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Energies, Volume 10, Issue 4 (April 2017) – 170 articles

Cover Story (view full-size image): Optimized component behavior, for instance of check valves, turned out to be crucial for good energetic performance of a hydraulic stepper converter. Despite their simple mechanical design, check valves have very complicated dynamical behavior due to different flow regimes which interact in a highly nonlinear way with the check valve’s plate motion. A model in the free CFD software OpenFOAM was able to simulate quite accurately this coupled fluid and solid body mechanics. The insight into the flow and motion details allowed an optimized design of the fast-check valves. View this paper
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3707 KiB  
Article
Double-Carrier Phase-Disposition Pulse Width Modulation Method for Modular Multilevel Converters
by Fayun Zhou, An Luo, Yan Li, Qianming Xu, Zhixing He and Josep M. Guerrero
Energies 2017, 10(4), 581; https://doi.org/10.3390/en10040581 - 23 Apr 2017
Cited by 8 | Viewed by 5616
Abstract
Modular multilevel converters (MMCs) have become one of the most attractive topologies for high-voltage and high-power applications. A double-carrier phase disposition pulse width modulation (DCPDPWM) method for MMCs is proposed in this paper. Only double triangular carriers with displacement angle are needed for [...] Read more.
Modular multilevel converters (MMCs) have become one of the most attractive topologies for high-voltage and high-power applications. A double-carrier phase disposition pulse width modulation (DCPDPWM) method for MMCs is proposed in this paper. Only double triangular carriers with displacement angle are needed for DCPDPWM, one carrier for the upper arm and the other for the lower arm. Then, the theoretical analysis of DCPDPWM for MMCs is presented by using a double Fourier integral analysis method, and the Fourier series expression of phase voltage, line-to-line voltage and circulating current are deduced. Moreover, the impact of carrier displacement angle between the upper and lower arm on harmonic characteristics is revealed, and further the optimum displacement angles are specified for the circulating current harmonics cancellation scheme and output voltage harmonics minimization scheme. Finally, the proposed method and theoretical analysis are verified by simulation and experimental results. Full article
(This article belongs to the Special Issue Distribution Power Systems and Power Quality)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of three phase modular multilevel converter (MMC). SM: sub-module.</p> Full article ">Figure 2
<p>Principle of double-carrier phase-disposition pulse width modulation (DCPDPWM) for MMCs: (<b>a</b>) integer portion of the upper arm modulation signal; (<b>b</b>) integer portion of the lower arm modulation signal; (<b>c</b>) modulation of the remainder for the upper arm; (<b>d</b>) modulation of the remainder for the lower arm; (<b>e</b>) pulse width modulation (PWM) signal of the upper arm; (<b>f</b>) PWM signal of the lower arm; (<b>g</b>) number of on-state SMs for the upper arm; and (<b>h</b>) number of on-state SMs for the lower arm.</p> Full article ">Figure 3
<p>Block diagram of double-carrier phase-disposition pulse width modulation for MMCs.</p> Full article ">Figure 4
<p>Magnitudes of the harmonic components for the phase voltage and circulating current with different displacement angles: (<b>a</b>) magnitudes of harmonic components for phase voltage; and (<b>b</b>) magnitudes of harmonic components circulating current.</p> Full article ">Figure 5
<p>Comparison of simulation waveforms between DCPDPWM and phase-shifted carrier pulse-width modulation (PSCPWM) methods with the circulating current harmonics cancellation scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 6
<p>Comparison of harmonic spectra between DCPDPWM and PSCPWM methods with the circulating current harmonics cancellation scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 7
<p><span class="html-italic">THD</span> of line-to-line voltage for DCPDPWM and PSCPWM methods in different modulation index with circulating current harmonics cancellation scheme.</p> Full article ">Figure 8
<p>Comparison of simulation waveforms between the DCPDPWM and PSCPWM methods with output voltage harmonics minimization scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 9
<p>Comparison of harmonic spectra between DCPDPWM and PSCPWM methods with output voltage harmonics minimization scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 10
<p><span class="html-italic">THD</span> of line-to-line output voltage for DCPDPWM and PSCPWM methods in different modulation index with output voltage harmonics minimization scheme.</p> Full article ">Figure 11
<p>Experimental waveforms of DCPDPWM method and PSCPWM method with circulating current harmonics cancellation scheme: (<b>a</b>) phase voltage and circulating current of DCPDPWM; (<b>b</b>) phase voltage and circulating current of PSCPWM; (<b>c</b>) line-to-line voltage and phase current of DCPDPWM; and (<b>d</b>) line-to-line voltage and phase current of PSCPWM.</p> Full article ">Figure 12
<p>Harmonic spectra of experimental waveforms for the DCPDPWM and PSCPWM methods with circulating current harmonics cancellation scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 12 Cont.
<p>Harmonic spectra of experimental waveforms for the DCPDPWM and PSCPWM methods with circulating current harmonics cancellation scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">Figure 13
<p>Experimental waveforms of DCPDPWM method and PSCPWM method with output voltage harmonics minimization scheme: (<b>a</b>) phase voltage and circulating current of DCPDPWM; (<b>b</b>) phase voltage and circulating current of PSCPWM; (<b>c</b>) line-to-line voltage and phase current of DCPDPWM; (<b>d</b>) line-to-line voltage and phase current of PSCPWM.</p> Full article ">Figure 14
<p>Harmonic spectra of experimental results for DCPDPWM and PSCPWM methods with output voltage harmonics minimization scheme: (<b>a</b>) phase voltage of DCPDPWM; (<b>b</b>) phase voltage of PSCPWM; (<b>c</b>) line-to-line voltage of DCPDPWM; (<b>d</b>) line-to-line voltage of PSCPWM; (<b>e</b>) phase current of DCPDPWM; (<b>f</b>) phase current of PSCPWM; (<b>g</b>) circulating current of PDPWM; and (<b>h</b>) circulating current of PSCPWM.</p> Full article ">
2671 KiB  
Article
Influences of Winding MMF Harmonics on Torque Characteristics in Surface-Mounted Permanent Magnet Vernier Machines
by Daekyu Jang and Junghwan Chang
Energies 2017, 10(4), 580; https://doi.org/10.3390/en10040580 - 23 Apr 2017
Cited by 10 | Viewed by 5298
Abstract
This paper presents the influences of winding magneto-motive force (MMF) harmonics on the torque characteristics in surface-mounted permanent magnet vernier (SPMV) machines. Based on the magnetic gearing effects, the armature magnetic field of the SPMV machines is modulated by flux modulation poles (FMPs). [...] Read more.
This paper presents the influences of winding magneto-motive force (MMF) harmonics on the torque characteristics in surface-mounted permanent magnet vernier (SPMV) machines. Based on the magnetic gearing effects, the armature magnetic field of the SPMV machines is modulated by flux modulation poles (FMPs). In the modulated magnetic field, a working harmonic which corresponds to the number of the rotor pole pairs generates torque. Unlike regular PM machines, the winding MMF harmonics in the SPMV machines can produce the working harmonic by adjusting the FMP shapes. In order to investigate the effects of the winding MMF harmonics, the operating principle of the SPMV machines is elaborated by an analytical method using the winding MMF distribution and air-gap permeance function. After that, the design method of the FMP shapes that can improve the output torque by using the winding MMF harmonics is proposed. For the SPMV machine having 6 slots and 24 FMPs, the effects of the winding MMF harmonics and the validity of the proposed design method are confirmed by the finite element analysis method. It is shown that the proposed design method can improve the performances of the SPMV machine in terms of the torque density, induced electromagnetic force, and efficiency. Full article
(This article belongs to the Section D: Energy Storage and Application)
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Figure 1

Figure 1
<p>A structure of the magnetic gear.</p> Full article ">Figure 2
<p>A structure of the SPMV machines with concentrated windings.</p> Full article ">Figure 3
<p>Winding MMF produced by the winding of the phase <span class="html-italic">a</span>. (<b>a</b>) Magnetic flux path; (<b>b</b>) Equivalent magnetic circuit; (<b>c</b>) Winding MMF distribution.</p> Full article ">Figure 4
<p>Distributions of (<b>a</b>) the radial components of the air-gap flux density by the armature windings; (<b>b</b>) the winding MMF; and (<b>c</b>) the air-gap permeance.</p> Full article ">Figure 5
<p>FFT results for (<b>a</b>) the air-gap permeance function; and (<b>b</b>) the radial flux density distribution calculated by the winding MMF distribution and the air-gap permeance function.</p> Full article ">Figure 6
<p>Basic and designed shapes of the FMPs. (<b>a</b>) Basic shape; (<b>b</b>) Designed shape including the 18th harmonic of the air-gap permeance function (<span class="html-italic">β</span> = 1).</p> Full article ">Figure 7
<p>Air-gap permeance functions according to the values of <span class="html-italic">β</span>. (<b>a</b>) Waveforms; (<b>b</b>) Spatial harmonic components.</p> Full article ">Figure 8
<p>Working harmonics obtained by the FEA and analytical methods according to the values of <span class="html-italic">β</span>.</p> Full article ">Figure 9
<p>Torque characteristics according to the values of <span class="html-italic">β</span>. (<b>a</b>) Waveforms; (<b>b</b>) Average torque and working harmonic and (<b>c</b>) Torque ripple and cogging torque.</p> Full article ">Figure 10
<p>Induced EMF characteristics under the no-load condition according to the values of <span class="html-italic">β</span>.</p> Full article ">Figure 11
<p>Stator iron losses and the flux density in the stator yoke according to the values of <span class="html-italic">β</span>.</p> Full article ">Figure 12
<p>Efficiency and power according to the values of <span class="html-italic">β</span>.</p> Full article ">
3553 KiB  
Article
Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir
by Jaejun Kim, Joe M. Kang, Changhyup Park, Yongjun Park, Jihye Park and Seojin Lim
Energies 2017, 10(4), 579; https://doi.org/10.3390/en10040579 - 23 Apr 2017
Cited by 12 | Viewed by 8368
Abstract
This paper presents a fast, reliable multi-objective history-matching method based on proxy modeling to forecast the production performances of shale gas reservoirs for which all available post-hydraulic-fracturing production data, i.e., the daily gas rate and cumulative-production volume until the given date, are honored. [...] Read more.
This paper presents a fast, reliable multi-objective history-matching method based on proxy modeling to forecast the production performances of shale gas reservoirs for which all available post-hydraulic-fracturing production data, i.e., the daily gas rate and cumulative-production volume until the given date, are honored. The developed workflow consists of distance-based generalized sensitivity analysis (DGSA) to determine the spatiotemporal-parameter significance, fast marching method (FMM) as a proxy model, and a multi-objective evolutionary algorithm to integrate the dynamic data. The model validation confirms that the FMM is a sound surrogate model working within an error of approximately 2% for the estimated ultimate recovery (EUR), and it is 11 times faster than a full-reservoir simulation. The predictive accuracy on future production after matching 1.5-year production histories is assessed to examine the applicability of the proposed method. The DGSA determines the effective parameters with respect to the gas rate and the cumulative volume, including fracture permeability, fracture half-length, enhanced permeability in the stimulated reservoir volume, and average post-fracturing porosity. A comparison of the prediction accuracy for single-objective optimization shows that the proposed method accurately estimates the recoverable volume as well as the production profiles to within an error of 0.5%, while the single-objective consideration reveals the scale-dependency problem with lesser accuracy. The results of this study are useful to overcome the time-consuming effort of using a multi-objective evolutionary algorithm and full-scale reservoir simulation as well as to conduct a more-realistic prediction of the shale gas reserves and the corresponding production performances. Full article
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Figure 1

Figure 1
<p>Flow diagram developed for this work.</p> Full article ">Figure 2
<p>Example of the distance-based generalized sensitivity analysis (DGSA) workflow: (<b>a</b>) distance-based map consisting of three clusters, and (<b>b</b>) illustration of cumulative-density functions (CDF) curves that are allocated to each cluster and the population.</p> Full article ">Figure 3
<p>CDF distances for the DGSA-derived determination of the influence parameters: (<b>a</b>) sensitivity by cluster and (<b>b</b>) standardized sensitivity.</p> Full article ">Figure 4
<p>Schematic diagram explaining the NSGA-II procedure.</p> Full article ">Figure 5
<p>Schematic diagram explaining the NSGA-II sorting methods: (<b>a</b>) non-dominated sorting and (<b>b</b>) crowding-distance sorting (modified from Han et al. [<a href="#B21-energies-10-00579" class="html-bibr">21</a>]).</p> Full article ">Figure 6
<p>Schematic diagram of the synthetic reservoir to examine the reliability of the fast marching method (FMM)-based proxy modeling.</p> Full article ">Figure 7
<p>Comparison of the production performance relevant to the simulated P50 of the FMM-based proxy modeling and the ECL100: (<b>a</b>) daily gas rate and (<b>b</b>) cumulative volume.</p> Full article ">Figure 8
<p>Errors of the production performance of the FMM-based proxy modeling and the ECL100: (<b>a</b>) daily gas rate and (<b>b</b>) cumulative volume.</p> Full article ">Figure 9
<p>True profile of the daily gas rate at the shale gas reservoir after hydraulic fracturing.</p> Full article ">Figure 10
<p>Sensitivity analysis for which the DGSA is used. Determination of the significant parameters affecting: (<b>a</b>) the objective function <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> and (<b>b</b>) the objective function <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> </semantics> </math>. ‘<span class="html-italic">kf</span>’, ‘<span class="html-italic">ke</span>’, and ‘<span class="html-italic">km</span>’ represent the fracture permeability, the permeability in the stimulated reservoir volume, and the matrix permeability, respectively. ‘<span class="html-italic">xf</span>’ is the fracture half-length, and ‘<span class="html-italic">po</span>’ is the matrix porosity. ‘<span class="html-italic">ver</span>’ is the vertical enhanced ratio and ‘<span class="html-italic">her</span>’ is the horizontal enhanced ratio.</p> Full article ">Figure 11
<p>Errors of the optimum solutions for the three comparisons and the proposed model with respect to the single-objective function: (<b>a</b>) during the history-matching period and (<b>b</b>) during the prediction period.</p> Full article ">Figure 12
<p>Production performance of the four optimal solutions of the proposed method and the true data: (<b>a</b>) daily gas rate and (<b>b</b>) cumulative volume.</p> Full article ">Figure 13
<p>Box plots of the errors of the optimal solutions and the true profile: (<b>a</b>) daily gas rate and (<b>b</b>) cumulative volume.</p> Full article ">
1654 KiB  
Review
Heat Roadmap Europe: Large-Scale Electric Heat Pumps in District Heating Systems
by Andrei David, Brian Vad Mathiesen, Helge Averfalk, Sven Werner and Henrik Lund
Energies 2017, 10(4), 578; https://doi.org/10.3390/en10040578 - 22 Apr 2017
Cited by 201 | Viewed by 20007
Abstract
The Heat Roadmap Europe (HRE) studies estimated a potential increase of the district heating (DH) share to 50% of the entire heat demand by 2050, with approximately 25–30% of it being supplied using large-scale electric heat pumps. This study builds on this potential [...] Read more.
The Heat Roadmap Europe (HRE) studies estimated a potential increase of the district heating (DH) share to 50% of the entire heat demand by 2050, with approximately 25–30% of it being supplied using large-scale electric heat pumps. This study builds on this potential and aims to document that such developments can begin now with technologies currently available. We present a database and the status of the technology and its ability of expansion to other European locations by reviewing experiences aimed at further research or application in the heating industry. This is based on a survey of the existing capacity of electric large-scale heat pumps with more than 1 MW thermal output, operating in European DH systems. The survey is the first database of its kind containing the technical characteristics of these heat pumps, and provides the basis for the analysis of this paper. By quantifying the heat sources, refrigerants, efficiency and types of operation of 149 units with 1580 MW of thermal output, the study further uses this data to analyze if the deployment of this technology on a large-scale is possible in other locations in Europe. It finally demonstrates that the technical level of the existing heat pumps is mature enough to make them suitable for replication in other locations in Europe. Full article
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Figure 1

Figure 1
<p>Establishment years and capacities of heat pumps in seven countries with the greatest capacities installed and currently operating.</p> Full article ">Figure 2
<p>Breakdown of the refrigerants used by the heat pumps in the survey.</p> Full article ">Figure 3
<p>Operating temperatures for the heat pumps built before 2006 (orange) and beyond 2006 (blue), for the units where all the data was available (input and output temperature, coefficient of performance (COP) and establishment year). Each bar represents one heat pump.</p> Full article ">Figure 4
<p>Higher renewable energy sources (RES) production, lower combined heat and power (CHP) use (<b>A)</b> and lower RES production, higher CHP use (<b>B</b>).</p> Full article ">
7122 KiB  
Article
Performance Comparison between Selected Evaporative Air Coolers
by Demis Pandelidis, Sergey Anisimov and Paweł Drąg
Energies 2017, 10(4), 577; https://doi.org/10.3390/en10040577 - 22 Apr 2017
Cited by 9 | Viewed by 4358
Abstract
The aim of this study is to determine which of the heat exchangers is characterized by the highest efficiency in different applications. Various types of evaporative air coolers were compared: a typical counter-flow unit, the same unit operating as a heat recovery exchanger, [...] Read more.
The aim of this study is to determine which of the heat exchangers is characterized by the highest efficiency in different applications. Various types of evaporative air coolers were compared: a typical counter-flow unit, the same unit operating as a heat recovery exchanger, a regenerative unit and a novel, modified regenerative exchanger. The analysis includes comparing the work of evaporative heat exchangers during summer and winter season. The analysis is based on the original mathematical models. The numerical models are based on the modified ε-NTU (number of heat transfer units) method. It was established that selected arrangements of the presented exchangers are characterized by the different efficiency in different air-conditioning applications. The analysis faces the main construction aspects of those evaporative coolers and also compares two above-mentioned devices with modified regenerative air cooler, which can partly operate on cooled outdoor airflow and on the exhaust air from conditioned spaces. This solution can be applied in any climate and it is less dependent on the outdoor conditions. The second part of the study focuses on winter season and the potential of recovering heat with the same exchangers, but with dry working air channels. This allows establishing their total potential of generating energy savings during the annual operation. Full article
(This article belongs to the Special Issue The 1st “The Belt and Road Initiative” International Conference on Sustainable Refrigeration and Air Conditioning)
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Figure 1

Figure 1
<p>Counter-flow indirect evaporative air coolers. (<b>a</b>) “Classical” counter-flow exchanger; (<b>b</b>) regenerative exchanger; (<b>c</b>) counter-flow exchanger with cooling coil; (<b>d</b>) perforated regenerative exchanger; (<b>e</b>) regenerative exchanger with the desiccant wheel; and (<b>f</b>) novel modified counter-flow exchanger.</p> Full article ">Figure 2
<p>Initial conditions for considered exchangers. (<b>a</b>) Counter-flow exchanger; (<b>b</b>) regenerative exchanger; (<b>c</b>) perforated regenerative exchanger; and (<b>d</b>) novel counter-flow exchanger.</p> Full article ">Figure 3
<p>Energy balance for differential control volume: (<b>a</b>) view on the channels along the <span class="html-italic">X</span> axis; and (<b>b</b>) view on the channels along the <span class="html-italic">Y</span> axis.</p> Full article ">Figure 4
<p>Fin geometry and the boundary conditions for the fins in working and main air-flow passages.</p> Full article ">Figure 5
<p>Primary air temperature distribution in dry channel - comparison between modeled results and experimental data from [<a href="#B11-energies-10-00577" class="html-bibr">11</a>].</p> Full article ">Figure 6
<p>Comparison between calculated results and experimental data from [<a href="#B12-energies-10-00577" class="html-bibr">12</a>]. (<b>a</b>) Effect of the inlet humidity for constant inlet air temperature; and (<b>b</b>) effect of the inlet temperature for constant RH.</p> Full article ">Figure 7
<p>Comparison between the counter-flow and the regenerative exchanger. (<b>a</b>) Operation on ambient air- outlet air temperatures; (<b>b</b>) operation on ambient air- obtained cooling capacity; (<b>c</b>) counter-flow exchanger operating on exhaust airflow (wet channels) and ambient air (dry channels), regenerative exchanger operating only on ambient air- outlet air temperatures; and (<b>d</b>) counter-flow exchanger operating on exhaust airflow (wet channels) and ambient air (dry channels), regenerative exchanger operating only on ambient air- obtained cooling capacity.</p> Full article ">Figure 8
<p>Comparison of the novel exchanger with counter-flow and regenerative unit. (<b>a</b>) Comparison with the counter-flow exchanger operating on exhaust airflow; (<b>b</b>) Comparison with the regenerative air cooler operating on ambient air; (<b>c</b>) Comparison with the regenerative air cooler with the same amount of airflow returned to the wet channel; (<b>d</b>) cooling capacities obtained by the novel unit, counter-flow unit and a regenerative unit; (<b>e</b>) Switch points relative humidity at ambient air temperature equal 30 °C; and (<b>f</b>) Comparison with the regenerative air cooler operating with the desiccant wheel.</p> Full article ">Figure 9
<p>Analysis of indirect evaporative air cooler in summer season. (<b>a</b>) Obtained supply air temperature at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 30 °C; (<b>b</b>) obtained cooling capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 30 °C; (<b>c</b>) obtained equivalent effectiveness at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 30 °C; (<b>d</b>) obtained supply air temperature at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 35 °C; (<b>e</b>) obtained cooling capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 35 °C; and (<b>f</b>) obtained equivalent effectiveness at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = 35 °C.</p> Full article ">Figure 10
<p>Analysis of heat recovery exchanger in winter season. (<b>a</b>) Obtained safe working temperature at <span class="html-italic">t</span><sub>2<span class="html-italic">i</span></sub> = 18 °C; (<b>b</b>) obtained safe working temperature at <span class="html-italic">t</span><sub>2<span class="html-italic">i</span></sub> = 20 °C; (<b>c</b>) obtained supply air temperature t<sub>1o</sub> at outdoor air temperature <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = −20 °C; (<b>d</b>) obtained supply air temperature t<sub>1o</sub> at safe working temperature <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = <span class="html-italic">t<sub>swc</sub></span>; (<b>e</b>) obtained heating capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = −20 °C; and (<b>f</b>) obtained heating capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = <span class="html-italic">t<sub>swc</sub></span>.</p> Full article ">Figure 10 Cont.
<p>Analysis of heat recovery exchanger in winter season. (<b>a</b>) Obtained safe working temperature at <span class="html-italic">t</span><sub>2<span class="html-italic">i</span></sub> = 18 °C; (<b>b</b>) obtained safe working temperature at <span class="html-italic">t</span><sub>2<span class="html-italic">i</span></sub> = 20 °C; (<b>c</b>) obtained supply air temperature t<sub>1o</sub> at outdoor air temperature <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = −20 °C; (<b>d</b>) obtained supply air temperature t<sub>1o</sub> at safe working temperature <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = <span class="html-italic">t<sub>swc</sub></span>; (<b>e</b>) obtained heating capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = −20 °C; and (<b>f</b>) obtained heating capacity at <span class="html-italic">t</span><sub>1<span class="html-italic">i</span></sub> = <span class="html-italic">t<sub>swc</sub></span>.</p> Full article ">Figure 11
<p>Analysis of round-year operation. (<b>a</b>) Equivalent effectiveness of heat recovery exchanger as function of NTU number; and (<b>b</b>) obtained cooling capacity by heat recovery exchanger as function of NTU number.</p> Full article ">
4688 KiB  
Article
Enhancing Oil Recovery from Chalk Reservoirs by a Low-Salinity Water Flooding Mechanism and Fluid/Rock Interactions
by Aly A. Hamouda and Sachin Gupta
Energies 2017, 10(4), 576; https://doi.org/10.3390/en10040576 - 22 Apr 2017
Cited by 21 | Viewed by 5308
Abstract
Different Low Salinity Waters (LSWs) are investigated in this work to understand the role of some ions, which were recognized from our previous work and the literature for their effect on wettability alteration. Different flooding stages were followed. The primary stage was by [...] Read more.
Different Low Salinity Waters (LSWs) are investigated in this work to understand the role of some ions, which were recognized from our previous work and the literature for their effect on wettability alteration. Different flooding stages were followed. The primary stage was by injecting synthetic seawater (SSW) and the secondary stage was with SSW diluted by 10 (LSW 1:10) and 50 (LSW 1:50) times, single and two salt brines, such as Na2SO4, MgCl2, and NaCl+MgCl2 at 70 °C. The flooding sequence was due to that most of the fields in the North Sea were flooded with seawater. Two flooding rates were followed, 4 PV/day (PV = Pore Volume) and 16 PV/day in all the experiments. One of the observations was the increase of the pH during the flooding with LSW and single salt brines. The increase of the pH was attributed to mineral precipitation/dissolution as the results of ionic interactions. The effluent ion concentrations measured to understand the most likely oil recovery mechanisms. The results showed that the higher the SSW dilution the slower the oil recovery response. In presence of SO42−, Ca/Mg, higher oil recovery. The exchange between Ca/Mg, was in line with field observations. A geochemical simulation was done for a comparison with the experimental data. Full article
(This article belongs to the Special Issue Oil and Gas Engineering)
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<p>Schematic of the flooding system.</p> Full article ">Figure 2
<p>Comparison of Oil recovery as a function of PV after flooding with SSW as primary fluid and different secondary injection fluid (LSW and LS brines): (<b>A</b>) LSW (1:10 &amp; 1:50); (<b>B</b>) SO<sub>4</sub> (1:10 &amp; 1:50); (<b>C</b>) Mg (70C &amp; 90C), Mg+Na; (<b>D</b>) SO<sub>4</sub> 1:10, Mg70C &amp; Mg+Na.</p> Full article ">Figure 2 Cont.
<p>Comparison of Oil recovery as a function of PV after flooding with SSW as primary fluid and different secondary injection fluid (LSW and LS brines): (<b>A</b>) LSW (1:10 &amp; 1:50); (<b>B</b>) SO<sub>4</sub> (1:10 &amp; 1:50); (<b>C</b>) Mg (70C &amp; 90C), Mg+Na; (<b>D</b>) SO<sub>4</sub> 1:10, Mg70C &amp; Mg+Na.</p> Full article ">Figure 3
<p>Pressure drop across the core during Primary secondary injection of SSW and different LS brines, respectively as a function of the flooded PV of brines: (<b>A</b>) LSW 1:10, (<b>B</b>) LSW 1:50, (<b>C</b>) SO<sub>4</sub> 1:10 and 1:50, (<b>D</b>) Mg70C and Mg90C, (<b>E</b>) Mg+Na.</p> Full article ">Figure 3 Cont.
<p>Pressure drop across the core during Primary secondary injection of SSW and different LS brines, respectively as a function of the flooded PV of brines: (<b>A</b>) LSW 1:10, (<b>B</b>) LSW 1:50, (<b>C</b>) SO<sub>4</sub> 1:10 and 1:50, (<b>D</b>) Mg70C and Mg90C, (<b>E</b>) Mg+Na.</p> Full article ">Figure 4
<p>The effluent pH by flooding with SSW, LSW 10, LSW 50, SO<sub>4</sub><sup>2−</sup> (1:10 &amp; 1:50) and Mg<sup>2+</sup> (70 and 90C), Mg+Na brine.</p> Full article ">Figure 5
<p>Dimensionless ion concentrations (ratio of the ions from the effluent to corresponding ion in SSW) as a function of PV: (<b>A</b>) calcium, (<b>B</b>) magnesium, (<b>C</b>) sodium, (<b>D</b>) carbonate and (<b>E</b>) sulfate.</p> Full article ">Figure 5 Cont.
<p>Dimensionless ion concentrations (ratio of the ions from the effluent to corresponding ion in SSW) as a function of PV: (<b>A</b>) calcium, (<b>B</b>) magnesium, (<b>C</b>) sodium, (<b>D</b>) carbonate and (<b>E</b>) sulfate.</p> Full article ">Figure 6
<p>Comparison between experimental (points) and simulated (lines) ion concentrations relative to SSW of Ca<sup>2+</sup>, Mg<sup>2+</sup>, SO<sub>4</sub><sup>2−</sup> and Na<sup>+</sup>: (<b>A</b>) LSW 1:10, (<b>B</b>) LSW 1:50, (<b>C</b>) SO<sub>4</sub> 1:10, (<b>D</b>) SO<sub>4</sub> 1:50 and (<b>E</b>) Mg brine.</p> Full article ">Figure 6 Cont.
<p>Comparison between experimental (points) and simulated (lines) ion concentrations relative to SSW of Ca<sup>2+</sup>, Mg<sup>2+</sup>, SO<sub>4</sub><sup>2−</sup> and Na<sup>+</sup>: (<b>A</b>) LSW 1:10, (<b>B</b>) LSW 1:50, (<b>C</b>) SO<sub>4</sub> 1:10, (<b>D</b>) SO<sub>4</sub> 1:50 and (<b>E</b>) Mg brine.</p> Full article ">Figure 7
<p>Comparison between experimental (points) and simulated (lines) ion concentrations relative to SSW for SO<sub>4</sub> 1:10 and Mg brines: (<b>A</b>) calcium, (<b>B</b>) magnesium and (<b>C</b>) sulfate.</p> Full article ">
10079 KiB  
Article
Aerodynamic Analysis of a Helical Vertical Axis Wind Turbine
by Qian Cheng, Xiaolan Liu, Ho Seong Ji, Kyung Chun Kim and Bo Yang
Energies 2017, 10(4), 575; https://doi.org/10.3390/en10040575 - 22 Apr 2017
Cited by 43 | Viewed by 12355
Abstract
Vertical axis wind turbines (VAWTs) are gradually receiving more and more interest due to their lower sensitivity to the yawed wind direction. Compared with straight blades VAWT, blades with a certain helicity show a better aerodynamic performance and less noise emission. Nowadays computational [...] Read more.
Vertical axis wind turbines (VAWTs) are gradually receiving more and more interest due to their lower sensitivity to the yawed wind direction. Compared with straight blades VAWT, blades with a certain helicity show a better aerodynamic performance and less noise emission. Nowadays computational fluid dynamics technology is frequently applied to VAWTs and gives results that can reflect real flow phenomena. In this paper, a 2D flow field simulation of a helical vertical axis wind turbine (HVAWT) with four blades has been carried out by means of a large eddy simulation (LES). The power output and fluctuation at each azimuthal position are studied with different tip speed ratio (TSR). The result shows that the variation of angle of attack (AOA) and blade-wake interaction under different TSR conditions are the two main reasons for the effects of TSR on power output. Furthermore, in order to understand the characteristics of the HVAWT along the spanwise direction, the 3D full size flow field has also been studied by the means of unsteady Reynold Averaged Navier-Stokes (U-RANS) and 3D effects on the turbine performance can be observed by the spanwise pressure distribution. It shows that tip vortex near blade tips and second flow in the spanwise direction also play a major role on the performance of VAWTs. Full article
(This article belongs to the Special Issue Wind Turbine 2017)
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<p>The sketch of HVAWT.</p> Full article ">Figure 2
<p>The computational zone division and boundary condition setting.</p> Full article ">Figure 3
<p>The total torque output at 1.46 TSR condition. (<b>a</b>,<b>b</b>) represent the mesh density and time step independence check respectively.</p> Full article ">Figure 4
<p>The mesh adjacent to the blade. (<b>a</b>,<b>b</b>) represent the mesh applied in 2D and 3D simulation respectively.</p> Full article ">Figure 5
<p>Experiment rigs in the wind tunnel. (<b>a</b>,<b>b</b>) represent the wind tunnel and measuring devices respectively.</p> Full article ">Figure 6
<p>Power coefficient at different TSR with Reynold number 60,800.</p> Full article ">Figure 7
<p>The power output at different TSR conditions, (<b>a</b>–<b>f</b>) represent 0.9, 1.14, 1.25, 1.46, 1.8, 2.3 TSR λ respectively, red line and black lines represent the 2D LES and U-RANS results, respectively.</p> Full article ">Figure 7 Cont.
<p>The power output at different TSR conditions, (<b>a</b>–<b>f</b>) represent 0.9, 1.14, 1.25, 1.46, 1.8, 2.3 TSR λ respectively, red line and black lines represent the 2D LES and U-RANS results, respectively.</p> Full article ">Figure 8
<p>(<b>a</b>,<b>b</b>) represent the angle of attack and power output of single blade along one revolution under different TSR conditions respectively.</p> Full article ">Figure 9
<p>Instantaneous torque generation of each blade during one revolution; subfigures (<b>a</b>–<b>c</b>) correspond to the results at 0.9, 1.46, 2.3 TSR, respectively.</p> Full article ">Figure 10
<p>The vorticity distributions of HVAWT at different azimuthal positions.</p> Full article ">Figure 11
<p>The streamlines at 60°, 90°, 150°, 180°, (<b>a</b>–<b>c</b>) corresponding to the results at TSR 0.9, 1.46 and 2.3 respectively, red frames represent the wake vortex generated from another blade.</p> Full article ">Figure 12
<p>The streamlines and pressure distribution around blade leading edge at azimuth 270°, (<b>a</b>–<b>c</b>) corresponding to the results at TSR 0.9, 1.46 and 2.3, respectively.</p> Full article ">Figure 13
<p>Power fluctuation coefficient at different azimuthal points and TSR conditions, the lines on axis plane are isoline of power fluctuation coefficient.</p> Full article ">Figure 14
<p>Power coefficient results derived by 2D LES and 3D U-RANS methods for <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>R</mi> <mi>e</mi> </mrow> <mi>c</mi> </msub> </mrow> </semantics> </math> = 60,800, TSR = 1.46.</p> Full article ">Figure 15
<p>Chord position definition scheme.</p> Full article ">Figure 16
<p>Pressure distribution along blades at different height positions at TSR 1.46, (<b>a</b>–<b>d</b>) corresponded to 30°, 60°, 90°, 120° respectively.</p> Full article ">Figure 17
<p>Streamlines around blades when the mid-plane of rotor at different azimuthal positions at TSR 1.46.</p> Full article ">Figure 18
<p>Pressure distribution at inner blade surface, (<b>a</b>,<b>b</b>) represents the results when the 95% and 50% span-wise plane reached 120° azimuths, respectively.</p> Full article ">Figure 19
<p>Wake vortex diffusion pattern at 2.3 TSR condition.</p> Full article ">
4761 KiB  
Article
Automatic Tracking of the Modal Parameters of an Offshore Wind Turbine Drivetrain System
by Mahmoud El-Kafafy, Christof Devriendt, Patrick Guillaume and Jan Helsen
Energies 2017, 10(4), 574; https://doi.org/10.3390/en10040574 - 22 Apr 2017
Cited by 14 | Viewed by 5197
Abstract
An offshore wind turbine (OWT) is a complex structure that consists of different parts (e.g., foundation, tower, drivetrain, blades, et al.). The last decade, there has been continuous trend towards larger machines with the goal of cost reduction. Modal behavior is an important [...] Read more.
An offshore wind turbine (OWT) is a complex structure that consists of different parts (e.g., foundation, tower, drivetrain, blades, et al.). The last decade, there has been continuous trend towards larger machines with the goal of cost reduction. Modal behavior is an important design aspect. For tackling noise, vibration, and harshness (NVH) issues and validating complex simulation models, it is of high interest to continuously track the vibration levels and the evolution of the modal parameters (resonance frequencies, damping ratios, mode shapes) of the fundamental modes of the turbine. Wind turbines are multi-physical machines with significant interaction between their subcomponents. This paper will present the possibility of identifying and automatically tracking the structural vibration modes of the drivetrain system of an instrumented OWT by using signals (e.g., acceleration responses) measured on the drivetrain system. The experimental data has been obtained during a measurement campaign on an OWT in the Belgian North Sea where the OWT was in standstill condition. The drivetrain, more specifically the gearbox and generator, is instrumented with a dedicated measurement set-up consisting of 17 sensor channels with the aim to continuously track the vibration modes. The consistency of modal parameter estimates made at consequent 10-min intervals is validated, and the dominant drivetrain modal behavior is identified and automatically tracked. Full article
(This article belongs to the Collection Wind Turbines)
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<p>(<b>a</b>) A layout of the drivetrain; (<b>b</b>) the structure of the instrumented wind turbine; (<b>c</b>) Measurement locations on the drivetrain unit with zoom in on the sensor locations; (<b>d</b>) a simple geometry representing the locations of the tri-axial sensors (indicated by three axes) and the uni-axial sensors (indicated by one axis) at the different stages of the drivetrain unit (<b>Black boxes</b>: the sensors on the gearbox unit. <b>Blue box</b>: sensor on the generator unit.).</p> Full article ">Figure 2
<p>Low frequency band analysis: Dominant modes of the tower of the instrumented offshore wind turbine (OWT) [<a href="#B7-energies-10-00574" class="html-bibr">7</a>].</p> Full article ">Figure 3
<p>A typical stabilization chart constructed by the polyreference linear least-squares complex exponential (pLSCE) estimator in the low frequency band.</p> Full article ">Figure 4
<p>(<b>a</b>) The drivetrain mode shape at the tower FA1 frequency: 0.359 Hz. (<b>b</b>) The drivetrain mode shape at the tower SS1 frequency: 0.369 Hz. (Y: Side-Side direction X: For-Aft direction Grey boxes: undeformed model Black boxes: deformed model).</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) The drivetrain mode shape at the tower FA1 frequency: 0.359 Hz. (<b>b</b>) The drivetrain mode shape at the tower SS1 frequency: 0.369 Hz. (Y: Side-Side direction X: For-Aft direction Grey boxes: undeformed model Black boxes: deformed model).</p> Full article ">Figure 5
<p>The different steps of the automatic tracking procedure of the drivetrain modes.</p> Full article ">Figure 6
<p>Periodogram approach applied to one data record.</p> Full article ">Figure 7
<p>Obtained averaged cross power spectra between all the outputs and the first reference.</p> Full article ">Figure 8
<p>Drivetrain modes tracking criteria.</p> Full article ">Figure 9
<p>Stabilization chart constructed by the <span class="html-italic">pLSCF</span> estimator when applied to the first data record.</p> Full article ">Figure 10
<p>The mode shapes of the 10 tracked drivetrain modes.</p> Full article ">Figure 10 Cont.
<p>The mode shapes of the 10 tracked drivetrain modes.</p> Full article ">Figure 11
<p>Evolution of the frequencies (<b>top</b>) and damping ratios (<b>bottom</b>) of the 10 most dominant drivetrain modes during the tracking period.</p> Full article ">Figure 12
<p>Evolution of the MAC value calculated between the 10 most dominant drivetrain modes and the reference mode shapes during the tracking period.</p> Full article ">Figure 13
<p>Boxplots of the frequencies (<b>left</b>) and damping ratios (<b>right</b>) of the 10 most dominant drivetrain modes over the tracking period: On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme values that were not considered as outliers and the outliers are plotted individually using the “+” symbol.</p> Full article ">Figure 14
<p>Boxplots of the MAC value calculated between the 10 most dominant drivetrain modes and the reference mode shapes over the tracking period: On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme values that were not considered as outliers and the outliers are plotted individually using the “+” symbol.</p> Full article ">
2177 KiB  
Article
Primary and Albedo Solar Energy Sources for High Altitude Persistent Air Vehicle Operation
by Tim Smith, Michele Trancossi, Dean Vucinic, Chris Bingham and Paul Stewart
Energies 2017, 10(4), 573; https://doi.org/10.3390/en10040573 - 22 Apr 2017
Cited by 7 | Viewed by 6185
Abstract
A new class of the all electric airship to globally transport both passengers and freight using a ‘feeder-cruiser’ concept, and powered by renewable electric energy, is considered. Specific focus is given to photo-electric harvesting as the primary energy source and the associated hydrogen-based [...] Read more.
A new class of the all electric airship to globally transport both passengers and freight using a ‘feeder-cruiser’ concept, and powered by renewable electric energy, is considered. Specific focus is given to photo-electric harvesting as the primary energy source and the associated hydrogen-based energy storage systems. Furthermore, it is shown that the total PV output may be significantly increased by utilising cloud albedo effects. Appropriate power architectures and energy audits required for life support, and the propulsion and ancillary loads to support the continuous daily operation of the primary airship (cruiser) at stratospheric altitudes (circa 18 km), are also considered. The presented solution is substantially different from those of conventional aircraft due to the airship size and the inherent requirement to harvest and store sufficient energy during “daylight” operation, when subject to varying seasonal conditions and latitudes, to ensure its safe and continued operation during the corresponding varying “dark hours”. This is particularly apparent when the sizing of the proposed electrolyser is considered, as its size and mass increase nonlinearly with decreasing day-night duty. As such, a Unitized Regenerative Fuel Cell is proposed. For the first time the study also discusses the potential benefits of integrating the photo-voltaic cells into airship canopy structures utilising TENSAIRITY®-based elements in order to eliminate the requirements for separate inter-PV array wiring and the transport of low pressure hydrogen between fuel cells. Full article
(This article belongs to the Special Issue Next-Generation Low-Carbon Power and Energy Systems)
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<p>MAAT Cruiser-Feeder Concept.</p> Full article ">Figure 2
<p>Operational Day Length.</p> Full article ">Figure 3
<p>Energy Flow for day and night operation.</p> Full article ">Figure 4
<p>(<b>a</b>) Photovoltaic array mass required per kW Electrical Load for varying day lengths; (<b>b</b>) Hydrogen and Water storage tank mass required per kW Electrical Load for varying day lengths; (<b>c</b>) Electrolyser mass required per kW Electrical Load for varying day lengths.</p> Full article ">Figure 5
<p>MAAT System operational altitude and Air Mass effect.</p> Full article ">Figure 6
<p>Variation of beam irradiance with respect to altitude at a sun zenith angle, <span class="html-italic">θ</span>, of zero degrees.</p> Full article ">Figure 7
<p>Direct Spectral Irradiance for TOA, 15,000 m and MSL.</p> Full article ">Figure 8
<p>(<b>a</b>) Cruiser and (<b>b</b>) Feeder outline shape and projected area.</p> Full article ">
5838 KiB  
Article
PTC Self-Heating Experiments and Thermal Modeling of Lithium-Ion Battery Pack in Electric Vehicles
by Chengning Zhang, Xin Jin and Junqiu Li
Energies 2017, 10(4), 572; https://doi.org/10.3390/en10040572 - 22 Apr 2017
Cited by 31 | Viewed by 6355
Abstract
This paper proposes a positive temperature coefficient (PTC) self-heating method, in which EVs can be operated independently of external power source at low temperature, with a lithium-ion battery (LIB) pack discharging electricity to provide PTC material with power. Three comparative heating experiments have [...] Read more.
This paper proposes a positive temperature coefficient (PTC) self-heating method, in which EVs can be operated independently of external power source at low temperature, with a lithium-ion battery (LIB) pack discharging electricity to provide PTC material with power. Three comparative heating experiments have been carried out respectively. With charge/discharge tests implemented, results demonstrate the superiority of the self-heating method, proving that the discharge capability, especially the discharge capacity of the self-heated pack is better than that of the external power heated pack. In order to evaluate the heating effect of this method, further studies are conducted on temperature distribution uniformity in the heated pack. Firstly, a geometric model is established, and heat-generation rate of PTC materials and LIB are calculated. Then, thermal characteristics of the self-heating experiment processes are numerically simulated, validating the accuracy of our modeling and confirming that temperature distributions inside the pack after heating are kept in good uniformity. Therefore, the PTC self-heating method is verified to have a significant effect on the improvement of performance of LIB at low temperature. Full article
(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))
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<p>The 1C charge characteristics of a cell at different temperatures.</p> Full article ">Figure 2
<p>Schematic diagram of PTC self-heating method.</p> Full article ">Figure 3
<p>Product of battery pack with PTC.</p> Full article ">Figure 4
<p>The characteristics of the PTC material: (<b>a</b>) The resistance characteristic of PTC; and (<b>b</b>) the current curve in the heating process at −40 °C.</p> Full article ">Figure 5
<p>The scene photos of the experiments.</p> Full article ">Figure 6
<p>The first heating curves of Self-heating Experiment I.</p> Full article ">Figure 7
<p>The second heating curves of Self-heating Experiment I.</p> Full article ">Figure 8
<p>The first heating curves of Self-heating Experiment II.</p> Full article ">Figure 9
<p>The second heating curves of Self-heating Experiment II.</p> Full article ">Figure 10
<p>Pulse discharge capability of the pack after the first heating with an external power source.</p> Full article ">Figure 11
<p>Pulse discharge capability of the pack after the second heating with an external power source.</p> Full article ">Figure 12
<p>Pulse discharge capability of the pack after the first heating in Self-heating Experiment I.</p> Full article ">Figure 13
<p>Pulse discharge capability of the pack after the second heating in Self-heating Experiment I.</p> Full article ">Figure 14
<p>Pulse discharge capability of the pack after the first heating in Self-heating Experiment II.</p> Full article ">Figure 15
<p>Pulse discharge capability of the pack after the second heating in Self-heating Experiment II.</p> Full article ">Figure 16
<p>Comparison of 1C CC discharge tests.</p> Full article ">Figure 17
<p>Comparison of simulation results of two models and the experimental results. (<b>a</b>) Temperature variation in the charge process; and (<b>b</b>) temperature variation in the discharge process.</p> Full article ">Figure 18
<p>The micro-unit of the cell.</p> Full article ">Figure 19
<p>The 1/4 geometry model.</p> Full article ">Figure 20
<p>Heating current and heating power in the first self-heating process.</p> Full article ">Figure 21
<p>Heating current and heating power in the second self-heating process.</p> Full article ">Figure 22
<p>Variation of the internal and external heat generation rates of the whole self-heating process.</p> Full article ">Figure 23
<p>Comparison of simulation and experimental results in the first self-heating process.</p> Full article ">Figure 24
<p>Comparison of simulation and experimental results in the second self-heating process.</p> Full article ">Figure 25
<p>Temperature distribution of 12 cells on the <span class="html-italic">z</span> = 0 section (the center) at the end of the first self-heating process.</p> Full article ">Figure 26
<p>Temperature distribution of 12 cells on the <span class="html-italic">z</span> = 0 section (the center) at the end of the second self-heating process.</p> Full article ">
1973 KiB  
Article
Performance Evaluation of Waste Heat Recovery Systems Based on Semiconductor Thermoelectric Generators for Hypersonic Vehicles
by Kunlin Cheng, Yu Feng, Chuanwen Lv, Silong Zhang, Jiang Qin and Wen Bao
Energies 2017, 10(4), 570; https://doi.org/10.3390/en10040570 - 22 Apr 2017
Cited by 19 | Viewed by 4946
Abstract
The types and the characteristics of the waste heat on hypersonic vehicles and the application feasibility of thermoelectric generators (TEGs) for hypersonic aircraft are discussed in this paper. Two thermoelectric generator schemes with an isothermal heat source and a variable temperature heat source [...] Read more.
The types and the characteristics of the waste heat on hypersonic vehicles and the application feasibility of thermoelectric generators (TEGs) for hypersonic aircraft are discussed in this paper. Two thermoelectric generator schemes with an isothermal heat source and a variable temperature heat source were proposed, and the corresponding models were developed to predict the performance of the waste heat recovery systems on a hypersonic vehicle with different heat sources. The thermoelectric efficiency variation with electric current, the temperature distribution of fuel and junctions, and the distribution of the thermoelectric figure of merit (ZT value) are described by diagrams. Besides, some improvements for a better performance are analyzed. The results indicate that the maximum values of thermoelectric efficiency are 5% and 2.5% for the isothermal heat source and the variable temperature heat source, respectively, and the thermoelectric efficiency improves with the temperature of the hot junction. The performance of the TEGs with variable temperature heat source is worse than that of the other TEGs under the same highest hot junction temperature conditions, and the former has a better conversion efficiency than the latter when the average temperatures are identical. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Waste heat types on a hypersonic flight vehicle.</p> Full article ">Figure 2
<p>Sketch of a thermoelectric device with an isothermal heat source.</p> Full article ">Figure 3
<p>Calculation model of the cooling process.</p> Full article ">Figure 4
<p>Sketch of a thermoelectric device with a variable temperature heat source.</p> Full article ">Figure 5
<p>Variation of thermoelectric efficiency of the electric current with different hot junction temperatures for TEGs with isothermal heat sources.</p> Full article ">Figure 6
<p>Temperature distribution along <span class="html-italic">x</span> direction with different hot junction temperatures for TEGs with isothermal heat source.</p> Full article ">Figure 7
<p><span class="html-italic">ZT</span> value distribution along the <span class="html-italic">x</span> direction with different hot junction temperatures for TEGs with isothermal heat sources.</p> Full article ">Figure 8
<p>Variation of the thermoelectric efficiency of the electric current for TEGs with variable temperature heat sources.</p> Full article ">Figure 9
<p>Temperature distribution along the <span class="html-italic">x</span> direction for TEGs with variable temperature heat sources.</p> Full article ">Figure 10
<p><span class="html-italic">ZT</span> value distribution along the <span class="html-italic">x</span> direction for TEGs with a variable temperature heat source.</p> Full article ">
7402 KiB  
Article
Research on the Common Rail Pressure Overshoot of Opposed-Piston Two-Stroke Diesel Engines
by Yi Lu, Changlu Zhao, Zhe Zuo, Fujun Zhang and Shuanlu Zhang
Energies 2017, 10(4), 571; https://doi.org/10.3390/en10040571 - 21 Apr 2017
Cited by 2 | Viewed by 6562
Abstract
The common rail pressure has a direct influence on the working stability of Opposed-Piston Two-Stroke (OP2S) diesel engines, especially on performance indexes such as power, economy and emissions. Meanwhile, the rail pressure overshoot phenomenon occurs frequently due to the operating characteristics of OP2S [...] Read more.
The common rail pressure has a direct influence on the working stability of Opposed-Piston Two-Stroke (OP2S) diesel engines, especially on performance indexes such as power, economy and emissions. Meanwhile, the rail pressure overshoot phenomenon occurs frequently due to the operating characteristics of OP2S diesel engines, which could lead to serious consequences. In order to solve the rail pressure overshoot problem of OP2S diesel engines, a nonlinear concerted algorithm adding a speed state feedback was investigated. First, the nonlinear Linear Parameter Varying (LPV) model was utilized to describe the coupling relationship between the engine speed and the rail pressure. The Linear Quadratic Regulator (LQR) optimal control algorithm was applied to design the controller by the feedback of speed and rail pressure. Second, cooperating with the switching characteristics of injectors, the co-simulation of MATLAB/Simulink and GT-Power was utilized to verify the validity of the control algorithm and analyze workspaces for both normal and special sections. Finally, bench test results showed that the accuracy of the rail pressure control was in the range of ±1 MPa, in the condition of sudden 600 r/min speed increases. In addition, the fuel mass was reduced 76.3% compared with the maximum fuel supply quantity and the rail pressure fluctuation was less than 20 MPa. The algorithm could also be appropriate for other types of common rail system thanks to its universality. Full article
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<p>Experimental prototype test bench and measurement equipment.</p> Full article ">Figure 2
<p>Configuration of OP2S diesel engine.</p> Full article ">Figure 3
<p>A block scheme of the common rail injection system for diesel engines.</p> Full article ">Figure 4
<p>LPV control principle.</p> Full article ">Figure 5
<p>Design flow of the LQR scheduling control algorithm based on the LPV model.</p> Full article ">Figure 6
<p><span class="html-italic">K</span><sub>p</sub> distribution at different rail pressures and <span class="html-italic">ET.</span></p> Full article ">Figure 7
<p><span class="html-italic">K</span>n distribution at different rail pressures and <span class="html-italic">ET.</span></p> Full article ">Figure 8
<p><span class="html-italic">K</span>e distribution at different rail pressures and <span class="html-italic">ET.</span></p> Full article ">Figure 9
<p>Control principle of PID optimized by optimal control.</p> Full article ">Figure 10
<p>Co-simulation of OP2S engine using MATLAB/Simulink &amp; GT-Power.</p> Full article ">Figure 11
<p>Rail pressure, engine speed and fuel mass vs. time controlled by PID.</p> Full article ">Figure 12
<p>Map for injector characteristics.</p> Full article ">Figure 13
<p>Off-delay characteristics of the injector.</p> Full article ">Figure 14
<p>In-cylinder pressure in the special section.</p> Full article ">Figure 15
<p>Rail pressure, engine speed and fuel mass vs. time controlled by LQR.</p> Full article ">Figure 16
<p>Comparison of rail pressure overshoot controlled by PID &amp; LQR.</p> Full article ">Figure 17
<p>Rail pressure, engine speed and fuel mass vs. time on the experimental prototype.</p> Full article ">
3465 KiB  
Article
Development of Novel Robust Regulator for Maximum Wind Energy Extraction Based upon Perturbation and Observation
by Bo Li, Wenhu Tang, Kaishun Xiahou and Qinghua Wu
Energies 2017, 10(4), 569; https://doi.org/10.3390/en10040569 - 21 Apr 2017
Cited by 8 | Viewed by 4431
Abstract
This paper develops a robust regulator design approach to maximum power point tracking (MPPT) of a variable-speed wind energy conversion system (WECS) under the concept of perturbation and observation. The proposed perturb and observe regulators (PORs) rooted on the sliding mode method employs [...] Read more.
This paper develops a robust regulator design approach to maximum power point tracking (MPPT) of a variable-speed wind energy conversion system (WECS) under the concept of perturbation and observation. The proposed perturb and observe regulators (PORs) rooted on the sliding mode method employs the optimal power curve (OPC) to realize MPPT operations by continuously adjusting rotor speeds and the duty cycles, which can ensure control performance against system parameter variations. The proposed PORs can detect sudden wind speed changes indirectly through the mechanical power coefficient, which is used to acquire the rotor speed reference by comparing it with the optimal power constant. For the speed and duty cycle regulation, two novel controllers based on the proposed POR, i.e., an MPPT controller and a speed controller, are devised in this research. Moreover, by applying the small-signal analysis on a nonlinear wind turbine system, the convergence of the proposed speed controller is proven for the first time based on the Lyapunov theory, and meanwhile, a single-pole transfer function, to describe the effect of duty cycle variations on rotor speeds, is designed to ensure its stability. The proposed strategy is verified by simulation cases operated in MATLAB/Simulink and experimental results performed from a 0.5-kW wind turbine generator simulator. Full article
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<p>Block diagram-system configuration of the proposed MPPT algorithm.</p> Full article ">Figure 2
<p>Mechanical power coefficient as a function of the rotor speed at various wind speeds.</p> Full article ">Figure 3
<p>Operational principle of the proposed MPPT strategy: (<b>a</b>) Operating points in MPPT curve; (<b>b</b>) Operating points in OPC curve.</p> Full article ">Figure 4
<p>Estimator of mechanical power coefficient.</p> Full article ">Figure 5
<p>(<b>a</b>) HCS algorithm direction misled under wind speed changes; (<b>b</b>) HCS algorithm oscillates around the MPP.</p> Full article ">Figure 6
<p>(<b>a</b>) Flowchart of the POR-based MPPT controller for estimating <math display="inline"> <semantics> <msubsup> <mi>ω</mi> <mrow> <mi>ref</mi> </mrow> <mo>*</mo> </msubsup> </semantics> </math>; (<b>b</b>) Flowchart of the POR-based speed controller for estimating <math display="inline"> <semantics> <msub> <mi>d</mi> <mi>ref</mi> </msub> </semantics> </math>.</p> Full article ">Figure 7
<p>Small signal analysis of generator torque including the speed controller.</p> Full article ">Figure 8
<p>Dynamic responses of the PORs under step changes of wind speeds: (<b>a</b>) Wind speed; (<b>b</b>) Power coefficient; (<b>c</b>) Tip speed ratio; (<b>d</b>) Rotor speed; (<b>e</b>) Rotor speed; (<b>f</b>) Inductor current; (<b>g</b>) q axis current; and (<b>h</b>) Output voltage.</p> Full article ">Figure 8 Cont.
<p>Dynamic responses of the PORs under step changes of wind speeds: (<b>a</b>) Wind speed; (<b>b</b>) Power coefficient; (<b>c</b>) Tip speed ratio; (<b>d</b>) Rotor speed; (<b>e</b>) Rotor speed; (<b>f</b>) Inductor current; (<b>g</b>) q axis current; and (<b>h</b>) Output voltage.</p> Full article ">Figure 9
<p>Dynamic performances compared among the POR-MPPT, classic HCS and IHCS methods under step changes of wind speeds: (<b>a</b>) <math display="inline"> <semantics> <msub> <mi>k</mi> <mi>wt</mi> </msub> </semantics> </math> in POR-MPPT; (<b>b</b>) <math display="inline"> <semantics> <msub> <mi>k</mi> <mi>wt</mi> </msub> </semantics> </math> in HCS; (<b>c</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in POR-MPPT; (<b>d</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in HCS; (<b>e</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in POR-MPPT; (<b>f</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in HCS; (<b>g</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in IHCS; and (<b>h</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in IHCS.</p> Full article ">Figure 9 Cont.
<p>Dynamic performances compared among the POR-MPPT, classic HCS and IHCS methods under step changes of wind speeds: (<b>a</b>) <math display="inline"> <semantics> <msub> <mi>k</mi> <mi>wt</mi> </msub> </semantics> </math> in POR-MPPT; (<b>b</b>) <math display="inline"> <semantics> <msub> <mi>k</mi> <mi>wt</mi> </msub> </semantics> </math> in HCS; (<b>c</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in POR-MPPT; (<b>d</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in HCS; (<b>e</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in POR-MPPT; (<b>f</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in HCS; (<b>g</b>) <math display="inline"> <semantics> <msub> <mi>ω</mi> <mi mathvariant="normal">r</mi> </msub> </semantics> </math> in IHCS; and (<b>h</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">m</mi> </msub> </semantics> </math> in IHCS.</p> Full article ">Figure 10
<p>Robustness of the controllers against wind harmonics, PM aging phenomenon: (<b>a</b>) Wind speed; (<b>b</b>) Wind speed; (<b>c</b>) With 85% <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>pm</mi> </msub> </semantics> </math> errors in PI; and (<b>d</b>) With 85% <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>pm</mi> </msub> </semantics> </math> errors in POR.</p> Full article ">Figure 11
<p>Robustness of the controllers against changed temperature under step wind speeds: (<b>a</b>) With −15% <math display="inline"> <semantics> <msub> <mi>R</mi> <mi mathvariant="normal">s</mi> </msub> </semantics> </math> and +15% <math display="inline"> <semantics> <msub> <mi>L</mi> <mi mathvariant="normal">s</mi> </msub> </semantics> </math> errors in PI; (<b>b</b>) With -15% <math display="inline"> <semantics> <msub> <mi>R</mi> <mi mathvariant="normal">s</mi> </msub> </semantics> </math> and +15% <math display="inline"> <semantics> <msub> <mi>L</mi> <mi mathvariant="normal">s</mi> </msub> </semantics> </math> errors in POR; (<b>c</b>) Random changed <math display="inline"> <semantics> <mi>ρ</mi> </semantics> </math>; and (<b>d</b>) With random changed <math display="inline"> <semantics> <mi>ρ</mi> </semantics> </math> in POR.</p> Full article ">Figure 12
<p>Maximum power extraction under random wind speeds: (<b>a</b>) 3 Hz wind speed variations; (<b>b</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 3 Hz variations between POR-MPPT and HCS; (<b>c</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 3 Hz variations between POR-MPPT and PI; (<b>d</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 3 Hz variations between POR-MPPT and IHCS; (<b>e</b>) 6 Hz wind speed variations; (<b>f</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 6 Hz variations between POR-MPPT and HCS; (<b>g</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 6 Hz variations between POR-MPPT and PI; and (<b>h</b>) <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>loss</mi> </msub> </semantics> </math> under 6 Hz variations between POR-MPPT and IHCS.</p> Full article ">Figure 13
<p>Layout of the experimental equipment. SCIM: squirrel-cage induction motor.</p> Full article ">Figure 14
<p>Responses of POR control in 16-m/s wind speed. (<b>a</b>) Inductor current; (<b>b</b>) Line current and line voltage.</p> Full article ">Figure 15
<p>Responses of POR control in stepwise wind speed variation. (<b>a</b>) Wind speed; (<b>b</b>) Rotor speed, output voltage and line voltage; and (<b>c</b>) Rotor speed, line current and inductor current.</p> Full article ">Figure 16
<p>Block diagram of the PI-based control strategy.</p> Full article ">Figure 17
<p>Block diagram of the HCS-based control strategy.</p> Full article ">Figure 18
<p>Block diagram of the IHCS-based control strategy.</p> Full article ">
3928 KiB  
Article
Simulations of Melting of Encapsulated CaCl2·6H2O for Thermal Energy Storage Technologies
by Antonio M. Puertas, Manuel S. Romero-Cano, Francisco Javier De Las Nieves, Sabina Rosiek and Francisco J. Batlles
Energies 2017, 10(4), 568; https://doi.org/10.3390/en10040568 - 21 Apr 2017
Cited by 7 | Viewed by 4133
Abstract
We present in this work simulations using the finite difference approximation in 2D for the melting of an encapsulated phase-change material suitable for heat storage applications; in particular, we study CaCl2·6H2O in a cylindrical encapsulation of internal radius 8 [...] Read more.
We present in this work simulations using the finite difference approximation in 2D for the melting of an encapsulated phase-change material suitable for heat storage applications; in particular, we study CaCl2·6H2O in a cylindrical encapsulation of internal radius 8 mm. We choose this particular salt hydrate due to its availability and economic feasibility in high thermal mass building walls or storage. Considering only heat conduction, a thermostat is placed far from the capsule, providing heat for the melting of the phase-change material (PCM), which is initially frozen in a water bath. The difference in density between the solid and liquid phases is taken into account by considering a void in the solid PCM. A simple theoretical model is also presented, based on solving the heat equation in the steady state. The kinetics of melting is monitored by the total solid fraction and temperatures in the inner and outer surfaces of the capsule. The effect of different parameters is presented (thermostat temperature, capsule thickness, capsule conductivity and natural convection in the bath), showing the potential application of the method to select materials or geometries of the capsule. Full article
(This article belongs to the Section D: Energy Storage and Application)
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<p>Schematic model of the configuration used in the simulations. The numbers correspond to the different materials: The thermostat (1) has a radius of <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>25</mn> </mrow> </semantics> </math> mm; the capsule (3) has external and internal radii <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> mm and <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>8</mn> </mrow> </semantics> </math> mm, respectively; and the PCM has a void initially with radius <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>v</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>4</mn> </mrow> </semantics> </math> mm.</p> Full article ">Figure 2
<p>Snapshots of the capsule and temperature maps (left and right panels, respectively) for different times: <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000 and 4500 s, from left to right and top to bottom. The different colors represent different materials: bath (light blue), capsule (yellow), solid PCM (red), liquid PCM (orange) and void (dark blue). The color scales apply to the temperature maps.</p> Full article ">Figure 3
<p>Upper panel: Evolution of the temperature in the inner and outer surfaces of the capsule and the minimum temperature of the PCM, as labeled. The horizontal blue lines mark the predictions of the theoretical model for the inner and outer surfaces of the capsule (see text). Lower panel: Evolution of the PCM solid fraction.</p> Full article ">Figure 4
<p>Temperature profiles (red lines, left <span class="html-italic">y</span>-axis) and PCM solid fraction (blue line, right <span class="html-italic">y</span>-axis) as a function of the radial coordinate. The vertical lines mark the capsule, and the horizontal green line shows the melting temperature. The upper panel shows the results for a short time (<math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>500</mn> <mspace width="3.33333pt"/> </mrow> </semantics> </math> s) and the lower one for a long time (<math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>2500</mn> <mspace width="3.33333pt"/> </mrow> </semantics> </math> s).</p> Full article ">Figure 5
<p>Temperature profiles along the <span class="html-italic">x</span>-axis (horizontal) for different times. The dashed black line is at a short time, evolving to lower temperatures inside the capsule (<math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>&lt;</mo> <mn>0</mn> <mo>.</mo> <mn>010</mn> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>500</mn> </mrow> </semantics> </math> s), and the violet lines correspond to longer times, increasing from right to left (<math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1000</mn> <mo>,</mo> <mn>1500</mn> <mo>,</mo> <mn>2000</mn> <mo>,</mo> <mn>2500</mn> </mrow> </semantics> </math> and 3000 s). The profiles calculated from the model at the start and end of the melting are shown by the black and red lines, respectively.</p> Full article ">Figure 6
<p>Upper panel: Temperatures in the outer and inner surfaces of the capsule as a function of time, for different thermostat temperatures, as labeled (continuous lines for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> and dashed ones for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </msub> </semantics> </math>). Lower panel: Solid fraction of the PCM for the same thermostat temperatures.</p> Full article ">Figure 7
<p>Upper panel: Temperatures in the outer surface of the capsule at the start and end of the melting of the PCM and the temperature in the inner surface at the end of the melting, as a function of the thermostat temperature, as labeled (see the text for details). Lower panel: Time for the total melting of the PCM. The lines show the predictions of the theoretical model, with the same color code as the simulation data.</p> Full article ">Figure 8
<p>Upper panel: Temperatures in the outer and inner surfaces of the capsule as a function of time, for different thickness of the capsule, as labeled (continuous lines for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> and dashed ones for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </msub> </semantics> </math>). Lower panel: Solid fraction of the PCM for the same thickness.</p> Full article ">Figure 9
<p>Upper panel: Temperatures in the outer surface of the capsule at the start and end of the melting of the PCM and the temperature in the inner surface at the end of the melting, as a function of the thickness capsule, as labeled (see the text for details). Lower panel: Time for the total melting of the PCM. The lines show the predictions of the theoretical model, with the same color code as the simulation data.</p> Full article ">Figure 10
<p>Temperature profiles from the simulations (upper panel) and model (lower panel) for different capsule thickness, with the same color code as <a href="#energies-10-00568-f008" class="html-fig">Figure 8</a>. In the simulations, we show the profiles at a constant time <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1000</mn> <mspace width="3.33333pt"/> </mrow> </semantics> </math> s and in the model, the profile at the end of the melting.</p> Full article ">Figure 11
<p>Upper panel: Temperatures in the outer and inner surfaces of the capsule as a function of time, for two different values of the conductivity of the capsule, as labeled (continuous lines for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> and dashed ones for <math display="inline"> <semantics> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> </mrow> </msub> </semantics> </math>). The horizontal lines show the predictions from the model for the initial and final external temperature, with the same color code. Lower panel: Solid fraction of the PCM.</p> Full article ">Figure 12
<p>Upper panel: Temperatures in the outer surface of the capsule at the start and end of the melting of the PCM and the temperature in the inner surface at the end of the melting, as a function of the thermal conductivity of the capsule, as labeled. Lower panel: Time for the total melting of the PCM. The lines show the predictions of the theoretical model, with the same color code as the simulation data.</p> Full article ">Figure 13
<p>Upper panel: Evolution of the temperatures in the outer and inner surfaces of the capsule including convection in the bath (solid lines) and without convection (dashed lines). The effective conductivity is also included (right scale). Lower panel: Evolution of the solid fraction with convection (solid line) and without convection (dashed line).</p> Full article ">
2168 KiB  
Article
Thermal-Flow Analysis of a Simple LTD (Low-Temperature-Differential) Heat Engine
by Yeongmin Kim, Wongee Chun and Kuan Chen
Energies 2017, 10(4), 567; https://doi.org/10.3390/en10040567 - 21 Apr 2017
Cited by 7 | Viewed by 4274
Abstract
A combined thermal and flow analysis was carried out to study the behavior and performance of a simple, commercial LTD (low-temperature-differential) heat engine. Laminar-flow solutions for annulus and channel flows were employed to estimate the viscous drags on the piston and the displacer, [...] Read more.
A combined thermal and flow analysis was carried out to study the behavior and performance of a simple, commercial LTD (low-temperature-differential) heat engine. Laminar-flow solutions for annulus and channel flows were employed to estimate the viscous drags on the piston and the displacer, and the pressure difference across the displacer. Temperature correction factors were introduced in the thermal analysis to account for the departures from the ideal heat transfer processes. The flow analysis results indicate that the work required to overcome the viscous drags on engine moving parts is very small for engine speeds below 10 RPS (revolutions per second). The work required to move the displacer due to the pressure difference across the displacer is also one-to-two orders of magnitude smaller than the moving-boundary work of the piston for temperature differentials in the neighborhood of 20 °C and engine speeds below 10 RPS. A comparison with experimental data reveals large degradations from the ideal heat transfer processes inside the engine. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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<p>The American Stirling Company MM-7 engine.</p> Full article ">Figure 2
<p>Nomenclature and major components of the MM-7 engine.</p> Full article ">Figure 3
<p>Piston and displacer positions for <span class="html-italic">α</span> = −90°.</p> Full article ">Figure 4
<p>Volumes in thermal-flow analysis.</p> Full article ">Figure 5
<p>Moving-boundary work produced by the piston for different phase differences. (∆<span class="html-italic">T</span> = 20 °C, <span class="html-italic">ω</span> = 1 RPS, <span class="html-italic">C<sub>c</sub></span> = <span class="html-italic">C<sub>h</sub></span> = 0).</p> Full article ">Figure 6
<p>Pressure and pressure difference in the displacer cylinder for different engine speeds. (∆<span class="html-italic">T</span> = 20 °C, <span class="html-italic">ω</span> = 1 to 10 RPS, <span class="html-italic">C<sub>c</sub></span> = <span class="html-italic">C<sub>h</sub></span> = 0).</p> Full article ">Figure 7
<p>Shear stresses on the lateral surfaces of the piston and the displacer. (∆<span class="html-italic">T</span> = 20 °C, <span class="html-italic">ω</span> = 1 RPS, <span class="html-italic">C<sub>c</sub></span> = <span class="html-italic">C<sub>h</sub></span> = 0).</p> Full article ">Figure 8
<p>Calculated engine power outputs for different temperature differentials and a comparison with experimental data for ∆<span class="html-italic">T</span> = 20 °C: (<b>a</b>) ∆<span class="html-italic">T</span> = 10 °C; (<b>b</b>) ∆<span class="html-italic">T</span> = 20 °C; (<b>c</b>) ∆<span class="html-italic">T</span> = 30 °C.</p> Full article ">Figure 9
<p>Different forms of work produced or consumed by the engine per unit time for ∆<span class="html-italic">T</span> = 20 °C, <span class="html-italic">ω</span> = 1 to 10 RPS, and <span class="html-italic">C<sub>c</sub></span> = <span class="html-italic">C<sub>h</sub></span> = 0. (<span class="html-italic">P<sub>p</sub></span> = boundary work produced by the piston per unit time; <span class="html-italic">P<sub>fp</sub></span>, <span class="html-italic">P<sub>fDR</sub></span>, and <span class="html-italic">P<sub>fD</sub></span> = power required to overcome the viscous drags on the piston, the displacer rod, and the displacer; <span class="html-italic">P<sub>pD</sub></span> = power required to overcome the pressure difference across the displacer).</p> Full article ">
532 KiB  
Article
Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids
by Boram Kim, Sunghwan Bae and Hongseok Kim
Energies 2017, 10(4), 566; https://doi.org/10.3390/en10040566 - 21 Apr 2017
Cited by 23 | Viewed by 4644
Abstract
In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a [...] Read more.
In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a distributed network layer and a market layer. In the distributed network layer, we first solve optimal power flow (OPF) using a predictor corrector proximal multiplier algorithm to optimally dispatch diesel generation considering renewable energy and power loss within a microgrid. Then, in the market layer, the agent of microgrid behaves either as a load agent or generator agent so that the auctioneer sets a reasonable transaction price for both agents by using the naive auction-inspired algorithm. Finally, energy surplus/deficit is traded among microgrids at a determined transaction price while the main grid balances the transaction. We implement the proposed mechanism in MATLAB (Matlab Release 15, The MathWorks Inc., Natick, MA, USA) using an optimization solver, CVX. In the case studies, we compare four scenarios depending on whether OPF and/or energy transaction is performed or not. Our results show that the joint consideration of OPF and energy transaction achieves as minimal a cost as the ideal case where all microgrids are combined into a single microgrid (or called grand-microgrid) and OPF is performed. We confirm that, even though microgrids are operated by private owners who are not collaborated, a transaction-based mechanism can mimic the optimal operation of a grand-microgrid in a scalable way. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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<p>The overall structure of multiple microgrids. PCPM: predictor corrector proximal multiplier; TOU: time of use; and DSO: distribution system operator.</p> Full article ">Figure 2
<p>Examples of balancing scenarios by auction agent. (<math display="inline"> <semantics> <mrow> <mi>E</mi> <mo stretchy="false">(</mo> <mo>·</mo> <mo stretchy="false">)</mo> </mrow> </semantics> </math> denotes the amount of energy of either LA, GA or DSO.)</p> Full article ">Figure 3
<p>(<b>a</b>) The topology of microgrids; (<b>b</b>) transaction price from the main grid; and (<b>c</b>) renewable energy profile.</p> Full article ">Figure 4
<p>Case 1: load profile and power scheduling based on non-OPF operation. (<b>a</b>) Microgrid 1; and (<b>b</b>) microgrid 2. OPF: optimal power flow.</p> Full article ">Figure 5
<p>Load profile and power scheduling based on OPF operation: (<b>a</b>) microgrid 1; and (<b>b</b>) microgrid 2.</p> Full article ">Figure 6
<p>Energy transaction price between microgrids.</p> Full article ">Figure 7
<p>Case 3: load profile and power scheduling based on the transaction-enabled OPF algorithm. (<b>a</b>) Microgrid 1; and (<b>b</b>) microgrid 2.</p> Full article ">Figure 8
<p>Load profile and power scheduling based on the centralized optimal power flow.</p> Full article ">
4204 KiB  
Article
Simultaneous Robust Coordinated Damping Control of Power System Stabilizers (PSSs), Static Var Compensator (SVC) and Doubly-Fed Induction Generator Power Oscillation Dampers (DFIG PODs) in Multimachine Power Systems
by Jian Zuo, Yinhong Li, Dongyuan Shi and Xianzhong Duan
Energies 2017, 10(4), 565; https://doi.org/10.3390/en10040565 - 20 Apr 2017
Cited by 25 | Viewed by 6021
Abstract
The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping [...] Read more.
The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping controllers is rarely involved. A simultaneous robust coordinated multiple damping controller design strategy for a power system incorporating power system stabilizer (PSS), static var compensator (SVC) POD and DFIG POD is presented in this paper. This coordinated damping control design strategy is addressed as an eigenvalue-based optimization problem to increase the damping ratios of oscillation modes. Both local and inter-area electromechanical oscillation modes are intended in the optimization design process. Wide-area phasor measurement unit (PMU) signals, selected by the joint modal controllability/ observability index, are utilized as SVC and DFIG POD feedback modulation signals to suppress inter-area oscillation modes. The robustness of the proposed coordinated design strategy is achieved by simultaneously considering multiple power flow situations and operating conditions. The recently proposed Grey Wolf optimizer (GWO) algorithm is adopted to efficiently optimize the parameter values of multiple damping controllers. The feasibility and effectiveness of the proposed coordinated design strategy are demonstrated through frequency-domain eigenvalue analysis and nonlinear time-domain simulation studies in two modified benchmark test systems. Moreover, the dynamic response simulation results also validate the robustness of the recommended coordinated multiple damping controllers under various system operating conditions. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>A simple automatic voltage regulator (AVR) with fixed-structure lead-lag type power system stabilizer (PSS).</p> Full article ">Figure 2
<p>Static var compensator (SVC) regulator structure.</p> Full article ">Figure 3
<p>The structure of static var compensator power oscillation damper (SVC POD).</p> Full article ">Figure 4
<p>The structure of the doubly-fed induction generator (DFIG) and vector control strategy.</p> Full article ">Figure 5
<p>The specified sector region in left half <span class="html-italic">s</span>-plane.</p> Full article ">Figure 6
<p>The flow chart of suggested GWO algorithm for tuning parameters of PSSs and PODs.</p> Full article ">Figure 7
<p>Modified Two-area Kundur test system.</p> Full article ">Figure 8
<p>The joint modal controllability/observability index (COBI) of line active power as the input feedback signal of SVC POD.</p> Full article ">Figure 9
<p>COBI of line active power as the input feedback signal of DFIG POD.</p> Full article ">Figure 10
<p>System bus 7 3-phase fault responses under light inter-area power flow transmission condition. (<b>a</b>) Relative power angle of generator G1 and G3; (<b>b</b>) Relative power angle of generator G2 and G3; and (<b>c</b>) Relative power angle of generator G4 and G3.</p> Full article ">Figure 11
<p>System bus 7 3-phase fault responses under normal inter-area power flow transmission condition. (<b>a</b>) Relative power angle of generator G1 and G3; (<b>b</b>) Relative power angle of generator G2 and G3; and (<b>c</b>) Relative power angle of generator G4 and G3.</p> Full article ">Figure 12
<p>System bus 7 3-phase fault responses under heavy inter-area power flow transmission condition. (<b>a</b>) Relative power angle of generator G1 and G3; (<b>b</b>) Relative power angle of generator G2 and G3; and (<b>c</b>) Relative power angle of generator G4 and G3.</p> Full article ">Figure 13
<p>System bus 7 3-phase fault responses under different system operating scenarios. (<b>a</b>) Line 7–8 outage; (<b>b</b>) Line 8–9 outage; and (<b>c</b>) Tie-line active power is −310 MW.</p> Full article ">Figure 14
<p>Modified IEEE 39 bus New England test system.</p> Full article ">Figure 15
<p>Optimum objective function convergence curve.</p> Full article ">Figure 16
<p>Relative power angle response of synchronous generators with different coordinated damping controllers. (<b>a</b>) Coordinated damping controllers; (<b>b</b>) Uncoordinated damping controllers; (<b>c</b>) G<sub>10,1</sub> response under different control strategy; (<b>d</b>) G<sub>8,1</sub> response under different control strategy; (<b>e</b>) G<sub>4,1</sub> response under different control strategy; and (<b>f</b>) G<sub>2,1</sub> response under different control strategy.</p> Full article ">Figure 16 Cont.
<p>Relative power angle response of synchronous generators with different coordinated damping controllers. (<b>a</b>) Coordinated damping controllers; (<b>b</b>) Uncoordinated damping controllers; (<b>c</b>) G<sub>10,1</sub> response under different control strategy; (<b>d</b>) G<sub>8,1</sub> response under different control strategy; (<b>e</b>) G<sub>4,1</sub> response under different control strategy; and (<b>f</b>) G<sub>2,1</sub> response under different control strategy.</p> Full article ">Figure 17
<p>Relative power angle response of synchronous generators under different test scenarios (solid line: coordinated; dot line: uncoordinated). (<b>a</b>) Power outputs of wind farms are decreased to 100 MW; (<b>b</b>) Load increase 10%; and (<b>c</b>) Line 14–15 and 21–22 outage.</p> Full article ">Figure 17 Cont.
<p>Relative power angle response of synchronous generators under different test scenarios (solid line: coordinated; dot line: uncoordinated). (<b>a</b>) Power outputs of wind farms are decreased to 100 MW; (<b>b</b>) Load increase 10%; and (<b>c</b>) Line 14–15 and 21–22 outage.</p> Full article ">
3112 KiB  
Article
Assessment of the Usability and Accuracy of Two-Diode Models for Photovoltaic Modules
by Vincenzo Franzitta, Aldo Orioli and Alessandra Di Gangi
Energies 2017, 10(4), 564; https://doi.org/10.3390/en10040564 - 20 Apr 2017
Cited by 28 | Viewed by 4331
Abstract
Many diode-based equivalent circuits for simulating the electrical behaviour of photovoltaic (PV) cells and panels are reported in the scientific literature. Two-diode equivalent circuits, which require more complex procedures to calculate the seven model parameters, are less numerous. The model parameters are generally [...] Read more.
Many diode-based equivalent circuits for simulating the electrical behaviour of photovoltaic (PV) cells and panels are reported in the scientific literature. Two-diode equivalent circuits, which require more complex procedures to calculate the seven model parameters, are less numerous. The model parameters are generally calculated using the data extracted from the datasheets issued by the PV panel manufactures and adopting simplifying hypotheses and numerical solving techniques. A criterion for rating both the usability and accuracy of two-diode models is proposed in this paper with the aim of supporting researchers and designers, working in the area of PV systems, to select and use a model that may be fit for purpose. The criterion adopts a three-level rating scale that considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power. The analytical procedures, the simplifying hypotheses and the operative steps to calculate the parameters of the most famous two-diode equivalent circuits are exhaustively described in this paper. The accuracy of the models is tested by comparing the characteristics issued by the PV panel manufacturers with the current-voltage (I-V) curves, at constant solar irradiance and/or cell temperature, calculated with the analysed models with. The results of the study show that the two-diode models recently proposed reach accuracies that are comparable with the values derived from the one-diode models. Full article
(This article belongs to the Special Issue Solar Photovoltaics Trilemma: Efficiency, Stability and Cost Reduction 2017)
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<p>Two-diode equivalent circuit for a PV panel.</p> Full article ">Figure 2
<p>Range-scaled <span class="html-italic">I-V</span> characteristics of crystalline and thin-film PV panels at the SRC.</p> Full article ">Figure 3
<p>Effects of the series resistance on the <span class="html-italic">I-V</span> characteristic.</p> Full article ">Figure 4
<p>Effects of the shunt resistance on the <span class="html-italic">I-V</span> characteristic.</p> Full article ">Figure 5
<p>Effects of the saturation currents on the <span class="html-italic">I-V</span> characteristic.</p> Full article ">Figure 6
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Kyocera KD245GH-4FB2 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of the Chan et al. models.</p> Full article ">Figure 7
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Kyocera KD245GH-4FB2 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of the Enebish et al. and the Hovinen models.</p> Full article ">Figure 8
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Sanyo HIT-240 HDE4 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of Chan et al. models.</p> Full article ">Figure 9
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Sanyo HIT-240 HDE4 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of Enebish et al. and the Hovinen models.</p> Full article ">Figure 10
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Kyocera KD245GH-4FB2 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of the Hejri et al., the Gupta et al. and the Ishaque et al. models.</p> Full article ">Figure 11
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Sanyo HIT-240 HDE4 at <span class="html-italic">T</span> = 25 °C and the characteristics calculated by means of the Hejri et al., the Gupta et al. and the Ishaque et al. models.</p> Full article ">Figure 12
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Kyocera KD245GH-4FB2 at <span class="html-italic">G</span> = 1000 W/m<sup>2</sup> and the characteristics calculated by means of the Hejri et al., the Gupta et al. and the Ishaque et al. models.</p> Full article ">Figure 13
<p>Comparison between the issued <span class="html-italic">I-V</span> characteristics of Sanyo HIT-240 HDE4at <span class="html-italic">G</span> = 1000 W/m<sup>2</sup> and the characteristics calculated by means of the Hejri et al., the Gupta et al. and the Ishaque et al. models.</p> Full article ">
5100 KiB  
Article
Jointed Surrounding Rock Mass Stability Analysis on an Underground Cavern in a Hydropower Station Based on the Extended Key Block Theory
by Chao Jia, Yong Li, Mingyuan Lian and Xiaoyong Zhou
Energies 2017, 10(4), 563; https://doi.org/10.3390/en10040563 - 20 Apr 2017
Cited by 17 | Viewed by 4547
Abstract
The jointed surrounding rock mass stability is of utmost importance to integral stability during the construction and long-term safety operation of the underground caverns in hydropower stations. The key blocks play a significant role in the integral stability of the jointed surrounding rock [...] Read more.
The jointed surrounding rock mass stability is of utmost importance to integral stability during the construction and long-term safety operation of the underground caverns in hydropower stations. The key blocks play a significant role in the integral stability of the jointed surrounding rock mass, therefore it is critical to determine the location, size, and failure mode of random key blocks. This paper proposes an improved method combining the traditional key block theory (KBT) and the force transfer algorithm to accurately calculate the safety factors of probabilistic key blocks in the surrounding rock mass. The force transfer algorithm can consider the interactions between the internal blocks. After the probabilistic characteristics of the joint fissures are obtained, the stereographic projection method is employed to determine the locations of dangerous joints. Then the vector analysis method is used to search the random blocks, determine the sliding directions of random blocks, and calculate the block sizes and safety factors near the free surface of the underground cavern, which can be used to comprehensively evaluate the surrounding rock mass stability. The above numerical results have provided powerful guidance for developing a reinforcement system for the surrounding rock mass. Full article
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<p>Vector analysis of the probabilistic blocks: (<b>a</b>) Stable block; (<b>b</b>) Free falling block; (<b>c</b>) Sliding along one face; and (<b>d</b>) Double plane sliding.</p> Full article ">Figure 2
<p>A sketch map for a two-dimensional condition of the force transfer algorithm in key blocks.</p> Full article ">Figure 3
<p>A flowchart for the detailed procedure of the methodology for the stability analysis on extended KBT.</p> Full article ">Figure 4
<p>Layout diagram of the Taishan pumped-storage hydropower station.</p> Full article ">Figure 5
<p>Stereographic projection of the joints.</p> Full article ">Figure 6
<p>Failure probability analysis in planar sliding: (<b>a</b>) Failure probability when the average dip direction is 58°;(<b>b</b>) Failure probability when the mode dip direction is 70°; and (<b>c</b>) Failure probability at the most critical dip direction.</p> Full article ">Figure 7
<p>Probability analysis of five typical failure modes.</p> Full article ">Figure 8
<p>The dimensions of the main powerhouse (Unit: m).</p> Full article ">Figure 9
<p>Search of key blocks and calculations of safety factors: (<b>a</b>) The key blocks in the arch crown; (<b>b</b>) The key blocks in the upstream side wall; (<b>c</b>) The key blocks in the downstream side wall; and (<b>d</b>) The key blocks in the bottom floor.</p> Full article ">Figure 10
<p>Cumulative frequency curves: (<b>a</b>) The buried depths of the key blocks; (<b>b</b>) The volumes of the key blocks; and (<b>c</b>) The safety factors of the blocks need to be supported.</p> Full article ">Figure 11
<p>Safety factors of some typical key blocks before force transfer and after force transfer.</p> Full article ">Figure 12
<p>A cross-sectional drawing for the supporting design of the main powerhouse.</p> Full article ">Figure 13
<p>The supporting scheme of rock bolts and sprayed concrete lining and effectiveness verifications: (<b>a</b>) The key blocks in the arch crown; (<b>b</b>) The key blocks in the upstream side wall; (<b>c</b>) The key blocks in the bottom floor; and (<b>d</b>) The key blocks in the downstream side wall.</p> Full article ">
2869 KiB  
Article
Evaluating Heat Flux Profiles in Aluminum Reheating Furnace with Regenerative Burner
by Hang Zhang and Shengxiang Deng
Energies 2017, 10(4), 562; https://doi.org/10.3390/en10040562 - 20 Apr 2017
Cited by 3 | Viewed by 6192
Abstract
Properly understanding heat flux characteristics is a crucial prerequisite to efficiently applying a regenerative burner in an aluminum reheating furnace. A series of experiments was conducted in this study in order to establish a database of the best available burners according to furnace [...] Read more.
Properly understanding heat flux characteristics is a crucial prerequisite to efficiently applying a regenerative burner in an aluminum reheating furnace. A series of experiments was conducted in this study in order to establish a database of the best available burners according to furnace temperature, excess air ratio, and flame combustion mode as they affect heat flux characteristics at the burner plane (Z = 0 mm). A heat flux model was developed to estimate heat transfer in the furnace, and the heat flux proportions of the other two horizontal levels (Z = 400 mm and Z = 750 mm) were investigated. The contour profile of heat flux indicates that total heat flux (THF) and radiation heat flux (RHF) increases with furnace temperature increment (900–1100 °C). Low excess air ratio (1.2–1.3, at furnace temperature 1100 °C) not only reduced the heat flux gradient, but also contributed to enlarge high THF areas and the maximum RHF. The flameless combustion mode displayed larger average THF and RHF uniformity than that of conventional combustion mode. Therefore, the burning effect of operating condition 1 (gas velocity, 90 m/s; excess air ratio, 1.2; flameless combustion) is better than the other conditions. A change of furnace temperature and excess air ratio had mildly effect on convection coefficient, but combustion mode was in contrast. The estimated heat flux distribution from the measured heat flux at the whole burner plane was in agreement with the fitted line of the axis of burner B. Although the intercept of the simulated equation was slightly underestimated, the error can be eliminated by improving the experimental conditions. The results presented here similarly apply to all regenerative burners. A comparison of heat flux among the three horizontal levels indicated that the RHF proportion comprised about 80% of the THF at each level, and a slightly increase (21.1 kW/m2) of THF in the high level from the low levels. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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<p>A photograph of the burning aluminum reheating furnace.</p> Full article ">Figure 2
<p>Orthographic views of the reheating furnace: (<b>a</b>) top view; (<b>b</b>) front view; and (<b>c</b>) side view.</p> Full article ">Figure 3
<p>Schematic of regenerative burner manufactured by NFK type HRS-DF1: (<b>a</b>) firing in flame mode with heating up air (F1 mode); and (<b>b</b>) flameless mode (F2 mode).</p> Full article ">Figure 4
<p>Structure and function of water-cooled heat flux meter.</p> Full article ">Figure 5
<p>The total heat flux distribution at burner horizontal plane: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 5 Cont.
<p>The total heat flux distribution at burner horizontal plane: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 6
<p>The total heat flux at <span class="html-italic">X</span> = 1250 mm and <span class="html-italic">X</span> = 350 mm as function of distance along burner-axis: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 7
<p>The radiation heat flux distribution at burner horizontal plane: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 7 Cont.
<p>The radiation heat flux distribution at burner horizontal plane: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 8
<p>The radiation heat flux at <span class="html-italic">X</span> = 1250 mm and <span class="html-italic">X</span> = 350 mm as function of distance along burner-axis: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 9
<p>The convection heat transfer coefficient variation of six cases.</p> Full article ">Figure 10
<p>Heat flux as function of the distance along burner B axis: (<b>a</b>) OP1; (<b>b</b>) OP2; (<b>c</b>) OP3; (<b>d</b>) OP4; (<b>e</b>) OP5; and (<b>f</b>) OP6.</p> Full article ">Figure 11
<p>The heat flux measured from three height levels at No. 10 hole with standard operating condition.</p> Full article ">
1849 KiB  
Article
Methane Hydrate Formation in Marine Sediment from South China Sea with Different Water Saturations
by Yu Zhang, Xiaosen Li, Yi Wang, Zhaoyang Chen and Gang Li
Energies 2017, 10(4), 561; https://doi.org/10.3390/en10040561 - 20 Apr 2017
Cited by 18 | Viewed by 4559
Abstract
The kinetics of methane hydrate formation in marine sediments with different water saturations are important to assess the feasibility of the hydrate production and understand the process of the secondary hydrate formation in the gas production from hydrate reservoir. In this paper, the [...] Read more.
The kinetics of methane hydrate formation in marine sediments with different water saturations are important to assess the feasibility of the hydrate production and understand the process of the secondary hydrate formation in the gas production from hydrate reservoir. In this paper, the behaviors of methane hydrate formation in marine sediments from the South China Sea at different water saturation levels were experimentally studied in isobaric conditions. The marine sediments used in the experiments have the mean pore diameter of 12.178 nm, total pore volume of 4.997 × 10−2 mL/g and surface area of 16.412 m2/g. The volume fraction of water in the marine sediments ranges from 30% to 50%. The hydrate formation rate and the final water conversion increase with the decrease of the formation temperature at the water saturation of 40%. At the same experimental conditions, the hydrate formation rate decreases with the increase of the water saturation from 40% to 50% due to the reduction of the gas diffusion speed. At the water saturation of 30%, the hydrate formation rate is lower than that at the water saturation of 40% due to the effect of the equilibrium hydrate formation pressure, which increases with the decrease of the water saturation. The final water conversion is shown to increase with the increase of the water saturation, even the formation process at higher water did not end. The experiments at low water saturation show a better repeatability than that at high water saturation. Full article
(This article belongs to the Special Issue Methane Hydrate Research and Development)
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<p>Schematic of the experimental apparatus.</p> Full article ">Figure 2
<p>Particle size distribution of the sample.</p> Full article ">Figure 3
<p>Pressure and temperature changes with time for the experiments at various formation temperatures.</p> Full article ">Figure 4
<p>Comparison of the gas uptake for the experiments at various formation temperatures.</p> Full article ">Figure 5
<p>Comparison of the gas uptake rate for the experiments at various formation temperatures.</p> Full article ">Figure 6
<p>Comparison of gas uptake for the experiments at different water saturations.</p> Full article ">Figure 7
<p>Comparison of temperature change for the experiments at different water saturations.</p> Full article ">Figure 8
<p>Temperature and gas uptake measurement curves of the repeated experiments at the water saturation of 40%.</p> Full article ">Figure 9
<p>Gas uptake measurement curves of the repeated experiments at the water saturation of 50% and the experiment at the water saturation of 45%.</p> Full article ">
16730 KiB  
Article
Flow Behavior and Displacement Mechanisms of Nanoparticle Stabilized Foam Flooding for Enhanced Heavy Oil Recovery
by Teng Lu, Zhaomin Li and Yan Zhou
Energies 2017, 10(4), 560; https://doi.org/10.3390/en10040560 - 20 Apr 2017
Cited by 28 | Viewed by 6469
Abstract
In this study, nanoparticle stabilized foam experiments were performed in bulk tests, micromodels, and sandpacks at elevated temperatures and pressures to investigate the flow behavior and displacement mechanisms for enhanced heavy oil recovery. The results from the bulk tests showed that the stability [...] Read more.
In this study, nanoparticle stabilized foam experiments were performed in bulk tests, micromodels, and sandpacks at elevated temperatures and pressures to investigate the flow behavior and displacement mechanisms for enhanced heavy oil recovery. The results from the bulk tests showed that the stability of the foam and oil in water (O/W) emulsion improved when silica nanoparticles (SiO2) were added, compared with the anionic surfactant alone. Also, the SiO2 nanoparticles increased the dilatational viscoelasticity of the gas-water interface, which is an important fluid property and mechanism for improving heavy oil recovery. The micromodel studies demonstrated that several gas bubbles and oil droplets were stably dispersed during the nanoparticle stabilized foam flooding. The gas bubbles and oil droplets plug pores through capture-plugging and bridge-plugging, thereby increasing the sweep efficiency. The trapped residual oil is gradually pushed to the pores by the elastic forces of bubbles. Subsequently, the residual oil is pulled into oil threads by the flowing gas bubbles. Then, a greater improvement in displacement efficiency is obtained. The sandpack tests showed that the tertiary oil recovery of nanoparticle stabilized foam flooding can reach about 27% using 0.5 wt % SiO2 nanoparticles. The foam slug size of 0.3 pore volume (PV) and the gas liquid ratio (GLR) of 3:1 were found to be the optimum conditions in terms of heavy oil recovery by nanoparticle stabilized foam flooding in this study. A continuous nanoparticle dispersion and N2 could be more effective compared with the cyclic injection pattern. Full article
(This article belongs to the Special Issue Nanotechnology for Oil and Gas Applications)
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<p>(<b>a</b>) Image of the micromodel test experimental setup; (<b>b</b>) a simplified schematic of the micromodel test experimental setup.</p> Full article ">Figure 2
<p>Image of the micromodel.</p> Full article ">Figure 3
<p>Simplified schematic of the sandpack test experimental setup.</p> Full article ">Figure 4
<p>Half-life of various foam systems at different temperatures: 0.5 wt % HY-2 solution and1.0 wt % SiO<sub>2</sub> nanoparticles with 0.5 wt % HY-2 dispersion.</p> Full article ">Figure 5
<p>Relationship of dilational viscoelasticity with the frequency of different systems at 30 °C: 0.5 wt % HY-2 solution/N<sub>2</sub> interface and 1.0 wt % SiO<sub>2</sub> nanoparticles with 0.5 wt % HY-2 aqueous dispersion/N<sub>2</sub> interface.</p> Full article ">Figure 6
<p>Photos of the bottle test for oil and brine at room temperature. (<b>a</b>) Oil and brine before mixing; (<b>b</b>) Oil and brine after being turned upside down five times; (<b>c</b>) Oil and nanoparticle-surfactant before mixing; (<b>d</b>) Oil and nanoparticle-surfactant after being turned upside down five times.</p> Full article ">Figure 7
<p>Half-life of the emulsions at different temperatures (surfactant emulsion, 0.5 wt % HY-2 solution + oil; nanoparticle-surfactant dispersion emulsion, 0.5 wt % HY-2 + 1 wt % nanoparticle + oil).</p> Full article ">Figure 8
<p>Schematic of nanoparticle bridging between oil droplets.</p> Full article ">Figure 9
<p>Nanoparticle-surfactant-stabilized gas bubbles near the inlet of the micromodel. (<b>a</b>) 0.01 PV foam injected; (<b>b</b>) 0.2 PV foam injected.</p> Full article ">Figure 10
<p>Oil droplets in the porous media.</p> Full article ">Figure 11
<p>Flowing gas bubbles accelerating the formation of oil droplets. (<b>a</b>) Gas bubbles deform when in contact with an oil droplet; (<b>b</b>) Oil droplets are formed under the action of gas bubbles.</p> Full article ">Figure 12
<p><b>The</b> micro-elastic force (<span class="html-italic">F</span><sub>e</sub>) of gas bubbles to strip the oil droplet.</p> Full article ">Figure 13
<p>Gas bubbles and oil droplets flowing in the porous media.</p> Full article ">Figure 14
<p>An oil droplet driven by gas bubbles.</p> Full article ">Figure 15
<p>Micro-elastic force (<span class="html-italic">F</span><sub>e</sub>) acting on the flowing oil droplets.</p> Full article ">Figure 16
<p>Capture-plugging in pore-throat. (<b>a</b>) Gas bubble capture-plugging; (<b>b</b>) Oil droplet capture-plugging.</p> Full article ">Figure 17
<p>Force analysis for the capture-plugging of pore-throats with gas bubbles.</p> Full article ">Figure 18
<p>Gas bubble remigration in a pore-throat. (<b>a</b>) 0.251 PV foam injected; (<b>b</b>) 0.262 PV foam injected; (<b>c</b>) 0.271 PV foam injected; (<b>d</b>) 0.275 PV foam injected.</p> Full article ">Figure 19
<p>Force analysis during the remigration of gas bubbles in pore-throats. (<b>a</b>) Capture-plugging; (<b>b</b>) Elastic deformation; (<b>c</b>) Steady migration; (<b>d</b>) Deformation recovery.</p> Full article ">Figure 20
<p>Bridge-plugging in pore-throats. (<b>a</b>) Gas bubble bridge-plugging; (<b>b</b>) Oil droplet bridge-plugging.</p> Full article ">Figure 21
<p>Force analysis for bridge-plugging in pore-throats with gas bubbles.</p> Full article ">Figure 22
<p>Image of residual oil droplets trapped in the sand grains.</p> Full article ">Figure 23
<p>Residual oil droplet trapping after water flooding.</p> Full article ">Figure 24
<p>The micro-elastic force (<span class="html-italic">F</span><sub>e</sub>) acting on residual oil droplets trapped in pore-throats.</p> Full article ">Figure 25
<p>Pore-scale images of the residual oil droplets pushed by the gas bubbles. (<b>a</b>) 0.105 PV; (<b>b</b>) 0.124 PV.</p> Full article ">Figure 26
<p>Pore-level images of the formation of oil threads. (<b>a</b>) 0.108 PV; (<b>b</b>) 0.135 PV.</p> Full article ">Figure 27
<p>Pore-level images of the formation of oil threads in the interchange of pores. (<b>a</b>) 0.148 PV; (<b>b</b>) 0.159 PV.</p> Full article ">Figure 28
<p>The micro-elastic force (<span class="html-italic">F</span><sub>e</sub>) of gas bubbles mobilizing oil droplets.</p> Full article ">Figure 29
<p>The micro-elastic force (<span class="html-italic">F</span><sub>e</sub>) of gas bubbles mobilizing oil droplets at the interchange of pores.</p> Full article ">Figure 30
<p>Effect of the nanoparticle concentration on oil recovery.</p> Full article ">Figure 31
<p>Differential pressure changes as a function of the fluid injected for Runs 1 and 4.</p> Full article ">Figure 32
<p>Effect of foam slug size on oil recovery.</p> Full article ">Figure 33
<p>Effect of the GLR on oil recovery.</p> Full article ">Figure 34
<p>Injection patterns of the nanoparticle dispersion and N<sub>2</sub>.</p> Full article ">Figure 35
<p>Effect of the injection pattern on oil recovery.</p> Full article ">
4483 KiB  
Article
Frequency Control Ancillary Service Provided by Efficient Power Plants Integrated in Queuing-Controlled Domestic Water Heaters
by Yebai Qi, Dan Wang, Xuyang Wang, Hongjie Jia, Tianjiao Pu, Naishi Chen and Kaixin Liu
Energies 2017, 10(4), 559; https://doi.org/10.3390/en10040559 - 19 Apr 2017
Cited by 10 | Viewed by 4251
Abstract
Frequency is an important parameter of a power system. It is of great significance to maintain its stability, especially in the current development scenario of large-scale interconnected power systems. Thermostatically controlled appliances (TCAs) are good controllable resources for demand response owing to their [...] Read more.
Frequency is an important parameter of a power system. It is of great significance to maintain its stability, especially in the current development scenario of large-scale interconnected power systems. Thermostatically controlled appliances (TCAs) are good controllable resources for demand response owing to their rapid response capabilities and relatively wide controllable ranges. In this study, domestic water heaters, which have wider deadbands compared with other typical TCAs, such as heat pumps, are used as frequency regulation resources. The main contribution of this paper is that it proposes a queuing-controlled strategy with lock-on and off constraints for controlling an efficient power plant consisting of water heaters (EPP-WH). The queuing-controlled strategy enables TCAs to provide frequency regulation ancillary service for the normal operation of the power system. The thermal dynamic process of the water heater and the formation of the EPP-WH are first discussed. Based on the developed model, a series of strategies are proposed, including load shedding calculation, top layer optimization, and improved temperature priority list (TPL) strategy with lock-on and off constraints. Finally, typical case studies are discussed to illustrate the frequency regulation effects and the effects of two characteristic parameters—users’ willingness and lock time limits. Reasonable targets are generated based on various consideration from top layer optimization module. The results indicate that using the model and proposed strategies, the EPP-WH has good frequency regulation performance. Full article
(This article belongs to the Special Issue Energy Management Control)
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<p>Thermal dynamic process of single water heater.</p> Full article ">Figure 2
<p>Efficient power plant model.</p> Full article ">Figure 3
<p>Changes in limits taking lock-on and off constraints into account.</p> Full article ">Figure 4
<p>The relationships of related factors and uses’ willingness. (<b>a</b>) Educational level influence; (<b>b</b>) Family income influence; and (<b>c</b>) Family members’ age influence.</p> Full article ">Figure 5
<p>Different periods of frequency response and corresponding resources.</p> Full article ">Figure 6
<p>Flowchart of control structure and strategies.</p> Full article ">Figure 7
<p>Structure of TPL method.</p> Full article ">Figure 8
<p>The 10 generator 39 bus system.</p> Full article ">Figure 9
<p>System frequency without adjustment strategies.</p> Full article ">Figure 10
<p>System frequency with different strategies.</p> Full article ">Figure 11
<p>EPP-WH power with deficient resource.</p> Full article ">Figure 12
<p>EPP-WH power with sufficient resource.</p> Full article ">Figure 13
<p>EPP-WH’s performance of frequency regulation with different users’ willingness.</p> Full article ">Figure 14
<p>EPP-WH’s targets and responses based on different users’ willingness.</p> Full article ">Figure 15
<p>EPP-WH’s performance of frequency regulation without abundant supplemental resources.</p> Full article ">Figure 16
<p>EPP-WH’s operating limits without lock-on and off constraints.</p> Full article ">Figure 17
<p>EPP-WH’s operating status taking lock-on and off constraints into account.</p> Full article ">Figure 18
<p>Response of a single water heater.</p> Full article ">Figure 19
<p>Temperature trajectories of EPP-WH.</p> Full article ">
2453 KiB  
Article
Synchronization of Low-Frequency Oscillation in Power Systems
by Kwan-Shik Shim, Seon-Ju Ahn and Joon-Ho Choi
Energies 2017, 10(4), 558; https://doi.org/10.3390/en10040558 - 19 Apr 2017
Cited by 9 | Viewed by 5069
Abstract
This paper presents the well-documented concept of synchronization of low frequency oscillation occurring in power systems and describes the characteristics of sync occurring in basic electrical circuits. The theory of sync, observed in basic circuits, is extended to analyze the dynamic characteristics of [...] Read more.
This paper presents the well-documented concept of synchronization of low frequency oscillation occurring in power systems and describes the characteristics of sync occurring in basic electrical circuits. The theory of sync, observed in basic circuits, is extended to analyze the dynamic characteristics of low-frequency oscillation in power systems. Full article
(This article belongs to the Special Issue Advances in Power System Operations and Planning)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sync of signal. (<b>a</b>) –sync; and (<b>b</b>) +sync.</p> Full article ">Figure 2
<p>Serial and parallel circuits with two voltage and current sources. (<b>a</b>) Parallel circuit; and (<b>b</b>) serial circuit.</p> Full article ">Figure 3
<p>Voltage-current phase relationship of serial and parallel circuit. (<b>a</b>) +Sync in parallel circuit (large current); (<b>b</b>) −sync in parallel circuit (small current); (<b>c</b>) +sync in serial circuit (small current); and (<b>d</b>) −sync in parallel circuit (large current).</p> Full article ">Figure 4
<p>Local and wide area network.</p> Full article ">Figure 5
<p>Equivalent system with infinite bus.</p> Full article ">Figure 6
<p>Equivalent system with infinite bus.</p> Full article ">Figure 7
<p>Two-area systems (Case 1: 50 MW; and Case 2: 400 MW).</p> Full article ">Figure 8
<p>Sync of generator and bus voltage in two-area system: (<b>a</b>) Case 1 (power flows: 50 MW); and (<b>b</b>) Case 2 (power flows: 400 MW).</p> Full article ">Figure 9
<p>Simplified diagram of the Korea Electric Power Corporation (KEPCO) system.</p> Full article ">Figure 10
<p>Sync of the interarea mode. GR: Gori area generators; US: Ulsan area generators; IC: Incheon area generators; and SIC: Seoincheon area generators.</p> Full article ">Figure 11
<p>Sync of the local mode. IC 1ST: IC steam turbine unit 1; IC 2ST: IC steam turbine unit 2; IC 1GT: IC gas turbine unit 1; IC 3GT: IC gas turbine unit 3; SIC GT1: SIC gas turbine unit 1; and SIC GT4: SIC gas turbine unit 4.</p> Full article ">
15679 KiB  
Article
Deformation Behavior of Hard Roofs in Solid Backfill Coal Mining Using Physical Models
by Nan Zhou, Jixiong Zhang, Hao Yan and Meng Li
Energies 2017, 10(4), 557; https://doi.org/10.3390/en10040557 - 18 Apr 2017
Cited by 44 | Viewed by 5386
Abstract
Solid backfill coal mining technology has been widely applied in coal seams that are at risk of hard roof. Using actual measured strain–stress curves of the backfill body and the similarity theory, this study designed and employed four experimental models for physical simulation, [...] Read more.
Solid backfill coal mining technology has been widely applied in coal seams that are at risk of hard roof. Using actual measured strain–stress curves of the backfill body and the similarity theory, this study designed and employed four experimental models for physical simulation, corresponding to roof-controlled backfilling ratios of 0%, 40%, 82.5% and 97% using the geological conditions of Face No. 6304 in the Jining No. 3 coal mine—a solid backfill coal mining face under a hard roof. A non-contact strain measurement system and pressure sensors were used to monitor the deformation of the overlying strata and changes in abutment stress ahead of the face during mining of the models for varying roof-controlled backfilling ratios. The results indicated that the solid backfill body was able to support the roof. As the roof-controlled backfilling ratio was increased, the maximum subsidence of the roof and the maximum height of the cracks decreased. When the roof-controlled backfilling ratio was 82.5% or higher, the working face did not display any obvious initial fractures or periodic fractures, and both the value and the impact range of the abutment stress ahead of the face decreased. Full article
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<p>Backfilling hydraulic support at the working face.</p> Full article ">Figure 2
<p>Mine layout around Panel No. 6304-1.</p> Full article ">Figure 3
<p>Non-contact strain measurement system. (<b>a</b>) Monitoring equipment and its placement; (<b>b</b>) Data processing system.</p> Full article ">Figure 4
<p>Completed model.</p> Full article ">Figure 5
<p>The model mining and backfilling sequence and the distribution of pressure sensors.</p> Full article ">Figure 6
<p>The universal material testing system and specially-made compact steel cylinder.</p> Full article ">Figure 7
<p>Diagram and field photo of the material simulating the backfill body.</p> Full article ">Figure 8
<p>Comparisons of strain–stress curves between similar materials and backfilling bodies.</p> Full article ">Figure 9
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 157.5 m with a roof-controlled backfilling ratio of 0 (caving method).</p> Full article ">Figure 10
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 217.5 m with a roof-controlled backfilling ratio of 0 (caving method).</p> Full article ">Figure 11
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 270 m with a roof-controlled backfilling ratio of 0 (caving method). (<b>a</b>) Deformation of overlying strata; (<b>b</b>) Displacement distribution cloud graph of overlying strata.</p> Full article ">Figure 12
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 165 m with a roof-controlled backfilling ratio of 40%.</p> Full article ">Figure 13
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 225 m with a roof-controlled backfilling ratio of 40%.</p> Full article ">Figure 14
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 270 m with a roof-controlled backfilling ratio of 40%. (<b>a</b>) Deformation of overlying strata; (<b>b</b>) Displacement distribution cloud graph of overlying strata.</p> Full article ">Figure 14 Cont.
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 270 m with a roof-controlled backfilling ratio of 40%. (<b>a</b>) Deformation of overlying strata; (<b>b</b>) Displacement distribution cloud graph of overlying strata.</p> Full article ">Figure 15
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 150 m with a roof-controlled backfilling ratio of 82.5%.</p> Full article ">Figure 16
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 270 m with a roof-controlled backfilling ratio of 82.5%. (<b>a</b>) Deformation of overlying strata; (<b>b</b>) Displacement distribution cloud graph of overlying strata.</p> Full article ">Figure 17
<p>Deformation and displacement distribution cloud graph of overlying strata when the mining had advanced to 270 m with a roof-controlled backfilling ratio of 97%. (<b>a</b>) Deformation of overlying strata; (<b>b</b>) Displacement distribution cloud graph of overlying strata.</p> Full article ">Figure 18
<p>Stabilized subsidence curves for different roof-controlled backfilling ratios.</p> Full article ">Figure 19
<p>Distribution of abutment stress ahead of the face for different roof-controlled backfilling ratios. (<b>a</b>) 0%; (<b>b</b>) 40%; (<b>c</b>) 82.5%; and (<b>d</b>) 97%.</p> Full article ">Figure 20
<p>Variation of the mass ratio of backfilled waste to mined coal.</p> Full article ">Figure 21
<p>Measured roof subsidence in the backfilling area.</p> Full article ">Figure 22
<p>Measured abutment pressures at the 120 m station.</p> Full article ">
1997 KiB  
Article
Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions
by Ju Tang, Xu Yang, Gaoxiang Ye, Qiang Yao, Yulong Miao and Fuping Zeng
Energies 2017, 10(4), 556; https://doi.org/10.3390/en10040556 - 18 Apr 2017
Cited by 21 | Viewed by 5081
Abstract
Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition [...] Read more.
Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF6 in diagnosing the type and severity of insulation fault in DC SF6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF6 gas and generate five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentration, effective formation rate, and concentration ratio of SF6 decomposed components can be associated with the PD types. Furthermore, back propagation neural network algorithm is used to recognize the PD types. The recognition results show that compared with the concentrations of SF6 decomposed components, their concentration ratios are more suitable as the characteristic quantities for PD recognition, and using those concentration ratios in recognizing the PD types can obtain a good effect. Full article
(This article belongs to the Special Issue Selected Papers from 2016 IEEE International Conference on High Voltage Engineering (ICHVE 2016))
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<p>Experimental wiring of SF<sub>6</sub> decomposition under negative DC-PD.</p> Full article ">Figure 2
<p>Structure of the gas chamber: 1 stainless-steel shell; 2 top cover; 3 HV bushing; 4 HV conductor; 5 flange; 6 screw; 7 ball valve; 8 vacuum pressure gauge; 9 vacuum pump; 10 injection port; 11 supporting foot; 12 epoxy loop; 13 ground conductor; 14 insulation defect; and 15 sampling port.</p> Full article ">Figure 3
<p>Typical defects in DC-GIE.</p> Full article ">Figure 4
<p>Four kinds of insulation defect models (Unit: mm): (<b>a</b>) Protrusion defect; (<b>b</b>) Particle defect; (<b>c</b>) Pollution defect; and (<b>d</b>) Gap defect.</p> Full article ">Figure 5
<p>Change curves of the concentrations of CF<sub>4</sub> and CO<sub>2</sub> with time: (<b>a</b>) CF<sub>4</sub>; and (<b>b</b>) CO<sub>2</sub>.</p> Full article ">Figure 6
<p>Change curves of the concentrations of SO<sub>2</sub>F<sub>2</sub>, SOF<sub>2</sub>, and SO<sub>2</sub> with time: (<b>a</b>) SO<sub>2</sub>F<sub>2</sub>; (<b>b</b>) SO<sub>2</sub>F<sub>2</sub>; and (<b>c</b>) SO<sub>2</sub>.</p> Full article ">Figure 7
<p>Formation process of SF<sub>6</sub> decomposed components.</p> Full article ">Figure 8
<p>Change curves of the concentration ratios of SF<sub>6</sub> decomposed components with time: (<b>a</b>) <span class="html-italic">C</span>(SOF<sub>2</sub>)/<span class="html-italic">C</span>(SO<sub>2</sub>F<sub>2</sub>); (<b>b</b>) Ln(<span class="html-italic">C</span>(CO<sub>2</sub>)/<span class="html-italic">C</span>(CF<sub>4</sub>)); and (<b>c</b>) <span class="html-italic">C</span>(SOF<sub>2</sub> + SO<sub>2</sub>F<sub>2</sub> + SO<sub>2</sub>)/<span class="html-italic">C</span>(CF<sub>4</sub> + CO<sub>2</sub>).</p> Full article ">Figure 9
<p>Structure of BP neural network.</p> Full article ">
4843 KiB  
Article
Power-Smoothing Scheme of a DFIG Using the Adaptive Gain Depending on the Rotor Speed and Frequency Deviation
by Hyewon Lee, Min Hwang, Eduard Muljadi, Poul Sørensen and Yong Cheol Kang
Energies 2017, 10(4), 555; https://doi.org/10.3390/en10040555 - 18 Apr 2017
Cited by 8 | Viewed by 4290
Abstract
In an electric power grid that has a high penetration level of wind, the power fluctuation of a large-scale wind power plant (WPP) caused by varying wind speeds deteriorates the system frequency regulation. This paper proposes a power-smoothing scheme of a doubly-fed induction [...] Read more.
In an electric power grid that has a high penetration level of wind, the power fluctuation of a large-scale wind power plant (WPP) caused by varying wind speeds deteriorates the system frequency regulation. This paper proposes a power-smoothing scheme of a doubly-fed induction generator (DFIG) that significantly mitigates the system frequency fluctuation while preventing over-deceleration of the rotor speed. The proposed scheme employs an additional control loop relying on the system frequency deviation that operates in combination with the maximum power point tracking control loop. To improve the power-smoothing capability while preventing over-deceleration of the rotor speed, the gain of the additional loop is modified with the rotor speed and frequency deviation. The gain is set to be high if the rotor speed and/or frequency deviation is large. The simulation results based on the IEEE 14-bus system clearly demonstrate that the proposed scheme significantly lessens the output power fluctuation of a WPP under various scenarios by modifying the gain with the rotor speed and frequency deviation, and thereby it can regulate the frequency deviation within a narrow range. Full article
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<p>Typical topology and pitch control scheme of a doubly-fed induction generator (DFIG). (<b>a</b>) Typical configuration of a DFIG; (<b>b</b>) Electrical equivalent circuit of an induction generator; (<b>c</b>) Simplified rotor-side converter (RSC) control scheme; (<b>d</b>) Simplified grid-side converter (GSC) control scheme; (<b>e</b>) Pitch-angle control scheme used in this paper. MPPT: maximum power point tracking.</p> Full article ">Figure 2
<p>Input–output power characteristics of a DFIG.</p> Full article ">Figure 3
<p>Conventional power-smoothing scheme in [<a href="#B12-energies-10-00555" class="html-bibr">12</a>].</p> Full article ">Figure 4
<p>Proposed power-smoothing scheme of a DFIG. (<b>a</b>) Proposed power-smoothing scheme; (<b>b</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) represented in the three-dimensional space used in this paper.</p> Full article ">Figure 4 Cont.
<p>Proposed power-smoothing scheme of a DFIG. (<b>a</b>) Proposed power-smoothing scheme; (<b>b</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) represented in the three-dimensional space used in this paper.</p> Full article ">Figure 5
<p>IEEE 14-bus system with two wind power plants (WPPs).</p> Full article ">Figure 6
<p>Input wind speeds for WPPs.</p> Full article ">Figure 7
<p>Results for Case 1. (<b>a</b>) Frequency; (<b>b</b>) Total output power of two WPPs; (<b>c</b>) Output power of WPP<sub>1</sub>; (<b>d</b>) Output power of WPP<sub>2</sub>; (<b>e</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>1</sub>; (<b>f</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>2</sub>; (<b>g</b>) Δ<span class="html-italic">P</span> of WPP<sub>1</sub>; (<b>h</b>) Δ<span class="html-italic">P</span> of WPP<sub>2</sub>; (<b>i</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>1</sub>; (<b>j</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>2</sub>; (<b>k</b>) Rotor speed of WPP<sub>1</sub>; (<b>l</b>) Rotor speed of WPP<sub>2</sub>; (<b>m</b>) Pitch angle of WPP<sub>1</sub>.</p> Full article ">Figure 8
<p>Results for Case 2. (<b>a</b>) Frequency; (<b>b</b>) Total output power of two WPPs; (<b>c</b>) Output power of WPP<sub>1</sub>; (<b>d</b>) Output power of WPP<sub>2</sub>; (<b>e</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>1</sub>; (<b>f</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>2</sub>; (<b>g</b>) Δ<span class="html-italic">P</span> of WPP<sub>1</sub>; (<b>h</b>) Δ<span class="html-italic">P</span> of WPP<sub>2</sub>; (<b>i</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>1</sub>; (<b>j</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>2</sub>; (<b>k</b>) Rotor speed of WPP<sub>1</sub>; (<b>l</b>) Rotor speed of WPP<sub>2</sub>; (<b>m</b>) Pitch angle of WPP<sub>1</sub>.</p> Full article ">Figure 8 Cont.
<p>Results for Case 2. (<b>a</b>) Frequency; (<b>b</b>) Total output power of two WPPs; (<b>c</b>) Output power of WPP<sub>1</sub>; (<b>d</b>) Output power of WPP<sub>2</sub>; (<b>e</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>1</sub>; (<b>f</b>) <span class="html-italic">P<sub>MPPT</sub></span> of WPP<sub>2</sub>; (<b>g</b>) Δ<span class="html-italic">P</span> of WPP<sub>1</sub>; (<b>h</b>) Δ<span class="html-italic">P</span> of WPP<sub>2</sub>; (<b>i</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>1</sub>; (<b>j</b>) <span class="html-italic">K</span> (<span class="html-italic">ω<sub>r</sub></span>, Δ<span class="html-italic">f</span>) of WPP<sub>2</sub>; (<b>k</b>) Rotor speed of WPP<sub>1</sub>; (<b>l</b>) Rotor speed of WPP<sub>2</sub>; (<b>m</b>) Pitch angle of WPP<sub>1</sub>.</p> Full article ">
2858 KiB  
Article
A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration
by Yuewen Jiang, Meisen Chen and Shi You
Energies 2017, 10(4), 554; https://doi.org/10.3390/en10040554 - 18 Apr 2017
Cited by 14 | Viewed by 3783
Abstract
In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast [...] Read more.
In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO) is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO). Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Frequency distribution histogram of <span class="html-italic">X<sub>i</sub></span>(2).</p> Full article ">Figure 2
<p>Improved frequency distribution histogram of <span class="html-italic">X<sub>i</sub></span>(2).</p> Full article ">Figure 3
<p>Hourly load and wind power forecasts.</p> Full article ">Figure 4
<p>Purchase and sale power price forecasts in the real-time market.</p> Full article ">Figure 5
<p>Standard deviations of the hourly load forecast and wind power forecast.</p> Full article ">Figure 6
<p>Thermal generators’ quotation parameter <span class="html-italic">b<sub>j</sub></span>(<span class="html-italic">t</span>).</p> Full article ">Figure 7
<p>Thermal generators’ quotation parameter <span class="html-italic">a<sub>j</sub></span>(<span class="html-italic">t</span>).</p> Full article ">Figure 8
<p>Convergence curves of the two algorithms (QPSO <b>left</b> IQPSO <b>right</b>).</p> Full article ">Figure 9
<p>Impact of real-time prices on <math display="inline"> <semantics> <msubsup> <mi>P</mi> <mi>r</mi> <mi>o</mi> </msubsup> </semantics> </math>(<span class="html-italic">t</span>) in the day-ahead market.</p> Full article ">Figure 10
<p><math display="inline"> <semantics> <msubsup> <mi>P</mi> <mi>r</mi> <mi>o</mi> </msubsup> </semantics> </math>(<span class="html-italic">t</span>) in the day-ahead market when <span class="html-italic">σ<sub>r</sub></span>, <span class="html-italic">Γ</span>, and <span class="html-italic">e<sub>r</sub></span> change, respectively.</p> Full article ">Figure 11
<p>The impact of <span class="html-italic">Γ</span> on the total cost.</p> Full article ">
3176 KiB  
Article
Maximum Boost Control Method for Single-Phase Quasi-Switched-Boost and Quasi-Z-Source Inverters
by Minh-Khai Nguyen and Youn-Ok Choi
Energies 2017, 10(4), 553; https://doi.org/10.3390/en10040553 - 18 Apr 2017
Cited by 20 | Viewed by 6813
Abstract
The maximum boost control method for a single-phase switched-boost inverter (SBI) and single-phase Z-source inverter (ZSI) is proposed in this paper. In the proposed method, the low frequency voltage is added to the constant voltage for generating the variable shoot-through time intervals. For [...] Read more.
The maximum boost control method for a single-phase switched-boost inverter (SBI) and single-phase Z-source inverter (ZSI) is proposed in this paper. In the proposed method, the low frequency voltage is added to the constant voltage for generating the variable shoot-through time intervals. For improving the AC output quality of the inverter, an active power switch is used to replace one of the diodes in the single-phase SBI. The operating principles and circuit analysis using the proposed maximum boost control method for single-phase inverters are presented. Laboratory prototypes are built to verify the operation of the proposed pulse-width modulation (PWM) control method for both single-phase quasi-ZSI and single-phase quasi-SBI. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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<p>Single-phase quasi-Z-source inverter.</p> Full article ">Figure 2
<p>Single-phase qSBIs (<b>a</b>) type 1 and (<b>b</b>) type 2.</p> Full article ">Figure 3
<p>Operating states of qSBI type 2 under small shoot-through duty cycle; (<b>a</b>) shoot-through; (<b>b</b>) non-shoot-through when <span class="html-italic">i<sub>L</sub></span> &gt; <span class="html-italic">i<sub>PN</sub></span> and (<b>c</b>) non-shoot-through when <span class="html-italic">i<sub>L</sub></span> = <span class="html-italic">i<sub>PN</sub></span>.</p> Full article ">Figure 4
<p>Experimental waveforms for qSBI when <span class="html-italic">V<sub>dc</sub></span> = 120 V, <span class="html-italic">D</span> = 0.2, and <span class="html-italic">M</span> = 0.8. From top to bottom: (<b>a</b>) load current, inductor current, capacitor voltage, DC-link voltage; (<b>b</b>) capacitor current, capacitor voltage, inductor current, DC-link voltage; and (<b>c</b>) load current harmonics.</p> Full article ">Figure 4 Cont.
<p>Experimental waveforms for qSBI when <span class="html-italic">V<sub>dc</sub></span> = 120 V, <span class="html-italic">D</span> = 0.2, and <span class="html-italic">M</span> = 0.8. From top to bottom: (<b>a</b>) load current, inductor current, capacitor voltage, DC-link voltage; (<b>b</b>) capacitor current, capacitor voltage, inductor current, DC-link voltage; and (<b>c</b>) load current harmonics.</p> Full article ">Figure 5
<p>Improved single-phase qSBIs; (<b>a</b>) type 1 and (<b>b</b>) type 2.</p> Full article ">Figure 6
<p>Maximum boost control method for modulation index improvement.</p> Full article ">Figure 7
<p>Experimental prototype in the laboratory.</p> Full article ">Figure 8
<p>From top to bottom: (<b>a</b>) load current, inductor current, capacitor voltage, DC-link voltage; (<b>b</b>) capacitor current, capacitor voltage, inductor current, DC-link voltage; and (<b>c</b>) load current harmonics.</p> Full article ">Figure 9
<p>Experimental waveforms for qSBI when <span class="html-italic">V<sub>dc</sub></span> = 120 V, <span class="html-italic">M</span> = 0.8 and <span class="html-italic">A</span> = 0.01. From top to bottom: (<b>a</b>) load current, inductor current, capacitor voltage, DC-link voltage; (<b>b</b>) capacitor current, capacitor voltage, inductor current, DC-link voltage; and (<b>c</b>) load current harmonics.</p> Full article ">Figure 10
<p>Experimental waveforms for a qZSI when <span class="html-italic">V<sub>dc</sub></span> = 120 V, <span class="html-italic">M</span> = 0.75, and <span class="html-italic">A</span> = 0.01. From top to bottom: (<b>a</b>) load current, inductor current, capacitor voltage, DC-link voltage; (<b>b</b>) capacitor current, capacitor voltage, inductor current, DC-link voltage; and (<b>c</b>) load current harmonics.</p> Full article ">
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Article
A Fast-Acting Diagnostic Algorithm of Insulated Gate Bipolar Transistor Open Circuit Faults for Power Inverters in Electric Vehicles
by Lei Yu, Youtong Zhang, Wenqing Huang and Khaled Teffah
Energies 2017, 10(4), 552; https://doi.org/10.3390/en10040552 - 18 Apr 2017
Cited by 12 | Viewed by 6576
Abstract
To improve the diagnostic detection speed in electric vehicles, a novel diagnostic algorithm of insulated gate bipolar transistor (IGBT) open circuit faults for power inverters is proposed in this paper. The average of the difference between the actual three-phase current and referential three-phase [...] Read more.
To improve the diagnostic detection speed in electric vehicles, a novel diagnostic algorithm of insulated gate bipolar transistor (IGBT) open circuit faults for power inverters is proposed in this paper. The average of the difference between the actual three-phase current and referential three-phase current values over one electrical period is used as the diagnostic variable. The normalization method based on the amplitude of the d-q axis referential current is applied to the diagnostic variables to improve the response speed of diagnosis, and to avoid the noise and the delay caused by signal acquisition. In the parameter discretization process, the variable parameter moving average method (VPMAM) is adopted to solve the variation of the average value over a period with the speed of the motor; hence, the diagnostic reliability of the system is improved. This algorithm is robust, independent of load variations, and has a high resistivity against false alarms. Since only the three-phase current of the motor is utilized for calculations in the time domain, a fast diagnostic detection speed can be achieved, which is significantly essential for real-time control in electric vehicles. The effectiveness of the proposed algorithm is verified by both simulation and experimental results. Full article
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
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<p>Structure of the three-phase inverter.</p> Full article ">Figure 2
<p>The current of phase A with an open circuit fault in T1 at 1000 rpm/100 Nm.</p> Full article ">Figure 3
<p>Trajectory of the current with no fault at 1000 rpm/100 Nm.</p> Full article ">Figure 4
<p>Trajectory of stator current with an open circuit fault at 1000 rpm/100 Nm: (<b>a</b>) Open circuit fault in T1; (<b>b</b>) Open circuit fault in T2; (<b>c</b>) Open circuit fault in T3; (<b>d</b>) Open circuit fault in T4; (<b>e</b>) Open circuit fault in T5; and (<b>f</b>) Open circuit fault in T6.</p> Full article ">Figure 5
<p>Diagram of the proposed open circuit fault diagnostic algorithm.</p> Full article ">Figure 6
<p>Simulation model.</p> Full article ">Figure 7
<p>Comparison between the two diagnostic variables.</p> Full article ">Figure 8
<p>Diagnostic results at 2000 rpm/358 Nm.</p> Full article ">Figure 9
<p>Diagnostic results at 2000 rpm/36 Nm.</p> Full article ">Figure 10
<p>Diagnostic results at 600 rpm/358 Nm.</p> Full article ">Figure 11
<p>Diagnostic results at 2800 rpm/256 Nm.</p> Full article ">Figure 12
<p>Diagnostic results under a sudden change of load torque conditions.</p> Full article ">Figure 13
<p>Experimental Bench.</p> Full article ">Figure 14
<p>Experimental results at 600 rpm/358 Nm.</p> Full article ">Figure 15
<p>Experimental results under a sudden change of load torque conditions.</p> Full article ">
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