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Energies, Volume 10, Issue 2 (February 2017) – 114 articles

Cover Story (view full-size image): The development of distributed energy resources increases the needs of higher grid capacity. It is shown that by performing and comparing three power transformer capacity allocation mechanisms among virtual power plants (VPPs), the congestion problem at the transformer level can be prevented. Inside each VPP, an ACOPF problem is formulated to minimize operational costs, taking into account line thermal limits, voltage limits, and the transformer use power limits/congestion price resulting from the capacity allocation mechanism. View this paper.
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7639 KiB  
Article
Switchgrass-Based Bioethanol Productivity and Potential Environmental Impact from Marginal Lands in China
by Xun Zhang, Jingying Fu, Gang Lin, Dong Jiang and Xiaoxi Yan
Energies 2017, 10(2), 260; https://doi.org/10.3390/en10020260 - 21 Feb 2017
Cited by 26 | Viewed by 5811
Abstract
Switchgrass displays an excellent potential to serve as a non-food bioenergy feedstock for bioethanol production in China due to its high potential yield on marginal lands. However, few studies have been conducted on the spatial distribution of switchgrass-based bioethanol production potential in China. [...] Read more.
Switchgrass displays an excellent potential to serve as a non-food bioenergy feedstock for bioethanol production in China due to its high potential yield on marginal lands. However, few studies have been conducted on the spatial distribution of switchgrass-based bioethanol production potential in China. This study created a land surface process model (Environmental Policy Integrated Climate GIS (Geographic Information System)-based (GEPIC) model) coupled with a life cycle analysis (LCA) to explore the spatial distribution of potential bioethanol production and present a comprehensive analysis of energy efficiency and environmental impacts throughout its whole life cycle. It provides a new approach to study the bioethanol productivity and potential environmental impact from marginal lands based on the high spatial resolution GIS data, and this applies not only to China, but also to other regions and to other types of energy plant. The results indicate that approximately 59 million ha of marginal land in China are suitable for planting switchgrass, and 22 million tons of ethanol can be produced from this land. Additionally, a potential net energy gain (NEG) of 1.75 x 106 million MJ will be achieved if all of the marginal land can be used in China, and Yunnan Province offers the most significant one that accounts for 35% of the total. Finally, this study obtained that the total environmental effect index of switchgrass-based bioethanol is the equivalent of a population of approximately 20,300, and a reduction in the global warming potential (GWP) is the most significant environmental impact. Full article
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Figure 1
<p>The system boundary, energy flow, emission inventory and environmental impact categories of this study.</p> Full article ">Figure 2
<p>Marginal land resources suitable for switchgrass planting.</p> Full article ">Figure 3
<p>Spatial distribution of switchgrass.</p> Full article ">Figure 4
<p>NEG of switchgrass-based bioethanol production.</p> Full article ">
3072 KiB  
Article
A Nafion-Ceria Composite Membrane Electrolyte for Reduced Methanol Crossover in Direct Methanol Fuel Cells
by Parthiban Velayutham, Akhila K. Sahu and Sridhar Parthasarathy
Energies 2017, 10(2), 259; https://doi.org/10.3390/en10020259 - 21 Feb 2017
Cited by 44 | Viewed by 7989
Abstract
An alternative Nafion composite membrane was prepared by incorporating various loadings of CeO2 nanoparticles into the Nafion matrix and evaluated its potential application in direct methanol fuel cells (DMFCs). The effects of CeO2 in the Nafion matrix were systematically studied in [...] Read more.
An alternative Nafion composite membrane was prepared by incorporating various loadings of CeO2 nanoparticles into the Nafion matrix and evaluated its potential application in direct methanol fuel cells (DMFCs). The effects of CeO2 in the Nafion matrix were systematically studied in terms of surface morphology, thermal and mechanical stability, proton conductivity and methanol permeability. The composite membrane with optimum filler content (1 wt. % CeO2) exhibits a proton conductivity of 176 mS·cm−1 at 70 °C, which is about 30% higher than that of the unmodified membrane. Moreover, all the composite membranes possess a much lower methanol crossover compared to pristine Nafion membrane. In a single cell DMFC test, MEA fabricated with the optimized composite membrane delivered a peak power density of 120 mW·cm−2 at 70 °C, which is about two times higher in comparison with the pristine Nafion membrane under identical operating conditions. Full article
(This article belongs to the Special Issue Direct Alcohol Fuel Cells)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>X-ray diffraction pattern (<b>a</b>) and FE-SEM image (<b>b</b>) of CeO<sub>2</sub> nanoparticles.</p> Full article ">Figure 2
<p>FE-SEM images of pristine Nafion (<b>a</b>) and Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane (<b>b</b>); Corresponding quantitative EDX elemental mappings for carbon (<b>c</b>), oxygen (<b>d</b>), ceria (<b>e</b>) for Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane (quantitative EDX elemental mapping performed on a particular area is marked in <a href="#energies-10-00259-f002" class="html-fig">Figure 2</a>b); EDX spectra of pristine Nafion (<b>f</b>) and Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane (<b>g</b>).</p> Full article ">Figure 3
<p>(<b>a</b>) Topography of pristine Nafion membrane; (<b>b</b>) Topography of Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane; (<b>c</b>) Phase image of pristine Nafion membrane; (<b>d</b>) Phase image of Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane; (<b>e</b>) Line profile of pristine Nafion membrane; (<b>f</b>) Line profile of Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane.</p> Full article ">Figure 4
<p>(<b>a</b>) Thermogravimetric analysis of pristine Nafion and Nafion-CeO<sub>2</sub> composite membrane; (<b>b</b>) Tensile strength of pristine Nafion and Nafion-CeO<sub>2</sub> (1 wt. %) composite membrane.</p> Full article ">Figure 5
<p>Proton conductivity of pristine Nafion and Nafion-CeO<sub>2</sub> composite membranes at different temperatures under 100% RH.</p> Full article ">Figure 6
<p>Methanol crossover current densities of pristine Nafion and Nafion-CeO<sub>2</sub> composite membranes at room temperature.</p> Full article ">Figure 7
<p>(<b>a</b>) DMFC performances of pristine Nafion and Nafion-CeO<sub>2</sub> composite membrane at 70 °C under ambient pressure; (<b>b</b>) Stability test of the pristine Nafion and Nafion-CeO<sub>2</sub> (1 wt. %) membranes for 50 h under OCV condition.</p> Full article ">
2715 KiB  
Article
The Influence of Micro-Oxygen Addition on Desulfurization Performance and Microbial Communities during Waste-Activated Sludge Digestion in a Rusty Scrap Iron-Loaded Anaerobic Digester
by Renjun Ruan, Jiashun Cao, Chao Li, Di Zheng and Jingyang Luo
Energies 2017, 10(2), 258; https://doi.org/10.3390/en10020258 - 21 Feb 2017
Cited by 22 | Viewed by 6831
Abstract
In this study, micro-oxygen was integrated into a rusty scrap iron (RSI)-loaded anaerobic digester. Under an optimal RSI dosage of 20 g/L, increasing O2 levels were added stepwise in seven stages in a semi-continuous experiment. Results showed the average methane yield was [...] Read more.
In this study, micro-oxygen was integrated into a rusty scrap iron (RSI)-loaded anaerobic digester. Under an optimal RSI dosage of 20 g/L, increasing O2 levels were added stepwise in seven stages in a semi-continuous experiment. Results showed the average methane yield was 306 mL/g COD (chemical oxygen demand), and the hydrogen sulphide (H2S) concentration was 1933 ppmv with RSI addition. O2 addition induced the microbial oxidation of sulphide by stimulating sulfur-oxidizing bacteria and chemical corrosion of iron, which promoted the generation of FeS and Fe2S3. In the 6th phase of the semi-continuous test, deep desulfurization was achieved without negatively impacting system performance. Average methane yield was 301.1 mL/g COD, and H2S concentration was 75 ppmv. Sulfur mass balance was described, with 84.0%, 11.90% and 0.21% of sulfur present in solid, liquid and gaseous phases, respectively. The Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE) analysis revealed that RSI addition could enrich the diversity of hydrogenotrophic methanogens and iron-reducing bacteria to benefit methanogenesis and organic mineralization, and impoverish the methanotroph (Methylocella silvestris) to reduce the consumption of methane. Micro-oxygen supplementation could enhance the diversity of iron-oxidizing bacteria arising from the improvement of Fe(II) release rate and enrich the sulphur-oxidising bacteria to achieved desulfurization. These results demonstrated that RSI addition in combination with micro-oxygenation represents a promising method for simultaneously controlling biogas H2S concentration and improving digestion performance. Full article
(This article belongs to the Special Issue Recent development in the field of biofuels from agricultural industry)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Lab-scale bioreactor diagram for the semicontinuous anaerobic and microaerobic digestion of activated sludge supplemented with rusty scrap iron (RSI).</p> Full article ">Figure 2
<p>Variation of pH (■) and oxidation-reduction potential (ORP, ▲) under anaerobic and microaerobic conditions during semi-continuous digestion of activated sludge supplemented with RSI (error bars represent standard deviation, <span class="html-italic">n</span> = 3).</p> Full article ">Figure 3
<p>Daily COD removal (■) and daily methane yield (▼) under anaerobic and microaerobic conditions during the digestion of sludge (error bars represent standard deviation, <span class="html-italic">n</span> = 3).</p> Full article ">Figure 4
<p>Hydrogen sulphide (◆) and oxygen (▼) concentrations under anaerobic and microaerobic conditions during semi-continuous digestion of activated sludge supplemented with RSI (error bars represent standard deviation, <span class="html-italic">n</span> = 3).</p> Full article ">Figure 5
<p>Calculated sulfur balance during semicontinuous anaerobic and microaerobic digestion of activated sludge supplemented with RSI over seven periods of operation associated with stepwise increases in oxygen concentration: (1) sulfur compounds in waste sludge; (2) iron sulphide precipitation; (3) elemental sulfur S<sup>0</sup>; (4) sulfide in liquid phase; (5) sulphide oxidation products; and (6) sulfur in biogas.</p> Full article ">Figure 6
<p>The possible desulfurization mechanisms by combination of RSI and micro-oxygen during digestion system.</p> Full article ">Figure 7
<p>The DGGE profiles of archaea and bacterial 16S rRNA genes from the samples of digestion reactor in the last day of P1, P2 and P6. The gels with band were collected from the DGGE gel and labeled as bands A1–A11 (for archaea) and B1–B19 (for bacteria). The sequencing results of each band are shown in <a href="#energies-10-00258-t005" class="html-table">Table 5</a>.</p> Full article ">
2865 KiB  
Article
A Cluster Design on the Influence of Energy Taxation in Shaping the New EU-28 Economic Paradigm
by Marian Zaharia, Aurelia Pătrașcu, Manuela Rodica Gogonea, Ana Tănăsescu and Constanța Popescu
Energies 2017, 10(2), 257; https://doi.org/10.3390/en10020257 - 21 Feb 2017
Cited by 20 | Viewed by 4056
Abstract
Environmental and energy taxation are essential components for designing global economic policies and they often contribute to achieving the sustainable economic development goals in contemporary economies. Starting from the analysis of certain elements such as the share of environmental, energy, transport and pollution [...] Read more.
Environmental and energy taxation are essential components for designing global economic policies and they often contribute to achieving the sustainable economic development goals in contemporary economies. Starting from the analysis of certain elements such as the share of environmental, energy, transport and pollution taxation in GDP and using the Hierarchical Clustering methodology, the paper aims to identify economic models of behaviour and to understand the influence of energy taxation in designing an economic paradigm. In addition, another objective of the paper is to deepen the relationships that energy taxation has in designing certain economic models of behaviour and to group the EU-28 Member States based on the specified criteria. The research results confirm that at the EU-28 level could exist elements for achieving energy taxation convergence and that the states should promote a more accurate fiscal policy in order to improve the loss of competitivity caused by an inaccurate energy taxation. Full article
(This article belongs to the Special Issue Contemporary Sustainable Energy Paradigms and the Multifunctional Economy)
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Figure 1
<p>Dendrogram using the Ward linkage method.</p> Full article ">Figure 2
<p>Number of clusters.</p> Full article ">Figure 3
<p>Evolution of ENV_GDP, ENG_GDP, TRS_GDP, POL_GDP on clusters, for 2002.</p> Full article ">Figure 4
<p>Dendrogram using the Ward linkage method.</p> Full article ">Figure 5
<p>Number of clusters.</p> Full article ">Figure 6
<p>Evolution of ENV_GDP, ENG_GDP, TRS_GDP, POL_GDP in the 2012 clusters.</p> Full article ">
6035 KiB  
Article
A New Vertical JFET Power Device for Harsh Radiation Environments
by Pablo Fernández-Martínez, David Flores, Salvador Hidalgo, Xavier Jordà, Xavier Perpiñà, David Quirion, Lucia Ré, Miguel Ullán and Miquel Vellvehí
Energies 2017, 10(2), 256; https://doi.org/10.3390/en10020256 - 20 Feb 2017
Cited by 8 | Viewed by 6072
Abstract
An increasing demand for power electronic devices able to be operative in harsh radiation environments is now taking place. Specifically, in High Energy Physics experiments the required power devices are expected to withstand very high radiation levels which are normally too hard for [...] Read more.
An increasing demand for power electronic devices able to be operative in harsh radiation environments is now taking place. Specifically, in High Energy Physics experiments the required power devices are expected to withstand very high radiation levels which are normally too hard for most of the available commercial solutions. In this context, a new vertical junction field effect transistor (JFET) has been designed and fabricated at the Instituto de Microelectrónica de Barcelona, Centro Nacional de Microelectrónica (IMB-CNM, CSIC). The new silicon V-JFET devices draw upon a deep-trenched technology to achieve volume conduction and low switch-off voltage, together with a moderately high voltage capability. The first batches of V-JFET prototypes have been already fabricated at the IMB-CNM clean room, and several aspects of their design, fabrication and the outcome of their characterization are summarized and discussed in this paper. Radiation hardness of the fabricated transistors have been tested both with gamma and neutron irradiations, and the results are also included in the contribution. Full article
(This article belongs to the Special Issue Semiconductor Power Devices)
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Figure 1
<p>Schematic cross section through the center of two adjacent V-JFET cells.</p> Full article ">Figure 2
<p>Simulated switch-off voltage, <span class="html-italic">V</span><sub>OFF</sub>, (<b>red curves</b>) and inner-cell breakdown voltage, <span class="html-italic">V</span><sub>BD|CELL</sub>, (<b>black curves</b>) as a function of the trench depth, <span class="html-italic">D</span>, for a given V-JFET cell with 2<span class="html-italic">r</span> = 35 µm, <span class="html-italic">N</span><sub>A</sub> = 2 × 10<sup>13</sup> at/cm<sup>3</sup>, and <span class="html-italic">T</span> = 300 µm.</p> Full article ">Figure 3
<p>Simulated switch-off voltage, <span class="html-italic">V</span><sub>OFF</sub>, (<b>red curves</b>) and forward current at saturation, <span class="html-italic">I</span><sub>ON</sub>, (<b>black curves</b>) as a function of the substrate doping concentration, <span class="html-italic">N</span><sub>A</sub>, for a given V-JFET design with two different cell diameters (2<span class="html-italic">r</span> = 23 and 35 µm), and a total active area <span class="html-italic">A</span> = 5 × 5 mm<sup>2</sup>.</p> Full article ">Figure 4
<p>Simulated inner-cell breakdown voltage, <span class="html-italic">V</span><sub>BD|CELL</sub>, (<b>red curves</b>) and forward current at saturation, <span class="html-italic">I</span><sub>ON</sub>, (<b>black curves</b>) as a function of the substrate doping concentration, <span class="html-italic">N</span><sub>A</sub>, for a given V-JFET design with two different cell diameters (2<span class="html-italic">r</span> = 23 and 35 µm), and a total active area <span class="html-italic">A</span> = 5 × 5 mm<sup>2</sup>.</p> Full article ">Figure 5
<p>SEM image of a cross section through the center of a row of cells, obtained on a V-JFET sample before the metal deposition. Several peripheral elements (gate runners and guard rings) can be observed together with the arrangement of parallel cells. A depict of the layout is also shown in the bottom right inset.</p> Full article ">Figure 6
<p>SEM image of a cross section through the center of an individual V-JFET cell, obtained on a sample during the fabrication process. Regions with different doping levels are distinguishable thanks to a preparation of the sample with a reverse engineering technique.</p> Full article ">Figure 7
<p>Measured output characteristic curves (|<span class="html-italic">I</span><sub>D</sub>| vs. <span class="html-italic">V</span><sub>DS</sub> at <span class="html-italic">V</span><sub>GS</sub> = 0 V) for several representative V-JFET samples, with 2<span class="html-italic">r</span> = 35 µm, <span class="html-italic">S</span> = 10 µm and <span class="html-italic">A</span> = 5 × 5 mm<sup>2</sup>, coming from different wafers (A, B, and C), and compared with the corresponding simulated curves for different <span class="html-italic">N</span><sub>A</sub> values.</p> Full article ">Figure 8
<p>Measured transfer characteristic curves (|<span class="html-italic">I</span><sub>D</sub>| vs. <span class="html-italic">V</span><sub>GS</sub> at <span class="html-italic">V</span><sub>DS</sub> = −20 V) for several representative V-JFET samples, with 2<span class="html-italic">r</span> = 35 µm, <span class="html-italic">S</span> = 10 µm and <span class="html-italic">A</span> = 5 × 5 mm<sup>2</sup>, coming from different wafers (A, B, and C), and compared with the corresponding simulated curves for different <span class="html-italic">N</span><sub>A</sub> values.</p> Full article ">Figure 9
<p>Measured Gate current as a function of the Gate bias (<span class="html-italic">I</span><sub>G</sub> vs. <span class="html-italic">V</span><sub>GS</sub> at <span class="html-italic">V</span><sub>DS</sub> = −20 V) for several V-JFET samples with equal design, fabricated on the same wafer. The leakage current increase for <span class="html-italic">V</span><sub>GS</sub> &gt; <span class="html-italic">V</span><sub>OFF</sub>, observed in one malfunctioning sample is compared with the curves of other prototypes with standard performance.</p> Full article ">Figure 10
<p>Lock-in thermography amplitude and phase images, obtained with a voltage Gate modulation of Δ<span class="html-italic">V</span> = 4 V, for a V-JFET sample biased at <span class="html-italic">V</span><sub>GS</sub> = 2 V (<b>top side</b>), and at <span class="html-italic">V</span><sub>GS</sub> = 7.5 V (<b>bottom side</b>). A hot spot responsible of the Gate leakage increase is observed in the second case.</p> Full article ">Figure 11
<p>Lock-in thermography amplitude and phase images, obtained with a Drain voltage modulation of Δ<span class="html-italic">V</span> = 5 V around the breakdown value (<span class="html-italic">V</span><sub>DS</sub> = −300 V), for a V-JFET sample biased in conduction (<span class="html-italic">V</span><sub>GS</sub> = 0 V; <b>top side</b>), and blocking (<span class="html-italic">V</span><sub>GS</sub> = 5 V; <b>bottom side</b>) modes. Hot spots responsible for the breakdown are not uniformly located at the peripheral Gate runner.</p> Full article ">Figure 12
<p>Measured transfer characteristic curves for a V-JFET sample, before and after a neutron irradiation with equivalent fluence of 2 × 10<sup>13</sup> n<sub>eq</sub>/cm<sup>2</sup>. The effects on <span class="html-italic">I</span><sub>ON</sub>, <span class="html-italic">I</span><sub>OFF</sub>, and <span class="html-italic">V</span><sub>OFF</sub> are indicated in the figure.</p> Full article ">
3182 KiB  
Article
Research on the Combustion Characteristics and Kinetic Analysis of the Recycling Dust for a COREX Furnace
by Haiyang Wang, Jianliang Zhang, Guangwei Wang, Di Zhao, Jian Guo and Tengfei Song
Energies 2017, 10(2), 255; https://doi.org/10.3390/en10020255 - 20 Feb 2017
Cited by 14 | Viewed by 4077
Abstract
Thermogravimetric analysis of recycling dust (RD) from the melter gasifier of COREX, coke1 (C-1), coke2 (C-2) and coal char (CC) under 70% oxygen atmosphere was carried out using thermal balance. The chemical composition and physical structure of the samples were investigated. The characteristic [...] Read more.
Thermogravimetric analysis of recycling dust (RD) from the melter gasifier of COREX, coke1 (C-1), coke2 (C-2) and coal char (CC) under 70% oxygen atmosphere was carried out using thermal balance. The chemical composition and physical structure of the samples were investigated. The characteristic temperatures and comprehensive combustion characteristic indexes were calculated and kinetic parameters during the combustion process were calculated as well using a distributed activation energy model (DAEM). The results show that the carbon in the recycling dust originates from unconsumed CC and coke fines, and the average stacking height of carbon in RD is larger than that of C-1, C-2 and CC. The conversion curves of RD are different from those of C-1, C-2 and CC, and there are two peaks in the RD conversion rate curves. The combustion profiles of RD moves to a higher temperature zone with increasing heating rates. The average activation energies of their combustion process for RD, C-1, C-2 and CC range from 191.84 kJ/mol to 128.31 kJ/mol. The activation energy for recycling dust increases as the fractional conversion increases, but the value for C-1, C-2 and CC decreases with increasing conversion, indicating different combustion mechanisms. Full article
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Figure 1
<p>Microstructure of unconsumed pulverized coal in recycling dust: (<b>a</b>) completely unconsumed coal (CUC); (<b>b</b>) deformed coal (DC); and (<b>c</b>) residue coal (RC).</p> Full article ">Figure 2
<p>Microstructure of unconsumed fine coke in recycling dust: (<b>a</b>–<b>c</b>) granule inlay structure (GIS); (<b>d</b>) macro some (MS); (<b>e</b>) residue coke (RC); and (<b>f</b>) block structure (BS).</p> Full article ">Figure 3
<p>Morphology of carbonaceous materials in the recycling dust, coal char (CC) and coke: (<b>a</b>) RD; (<b>b</b>) C-1; (<b>c</b>) C-2; and (<b>d</b>) CC. <b>A</b>: ablative structure; <b>B</b>: irregular and sharp edge; <b>C</b>: irregular edge and rough surface; <b>D</b>: sharp edge and smooth surface; <b>E</b>: small pores; and <b>F</b>: irregular edge and smooth surface.</p> Full article ">Figure 4
<p>X-ray diffraction (XRD) spectra of RD, C-1, C-2 and CC.</p> Full article ">Figure 5
<p>Fractional conversion curves and conversion rate curves of samples: (<b>a</b>) RD; (<b>b</b>) C-1; (<b>c</b>) C-2; and (<b>d</b>) CC.</p> Full article ">Figure 6
<p>Plots of <math display="inline"> <semantics> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mfrac> <mi mathvariant="sans-serif">β</mi> <mrow> <msup> <mi>T</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo stretchy="false">)</mo> </mrow> </semantics> </math> vs. <math display="inline"> <semantics> <mrow> <mfrac> <mn>1</mn> <mi>T</mi> </mfrac> </mrow> </semantics> </math> at a given fractional conversion at different heating rates: (<b>a</b>) RD; (<b>b</b>) C-1; (<b>c</b>) C-2; and (<b>d</b>) CC.</p> Full article ">Figure 7
<p>Distributed activation energy curves of samples: (<b>a</b>) RD; (<b>b</b>) C-1; (<b>c</b>) C-2; and (<b>d</b>) CC.</p> Full article ">
2667 KiB  
Article
Power Consumption Efficiency Evaluation of Multi-User Full-Duplex Visible Light Communication Systems for Smart Home Technologies
by Muhammad Tabish Niaz, Fatima Imdad and Hyung Seok Kim
Energies 2017, 10(2), 254; https://doi.org/10.3390/en10020254 - 20 Feb 2017
Cited by 14 | Viewed by 7367
Abstract
Visible light communication (VLC) has recently gained significant academic and industrial attention. VLC has great potential to supplement the functioning of the upcoming radio-frequency (RF)-based 5G networks. It is best suited for home, office, and commercial indoor environments as it provides a high [...] Read more.
Visible light communication (VLC) has recently gained significant academic and industrial attention. VLC has great potential to supplement the functioning of the upcoming radio-frequency (RF)-based 5G networks. It is best suited for home, office, and commercial indoor environments as it provides a high bandwidth and high data rate, and the visible light spectrum is free to use. This paper proposes a multi-user full-duplex VLC system using red-green-blue (RGB), and white emitting diodes (LEDs) for smart home technologies. It utilizes red, green, and blue LEDs for downlink transmission and a simple phosphor white LED for uplink transmission. The red and green color bands are used for user data and smart devices, respectively, while the blue color band is used with the white LED for uplink transmission. The simulation was carried out to verify the performance of the proposed multi-user full-duplex VLC system. In addition to the performance evaluation, a cost-power consumption analysis was performed by comparing the power consumption and the resulting cost of the proposed VLC system to the power consumed and resulting cost of traditional Wi-Fi based systems and hybrid systems that utilized both VLC and Wi-Fi. Our findings showed that the proposed system improved the data rate and bit-error rate performance, while minimizing the power consumption and the associated costs. These results have demonstrated that a full-duplex VLC system is a feasible solution suitable for indoor environments as it provides greater cost savings and energy efficiency when compared to traditional Wi-Fi-based systems and hybrid systems that utilize both VLC and Wi-Fi. Full article
(This article belongs to the Special Issue Smart Home Energy Management)
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Figure 1

Figure 1
<p>Proposed multi-user full-duplex visible light communication (VLC) system.</p> Full article ">Figure 2
<p>Block diagram of the downlink section. S/P: serial to parallel; IFFT: inverse fast Fourier transform; P/S: parallel to serial; CP: cyclic prefix; DAC: digital-to-analogue converter; LPF: low pass filter; ACO-OFDM: asymmetrically clipped optical orthogonal frequency-division multiplexing; and ADC: analogue-to-digital converter.</p> Full article ">Figure 3
<p>Block diagram of the uplink section. LED: light emitting diode; and OOK: on-off keying.</p> Full article ">Figure 4
<p>(<b>a</b>) Traditional Wi-Fi model; (<b>b</b>) hybrid VLC-Wi-Fi model; and (<b>c</b>) proposed full-duplex multi-user VLC system. PD: photodiode.</p> Full article ">Figure 5
<p>Simulation of the proposed system: (<b>a</b>) bit error rate (BER) performance of the proposed system and (<b>b</b>) data rate against the number of users. OFDM: orthogonal frequency-division multiplexing; and SNR: signal-to-noise ratio.</p> Full article ">Figure 6
<p>Simulated average device cost and power consumption cost over data throughput for different SPUs (<a href="#energies-10-00254-t002" class="html-table">Table 2</a>).</p> Full article ">Figure 7
<p>Device cost comparison of architecture 1 for all three systems.</p> Full article ">Figure 8
<p>Power consumption cost comparison of architecture 1 for all three systems.</p> Full article ">Figure 9
<p>Total cost comparison of architecture 1 for all three systems.</p> Full article ">
4276 KiB  
Article
A Parameter Selection Method for Wind Turbine Health Management through SCADA Data
by Mian Du, Jun Yi, Peyman Mazidi, Lin Cheng and Jianbo Guo
Energies 2017, 10(2), 253; https://doi.org/10.3390/en10020253 - 20 Feb 2017
Cited by 16 | Viewed by 5217
Abstract
Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been [...] Read more.
Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copulabased mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough. Full article
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<p>Venn diagram for various information measures associated with variables <span class="html-italic">X</span> and <span class="html-italic">Y</span>.</p> Full article ">Figure 2
<p>The procedure of the application of ECMI.</p> Full article ">Figure 3
<p>Power curve from the cleaned dataset.</p> Full article ">Figure 4
<p>Cumulative copula distribution of the two parameters. In both figures, <span class="html-italic">X</span> and <span class="html-italic">Y</span> axis represent the bins in [0, 1] and <span class="html-italic">Z</span> axis shows the probability distribution of the copula distribution of the two parameters. In (<b>a</b>), the two parameters are active power and wind speed; while in (<b>b</b>) the two parameters are active power and yaw.</p> Full article ">Figure 5
<p>Copula density of the two parameters. In both figures, <span class="html-italic">X</span> and <span class="html-italic">Y</span> axis represent the bins in [0, 1] and <span class="html-italic">Z</span> axis shows the probability of the copula distribution of the two parameters. In (<b>a</b>) the two parameters are active power and wind speed; while in (<b>b</b>) the two parameters are active power and yaw.</p> Full article ">Figure 6
<p>Best validation performance with three approaches through NN training process. While (<b>a</b>) represent the result based on the PCCA list; (<b>b</b>) shows the result based on the KCCA list; (<b>c</b>) displays the result based on the ECMI list.</p> Full article ">
5480 KiB  
Article
Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data
by Fabrizio De Caro, Alfredo Vaccaro and Domenico Villacci
Energies 2017, 10(2), 252; https://doi.org/10.3390/en10020252 - 20 Feb 2017
Cited by 9 | Viewed by 5051
Abstract
The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address [...] Read more.
The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address these two conflicting objectives, this paper advocates the role of knowledge discovery from big-data, by proposing the integration of adaptive Case Based Reasoning models, and cardinality reduction techniques based on Partial Least Squares Regression, and Principal Component Analysis. The main idea is to learn from a large database of historical climatic observations, how to solve the windforecasting problem, avoiding complex and time-consuming computations. To assess the benefits derived by the application of the proposed methodology in complex application scenarios, the experimental results obtained in a real case study will be presented and discussed. Full article
(This article belongs to the Special Issue Advances in Power System Operations and Planning)
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<p>Physical downscaling model.</p> Full article ">Figure 2
<p>Input and output of physical method.</p> Full article ">Figure 3
<p>Flowchart of the proposed CBR-based framework.</p> Full article ">Figure 4
<p>Satellite view of the analyzed area.</p> Full article ">Figure 5
<p>Comparison between latent variables extracted from CFD solver output (green) and obtained by using of the PCA-based CBR for the 2nd forecasting hour.</p> Full article ">Figure 6
<p>Comparison between spatial wind speed obtained from CFD solver output (green) and obtained by using of the PCA-CBR for the 2nd hour forecasting.</p> Full article ">Figure 7
<p>Comparison between spatial power output obtained from CFD solver output (green) and obtained by using of PCA for the 2nd forecasting hour.</p> Full article ">Figure 8
<p>Comparison between spatial power output obtained from CFD solver output (green) and obtained by using of PCA for the 2nd forecasting hour.</p> Full article ">Figure 9
<p>Distances from NN 2th val. hour, Hist. Distances from NN, Distance <span class="html-italic">y</span> query.</p> Full article ">Figure 10
<p>NMAE, NBIAS, NRSME trend on validation period and their related histograms.</p> Full article ">Figure 11
<p>Probabilistic distribution of the error for the validation period.</p> Full article ">Figure 12
<p>Cumulative Variance in function of the PLS component number.</p> Full article ">Figure 13
<p>Scatter plot observed- fitted response in function of several number of PLS components used in this work.</p> Full article ">
1338 KiB  
Article
The Efficiency Improvement by Combining HHO Gas, Coal and Oil in Boiler for Electricity Generation
by Chia-Nan Wang, Min-Tsong Chou, Hsien-Pin Hsu, Jing-Wein Wang and Sridhar Selvaraj
Energies 2017, 10(2), 251; https://doi.org/10.3390/en10020251 - 20 Feb 2017
Cited by 13 | Viewed by 10593
Abstract
Electricity is an essential energy that can benefit our daily lives. There are many sources available for electricity generation, such as coal, natural gas and nuclear. Among these sources, coal has been widely used in thermal power plants that account for about 41% [...] Read more.
Electricity is an essential energy that can benefit our daily lives. There are many sources available for electricity generation, such as coal, natural gas and nuclear. Among these sources, coal has been widely used in thermal power plants that account for about 41% of the worldwide electricity supply. However, these thermal power plants are also found to be a big pollution source to our environment. There is a need to explore alternative electricity sources and improve the efficiency of electricity generation. This research focuses on improving the efficiency of electricity generation through the use of hydrogen and oxygen mixture (HHO) gas. In this research, experiments have been conducted to investigate the combined effects of HHO gas with other fuels, including coal and oil. The results show that the combinations of HHO with coal and oil can improve the efficiency of electricity generation while reducing the pollution to our environment. Full article
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<p>Oxy fuel firing in the coal-fired power plant [<a href="#B11-energies-10-00251" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>The configuration of a coal-fired power plant [<a href="#B12-energies-10-00251" class="html-bibr">12</a>].</p> Full article ">Figure 3
<p>The process of flue gas recycling [<a href="#B13-energies-10-00251" class="html-bibr">13</a>].</p> Full article ">Figure 4
<p>Test facility</p> Full article ">
2977 KiB  
Article
Environmental Assessment of Possible Future Waste Management Scenarios
by Yevgeniya Arushanyan, Anna Björklund, Ola Eriksson, Göran Finnveden, Maria Ljunggren Söderman, Jan-Olov Sundqvist and Åsa Stenmarck
Energies 2017, 10(2), 247; https://doi.org/10.3390/en10020247 - 19 Feb 2017
Cited by 35 | Viewed by 8867
Abstract
Waste management has developed in many countries and will continue to do so. Changes towards increased recovery of resources in order to meet climate targets and for society to transition to a circular economy are important driving forces. Scenarios are important tools for [...] Read more.
Waste management has developed in many countries and will continue to do so. Changes towards increased recovery of resources in order to meet climate targets and for society to transition to a circular economy are important driving forces. Scenarios are important tools for planning and assessing possible future developments and policies. This paper presents a comprehensive life cycle assessment (LCA) model for environmental assessments of scenarios and waste management policy instruments. It is unique by including almost all waste flows in a country and also allow for including waste prevention. The results show that the environmental impacts from future waste management scenarios in Sweden can differ a lot. Waste management will continue to contribute with environmental benefits, but less so in the more sustainable future scenarios, since the surrounding energy and transportation systems will be less polluting and also because less waste will be produced. Valuation results indicate that climate change, human toxicity and resource depletion are the most important environmental impact categories for the Swedish waste management system. Emissions of fossil CO2 from waste incineration will continue to be a major source of environmental impacts in these scenarios. The model is used for analyzing environmental impacts of several policy instruments including weight based collection fee, incineration tax, a resource tax and inclusion of waste in a green electricity certification system. The effect of the studied policy instruments in isolation are in most cases limited, suggesting that stronger policy instruments as well as combinations are necessary to reach policy goals as set out in for example the EU action plan on circular economy. Full article
(This article belongs to the Special Issue Energy and Waste Management)
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<p>General outline of SWEA model [<a href="#B19-energies-10-00247" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Relative environmental impacts of the waste treatment system (including avoided burdens) for the four base scenarios compared to the reference scenario set to 100% (average data).</p> Full article ">Figure 3
<p>Relative cumulative energy demand of the waste treatment system (including avoided burdens) for the four base scenarios compared to the reference scenario set to 100%.</p> Full article ">Figure 4
<p>Valuation results for the different scenarios using the Ecovalue method.</p> Full article ">Figure 5
<p>Relative environmental impacts from Weight based waste fee, alt.1 in comparison to the “no action” alternative. The “no action” alternative is set to 100%.</p> Full article ">Figure 6
<p>Relative results for the Cumulative energy demand for Weight based waste fee, alt.1 policy instrument compared to the “no action” alternative set to 100%</p> Full article ">Figure 7
<p>Hierarchical scheme of the SWEA model structure.</p> Full article ">
3235 KiB  
Article
Production Characteristics with Different Superimposed Modes Using Variogram: A Case Study of a Super-Giant Carbonate Reservoir in the Middle East
by Chenji Wei, Hongqing Song, Yong Li, Qi Zhang, Benbiao Song and Jiulong Wang
Energies 2017, 10(2), 250; https://doi.org/10.3390/en10020250 - 18 Feb 2017
Cited by 12 | Viewed by 4950
Abstract
Heterogeneity of permeability is an important factor affecting the production of a carbonate reservoir. How to correctly characterize the heterogeneity of permeability has become a key issue for carbonate reservoir development. In this study, the reservoirs were categorized into four superimposed modes based [...] Read more.
Heterogeneity of permeability is an important factor affecting the production of a carbonate reservoir. How to correctly characterize the heterogeneity of permeability has become a key issue for carbonate reservoir development. In this study, the reservoirs were categorized into four superimposed modes based on the actual logging data from a super-giant heterogeneous carbonate reservoir in the Middle East. A modified permeability formula in terms of the variogram method was presented to reflect the heterogeneity of the reservoirs. Furthermore, the models of oil production and water cut were established and the analytical solutions were obtained. The calculation results show that the present model can predict the productivity of wells with different heterogeneous layers more accurately and rapidly. The larger the varigoram value, the stronger the heterogeneity of the reservoirs, and the faster the decline of production owing to a quicker reduction of formation pressure. With the increase in variogram value, the relative permeability of the oil phase is smaller and the water phase larger, and the water cut becomes larger. This study has provided a quick and reasonable prediction model for heterogeneous reservoir. Full article
(This article belongs to the Special Issue Oil and Gas Engineering)
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<p>The schematic of four superimposed modes: (<b>a</b>) superimposed mode A; (<b>b</b>) superimposed mode B; (<b>c</b>) superimposed mode C; (<b>d</b>) superimposed mode D.</p> Full article ">Figure 1 Cont.
<p>The schematic of four superimposed modes: (<b>a</b>) superimposed mode A; (<b>b</b>) superimposed mode B; (<b>c</b>) superimposed mode C; (<b>d</b>) superimposed mode D.</p> Full article ">Figure 2
<p>Schematic of permeability distribution of different superimposed modes.</p> Full article ">Figure 3
<p>The flow chart of simulation.</p> Full article ">Figure 4
<p>Comparison of actual production data and the present model with different superimposed modes: (<b>a</b>) superimposed mode A; (<b>b</b>) superimposed mode B; (<b>c</b>) superimposed mode C; (<b>d</b>) superimposed mode D.</p> Full article ">Figure 5
<p>Production with different variogram values.</p> Full article ">Figure 6
<p>Water cuts with different variogram values.</p> Full article ">Figure 7
<p>Relative permeability with different variogram values.</p> Full article ">
4947 KiB  
Article
Sectoral Electricity Consumption and Economic Growth: The Time Difference Case of China, 2006–2015
by Jian Zhang, Zhaoguang Hu, Yanan Zheng, Yuhui Zhou and Ziwei Wan
Energies 2017, 10(2), 249; https://doi.org/10.3390/en10020249 - 18 Feb 2017
Cited by 7 | Viewed by 4133
Abstract
Unlike existing studies focused on the causal relationship between electricity consumption and economic growth at the macro level, this paper uses monthly data from January 2006 to December 2015 and applies the correlation coefficient, as well as Kullback-Leibler (KL) divergence, to study the [...] Read more.
Unlike existing studies focused on the causal relationship between electricity consumption and economic growth at the macro level, this paper uses monthly data from January 2006 to December 2015 and applies the correlation coefficient, as well as Kullback-Leibler (KL) divergence, to study the time difference relationship between sectoral electricity consumption and economic growth. The empirical results draw some main findings as follows: First, the time difference relationships show diversity at the sector level but will form a kind of overall characteristic between economic growth and total electricity consumption. Secondly, not all sectors have a remarkable correlation between sectoral electricity consumption and economic growth as only part of them have reasonable values to describe the time difference relationship. Thirdly, the results present both diversity and aggregation at the industry level, while lagging sectors mainly concentrate in the manufacturing industry. The relationship between sectoral electricity consumption and economic growth can be further explored and described from a new perspective based on the results. Further, the trend of economic development and sectoral electricity consumption can be predicted to help policy-makers formulate proper policies. Full article
(This article belongs to the Special Issue Energy Economics 2016)
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<p>GDP and industry value-added in China from 2006 to 2015.</p> Full article ">Figure 2
<p>Electricity consumption of China from 2006 to 2015.</p> Full article ">Figure 3
<p>Correlation coefficient results of 46 sectors.</p> Full article ">Figure 4
<p>Kullback-Leibler (KL) divergence results of 46 Sectors.</p> Full article ">Figure 5
<p>Time difference relationship results of selected sectors.</p> Full article ">
5788 KiB  
Article
A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations
by Martin Hofmann and Gunther Seckmeyer
Energies 2017, 10(2), 248; https://doi.org/10.3390/en10020248 - 18 Feb 2017
Cited by 41 | Viewed by 9446
Abstract
We present a new model for the calculation of the diffuse fraction of the global solar irradiance for solar system simulations. The importance of an accurate estimation of the horizontal diffuse irradiance is highlighted by findings that an inaccurately calculated diffuse irradiance can [...] Read more.
We present a new model for the calculation of the diffuse fraction of the global solar irradiance for solar system simulations. The importance of an accurate estimation of the horizontal diffuse irradiance is highlighted by findings that an inaccurately calculated diffuse irradiance can lead to significant over- or underestimations in the annual energy yield of a photovoltaic (PV) system by as much as 8%. Our model utilizes a time series of global irradiance in one-minute resolution and geographical information as input. The model is validated by measurement data of 28 geographically and climatologically diverse locations worldwide with one year of one-minute data each, taken from the Baseline Surface Radiation Network (BSRN). We show that on average the mean absolute deviation of the modelled and the measured diffuse irradiance is reduced from about 12% to about 6% compared to three reference models. The maximum deviation is less than 20%. In more than 80% of the test cases, the deviation is smaller 10%. The root mean squared error (RMSE) of the calculated diffuse fractions is reduced by about 18%. Full article
(This article belongs to the Special Issue Solar Photovoltaics Trilemma: Efficiency, Stability and Cost Reduction 2017)
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<p><span class="html-italic">Top</span>: Measured (grey) and modelled (green) time series of diffuse irradiance on a horizontal surface for four days in Lindenberg, Germany. Global irradiance (blue) for reference. The calculation of diffuse irradiance in this example was done with the reduced model of Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>]. The model underestimates the four-day sum of the diffuse irradiation by 18%. <span class="html-italic">Middle</span>: The global irradiance on a tilted photovoltaic (PV) module (facing south, tilted by 30°) for the same four days. The model used for calculating the irradiance on a tilted surface is from Hay and Davies [<a href="#B4-energies-10-00248" class="html-bibr">4</a>]. Due to the underestimation of the diffuse irradiance (see top), the four-day sum of the global irradiation on the PV module based on modelled values falls below the global irradiation based on measured values by −9%. <span class="html-italic">Bottom</span>: The resulting cumulated deviation of the modelled global irradiation on the tilted plane from the measured. The plot shows that one of the main sources of deviation is the modelling of highly variable irradiance situations, as observed e.g., on 10 June, between 08:00 and 12:00.</p> Full article ">Figure 2
<p>Measured diffuse fraction over clearness index <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> <mtext> </mtext> </mrow> </semantics> </math>for one year of measurement (grey points, extract of 2003) in Lindenberg, Germany. Line plots: Schematic overview of existing one-parameter models. Typically the models define three sections with varying <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mi>k</mi> <mi>t</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> functions.</p> Full article ">Figure 3
<p>Measured diffuse fraction over clearness index <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> <mtext> </mtext> </mrow> </semantics> </math>for one year of measurement (grey points, extract of 2003) in Lindenberg, Germany. Line plots: The model by Reindl, Beckman and Duffie [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] (reduced version), using two parameters (<math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> and sun height) as input.</p> Full article ">Figure 4
<p>A probability matrix of the diffuse fraction as a function of the clearness index <span class="html-italic">kt</span>. For each value of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math>, this matrix describes the probability with which a certain value of diffuse fraction will occur. The matrix correlates with the existing simple one-parameter models mentioned in <a href="#sec2dot3-energies-10-00248" class="html-sec">Section 2.3</a>, but it is based on measurements. Therefore the natural variability is better described by the model especially for high values of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> (<math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> &gt; 1.1, irradiance enhancement due to reflections by broken clouds) while preserving the strong relation at low levels of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> (<math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> &lt; 0.4, overcast sky).</p> Full article ">Figure 5
<p>Scatter plot of the relative changes of the diffuse fraction over the relative changes of the clearness index for Lindenberg, Germany, 2003. This strong relation is very valuable for modelling a realistic behaviour of the diffuse fraction over the day, since it depends highly on the behaviour of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math>. The area where the change of df is 0 while <span class="html-italic">kt</span> shows relatives changes between −0.5 and 0.5, i.e., <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> is changing while <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> is not, indicates days with movement of broken clouds, the reflection on which couses the measured global irradiance to change rapidly without changing its diffuse fraction.</p> Full article ">Figure 6
<p>The same relation between changes of <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> and changes of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> as in <a href="#energies-10-00248-f005" class="html-fig">Figure 5</a>, here as the probability matrix that is used in the model, corresponding to <a href="#energies-10-00248-f004" class="html-fig">Figure 4</a>. In the model, only relative <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> changes of −0.5 to 1 are computed with this matrix. In the matrix shown here, measurement values from the same locations and years as in <a href="#energies-10-00248-f004" class="html-fig">Figure 4</a> were incorporated.</p> Full article ">Figure 7
<p>Example for the geometric approach used to model clear sky diffuse fraction. The data shown is from Tateno, Japan, for 13 February 2006. While <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> (<b>top</b> plot, black) remains relatively constant, the measured diffuse fraction (blue) follows a typical scheme, starting with high <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> values in the morning, falling to a minimum at noon and rising again in the evening. This behaviour shows a strong correlation with the change of the air mass during the day (<b>bottom</b> plot, black). The clear sky diffuse fraction (green) is modelled as presented in Equation (9). The most important factor in this part of the model is the smallest value of <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> during the day, <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math>. Modelling <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> correctly is crucial for good algorithm results.</p> Full article ">Figure 8
<p>Measurement values for global irradiance (blue, <b>top</b>), <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> (black, <b>center</b>) and <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> (grey, <b>bottom</b>) for Tamanrasset, Algeria, from 21 to 26 March 2006. This plot illustrates the variation of the minimum daily <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>f</mi> </mrow> </semantics> </math> value (<math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math>) for consecutive clear sky days. <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> values for March 21 to 26 are: 0.328, 0.101, 0.062, 0.123, 0.139 and 0.105. One factor of influence seems to be the averaged maximum value of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> around noon. Another indicator is the shape of the <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> curve during day: A slow rise of <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> in the morning and slow fall in the evening indicate a high <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> like on 21 March, whereas steep ramps in the morning and evening with flat trends during the day indicate low <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> value (e.g., 26 March).</p> Full article ">Figure 9
<p>Variation of <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> (black) of days with clear skies over a year in Tamanrasset, 2006. While <math display="inline"> <semantics> <mrow> <mi>d</mi> <msub> <mi>f</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics> </math> is mostly close to 0.1 in wintertime, it varies strongly from spring to autumn, with no clear relation to the mean clearness index of the corresponding day (grey). It was found that changing levels of aerosols (red) and water vapour (dotted blue) may cause this effect.</p> Full article ">Figure 10
<p>Monthly means of aerosol optical depth (AOD) (plots <b>A</b> and <b>B</b>) and water vapour (<b>C</b>,<b>D</b>) for February (<b>A</b>,<b>C</b>) and June (<b>B</b>,<b>D</b>), from 2001 to 2015. Data taken from NASA Terra/MODIS satellite [<a href="#B29-energies-10-00248" class="html-bibr">29</a>,<a href="#B30-energies-10-00248" class="html-bibr">30</a>].</p> Full article ">Figure 11
<p>Three pictures made by an Hemispherical Sky Imager in Hannover (at the Institute for Meteorology and Climatology of the Leibniz University Hannover) in order to illustrate the three different weighing conditions presented in <a href="#energies-10-00248-t007" class="html-table">Table 7</a>. Time in UTC. (<b>A</b>) 02 May 2016 12:00–Clear Sky: <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> <mo>=</mo> <mn>1.03</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mi>a</mi> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>0.0025</mn> </mrow> </semantics> </math>; (<b>B</b>) 03 May 2016 12:00–Standard: <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> <mo>=</mo> <mn>0.147</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mi>a</mi> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>0.107</mn> </mrow> </semantics> </math>; (<b>C</b>) 06 May 2016 09:40–Transition: <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> <mo>=</mo> <mn>1.01</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mi>a</mi> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>0.028</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 12
<p>Plot of measured and modelled irradiance values for 14 consecutive days in Alice Springs, Australia, 2005, as an example. The total amount of analysed data sets comprises one year in minutes for each of the 28 test cases (refer to <a href="#sec2dot1-energies-10-00248" class="html-sec">Section 2.1</a>), equaling to seven million datapoints. Values at night are omitted in this plot. The measured global irradiance (green) is shown on top, the resulting clearness index <math display="inline"> <semantics> <mrow> <mi>k</mi> <mi>t</mi> </mrow> </semantics> </math> (black) for reference in the middle. The bottom part of the diagram displays measured (black) and modelled diffuse fractions (blue for the new model, grey for Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>], orange for Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>], yellow for Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>]). Most of the time, the output of the new model leads to good conformity for clear sky days as well as for days with broken clouds. The inherent problem of static one- or two-parameter models becomes apparent when comparing the measurement values to the output of the models by Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>], Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] and Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>], especially for clear sky days.</p> Full article ">Figure 13
<p>The root mean squared errors (<span class="html-italic">RMSE</span>) for all analysed datasets of the modelled versus the measured diffuse fraction. The <span class="html-italic">RMSE</span> of the new model is in all cases smaller than the <span class="html-italic">RMSE</span> of the model by Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>] (OH, grey), Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] (RR, orange) or Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>] (PZ, yellow), except for the location of Izaña, Spain (<span class="html-italic">iza 2011</span>), where the OH model produces a slightly smaller <span class="html-italic">RMSE</span>. The mean <span class="html-italic">RMSE</span> over all test cases is at 0.116 for the new model, 0.138 for OH, 0.134 for RR and 0.139 for PZ, which implies an amelioration of the <span class="html-italic">RMSE</span> of 16%–20%.</p> Full article ">Figure 14
<p>Relative deviation of the modelled annual diffuse irradiation from the measured diffuse irradiation for all analysed datasets. The new model performs better than the three reference models by Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>] (OH, grey), Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] (RR, orange) and Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>] in nearly all cases, except for desert-like locations such as Solar Village, Saudi Arabia (<span class="html-italic">sov</span>) or Tamanrasset, Algeria (<span class="html-italic">tam</span>). None of the test cases shows deviations of more than ±20% for the new model. The mean absolute deviation over all test cases for the new model is 6.4%, whereas it reaches 11.9% for OH, 12.7% for RR and 10.9% for PZ. The mean absolute deviation can thus approximately be halved when using the new model.</p> Full article ">Figure 15
<p>Histogram of the mean absolute deviations of the annual diffuse irradiation in classes of 5%. Most of the deviations produced by the new model are smaller than 10% (compare <a href="#energies-10-00248-f016" class="html-fig">Figure 16</a>). In none of the test cases deviations of more than 20% can be observed. While the model by Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] (RR, orange) has most of its test cases in classes &lt;15%, it still produces in some cases results of more than 40%. The model of Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>] (OH, grey) features less extreme deviations but shows a strong frequency of deviations between 10% and 20%. The model by Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>] (PZ, yellow) does not produce outliers and has its results distributed evenly between 0 and 15%.</p> Full article ">Figure 16
<p>When using the new model for calculating the diffuse irradiance, the annual deviation of the modelled irradiation will be smaller than 5% in more than 40% of the cases, and smaller than 10% in over 80% of the cases. When using the model of Orgill and Hollands [<a href="#B18-energies-10-00248" class="html-bibr">18</a>] (OH), these confidence probabilities reduce to 25% and 36%, while using the model of Reindl et al. [<a href="#B3-energies-10-00248" class="html-bibr">3</a>] (RR) reduces the probabilities to 36% and 54% respectively. The use of the model of Perez and Ineichen [<a href="#B23-energies-10-00248" class="html-bibr">23</a>] (PZ) results in probabilities of 25% and 50%.</p> Full article ">
6828 KiB  
Article
Development of Shale Gas Supply Chain Network under Market Uncertainties
by Jorge Chebeir, Aryan Geraili and Jose Romagnoli
Energies 2017, 10(2), 246; https://doi.org/10.3390/en10020246 - 18 Feb 2017
Cited by 17 | Viewed by 8439
Abstract
The increasing demand of energy has turned the shale gas and shale oil into one of the most promising sources of energy in the United States. In this article, a model is proposed to address the long-term planning problem of the shale gas [...] Read more.
The increasing demand of energy has turned the shale gas and shale oil into one of the most promising sources of energy in the United States. In this article, a model is proposed to address the long-term planning problem of the shale gas supply chain under uncertain conditions. A two-stage stochastic programming model is proposed to describe and optimize the shale gas supply chain network. Inherent uncertainty in final products’ prices, such as natural gas and natural gas liquids (NGL), is treated through the utilization of a scenario-based method. A binomial option pricing model is utilized to approximate the stochastic process through the generation of scenario trees. The aim of the proposed model is to generate an appropriate and realistic supply chain network configuration as well as scheduling of different operations throughout the planning horizon of a shale gas development project. Full article
(This article belongs to the Special Issue Energy Production Systems)
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<p>Binomial tree for determination of each possible scenario for crude oil price.</p> Full article ">Figure 2
<p>Framework for discretization of stochastic optimization problem.</p> Full article ">Figure 3
<p>Shale gas network superstructure for optimization problem resolution.</p> Full article ">Figure 4
<p>Number of scenarios and price (per bbl) variation in crude oil binomial tree.</p> Full article ">Figure 5
<p>Optimal shale gas supply chain network under market uncertainty.</p> Full article ">Figure 6
<p>(<b>a</b>) Optimal drilling and fracturing plan for deterministic case; (<b>b</b>) Optimal drilling and fracturing plan for stochastic case.</p> Full article ">Figure 7
<p>Amount of water required in shale sites for deterministic and stochastic cases.</p> Full article ">Figure 8
<p>Variation of crude oil, NGL, and natural gas prices during the planning horizon.</p> Full article ">Figure 9
<p>Histogram and cumulative probability function for two-stage stochastic model.</p> Full article ">Figure 10
<p>Comparison of sold NGL during the planning horizon for stochastic and deterministic cases studies.</p> Full article ">Figure 11
<p>Variation of NGL stored for deterministic and stochastic cases.</p> Full article ">Figure 12
<p>Variation of % of demand fulfilment for NGL for deterministic and stochastic cases.</p> Full article ">Figure 13
<p>Total amount of natural gas sold in Market 1 for both deterministic (base case) and stochastic cases during the planning horizon.</p> Full article ">Figure 14
<p>Total amount of natural gas sold in Market 2 for both deterministic (base case) and stochastic cases during the planning horizon.</p> Full article ">Figure 15
<p>Variation of % of demand fulfilment in Market 1 for both deterministic (base case) and stochastic cases during the planning horizon.</p> Full article ">Figure 16
<p>Variation of % of demand fulfilment in Market 2 for both deterministic (base case) and stochastic cases during the planning horizon.</p> Full article ">Figure 17
<p>Historical data for crude oil price for approximately twenty-eight years [<a href="#B45-energies-10-00246" class="html-bibr">45</a>].</p> Full article ">Figure 18
<p>Optimal shale gas supply chain network for deterministic model (base case).</p> Full article ">
2675 KiB  
Article
Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study
by Kangji Li, Lei Pan, Wenping Xue, Hui Jiang and Hanping Mao
Energies 2017, 10(2), 245; https://doi.org/10.3390/en10020245 - 17 Feb 2017
Cited by 66 | Viewed by 9257
Abstract
Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. [...] Read more.
Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study. Full article
(This article belongs to the Special Issue Zero-Carbon Buildings)
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<p>Usage of different multi-objective optimization algorithms (MOOAs) on building performance optimization problems.</p> Full article ">Figure 2
<p>The basic optimization frame.</p> Full article ">Figure 3
<p>Schematic view of the EnergyPlus model containing three thermal zones (Unit:mm).</p> Full article ">Figure 4
<p>The structure of the residential building containing five thermal zones (Unit:mm).</p> Full article ">Figure 5
<p>Pareto frontier (red points) with all candidates of the Pareto solutions (black points).</p> Full article ">Figure 6
<p>Pareto frontiers at 30, 60, 90 and 120 generations, NSGA-II. (<b>a</b>) 30 generations; (<b>b</b>) 60 generations; (<b>c</b>) 90 generations; (<b>d</b>) 120 generations.</p> Full article ">Figure 7
<p>Pareto frontiers at 30, 60, 90 and 120 generations, MOPSO. (<b>a</b>) 30 generations; (<b>b</b>) 60 generations; (<b>c</b>) 90 generations; (<b>d</b>) 120 generations.</p> Full article ">Figure 8
<p>Pareto frontiers at 30, 60, 90 and 120 generations, MOGA. (<b>a</b>) 30 generations; (<b>b</b>) 60 generations; (<b>c</b>) 90 generations; (<b>d</b>) 120 generations.</p> Full article ">Figure 9
<p>Pareto frontiers at 30, 60, 90 and 120 generations, MODE. (<b>a</b>) 30 generations; (<b>b</b>) 60 generations; (<b>c</b>) 90 generations; (<b>d</b>) 120 generations.</p> Full article ">Figure 10
<p>Boxplots of <span class="html-italic">GDn</span>, <span class="html-italic">DMn</span> and execution time for four algorithms. (<b>a</b>) <span class="html-italic">GDn</span>; (<b>b</b>) <span class="html-italic">DMn</span>; (<b>c</b>) execution time (min).</p> Full article ">Figure 11
<p>Frequency analysis of each discrete design variable on the best Pareto frontier.</p> Full article ">Figure 12
<p>Boxplot of the continuous design variables on the best Pareto frontier.</p> Full article ">Figure 13
<p>Daily schedule for occupancy, (<b>a</b>) Master bedroom; (<b>b</b>) Secondary bedroom; (<b>c</b>) Living room; (<b>d</b>) Toilet and Kitchen.</p> Full article ">Figure 14
<p>Daily schedule for lighting. (<b>a</b>) Master bedroom; (<b>b</b>) Secondary bedroom; (<b>c</b>) Living room; (<b>d</b>) Toilet and Kitchen.</p> Full article ">Figure 15
<p>Daily schedule for electrical equipment. (<b>a</b>) Master bedroom; (<b>b</b>) Secondary bedroom; (<b>c</b>) Living room; (<b>d</b>) Toilet and Kitchen.</p> Full article ">
6061 KiB  
Article
Maximum Safety Regenerative Power Tracking for DC Traction Power Systems
by Guifu Du, Dongliang Zhang, Guoxin Li, Yihua Hu, Yang Liu, Chonglin Wang and Jianhua Liu
Energies 2017, 10(2), 244; https://doi.org/10.3390/en10020244 - 17 Feb 2017
Cited by 7 | Viewed by 7271
Abstract
Direct current (DC) traction power systems are widely used in metro transport systems, with running rails usually being used as return conductors. When traction current flows through the running rails, a potential voltage known as “rail potential” is generated between the rails and [...] Read more.
Direct current (DC) traction power systems are widely used in metro transport systems, with running rails usually being used as return conductors. When traction current flows through the running rails, a potential voltage known as “rail potential” is generated between the rails and ground. Currently, abnormal rises of rail potential exist in many railway lines during the operation of railway systems. Excessively high rail potentials pose a threat to human life and to devices connected to the rails. In this paper, the effect of regenerative power distribution on rail potential is analyzed. Maximum safety regenerative power tracking is proposed for the control of maximum absolute rail potential and energy consumption during the operation of DC traction power systems. The dwell time of multiple trains at each station and the trigger voltage of the regenerative energy absorbing device (READ) are optimized based on an improved particle swarm optimization (PSO) algorithm to manage the distribution of regenerative power. In this way, the maximum absolute rail potential and energy consumption of DC traction power systems can be reduced. The operation data of Guangzhou Metro Line 2 are used in the simulations, and the results show that the scheme can reduce the maximum absolute rail potential and energy consumption effectively and guarantee the safety in energy saving of DC traction power systems. Full article
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<p>Diagram of regenerative power distribution and rail potential. (<b>a</b>) Regenerative energy transferring over sections with a long distance; (<b>b</b>) Regenerative energy absorbed with a short distance.</p> Full article ">Figure 2
<p>Simulation of DC traction power systems.</p> Full article ">Figure 3
<p>Flowchart of the simulation for DC traction power system.</p> Full article ">Figure 4
<p>Schematic diagram of the train’s running time.</p> Full article ">Figure 5
<p>Configuration of the Guangzhou Metro Line 2.</p> Full article ">Figure 6
<p>Train diagram with 30 s dwell time.</p> Full article ">Figure 7
<p>Rail potential distribution in the line with fixed dwell time (30 s). (<b>a</b>) Rail potential distribution in different positions; (<b>b</b>) Rail potential changes with time.</p> Full article ">Figure 8
<p>Rail potential and power consumption in the system with fixed dwell time (30 s). (<b>a</b>) Period A; (<b>b</b>) Period B.</p> Full article ">Figure 9
<p>Changes of energy distribution with trigger voltage of READ. (<b>a</b>) Changes of rail potential and energy consumption; (<b>b</b>) Changes of energy distribution in the system.</p> Full article ">Figure 10
<p>Variation of the parameters with per unit value.</p> Full article ">Figure 11
<p>Convergence curve of energy consumption during the optimization.</p> Full article ">Figure 12
<p>Rail potential distribution in the line. (<b>a</b>) Rail potential distribution in different position; (<b>b</b>) Rail potential changing with time.</p> Full article ">Figure 13
<p>Probability distribution of rail potential before and after optimization.</p> Full article ">
5402 KiB  
Article
An Optimized Energy Management Strategy for Preheating Vehicle-Mounted Li-ion Batteries at Subzero Temperatures
by Tao Zhu, Haitao Min, Yuanbin Yu, Zhongmin Zhao, Tao Xu, Yang Chen, Xinyong Li and Cong Zhang
Energies 2017, 10(2), 243; https://doi.org/10.3390/en10020243 - 17 Feb 2017
Cited by 50 | Viewed by 8479
Abstract
This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. In low-temperature environments, EVs suffer a sharp driving range loss resulting from the energy and power capability reduction of the battery. Simultaneously, because [...] Read more.
This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. In low-temperature environments, EVs suffer a sharp driving range loss resulting from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as the temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating the battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then, given a certain ambient temperature, a target preheating temperature is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with decreasing ambient temperature, however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save on total vehicle operation costs. Full article
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
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<p>Equivalent circuit of battery <span class="html-italic">Rint</span> model.</p> Full article ">Figure 2
<p>Cell SOC-OCV curve. SOC: state-of-charge. OCV: open circuit voltage.</p> Full article ">Figure 3
<p>Cell equivalent resistance as a function of SOC and temperature.</p> Full article ">Figure 4
<p>Discharge and charge efficiency as a function of temperature.</p> Full article ">Figure 5
<p>Entropy coefficient as a function of SOC.</p> Full article ">Figure 6
<p>Experiments results of battery capacity loss ratio along with simulation results given by the degradation model at (<b>a</b>) 1C current rate and (<b>b</b>) 2C current rate.</p> Full article ">Figure 7
<p>Coupled battery model.</p> Full article ">Figure 8
<p>Components of vehicle operation cost.</p> Full article ">Figure 9
<p>Driving cycle of a city bus in Changchun.</p> Full article ">Figure 10
<p>When ambient temperature <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> is −10 °C, driving range <span class="html-italic">L</span> is 20 km, SOC range of charging is 20%~80%, and vehicle is preheated to different temperatures, after vehicle operation, corresponding (<b>a</b>) electricity consumption, (<b>b</b>) battery fade ratio and (<b>c</b>) vehicle operation cost.</p> Full article ">Figure 11
<p>Optimal preheating target temperatures at different ambient temperatures and driving ranges.</p> Full article ">Figure 12
<p>Cell with circulating liquid inside.</p> Full article ">Figure 13
<p>Structure of the preheating system.</p> Full article ">Figure 14
<p>Electric bus and actual layout of the heating pipes in the battery box.</p> Full article ">Figure 15
<p>Experimental setup for measuring heat transfer coefficient <math display="inline"> <semantics> <mi>h</mi> </semantics> </math>.</p> Full article ">Figure 16
<p>The control strategy for coordinating the processes of preheating and charging.</p> Full article ">Figure 17
<p>Charging plug (<b>left</b>) and heating plug (<b>right</b>).</p> Full article ">Figure 18
<p>Data collection in vehicle tests.</p> Full article ">Figure 19
<p>Battery temperature curve during Test 2.</p> Full article ">
4548 KiB  
Article
Effects of SiO2/Al2O3 Ratios on Sintering Characteristics of Synthetic Coal Ash
by Hongwei Hu, Kun Zhou, Kesheng Meng, Lanbo Song and Qizhao Lin
Energies 2017, 10(2), 242; https://doi.org/10.3390/en10020242 - 16 Feb 2017
Cited by 19 | Viewed by 5640
Abstract
This article explores the effects of SiO2/Al2O3 ratios (S/A) on sintering characteristics and provides guidance for alleviating ash depositions in a large-scale circulation fluidized bed. Five synthetic coal ash (SCA) samples with different S/As were treated in a muffle furnace for 12 h [...] Read more.
This article explores the effects of SiO2/Al2O3 ratios (S/A) on sintering characteristics and provides guidance for alleviating ash depositions in a large-scale circulation fluidized bed. Five synthetic coal ash (SCA) samples with different S/As were treated in a muffle furnace for 12 h at different temperatures (from 773 K to 1373 K, in 100 K intervals). The morphological and chemical results of the volume shrinkage ratio (VSR), thermal deformation analysis by dilatometer (DIL), scanning electron microscope (SEM), X-ray photoelectron spectrometer (XPS), and X-ray diffraction (XRD) were combined to describe the sintering characteristics of different samples. The results showed that the sintering procedure mainly occurred in the third sintering stage when the temperature was over 1273 K, accompanied with significant decreases in the VSR curve. Excess SiO2 (S/A = 4.5) resulted in a porous structure while excess Al2O3 (S/A = 0.5) brought out large aggregations. The other three samples (S/A = 1.5, 2.5, 3.5) are made up of an amorphous compacted structure and are composed of low fusion temperature materials (e.g., augite and wadsleysite.). Sintering temperatures first dramatically decrease to a low level and then gradually rise to a high level as S/A increases, suggesting that Al2O3-enriched additives are more effective than SiO2enriched additives in alleviating depositions. Full article
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<p>Definition of sintering temperature (ST) in line deformation ratio (LDR) curve.</p> Full article ">Figure 2
<p>Volume shrinkage ratio (<span class="html-italic">VSR</span>) varies at different temperatures.</p> Full article ">Figure 3
<p><span class="html-italic">VSR</span> and morphology of SCA samples (S/A = 0.5, B/A = 0.8) at various temperatures.</p> Full article ">Figure 4
<p>SEM photos and elemental compositions of different structures at various temperatures: (<b>a</b>) the large agglomerates in sintered SCA sample (S/A = 2.5, B/A = 0.8) at 1073 K; (<b>b</b>) discrete particles in sintered SCA sample (S/A = 2.5, B/A = 0.8) at 1073 K.</p> Full article ">Figure 4 Cont.
<p>SEM photos and elemental compositions of different structures at various temperatures: (<b>a</b>) the large agglomerates in sintered SCA sample (S/A = 2.5, B/A = 0.8) at 1073 K; (<b>b</b>) discrete particles in sintered SCA sample (S/A = 2.5, B/A = 0.8) at 1073 K.</p> Full article ">Figure 5
<p><span class="html-italic">VSR</span> and morphology of SCA sintered samples (B/A = 0.8, 1373 K) at different S/As.</p> Full article ">Figure 6
<p>Chemical compositions of adhesives among large agglomerations.</p> Full article ">Figure 7
<p>Chemical compositions of dense amorphous structure and discrete materials: (<b>a</b>) dense amorphous structure; (<b>b</b>) discrete materials.</p> Full article ">Figure 8
<p>Chemical compositions of different micro structures: (<b>a</b>) slender rod crystal; (<b>b</b>) cubic crystal.</p> Full article ">Figure 9
<p>XRD graphs of SCA versus various S/As at 1373 K: 1-quartz 2-calcium magnesium iron silicate 3-augite 4-calcium silicate 5-wadsleysite 6-aluminium oxide 7-magnesium.</p> Full article ">Figure 10
<p>Sintering temperature of SCA samples versus different S/As.</p> Full article ">
2716 KiB  
Article
Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming
by Alberto Dolara, Francesco Grimaccia, Giulia Magistrati and Gabriele Marchegiani
Energies 2017, 10(2), 241; https://doi.org/10.3390/en10020241 - 16 Feb 2017
Cited by 33 | Viewed by 5562
Abstract
This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer [...] Read more.
This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production. Full article
(This article belongs to the Special Issue Selected Papers from 16 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2016))
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<p>General diagram of the micro-grid case study.</p> Full article ">Figure 2
<p>(<b>a</b>) Measure of fuel consumption rate for the case study; (<b>b</b>) Measure of the diesel generators efficiency for the case study.</p> Full article ">Figure 3
<p>(<b>a</b>) Measures of the main power flows into the micro-grid case study managed by the actual control logic; (<b>b</b>) Comparison between MPPT PV power (<span class="html-italic">Pmppt</span>) and measured PV power (<span class="html-italic">Ppv_meas</span>).</p> Full article ">Figure 4
<p>Results of the LP optimization model. (<b>a</b>) Main power flows; (<b>b</b>) Comparison between MPPT PV power and PV power of LP; (<b>c</b>) BESS energy compared to the usable quantity; (<b>d</b>) DG power; (<b>e</b>) usable SOC; (<b>f</b>) BESS power profile.</p> Full article ">Figure 4 Cont.
<p>Results of the LP optimization model. (<b>a</b>) Main power flows; (<b>b</b>) Comparison between MPPT PV power and PV power of LP; (<b>c</b>) BESS energy compared to the usable quantity; (<b>d</b>) DG power; (<b>e</b>) usable SOC; (<b>f</b>) BESS power profile.</p> Full article ">Figure 5
<p>Results of the MIP optimization model. (<b>a</b>) Main power flows; (<b>b</b>) Comparison between MPPT PV power and PV power of MIP; (<b>c</b>) BESS energy compared to the usable quantity; (<b>d</b>) DG power; (<b>e</b>) Usable SOC; (<b>f</b>) BESS power profile.</p> Full article ">Figure 6
<p>Comparison between spinning reserve of case study and MIP model. (<b>a</b>) BESS spinning reserve; (<b>b</b>) DG spinning reserve.</p> Full article ">
7779 KiB  
Article
Investigation of a Diesel-Engined Vehicle’s Performance and Emissions during the WLTC Driving Cycle—Comparison with the NEDC
by Evangelos G. Giakoumis and Alexandros T. Zachiotis
Energies 2017, 10(2), 240; https://doi.org/10.3390/en10020240 - 16 Feb 2017
Cited by 36 | Viewed by 8171
Abstract
The present work presents results from an experimentally validated simulation code, regarding a turbocharged diesel-powered vehicle running on the recently developed worldwide light-duty vehicles WLTC driving cycle. The simulation is based on an engine mapping approach, with correction coefficients applied vis-à-vis the transient [...] Read more.
The present work presents results from an experimentally validated simulation code, regarding a turbocharged diesel-powered vehicle running on the recently developed worldwide light-duty vehicles WLTC driving cycle. The simulation is based on an engine mapping approach, with correction coefficients applied vis-à-vis the transient discrepancies encountered. Both performance and engine-out emission results are presented and discussed. As regards the latter, the concerned pollutants are soot and nitrogen monoxide. Since the WLTC driving cycle is scheduled to replace the NEDC in Europe from September 2017 with regard to the certification of passenger cars and light-duty trucks, a comparative analysis between the two test schedules is also performed for the engine/vehicle under study. Full article
(This article belongs to the Special Issue Automotive Engines Emissions and Control)
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<p>Speed profile of the WLTC driving cycle for Classes 1, 2, and 3-2 (Class 3-1 differs slightly from 3-2, being intended for the special category of k-cars in Japan); discontinuous line along the Class 3-2 graph shows the currently employed in Europe NEDC speed/time trace (PMR: power to mass ratio).</p> Full article ">Figure 2
<p>Comparison of the speed/acceleration distribution between the WLTC Class 3-2 and the NEDC.</p> Full article ">Figure 3
<p>Illustration of the experimental set-up for steady-state and transient measurements.</p> Full article ">Figure 4
<p>Block diagram of the developed methodology.</p> Full article ">Figure 5
<p>Illustration of vehicle drivetrain.</p> Full article ">Figure 6
<p>Example of transient overshoot for the emissions correction procedure.</p> Full article ">Figure 7
<p>Development of vehicle parameters during the WLTC.</p> Full article ">Figure 8
<p>Development of engine parameters during the WLTC.</p> Full article ">Figure 9
<p>Development of engine-out emissions during the WLTC.</p> Full article ">Figure 10
<p>Comparison of the engine speed/power tested points between the WLTC Class 3-2 and the NEDC for the diesel-powered vehicle under study.</p> Full article ">Figure 11
<p>Comparison of the second-by-second engine-out emissions between the WLTC Class 3-2 and the NEDC (the NEDC lasts 1180 s and the WLTC 1800 s).</p> Full article ">
2300 KiB  
Article
Greenhouse Gas Mitigation of Rural Household Biogas Systems in China: A Life Cycle Assessment
by Jun Hou, Weifeng Zhang, Pei Wang, Zhengxia Dou, Liwei Gao and David Styles
Energies 2017, 10(2), 239; https://doi.org/10.3390/en10020239 - 16 Feb 2017
Cited by 29 | Viewed by 6281
Abstract
Rural household biogas (RHB) systems are at a crossroads in China, yet there has been a lack of holistic evaluation of their energy and climate (greenhouse gas mitigation) efficiency under typical operating conditions. We combined data from monitoring projects and questionnaire surveys across [...] Read more.
Rural household biogas (RHB) systems are at a crossroads in China, yet there has been a lack of holistic evaluation of their energy and climate (greenhouse gas mitigation) efficiency under typical operating conditions. We combined data from monitoring projects and questionnaire surveys across hundreds of households from two typical Chinese villages within a consequential life cycle assessment (LCA) framework to assess net GHG (greenhouse gas) mitigation by RHB systems operated in different contexts. We modelled biogas production, measured biogas losses and used survey data from biogas and non-biogas households to derive empirical RHB system substitution rates for energy and fertilizers. Our results indicate that poorly designed and operated RHB systems in northern regions of China may in fact increase farm household GHG emissions by an average of 2668 kg CO2-eq· year−1, compared with a net mitigation effect of 6336 kg CO2-eq per household and year in southern regions. Manure treatment (104 and 8513 kg CO2-eq mitigation) and biogas leakage (-533 and -2489 kg CO2-eq emission) are the two most important factors affecting net GHG mitigation by RHB systems in northern and southern China, respectively. In contrast, construction (−173 and −305 kg CO2-eq emission), energy substitution (−522 emission and 653 kg·CO2-eq mitigation) and nutrient substitution (−1544 and −37 kg CO2-eq emission) made small contributions across the studied systems. In fact, survey data indicated that biogas households had higher energy and fertilizer use, implying no net substitution effect. Low biogas yields in the cold northern climate and poor maintenance services were cited as major reasons for RHB abandonment by farmers. We conclude that the design and management of RHB systems needs to be revised and better adapted to local climate (e.g., digester insulation) and household energy demand (biogas storage and micro power generators to avoid discharge of unburned biogas). More precise nutrient management planning could ensure that digestate nutrients are more effectively utilized to substitute synthetic fertilizers. Full article
(This article belongs to the Special Issue Economics of Bioenergy 2016)
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<p>A typical digester is built below ground. Pipe joints, safety valve, purifier and other connectors are common gas leakage points. ① anaerobic digester, ② connecter in tank top (CTT), ③ digestate outlet, ④ safety valve, ⑤ purifier, ⑥ pipe joint, ⑦ inlet, ⑧ valve (switch).</p> Full article ">Figure 2
<p>Life cycle assessment of GHG mitigation from biogas facilities. The solid line represents the digester operation flow, energy and materials, and the dotted arrow represents GHG emissions from biogas facility and associated farming system. The dotted box represents nutrient recycling within farming system.</p> Full article ">Figure 3
<p>Annual GHG mitigation of household biogas in two villages. Positive (+) and negative (−) values represent GHG mitigation and net emission, respectively.</p> Full article ">Figure 4
<p>Gas usage by month (<b>a</b>) and leakage of household biogas by location (<b>b</b>) and by volume (<b>c</b>) in the two study villages during a 12-month period. Notes: CTT indicates connecter in tank top; the bars indirate Standard Deviation; 13 biogas household in Zhu and Shu were monitored.</p> Full article ">Figure 4 Cont.
<p>Gas usage by month (<b>a</b>) and leakage of household biogas by location (<b>b</b>) and by volume (<b>c</b>) in the two study villages during a 12-month period. Notes: CTT indicates connecter in tank top; the bars indirate Standard Deviation; 13 biogas household in Zhu and Shu were monitored.</p> Full article ">
4832 KiB  
Article
Effect of Injection Flow Rate on Product Gas Quality in Underground Coal Gasification (UCG) Based on Laboratory Scale Experiment: Development of Co-Axial UCG System
by Akihiro Hamanaka, Fa-qiang Su, Ken-ichi Itakura, Kazuhiro Takahashi, Jun-ichi Kodama and Gota Deguchi
Energies 2017, 10(2), 238; https://doi.org/10.3390/en10020238 - 16 Feb 2017
Cited by 21 | Viewed by 6311
Abstract
Underground coal gasification (UCG) is a technique to recover coal energy without mining by converting coal into a valuable gas. Model UCG experiments on a laboratory scale were carried out under a low flow rate (6~12 L/min) and a high flow rate (15~30 [...] Read more.
Underground coal gasification (UCG) is a technique to recover coal energy without mining by converting coal into a valuable gas. Model UCG experiments on a laboratory scale were carried out under a low flow rate (6~12 L/min) and a high flow rate (15~30 L/min) with a constant oxygen concentration. During the experiments, the coal temperature was higher and the fracturing events were more active under the high flow rate. Additionally, the gasification efficiency, which means the conversion efficiency of the gasified coal to the product gas, was 71.22% in the low flow rate and 82.42% in the high flow rate. These results suggest that the energy recovery rate with the UCG process can be improved by the increase of the reaction temperature and the promotion of the gasification area. Full article
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<p>Typical chemical reaction zone during UCG process.</p> Full article ">Figure 2
<p>Concept of co-axial UCG system.</p> Full article ">Figure 3
<p>A diagram of the UCG model experiment.</p> Full article ">Figure 4
<p>Gasification agents during experiments: (<b>a</b>) Experiment 1; (<b>b</b>) Experiment 2.</p> Full article ">Figure 5
<p>Layout of sensors: (<b>a</b>) Thermocouples; (<b>b</b>) Piezoelectric acceleration transducers.</p> Full article ">Figure 6
<p>Definition of AE event and AE count.</p> Full article ">Figure 7
<p>Temperature profiles: (<b>a</b>) Experiment 1; (<b>b</b>) Experiment 2.</p> Full article ">Figure 8
<p>Monitoring results of AE activities: (<b>a</b>) AE events (experiment 1); (<b>b</b>) AE counts (experiment 1); (<b>c</b>) AE events (experiment 2); (<b>d</b>) AE counts (experiment 2).</p> Full article ">Figure 8 Cont.
<p>Monitoring results of AE activities: (<b>a</b>) AE events (experiment 1); (<b>b</b>) AE counts (experiment 1); (<b>c</b>) AE events (experiment 2); (<b>d</b>) AE counts (experiment 2).</p> Full article ">Figure 9
<p>Main compositions and the calorific value of a product gas: (<b>a</b>) Experiment 1; (<b>b</b>) Experiment 2.</p> Full article ">Figure 10
<p>Cross-section study after the experiment: (<b>a</b>) Experiment 1; (<b>b</b>) Experiment 2.</p> Full article ">
9941 KiB  
Article
Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies
by Shiyu Liu, Gengfeng Li, Haipeng Xie and Xifan Wang
Energies 2017, 10(2), 237; https://doi.org/10.3390/en10020237 - 16 Feb 2017
Cited by 17 | Viewed by 6591
Abstract
As the scale of wind power bases rises, it becomes significant in power system planning and operation to provide detailed correlation characteristic of wind speed in different geographical hierarchies, that is among wind turbines, within a wind farm and its regional wind turbines, [...] Read more.
As the scale of wind power bases rises, it becomes significant in power system planning and operation to provide detailed correlation characteristic of wind speed in different geographical hierarchies, that is among wind turbines, within a wind farm and its regional wind turbines, and among different wind farms. A new approach to analyze the correlation characteristics of wind speed in different geographical hierarchies is proposed in this paper. In the proposed approach, either linear or nonlinear correlation of wind speed in each geographical hierarchy is firstly identified. Then joint sectionalized wind speed probability distribution is modeled for linear correlation analysis while a Copula function is adopted in nonlinear correlation analysis. By this approach, temporal-geographical correlations of wind speed in different geographical hierarchies are properly revealed. Results of case studies based on Jiuquan Wind Power Base in China are analyzed in each geographical hierarchy, which illustrates the feasibility of the proposed approach. Full article
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<p>Sectionalized wind speed points in selecting process.</p> Full article ">Figure 2
<p>Flow chart of establishing the joint probability table of sectionalized wind speed of two wind turbines A1 and A2.</p> Full article ">Figure 3
<p>Scatter diagrams of paired wind speed values: (<b>a</b>) linear correlation of wind speed A and B; (<b>b</b>) nonlinear correlation or no-correlation of wind speed A and B.</p> Full article ">Figure 4
<p>Flow chart of correlation characteristic analysis in different hierarchies.</p> Full article ">Figure 5
<p>Wind speeds series in time of wind turbine A and B in a certain period: (<b>a</b>) before shifting the time difference; (<b>b</b>) after shifting the time difference.</p> Full article ">Figure 6
<p>Gansu wind areas and geographical altitudes of the Jiuquan wind power base.</p> Full article ">Figure 7
<p>Joint scatter diagram of wind speeds of wind turbines A001 and A134.</p> Full article ">Figure 8
<p>Wind speed series in time of wind turbines A001 and A134 in a certain period in the Qiaodong II wind farm.</p> Full article ">Figure 9
<p>Linear correlation coefficients with different shifted time intervals</p> Full article ">Figure 10
<p>Correlation coefficients in different geographical distances between any two wind turbines in the Qiaodong II wind farm.</p> Full article ">Figure 11
<p>Probability density distribution of correlation coefficients of wind speed between wind farm QiaodongII and wind turbine A015 in different time scales: (<b>a</b>) 0.5 h; (<b>b</b>) 1 h; (<b>c</b>) 6 h; (<b>d</b>) 12 h.</p> Full article ">Figure 11 Cont.
<p>Probability density distribution of correlation coefficients of wind speed between wind farm QiaodongII and wind turbine A015 in different time scales: (<b>a</b>) 0.5 h; (<b>b</b>) 1 h; (<b>c</b>) 6 h; (<b>d</b>) 12 h.</p> Full article ">Figure 12
<p>Scatter diagram of wind speeds of the Qiaodong II and Changxi I wind farms.</p> Full article ">Figure 13
<p>Wind speeds in time series of the Qiaodong II and Changxi I wind farms in a certain period.</p> Full article ">Figure 14
<p>Bivariate frequency histogram based on wind speeds of Qiaodong II and Changxi I.</p> Full article ">Figure 15
<p>Gumbel-Copula function based on wind speeds of Qiaodong II and Changxi I: (<b>a</b>) Gumbel-Copula probability density function; (<b>b</b>) Gumbel-Copula cumulative function.</p> Full article ">
6044 KiB  
Article
Influence of Water Saturation on the Mechanical Behaviour of Low-Permeability Reservoir Rocks
by Decheng Zhang, Ranjith Pathegama Gamage, Mandadige Samintha Anne Perera, Chengpeng Zhang and Wanniarachchillage Ayal Maneth Wanniarachchi
Energies 2017, 10(2), 236; https://doi.org/10.3390/en10020236 - 16 Feb 2017
Cited by 56 | Viewed by 6015
Abstract
The influence of water on the mechanical properties of rocks has been observed by many researchers in rock engineering and laboratory tests, especially for sedimentary rocks. In order to investigate the effect of water saturation on the mechanical properties of low-permeability rocks, uniaxial [...] Read more.
The influence of water on the mechanical properties of rocks has been observed by many researchers in rock engineering and laboratory tests, especially for sedimentary rocks. In order to investigate the effect of water saturation on the mechanical properties of low-permeability rocks, uniaxial compression tests were conducted on siltstone with different water contents. The effects of water on the strength, elastic moduli, crack initiation and damage thresholds were observed for different water saturation levels. It was found that 10% water saturation level caused more than half of the reductions in mechanical properties. A new approach is proposed to analyze the stress-strain relations at different stages of compression by dividing the axial and lateral stress-strain curves into five equal stress zones, where stress zones 1–5 refer to 0%–20%, 20%–40%, 40%–60%, 60%–80% and 80%–100% of the peak stress, respectively. Stress zone 2 represents the elastic range better than stress zone 3 which is at half of the peak stress. The normalized crack initiation and crack damage stress thresholds obtained from the stress-strain curves and acoustic emission activities averaged 31.5% and 83% of the peak strength respectively. Pore pressure is inferred to take part in the deformation of low-permeability siltstone samples, especially at full saturation levels. A change of failure pattern from multi-fracturing to single shear failure with the increase of water saturation level was also observed. Full article
(This article belongs to the Special Issue Unconventional Natural Gas (UNG) Recoveries)
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<p>Scanning electron microscope (SEM) image showing the microstructure and minerals of the siltstone sample.</p> Full article ">Figure 2
<p>Incremental intrusion vs. pore size in the mercury intrusion test.</p> Full article ">Figure 3
<p>SEM image showing pore shape and size of the siltstone sample.</p> Full article ">Figure 4
<p>(<b>a</b>) Painted sample and (<b>b</b>) line strain calculations on the computed strain diagram by Aramis.</p> Full article ">Figure 5
<p>Calculated average uniaxial compressive strength (UCS) and deviations with water saturation level and the fitting curve.</p> Full article ">Figure 6
<p>Calculated stress-strain curves for different water saturation levels: (<b>a</b>) stress-axial &amp; lateral strain curve; (<b>b</b>) stress-volumetric strain curve; (<b>c</b>) maximum volumetric strain-water saturation level; and (<b>d</b>) stress-crack volumetric strain curve.</p> Full article ">Figure 7
<p>Axial and lateral stiffness calculated in different stress zones for (<b>a</b>) dry and (<b>b</b>) fully saturated samples.</p> Full article ">Figure 8
<p>Elastic moduli in different stress zones and with different water saturation levels for (<b>a</b>) axial stiffness; (<b>b</b>) lateral stiffness; and (<b>c</b>) Poisson’s ratio (Zones 1–5 means 0%–20%, 20%–40%, 40%–60%, 60%–80% and 80%–100% of the UCS ranges respectively).</p> Full article ">Figure 9
<p>Comparison of tangent, secant and stress zone 2 elastic moduli for (<b>a</b>) axial stiffness; (<b>b</b>) lateral stiffness and (<b>c</b>) Poisson’s ratio at different water saturation levels.</p> Full article ">Figure 10
<p>Accumulative AE counts vs. stress for (<b>a</b>) dry; (<b>b</b>) 10%; (<b>c</b>) 30%; (<b>d</b>) 60% and (<b>e</b>) 100% water saturation levels and their stress thresholds for crack initiation and crack damage.</p> Full article ">Figure 11
<p>Failure patterns of actual siltstone samples (38 mm × 76 mm) (<b>left</b>) and the computed strain distributions at failure (<b>right</b>) by Aramis with different water saturation levels (<b>a</b>) dry; (<b>b</b>) 10%; (<b>c</b>) 30%; (<b>d</b>) 60%; and (<b>e</b>) 100%.</p> Full article ">
3247 KiB  
Article
Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network
by Chi Cao, Jun Xie, Dong Yue, Chongxin Huang, Jixiang Wang, Shuyang Xu and Xingying Chen
Energies 2017, 10(2), 235; https://doi.org/10.3390/en10020235 - 16 Feb 2017
Cited by 31 | Viewed by 5623
Abstract
A virtual power plant (VPP) is aimed to integrate distributed energy resources (DERs). To solve the VPP economic dispatch (VPED) problem, the power supply-demand balance, power transmission constraints, and power output constraints of each DER must be considered. Meanwhile, the impacts of communication [...] Read more.
A virtual power plant (VPP) is aimed to integrate distributed energy resources (DERs). To solve the VPP economic dispatch (VPED) problem, the power supply-demand balance, power transmission constraints, and power output constraints of each DER must be considered. Meanwhile, the impacts of communication time delays, channel noises, and the time-varying topology on the communication networks cannot be ignored. In this paper, a VPED model is established and a distributed primal-dual sub-gradient method (DPDSM) is employed to address the presented VPED model. Compared with the traditional centralized dispatch, the distributed dispatch has the advantages of lower communication costs and stronger system robustness, etc. Simulations are realized in the modified IEEE-34 and IEEE-123 bus test VPP systems and the results indicate that the VPED strategy via DPDSM has the superiority of better convergence, more economic profits, and stronger system stability. Full article
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<p>Flowchart of DPDSM.</p> Full article ">Figure 2
<p>The modified IEEE-34 bus test system in Scenario A.</p> Full article ">Figure 3
<p>Dispatch results in Scenario A. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 4
<p>The simulation of power balance in Scenario A. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">Figure 5
<p>Dispatch results in Scenario B. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 6
<p>The simulation of power balance in Scenario B. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">Figure 7
<p>Dispatch results in Scenario C. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 8
<p>The simulation of power balance in Scenario C. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">Figure 9
<p>The modified IEEE-123 bus test system in Scenario D.</p> Full article ">Figure 10
<p>Dispatch results in Scenario D. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 11
<p>The simulation of power balance in Scenario D. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">Figure 12
<p>Dispatch results in Scenario E. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 13
<p>The simulation of power balance in Scenario E. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">Figure 14
<p>Dispatch results in Scenario F. (<b>a</b>) Power output of PV; (<b>b</b>) Power output of WG; (<b>c</b>) Power output of MGG; and (<b>d</b>) Power output of BE.</p> Full article ">Figure 15
<p>The simulation of power balance in Scenario F. (<b>a</b>) Power at PCC; and (<b>b</b>) The variation of power imbalance.</p> Full article ">
501 KiB  
Article
Computational Model of a Biomass Driven Absorption Refrigeration System
by Munyeowaji Mbikan and Tarik Al-Shemmeri
Energies 2017, 10(2), 234; https://doi.org/10.3390/en10020234 - 16 Feb 2017
Cited by 9 | Viewed by 5209
Abstract
The impact of vapour compression refrigeration is the main push for scientists to find an alternative sustainable technology. Vapour absorption is an ideal technology which makes use of waste heat or renewable heat, such as biomass, to drive absorption chillers from medium to [...] Read more.
The impact of vapour compression refrigeration is the main push for scientists to find an alternative sustainable technology. Vapour absorption is an ideal technology which makes use of waste heat or renewable heat, such as biomass, to drive absorption chillers from medium to large applications. In this paper, the aim was to investigate the feasibility of a biomass driven aqua-ammonia absorption system. An estimation of the solid biomass fuel quantity required to provide heat for the operation of a vapour absorption refrigeration cycle (VARC) is presented; the quantity of biomass required depends on the fuel density and the efficiency of the combustion and heat transfer systems. A single-stage aqua-ammonia refrigeration system analysis routine was developed to evaluate the system performance and ascertain the rate of energy transfer required to operate the system, and hence, the biomass quantity needed. In conclusion, this study demonstrated the results of the performance of a computational model of an aqua-ammonia system under a range of parameters. The model showed good agreement with published experimental data. Full article
(This article belongs to the Special Issue Biomass for Energy Country Specific Show Case Studies)
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<p>Absorption refrigeration test rig.</p> Full article ">Figure 2
<p>Schematic of the single-stage aqua-ammonia vapour absorption refrigeration system.</p> Full article ">Figure 3
<p>Heat exchanger.</p> Full article ">Figure 4
<p>Comparison of fuel consumed with varied generator temperature.</p> Full article ">Figure 5
<p>Effect of mass flow rate of hot water on fuel consumption.</p> Full article ">Figure 6
<p>Comparison of the present work with published data in [<a href="#B34-energies-10-00234" class="html-bibr">34</a>].</p> Full article ">
2939 KiB  
Article
Evaluation and Reliability Assessment of GaN-on-Si MIS-HEMT for Power Switching Applications
by Po-Chien Chou, Szu-Hao Chen, Ting-En Hsieh, Stone Cheng, Jesús A. Del Alamo and Edward Yi Chang
Energies 2017, 10(2), 233; https://doi.org/10.3390/en10020233 - 16 Feb 2017
Cited by 24 | Viewed by 7594
Abstract
This paper reports an extensive analysis of the physical mechanisms responsible for the failure of GaN-based metal–insulator–semiconductor (MIS) high electron mobility transistors (HEMTs). When stressed under high applied electric fields, the traps at the dielectric/III-N barrier interface and inside the III-N barrier cause [...] Read more.
This paper reports an extensive analysis of the physical mechanisms responsible for the failure of GaN-based metal–insulator–semiconductor (MIS) high electron mobility transistors (HEMTs). When stressed under high applied electric fields, the traps at the dielectric/III-N barrier interface and inside the III-N barrier cause an increase in dynamic on-resistance and a shift of threshold voltage, which might affect the long term stability of these devices. More detailed investigations are needed to identify epitaxy- or process-related degradation mechanisms and to understand their impact on electrical properties. The present paper proposes a suitable methodology to characterize the degradation and failure mechanisms of GaN MIS-HEMTs subjected to stress under various off-state conditions. There are three major stress conditions that include: VDS = 0 V, off, and off (cascode-connection) states. Changes of direct current (DC) figures of merit in voltage step-stress experiments are measured, statistics are studied, and correlations are investigated. Hot electron stress produces permanent change which can be attributed to charge trapping phenomena and the generation of deep levels or interface states. The simultaneous generation of interface (and/or bulk) and buffer traps can account for the observed degradation modes and mechanisms. These findings provide several critical characteristics to evaluate the electrical reliability of GaN MIS-HEMTs which are borne out by step-stress experiments. Full article
(This article belongs to the Special Issue Semiconductor Power Devices)
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<p>(<b>a</b>) Control voltages for step-stress experiments performed in this study. The device is stressed for a length of time: step-stress on gate and source (30 s) and step-stress on drain (3 min); bias is repeatedly switched to the ON-state for short intervals to evaluate the change in DC figures of merit induced by the OFF-state bias; (<b>b</b>) flow chart of a typical experiment. The device is stressed and regularly characterized in the process. The measurement loop is executed before reaching the critical voltage (key event), wherein irreversible damage to the device occurs.</p> Full article ">Figure 2
<p>Schematic structure of the AlGaN/GaN high electron mobility transistors (HEMTs) grown on 4-inch Si substrate.</p> Full article ">Figure 3
<p>Representative device characteristics of AlGaN/GaN metal-insulator-semiconductor (MIS)–HEMTs used in this work. (<b>a</b>) Output characteristics (I<sub>DS</sub>-V<sub>DS</sub>) measured at V<sub>GS</sub> from −20 to 2 V; (<b>b</b>) Transfer and transconductance characteristics (I<sub>DS</sub>-V<sub>GS</sub>, G<sub>m</sub>-V<sub>GS</sub>) measured at V<sub>DS</sub> = 10 V.</p> Full article ">Figure 4
<p>(<b>a</b>) Sketch of MIS-HEMT under step-stress of the gate reverse bias at V<sub>DS</sub> = 0 V from |V<sub>GS</sub>| = 25 to 135 V in 0.5 V. A high-field appears at the gate edges on both the drain and source sides; (<b>b</b>) Electrical figures of merit as a function of |V<sub>GS</sub>|: percent increase in dynamic R<sub>on</sub> (drain current degradation) and percent positive shift in V<sub>th</sub> (left scale). The inset in (<b>b</b>) depicts that a large positive V<sub>th</sub> shift is induced during stress, changing it from −18.95 to −12.45 V; (<b>c</b>) 2-terminals I-V characteristics acquired during stress. A sudden increase of gate current (I<sub>G_stress</sub>) is measured at |V<sub>GS</sub>| = 65 V; (<b>d</b>) 2-terminals leakage currents acquired after stress.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Sketch of MIS-HEMT under step-stress of the gate reverse bias at V<sub>DS</sub> = 0 V from |V<sub>GS</sub>| = 25 to 135 V in 0.5 V. A high-field appears at the gate edges on both the drain and source sides; (<b>b</b>) Electrical figures of merit as a function of |V<sub>GS</sub>|: percent increase in dynamic R<sub>on</sub> (drain current degradation) and percent positive shift in V<sub>th</sub> (left scale). The inset in (<b>b</b>) depicts that a large positive V<sub>th</sub> shift is induced during stress, changing it from −18.95 to −12.45 V; (<b>c</b>) 2-terminals I-V characteristics acquired during stress. A sudden increase of gate current (I<sub>G_stress</sub>) is measured at |V<sub>GS</sub>| = 65 V; (<b>d</b>) 2-terminals leakage currents acquired after stress.</p> Full article ">Figure 5
<p>Schematic diagram of a device illustrates possible electron trapping that mainly depletes 2DEG in the channel under high voltage off-state gate stress and causes R<sub>on</sub> degradation.</p> Full article ">Figure 6
<p>(<b>a</b>) Sketch of MIS-HEMT under drain voltage step-stress at off-state from V<sub>DS</sub> = 1 V to 150 V in 1 V step (3 min per step). A high-field appears at the drain-side edge of the gate electrode; (<b>b</b>) Change in normalized R<sub>on</sub>, V<sub>th</sub>, I<sub>G_stress</sub>, and I<sub>Goff</sub> as a function of stress voltage. There is a negligible degradation up to around V<sub>DS</sub> = 114 V. At this critical voltage, degradation in all figures of merit starts sharply and increases as the stress experiment proceeds.</p> Full article ">Figure 7
<p>(<b>a</b>) Sketch of MIS-HEMTs under source voltage step-stressing at off-state from V<sub>SG</sub> = 22 V to 122 V in 0.5 V step (measured at V<sub>DG</sub> = 20 V); (<b>b</b>) Normalized V<sub>th</sub>, R<sub>on</sub> (left axis), I<sub>G_stress</sub> and I<sub>Goff</sub> (right axis) as a function of V<sub>SG</sub> in a V<sub>DG</sub> = 20 V step–stress experiment; (<b>c</b>) 3-terminals I-V characteristics acquired during stress. A sudden increase of drain (I<sub>D_stress</sub>) and source (I<sub>S_stress</sub>) current is measured at V<sub>SG</sub> = 67 V; (<b>d</b>) 3-terminals leakage currents acquired after stress.</p> Full article ">
3612 KiB  
Article
Exploring Soot Particle Concentration and Emissivity by Transient Thermocouples Measurements in Laminar Partially Premixed Coflow Flames
by Gianluigi De Falco, Giulia Moggia, Mariano Sirignano, Mario Commodo, Patrizia Minutolo and Andrea D’Anna
Energies 2017, 10(2), 232; https://doi.org/10.3390/en10020232 - 15 Feb 2017
Cited by 15 | Viewed by 5791
Abstract
Soot formation in combustion represents a complex phenomenon that strongly depends on several factors such as pressure, temperature, fuel chemical composition, and the extent of premixing. The effect of partial premixing on soot formation is of relevance also for real combustion devices and [...] Read more.
Soot formation in combustion represents a complex phenomenon that strongly depends on several factors such as pressure, temperature, fuel chemical composition, and the extent of premixing. The effect of partial premixing on soot formation is of relevance also for real combustion devices and still needs to be fully understood. An improved version of the thermophoretic particle densitometry (TPD) method has been used in this work with the aim to obtain both quantitative and qualitative information of soot particles generated in a set of laminar partially-premixed coflow flames characterized by different equivalence ratios. To this aim, the transient thermocouple temperature response has been analyzed to infer particle concentration and emissivity. A variety of thermal emissivity values have been measured for flame-formed carbonaceous particles, ranging from 0.4 to 0.5 for the early nucleated soot particles up to the value of 0.95, representing the typical value commonly attributed to mature soot particles, indicating that the correct determination of the thermal emissivity is necessary to accurately evaluate the particle volume fraction. This is particularly true at the early stage of the soot formation, when particle concentration measurement is indeed particularly challenging as in the central region of the diffusion flames. With increasing premixing, an initial increase of particles is detected both in the maximum radial soot volume fraction region and in the central region of the flame, while the further addition of primary air determines the particle volume fraction drop. Finally, a modeling analysis based on a sectional approach has been performed to corroborate the experimental findings. Full article
(This article belongs to the Special Issue Combustion and Propulsion)
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Figure 1
<p>Temporal profiles of the thermocouple junction temperature measured in several laminar premixed ethylene/air flames: Φ = 1.6, height above the burner (HAB) = 6 mm, measured particle emissivity ε = 0.21 (<b>a</b>); Φ = 2.1, HAB = 6 mm, ε = 0.55 (<b>b</b>); Φ = 2.1, HAB = 18 mm, ε = 0.95 (<b>c</b>). Note: time plots do not start at zero time since they were magnified in the region between 1250 K and 1700 K to better show the decrease of temperature with time.</p> Full article ">Figure 2
<p>Images of the four different partially premixed flames, i.e., different equivalence ratios (Φ = ∞, 24, 12, 6).</p> Full article ">Figure 3
<p>Experimental and modelled centerline gas temperatures for (<b>a</b>) Flame Φ = ∞; (<b>b</b>) Flame Φ = 24; (<b>c</b>) Flame Φ = 12 and (<b>d</b>) Flame 6 = ∞.</p> Full article ">Figure 4
<p>Measured emissivity values vs. non dimensional flame height Z/H<sub>t</sub> along the centerline and the maximum radial soot volume fraction (MRSf<sub>v</sub>) for (<b>a</b>) Flame Φ = ∞; (<b>b</b>) Flame Φ = 24; (<b>c</b>) Flame Φ = 12 and (<b>d</b>) Flame 6 = ∞.</p> Full article ">Figure 5
<p>Measured particle volume fraction by thermophoretic particle densitometry (TPD) for the four partially premixed flames.</p> Full article ">Figure 6
<p>Particle volume fraction by TPD, particle volume fraction from laser induced incandescence (LII), benzene, naphthalene, and laser induced fluorescence (LIF), from [<a href="#B18-energies-10-00232" class="html-bibr">18</a>] and modelled OH profiles for (<b>a</b>) Flame Φ = ∞; (<b>b</b>) Flame Φ = 24; (<b>c</b>) Flame Φ = 12 and (<b>d</b>) Flame 6 = ∞.</p> Full article ">Figure 7
<p>Particle volume fraction by TPD, with and without correction for oxidation, particle volume fraction from LII [<a href="#B18-energies-10-00232" class="html-bibr">18</a>], and SEM images at Z/H<sub>t</sub> = 0.67 and Z/H<sub>t</sub> = 0.75.</p> Full article ">
1320 KiB  
Article
Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method
by Kangji Li, Wenping Xue and Guohai Liu
Energies 2017, 10(2), 231; https://doi.org/10.3390/en10020231 - 15 Feb 2017
Cited by 11 | Viewed by 5150
Abstract
This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based [...] Read more.
This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm—non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II)—is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method. Full article
(This article belongs to the Special Issue Zero-Carbon Buildings)
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<p>The basic optimization frame based on data interactive mechanism.</p> Full article ">Figure 2
<p>Thermal displacement ventilation system characteristics [<a href="#B24-energies-10-00231" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>The 3D office layout.</p> Full article ">Figure 4
<p>The steady temperature contour (K) and airflow pattern computed by the CFD program (the midsection through the diffuser).</p> Full article ">Figure 5
<p>Temperature comparisons between the CFD results and experimental data at: (<b>a</b>) Pole A; (<b>b</b>) Pole B; and (<b>c</b>) Pole C.</p> Full article ">Figure 6
<p>Velocity comparisons between the CFD results and experimental data at: (<b>a</b>) Pole A; (<b>b</b>) Pole B; and (<b>c</b>) Pole C.</p> Full article ">Figure 7
<p>Locations of five recording points in the computing domain, top view.</p> Full article ">Figure 8
<p>Bi-dimensional projections of the 3D objective space: (<b>a</b>) predicted percent dissatisfied (PPD)-indoor air quality (IAQ); (<b>b</b>) IAQ–Energy; (<b>c</b>) PPD–Energy; and (<b>d</b>) control variables space.</p> Full article ">Figure 9
<p>Limited bi-dimensional projections of the 3D objective space: (<b>a</b>) PPD–IAQ; (<b>b</b>) IAQ–Energy; (<b>c</b>) PPD–Energy; and (<b>d</b>) control variables space.</p> Full article ">Figure 10
<p>Radar diagram of the 23 best solutions identified by the optimization scheme.</p> Full article ">Figure 11
<p>Boxplot of the three indexes achieved by the 23 best solutions on the 3D Pareto front.</p> Full article ">Figure 12
<p>Resulted CO<math display="inline"> <semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics> </math> fraction distribution at: (<b>a</b>) sitting height (1.2 m ); and (<b>b</b>) standing height (1.8 m).</p> Full article ">
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