar, −<span class="html-italic">U</span>; (<b>c</b>) XLPE, cylindrical, +<span class="html-italic">U</span>; (<b>d</b>) XLPE, cylindrical, −<span class="html-italic">U</span>; (<b>e</b>) LDPE, planplanar, +<span class="html-italic">U</span>; (<b>f</b>) LDPE, planplanar, −<span class="html-italic">U</span>. The absolute value of the applied voltage is |<span class="html-italic">U</span>| = 20 kV.</p> Full article ">Figure 6 Cont.
<p>Measured and simulated charge distribution in a XLPE and LDPE insulation [<a href="#B6-energies-12-03018" class="html-bibr">6</a>]. (<b>a</b>) XLPE, planplanar, +<span class="html-italic">U</span>; (<b>b</b>) XLPE, planplanar, −<span class="html-italic">U</span>; (<b>c</b>) XLPE, cylindrical, +<span class="html-italic">U</span>; (<b>d</b>) XLPE, cylindrical, −<span class="html-italic">U</span>; (<b>e</b>) LDPE, planplanar, +<span class="html-italic">U</span>; (<b>f</b>) LDPE, planplanar, −<span class="html-italic">U</span>. The absolute value of the applied voltage is |<span class="html-italic">U</span>| = 20 kV.</p> Full article ">Figure 7
<p>(<b>a</b>) Measured and simulated charge distribution, using a common conductivity equation in literature (4) and the developed conductivity equation (5) in a planplanar XLPE insulation [<a href="#B20-energies-12-03018" class="html-bibr">20</a>]; (<b>b</b>) Measured and simulated charge distribution, using (4) and (5) in a cylindrical XLPE insulation [<a href="#B19-energies-12-03018" class="html-bibr">19</a>].</p> Full article ">
9 pages, 202 KiB  
Editorial
Special Issue “Intelligent Control in Energy Systems”
by Anastasios Dounis
Energies 2019, 12(15), 3017; https://doi.org/10.3390/en12153017 - 5 Aug 2019
Cited by 5 | Viewed by 3179
Abstract
The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which [...] Read more.
The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage. Full article
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
17 pages, 4515 KiB  
Article
Mining-Induced Failure Criteria of Interactional Hard Roof Structures: A Case Study
by Wenlong Shen, Meng Wang, Zhengzheng Cao, Faqiang Su, Hua Nan and Xuelong Li
Energies 2019, 12(15), 3016; https://doi.org/10.3390/en12153016 - 5 Aug 2019
Cited by 18 | Viewed by 3175
Abstract
Due to the additional abutment stress, interactional hard roof structures (IHRS) affect the normal operation of the coal production system in underground mining. The movement of IHRS may result in security problems, such as the failure of supporting body, large deformation, and even [...] Read more.
Due to the additional abutment stress, interactional hard roof structures (IHRS) affect the normal operation of the coal production system in underground mining. The movement of IHRS may result in security problems, such as the failure of supporting body, large deformation, and even roof caving for nearby openings. According to the physical configuration and loading conditions of IHRS in a simple two-dimensional physical model under the plane stress condition, mining-induced failure criteria were proposed and validated by the mechanical behavior of IHRS in a mechanical analysis model. The results indicate that IHRS, consisting of three interactional parts—the lower key structure, the middle soft interlayer, and the upper key structure—are governed by the additional abutment stress induced by the longwall mining working face. The fracture of the upper key structure in IHRS can be explained as follows: Due to the crushing failure, lower key structure, and middle soft interlayer yield, the action force between the upper and lower key structures vanishes, resulting in fracture of the upper key structure in IHRS. In a field case, when additional abutment stress reaches 7.37 MPa, the energy of 2.35 × 105 J is generated by the fracture of the upper key structure in IHRS. Under the same geological and engineering conditions, the energy generated by IHRS is much larger than that generated by a single hard roof. The mining-induced failure criteria are successfully applied in a field case. The in-situ mechanical behavior of the openings nearby IHRS under the mining abutment stress can be clearly explained by the proposed criteria. Full article
(This article belongs to the Section L: Energy Sources)
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<p>Entry conditions in the underground mining. The tail entry is located in the coal-seam 15 of the First Yangquan coal mine in the city of Yangquan, Shanxi Province, China. Panel 2 is approximately 2200 m long by 220 m wide. The average thickness and buried depth of coal-seam 15 are 6.5 m, 600 m, with the dip angle of 4°. As shown in <a href="#energies-12-03016-f002" class="html-fig">Figure 2</a>, the rock strata above coal-seam 15 are limestone, mudstone group, and fine sandstone, whereas below coal-seam 15 are mudstone and sandstone. The tail entry with dimensions of 5.0 m × 4.0 m is arranged along the immediate roof. The width of the coal pillar is 15 m.</p> Full article ">Figure 2
<p>Generalized stratigraphic column in First Yangquan coal mine.</p> Full article ">Figure 3
<p>Physical model of geological strata.</p> Full article ">Figure 4
<p>Generating process of interactional hard roof structures (IHRS). Excavation length of 25 cm (<b>a</b>), 65 cm (<b>b</b>), 115 cm (<b>c</b>), and 120 cm (<b>d</b>).</p> Full article ">Figure 4 Cont.
<p>Generating process of interactional hard roof structures (IHRS). Excavation length of 25 cm (<b>a</b>), 65 cm (<b>b</b>), 115 cm (<b>c</b>), and 120 cm (<b>d</b>).</p> Full article ">Figure 5
<p>Vertical displacement of the roofs above the gob in the physical model with an excavation of 120 cm.</p> Full article ">Figure 6
<p>Characteristics of IHRS. <span class="html-italic">L<sub>II</sub></span> is the length of the lower key structure, m; <span class="html-italic">L<sub>III</sub></span> is the length of the lower structure III, m; <span class="html-italic">L<sub>I</sub></span>′ is the cantilever length of the upper key structure, m; <span class="html-italic">L<sub>II</sub></span>′ is the length of the upper structure II, m; <span class="html-italic">L<sub>III</sub></span>′ is the length of the upper structure III, m; s is the length of the gob, m; <span class="html-italic">θ</span> is the dip angle of the lower key structure, degree; <span class="html-italic">d</span><sub>s</sub> is the distance between the gob floor and the contact position of the lower structure I, m.</p> Full article ">Figure 7
<p>Mining-induced failure process of IHRS. Process 1 is the increase process of additional abutment stress; Process 2 is the yielding process of the lower key structure; Process 3 is the fracture process of the upper key structure.</p> Full article ">Figure 8
<p>Distribution of the additional abutment stress [<a href="#B5-energies-12-03016" class="html-bibr">5</a>].</p> Full article ">Figure 9
<p>Mechanical model of the lower key structure.</p> Full article ">Figure 10
<p>Mechanical model of the upper structures. (<b>a</b>) Upper key structure; (<b>b</b>) combination of the upper structure II and upper structure III; (see <a href="#energies-12-03016-f006" class="html-fig">Figure 6</a>).</p> Full article ">Figure 11
<p>Mining-induced failure process. (<b>a</b>) Additional abutment stress; (<b>b</b>) shear stress and strength; (<b>c</b>) tensile stress and strength.</p> Full article ">Figure 12
<p>Deformation of the underground openings nearby IHRS. (<b>a</b>) Roof sink; (<b>b</b>) floor heave; (<b>c</b>) rib displacement; (<b>d</b>) support failure; (<b>e</b>) roof caving.</p> Full article ">Figure 13
<p>Deformation rate of the entry roof during the mining of the working face.</p> Full article ">
21 pages, 1269 KiB  
Article
Economic Health-Aware LPV-MPC Based on System Reliability Assessment for Water Transport Network
by Fatemeh Karimi Pour, Vicenç Puig and Gabriela Cembrano
Energies 2019, 12(15), 3015; https://doi.org/10.3390/en12153015 - 5 Aug 2019
Cited by 9 | Viewed by 3421
Abstract
This paper proposes a health-aware control approach for drinking water transport networks. This approach is based on an economic model predictive control (MPC) that considers an additional goal with the aim of extending the components and system reliability. The components and system reliability [...] Read more.
This paper proposes a health-aware control approach for drinking water transport networks. This approach is based on an economic model predictive control (MPC) that considers an additional goal with the aim of extending the components and system reliability. The components and system reliability are incorporated into the MPC model using a Linear Parameter Varying (LPV) modeling approach. The MPC controller uses additionally an economic objective function that determines the optimal filling/emptying sequence of the tanks considering that electricity price varies between day and night and that the demand also follows a 24-h repetitive pattern. The proposed LPV-MPC control approach allows considering the model nonlinearities by embedding them in the parameters. The values of these varying parameters are updated at each iteration taking into account the new values of the scheduling variables. In this way, the optimization problem associated with the MPC problem is solved by means of Quadratic Programming (QP) to avoid the use of nonlinear programming. This iterative approach reduces the computational load compared to the solution of a nonlinear optimization problem. A case study based on the Barcelona water transport network is used for assessing the proposed approach performance. Full article
(This article belongs to the Special Issue Approaches, Advances and Applications in Sustainable Development of Smart Cities )
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<p>Digram of the new proposed control model approach.</p> Full article ">Figure 2
<p>Drinking water network diagram (three-tanks).</p> Full article ">Figure 3
<p>Drinking water demand for the three tanks example.</p> Full article ">Figure 4
<p>Evaluation of the control actions results for three tanks.</p> Full article ">Figure 5
<p>Results of the evolutions of storage tanks for three tanks.</p> Full article ">Figure 6
<p>Evaluation of system reliability and accumulated economic cost for three tanks.</p> Full article ">Figure 7
<p>Barcelona drinking water network (17 tanks).</p> Full article ">Figure 8
<p>Graph of the Barcelona DWN.</p> Full article ">Figure 9
<p>Drinking water demand for several sinks.</p> Full article ">Figure 10
<p>Evaluation of the control actions results.</p> Full article ">Figure 11
<p>Results of the evolutions of storage tanks.</p> Full article ">Figure 12
<p>Evaluation of system reliability and accumulated economic cost.</p> Full article ">Figure 13
<p>Final system reliability vs. economic cost for different weight tuning.</p> Full article ">
22 pages, 9008 KiB  
Article
Impact of Electrically Assisted Turbocharger on the Intake Oxygen Concentration and Its Disturbance Rejection Control for a Heavy-duty Diesel Engine
by Chao Wu, Kang Song, Shaohua Li and Hui Xie
Energies 2019, 12(15), 3014; https://doi.org/10.3390/en12153014 - 5 Aug 2019
Cited by 6 | Viewed by 4086
Abstract
The electrically assisted turbocharger (EAT) shows promise in simultaneously improving the boost response and reducing the fuel consumption of engines with assist. In this paper, experimental results show that 7.8% fuel economy (FE) benefit and 52.1% improvement in transient boost response can be [...] Read more.
The electrically assisted turbocharger (EAT) shows promise in simultaneously improving the boost response and reducing the fuel consumption of engines with assist. In this paper, experimental results show that 7.8% fuel economy (FE) benefit and 52.1% improvement in transient boost response can be achieved with EAT assist. EAT also drives the need for a new feedback variable for the air system control, instead of the exhaust recirculation gas (EGR) rate that is widely used in conventional turbocharged engines (nominal system). Steady-state results show that EAT assist allows wider turbine vane open and reduces pre-turbine pressure, which in turn elevates the engine volumetric efficiency hence the engine air flow rate at fixed boost pressure. Increased engine air flow rate, together with the reduced fuel amount necessary to meet the torque demand with assist, leads to the increase of the oxygen concentration in the exhaust gas (EGR gas dilution). Additionally, transient results demonstrate that the enhanced air supply from the compressor and the diluted EGR gas result in a spike in the oxygen concentration in the intake manifold (Xoim) during tip-in, even though there is no spike in the EGR rate response profile. Consequently, there is Nitrogen Oxides (NOx) emission spike, although the response of boost pressure and EGR rate is smooth (no spike is seen). Therefore, in contrast to EGR rate, Xoim is found to be a better choice for the feedback variable. Additionally, a disturbance observer-based Xoim controller is developed to attenuate the disturbances from the turbine vane position variation. Simulation results on a high-fidelity GT-SUTIE model show over 43% improvement in disturbance rejection capability in terms of recovery time, relative to the conventional proportional-integral-differential (PID) controller. This Xoim-based disturbance rejection control solution is beneficial in the practical application of the EAT system. Full article
(This article belongs to the Special Issue Internal Combustion Engines Improving Performance, Fuel Economy and Emissions)
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<p>Schematic of the electrically assisted turbocharger (EAT)-assisted diesel engine. The nomenclature for all the symbols can be found in Nomenclature section.</p> Full article ">Figure 2
<p>Schematic of experimental platform.</p> Full article ">Figure 3
<p>Test bench for diesel engine with EAT.</p> Full article ">Figure 4
<p>Validation of the GT-SUITE model over a segment of the hot start FTP-75 drive cycle in terms of boost pressure, pre-turbine pressure, and compressor mass flow rate [<a href="#B27-energies-12-03014" class="html-bibr">27</a>].</p> Full article ">Figure 5
<p>Engine fuel efficiency improvement and <span class="html-italic">X<sub>oim</sub></span> surplus.</p> Full article ">Figure 6
<p>Volumetric efficiency improvement and in-cylinder composition change.</p> Full article ">Figure 7
<p>Comparison of three performance variables with NOx concentration.</p> Full article ">Figure 8
<p>Correlation between NOx concentration and three performance variables.</p> Full article ">Figure 9
<p>Regulations of three actuators during the transient process.</p> Full article ">Figure 10
<p>Tip-in test for the nominal system without assist.</p> Full article ">Figure 11
<p>Tip-in test for the EAT system</p> Full article ">Figure 12
<p>Control structure of the OADRC controller.</p> Full article ">Figure 13
<p>OADRC validation with step changes of desired intake oxygen concentration.</p> Full article ">Figure 14
<p>Comparison between OADRC and PID controller with step changes of intake oxygen concentration.</p> Full article ">Figure 15
<p>Disturbance rejection performance comparison with step change of <span class="html-italic">N<sub>T.</sub></span></p> Full article ">Figure 16
<p>Disturbance rejection performance comparison with step change of variable-geometry turbocharger (VGT) vane.</p> Full article ">Figure A1
<p>Experimental validation of oxygen concentration observer.</p> Full article ">
21 pages, 8310 KiB  
Article
Simultaneous Inertia Contribution and Optimal Grid Utilization with Wind Turbines
by Clemens Jauch and Arne Gloe
Energies 2019, 12(15), 3013; https://doi.org/10.3390/en12153013 - 5 Aug 2019
Cited by 7 | Viewed by 3651
Abstract
This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to [...] Read more.
This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilization and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilization and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants. Full article
(This article belongs to the Special Issue Modern Power System Dynamics, Stability and Control)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Setup of the case study of Flensburg campus supplemented by a 5 MW WT (5M) with connection to the external grid.</p> Full article ">Figure 2
<p>Measured grid frequency and <span class="html-italic">ROCOF</span> (top); frequency spectrum of grid frequency (bottom).</p> Full article ">Figure 3
<p>Close loop power control in the 1st eigenmodes model of a WT.</p> Full article ">Figure 4
<p>Bode plots of the open power control loop.</p> Full article ">Figure 5
<p>Variable inertia constant, H<sub>var</sub>, for the demanded rated inertia constants H<sub>dem</sub> = 6 s and H<sub>dem</sub> = 12 s. Constant inertia constant H = 6 s.</p> Full article ">Figure 6
<p>Control circuit for continuous FIM and inertial response with H<sub>var</sub> controller.</p> Full article ">Figure 7
<p>P vs. speed characteristic and linearization around the operating point.</p> Full article ">Figure 8
<p>WT drive train model.</p> Full article ">Figure 9
<p>Block diagram of WT system from <span class="html-italic">P_ref</span> to <span class="html-italic">speed_gen</span>.</p> Full article ">Figure 10
<p>Bode plots of the transfer functions <span class="html-italic">G<sub>WT</sub>(s)</span>, <span class="html-italic">G<sub>PI_P_dem</sub>(s)</span> and <span class="html-italic">G<sub>TR</sub>(s)</span> (top) in comparison to the frequency spectra of the different excitations: <span class="html-italic">ROCOF</span>, <span class="html-italic">P<sub>C</sub></span> and <span class="html-italic">Q<sub>C</sub></span> (bottom).</p> Full article ">Figure 11
<p>WT power (top) generator speed (middle) and pitch angle (bottom) of 5M for the case of continuous FIM operation (red curves) and for the case of continuous FIM with inertial response (blue curves in the background).</p> Full article ">Figure 12
<p>Generator acceleration (top) and frequency distribution of generator acceleration (bottom) of 5M in continuous FIM operation for two different generator-converter time constants.</p> Full article ">Figure 13
<p>Lateral tower top acceleration (top) and frequency distribution of lateral tower top acceleration (bottom) of 5M in continuous FIM operation for two different generator–converter time constants.</p> Full article ">Figure 14
<p>Generator acceleration (top) and frequency distribution of generator acceleration (bottom) of 5M when performing continuous FIM, with and without continuous inertia provision.</p> Full article ">Figure 15
<p>Lateral tower top acceleration (top) and frequency distribution of lateral tower top acceleration (bottom) of 5M when performing continuous FIM, with and without continuous inertia provision.</p> Full article ">Figure 16
<p><span class="html-italic">P<sub>inertia</sub></span> from an AC connected synchronous generator with H = 6 s (blue), <span class="html-italic">P<sub>inertia</sub></span> from the 5M with H<sub>var</sub> controller and H<sub>dem</sub> = 6 s (red) and H<sub>dem</sub> = 12 s (green). The bottom diagram is a zoom-in view of the top diagram.</p> Full article ">Figure 17
<p>The inertial response power setpoint, <span class="html-italic">P<sub>SI_var</sub></span>, (red) and the generator power, <span class="html-italic">P<sub>gen</sub></span>, from the 5M. <span class="html-italic">P<sub>gen</sub></span> is shown for the case of FIM only (green) and FIM with inertial response (blue).</p> Full article ">
20 pages, 5510 KiB  
Article
Island DC Microgrid Hierarchical Coordinated Multi-Mode Control Strategy
by Zhongbin Zhao, Jing Zhang, Yu He and Ying Zhang
Energies 2019, 12(15), 3012; https://doi.org/10.3390/en12153012 - 5 Aug 2019
Cited by 8 | Viewed by 3759
Abstract
As renewable energy sources connecting to power systems continue to improve and new-type loads, such as electric vehicles, grow rapidly, direct current (DC) microgrids are attracting great attention in distribution networks. In order to satisfy the voltage stability requirements of island DC microgrids, [...] Read more.
As renewable energy sources connecting to power systems continue to improve and new-type loads, such as electric vehicles, grow rapidly, direct current (DC) microgrids are attracting great attention in distribution networks. In order to satisfy the voltage stability requirements of island DC microgrids, the problem of inaccurate load power dispatch caused by line resistance must be solved and the defects of centralized communication and control must be overcome. A hierarchical, coordinated, multiple-mode control strategy based on the switch of different operation modes is proposed in this paper and a three-layer control structure is designed for the control strategy. Based on conventional droop control, a current-sharing layer and a multi-mode switching layer are used to ensure the stable operation of the DC microgrid. Accurate load power dispatch is satisfied using a difference discrete consensus algorithm. Furthermore, virtual bus voltage information is applied to guarantee smooth switching between various modes, which safeguards voltage stability. Simulation verification is carried out for the proposed control strategy by power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC). The results indicate that the proposed control strategy guarantees the voltage stability of island DC microgrids and accurate load power dispatch under different operation modes. Full article
(This article belongs to the Special Issue Clean Energy Microgrids)
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Figure 1
<p>Structure of the island DC microgrid.</p> Full article ">Figure 2
<p>Multi-mode of DC microgrid.</p> Full article ">Figure 3
<p>The hierarchical, coordinated control strategy of an island DC microgrid based on a multi-mode smooth switch.</p> Full article ">Figure 4
<p><span class="html-italic">I-U</span> droop control diagram.</p> Full article ">Figure 5
<p>Parallel structure of the DC microgrid multi-converter.</p> Full article ">Figure 6
<p>Simplified structure of the double parallel converters.</p> Full article ">Figure 7
<p>The current-sharing layer strategy based on the difference discrete consensus algorithm.</p> Full article ">Figure 8
<p>Equivalent model of the DC microgrid current-sharing control.</p> Full article ">Figure 9
<p>Communication topology and its Laplacian matrix. (<b>a</b>) A topological structure with five information nodes; (<b>b</b>) Laplacian matrix.</p> Full article ">Figure 10
<p>Converging speed comparison under different constant weights, <span class="html-italic">a.</span> (<b>a</b>) Consensus dynamic at <span class="html-italic">a</span> = 2/5; (<b>b</b>) Consensus dynamic at <span class="html-italic">a</span> = 1/2; (<b>c</b>) Consensus dynamic at <span class="html-italic">a</span> = 1/10.</p> Full article ">Figure 11
<p>Multi-mode switch strategy based on virtual bus voltage information.</p> Full article ">Figure 12
<p>Topological structure of four groups of energy storage communication.</p> Full article ">Figure 13
<p>Verification for the effectiveness of DDCA convergence.</p> Full article ">Figure 14
<p>Simulation waveform of Situation 2.</p> Full article ">Figure 15
<p>Simulation waveform of Situation 3.</p> Full article ">Figure 16
<p>Simulation waveform of Situation 4.</p> Full article ">Figure 17
<p>Simulation waveform of Situation 5.</p> Full article ">Figure 18
<p>Simulation waveform of Situation 6. (<b>a</b>) +20% disturbance; (<b>b</b>) −30% disturbance.</p> Full article ">
22 pages, 2423 KiB  
Review
Mapping Knowledge in the Economic Areas of Green Building Using Scientometric Analysis
by Xue Xiao, Martin Skitmore, Heng Li and Bo Xia
Energies 2019, 12(15), 3011; https://doi.org/10.3390/en12153011 - 5 Aug 2019
Cited by 39 | Viewed by 4347
Abstract
This paper presents the first inclusive scientometric review of the economic areas of green building (GBE). The aim is to methodically examine and summarize the state-of-the-art of the GBE body of knowledge. To this end, this study analyses 1713 GBE-related bibliographic records retrieved [...] Read more.
This paper presents the first inclusive scientometric review of the economic areas of green building (GBE). The aim is to methodically examine and summarize the state-of-the-art of the GBE body of knowledge. To this end, this study analyses 1713 GBE-related bibliographic records retrieved from the Web of Science by using the quantitative method of knowledge mapping. The knowledge base, knowledge domain, and knowledge evolution of how they interacted with each other are explored using document co-citation analysis and keywords co-citation analysis of the existing body of literature. The research findings are informative in recognizing and interpreting the underlying structure and trends in GBE. A knowledge map provides a valuable and instructive understanding of the evolution and status quo of the GBE knowledge body, as well as assisting in recognizing the gaps and deficiencies involved. The results will help in understanding how GBE knowledge is evolving and its role played in green building, and thus provide suggestions of how academic research can enhance sustainability practices in terms of economic area in the future. Full article
(This article belongs to the Special Issue Economic Development and Energy Policy)
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<p>Document co-citation network of GBE.</p> Full article ">Figure 2
<p>Keyword co-occurrence network.</p> Full article ">Figure 3
<p>Clusters of knowledge domains.</p> Full article ">Figure 4
<p>Top 25 keywords with strongest citation burst.</p> Full article ">Figure 5
<p>Top 20 reference with the strongest citation bursts.</p> Full article ">Figure 6
<p>GBE knowledge map.</p> Full article ">
15 pages, 3881 KiB  
Article
Distribution of the Strip Tensions with Slip Control in Strip Processing Lines
by Daniel Magura, Karol Kyslan, Sanjeevikumar Padmanaban and Viliam Fedák
Energies 2019, 12(15), 3010; https://doi.org/10.3390/en12153010 - 4 Aug 2019
Cited by 5 | Viewed by 4619
Abstract
The control of tension in processing lines for metal strips tackles several problems. The process of achieving high tension driven by a multi-motor drive system, where the motors are mechanically coupled by a strip, is affected by the maximal torque of each drive, [...] Read more.
The control of tension in processing lines for metal strips tackles several problems. The process of achieving high tension driven by a multi-motor drive system, where the motors are mechanically coupled by a strip, is affected by the maximal torque of each drive, by friction between the strip and the surface of the tension roll, and by the wrap angle. The friction itself and the wrap angle are described by the eµα factor, which can be also calculated as the ratio of tensions in the strip in the previous section and subsequent section of the multi-motor drive. In this paper, an algorithm for the proper distribution of tensions in the strip for a multi-motor drive system of a continuous processing line is revealed. The algorithm ensures the tension distribution among particular drives of the tension leveler while respecting the physical limits of the drives and also preserving the desired conditions of a constant ratio between the input and output tensions for all drives in the leveler. The algorithm also prevents overloading of the drives. Finally, the algorithm was implemented in a control system of a strip processing line, and the obtained results correspond with the simulations. This, in turn, confirms the correctness of the algorithm design. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Analysis of the contact surface between roll and strip in steady state.</p> Full article ">Figure 2
<p>Tensions in the strip passing rolls in the leveler section.</p> Full article ">Figure 3
<p>The arrangement of the bridle rolls in the real line used for simulation and experiment. The wrap angles α<sub>1</sub>–α<sub>5</sub> around tension rolls are different.</p> Full article ">Figure 4
<p>Symbolic control structure for the tension distribution algorithm for a leveler roll section with five drives.</p> Full article ">Figure 5
<p>Simulation results. The left-hand picture shows actual and limit factors for each drive. The right-hand picture shows tension setpoints and tension limits. Time <span class="html-italic">t</span> = 0 s to 6 s is for the uniform distribution of tensions; time <span class="html-italic">t</span> = 6 s to 12 s is for the dynamic distribution of tensions.</p> Full article ">Figure 6
<p>Implementation of the tension distribution algorithm in the CFC programming language.</p> Full article ">Figure 7
<p>Tension leveler on a realistic production line where experimental results were measured. The arrangement of the tension rolls in this figure is the same as that shown in <a href="#energies-12-03010-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 8
<p>Experimental results of dynamic tension distribution in a realistic production line.</p> Full article ">
21 pages, 13054 KiB  
Article
Control Technology of Soft Rock Floor in Mining Roadway with Coal Pillar Protection: A case study
by Housheng Jia, Luyao Wang, Kai Fan, Bo Peng and Kun Pan
Energies 2019, 12(15), 3009; https://doi.org/10.3390/en12153009 - 4 Aug 2019
Cited by 22 | Viewed by 2991
Abstract
This study considered the mining roadway with coal pillar protection in the fully mechanized caving face of the Dananhu No.1 Coal Mine, China. Theoretical analysis, numerical simulation, and field tests were conducted, and the stress environment, deformation, and failure characteristics of the mining [...] Read more.
This study considered the mining roadway with coal pillar protection in the fully mechanized caving face of the Dananhu No.1 Coal Mine, China. Theoretical analysis, numerical simulation, and field tests were conducted, and the stress environment, deformation, and failure characteristics of the mining roadway in the fully mechanized caving face were analyzed. The results revealed that the intrinsic cause for the large asymmetrical floor deformation in the mining roadway is the asymmetrical phenomenon of the surrounding rock’s stress environment, caused by mining. This also results in the non-uniform distribution of the mining roadway floor’s plastic zone. The degree of asymmetrical floor heave is internally related to the thickness of the caving coal. When the thickness of the caving coal was in the range of 5.9 m, the deformation of the asymmetrical floor heave, caused by the plastic failure in the floor, became more obvious as certain parameters increased. As the rotation angle of the principal stress direction increased, the maximum plastic failure depth position of the floor gradually moved toward the middle of the roadway. This caused a different distribution for the maximum deformation position. The control of the floor heave deformation was poor, and it was not feasible to use high-strength support under the existing engineering conditions. Hence, the control should mainly be applied to the floor heave deformation. When the thickness of the caving coal was more than 5.9 m, the main roof strata was prone to instability and being cut along the edge of the coal pillar; the rock stress environment surrounding the roadway tended to revert back to the initial geostress state. The proposed floor heave control strategy achieved good results, and as the deformation of the floor heave decreased, the workload of the floor heave was also greatly reduced. Full article
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Figure 1
<p>The location map of the Dananhu No.1 Coal Mine.</p> Full article ">Figure 2
<p>Roof, coal seam and floor strata structure peephole results for coal seam of panel 1306 return airway roadway.</p> Full article ">Figure 3
<p>Mining roadway layout of panels 1304 and 1306.</p> Full article ">Figure 4
<p>Statistical results of floor heave law and thickness of caving coal.</p> Full article ">Figure 5
<p>Side surface-connected fissure of panel 1304. (<b>a</b>) Fissure size 0.32m, Small size of fissure; (<b>b</b>) Fissure size 0.51m, Large size of fissure; (<b>c</b>) Fissure size 0.53m, Fractured fissure.</p> Full article ">Figure 6
<p>Stress environment of rock surrounding mining roadway with different caving coal thicknesses and roadway deformation profiles. (<b>a</b>) Small thickness of top coal (2 m); (<b>b</b>) Middle thickness of top coal (4 m); (<b>c</b>) Large thickness of top coal (6 m).</p> Full article ">Figure 6 Cont.
<p>Stress environment of rock surrounding mining roadway with different caving coal thicknesses and roadway deformation profiles. (<b>a</b>) Small thickness of top coal (2 m); (<b>b</b>) Middle thickness of top coal (4 m); (<b>c</b>) Large thickness of top coal (6 m).</p> Full article ">Figure 7
<p>Mechanical analysis of rotating rock block.</p> Full article ">Figure 8
<p>Stress environment of surrounding rock around roadway with main roof fractured and roadway deformation profile.</p> Full article ">Figure 9
<p>Numerical model with different top coal thicknesses. (<b>a</b>) Top 2 m of coal; (<b>b</b>) Top 4 m of coal; (<b>c</b>) Top 6 m of coal.</p> Full article ">Figure 10
<p>Simulation results of roadway’s plastic zone distribution under different stress environments at top 2 m of coal (<span class="html-italic">β</span> = 30°). Note: <span class="html-italic">λ</span> is the principal stress ratio; <span class="html-italic">β</span> is the deflection direction of principal stress.</p> Full article ">Figure 11
<p>Simulation results of roadway’s plastic zone distribution under different stress environments at top 4 m of coal (<span class="html-italic">β</span> = 45°). Note: <span class="html-italic">λ</span> is the principal stress ratio; <span class="html-italic">β</span> is the deflection direction of the principal stress.</p> Full article ">Figure 12
<p>Comparison of field photos of coal pillar roadway, and numerical simulation results of surrounding rock deformation profile.</p> Full article ">Figure 13
<p>Simulation results of roadway’s plastic zone distribution under different stress environments for top coal at 6 m (<span class="html-italic">β</span> = 10°). Note: <span class="html-italic">λ</span> is the principal stress ratio; <span class="html-italic">β</span> is the deflection direction of principal stress.</p> Full article ">Figure 14
<p>Calculation model of plastic zone for rock surrounding non-equal pressure circular roadway.</p> Full article ">Figure 15
<p>General shape of plastic zone in circular roadway obtained from theoretical calculation (<span class="html-italic">P</span><sub>3</sub> = 20 MPa, <span class="html-italic">a</span> = 2 m, <span class="html-italic">C</span> = 3 MPa, <span class="html-italic">φ</span> = 25°).</p> Full article ">Figure 16
<p>Drawing system interface of plastic zone for rock surrounding roadway.</p> Full article ">Figure 17
<p>Relationship between shape of butterfly-shaped plastic zone and direction of principal stress in rock surrounding roadway. Note: <span class="html-italic">α</span> is the angle between the maximum principal stress and the vertical direction; the clockwise direction is positive.</p> Full article ">Figure 18
<p>Layout of test roadway and measuring station.</p> Full article ">Figure 19
<p>Monitoring curve of floor heave deformation.</p> Full article ">Figure 19 Cont.
<p>Monitoring curve of floor heave deformation.</p> Full article ">Figure 20
<p>Curve of plastic zone range along with increase of support intensity.</p> Full article ">Figure 21
<p>Length of hardened layer and floor digging within 8 h.</p> Full article ">
27 pages, 3037 KiB  
Article
Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks
by Gheorghe Grigoras and Bogdan-Constantin Neagu
Energies 2019, 12(15), 3008; https://doi.org/10.3390/en12153008 - 4 Aug 2019
Cited by 20 | Viewed by 3886
Abstract
In this paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in three-phase networks with the voltage level below 0.4 kV (low voltage—LV) is presented. In the first stage, the input data regarding the hourly active and reactive powers [...] Read more.
In this paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in three-phase networks with the voltage level below 0.4 kV (low voltage—LV) is presented. In the first stage, the input data regarding the hourly active and reactive powers of the consumers and producers are introduced. The powers are loaded from the database of the smart metering system (SMS) for the consumers and producers integrated in this system or files containing the characteristic load profiles established by the Distribution Network Operator for the consumers, which have installed the conventional meters non-integrated in the SMS. In the second stage, a function, which is based on the work with the structure vectors, was implemented to easily identify the configuration of analysed networks. In the third stage, an improved version of a forward/backward sweep-based algorithm was proposed to quickly calculate the power/energy losses to three-phase LV distribution networks in a balanced and unbalanced regime. A real LV rural distribution network from a pilot zone belonging to a Distribution Network Operator from Romania was used to confirm the accuracy of the proposed algorithm. The comparison with the results obtained using the DigSilent PowerFactory Simulation Package certified the performance of the algorithm, with the mean absolute percentage error (MAPE) being 0.94%. Full article
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<p>The topology of a radial LV distribution network.</p> Full article ">Figure 2
<p>(<b>a</b>) The flow-chart of the proposed algorithm (the first and second stages); (<b>b</b>) The flow-chart of the proposed algorithm (the third stage).</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) The flow-chart of the proposed algorithm (the first and second stages); (<b>b</b>) The flow-chart of the proposed algorithm (the third stage).</p> Full article ">Figure 3
<p>The analyzed LV distribution network.</p> Full article ">Figure 4
<p>The phase loading on the first section of Feeder 1.</p> Full article ">Figure 5
<p>The phase loading on the first section of Feeder 2.</p> Full article ">Figure 6
<p>The phase loading on the first section of Feeder 3.</p> Full article ">Figure 7
<p>The errors between both approaches, [%].</p> Full article ">Figure 8
<p>The hourly energy losses computed with both approaches, [%].</p> Full article ">Figure 9
<p>The phase voltage (phase a) at Pillar P95—Feeder 2.</p> Full article ">Figure 10
<p>The phase voltage (phase b) at the Pillar P95—Feeder 2.</p> Full article ">Figure 11
<p>The phase voltage (phase c) at the Pillar P95—Feeder 2.</p> Full article ">Figure 12
<p>The phase voltage (phase a) at the Pillar P188—Feeder 3.</p> Full article ">Figure 13
<p>The phase voltage (phase b) at the Pillar P188—Feeder 3.</p> Full article ">Figure 14
<p>The phase voltage (phase c) at the Pillar P188—Feeder 3.</p> Full article ">Figure 15
<p>The MPE of the phase voltages, Pillar P95.</p> Full article ">Figure 16
<p>The MPE of the phase voltages, Pillar P188.</p> Full article ">
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