[go: up one dir, main page]
Next Issue
Volume 11, October
Previous Issue
Volume 11, August
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Volume 11
Issue 9
energies-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energies, Volume 11, Issue 9 (September 2018) – 313 articles

Cover Story (view full-size image): The article investigates the performance of cooling systems in the Polish hard-coal mining industry. A study conducted in six hard-coal mines revealed that despite implementing air conditioning systems, the permitted air temperature value of 28 °C was exceeded. The article mainly presents the cooling performance of movable spot air-coolers. The obtained results have allowed identifying the factors that diminish the efficiency of cooling under analysis. These factors can be treated on equal terms with key performance indicators, which can be used to monitor the performance of cooling system components and provide managers with high-level indicators on which decisions can be based. View this paper.
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
14 pages, 2628 KiB  
Article
Environmentally Friendly Compact Air-Insulated High-Voltage Substations
by Maurizio Albano, A. Manu Haddad, Huw Griffiths and Paul Coventry
Energies 2018, 11(9), 2492; https://doi.org/10.3390/en11092492 - 19 Sep 2018
Cited by 12 | Viewed by 6145
Abstract
This paper investigates the possible options for achieving a substantial reduction in a substation footprint using air-insulated switchgear as a more environmentally-friendly alternative to gas-insulated substations that use SF6 gas. Adopting a new approach to surge arrester location and numbers, International Electrotechnical [...] Read more.
This paper investigates the possible options for achieving a substantial reduction in a substation footprint using air-insulated switchgear as a more environmentally-friendly alternative to gas-insulated substations that use SF6 gas. Adopting a new approach to surge arrester location and numbers, International Electrotechnical Commission (IEC) minimum clearances can be successfully selected instead of the maximum clearances as currently adopted by many utilities, as is the case in the UK. In addition, innovative alternative compact busbar arrangements using vertical and delta configurations have been proposed by the authors. A further opportunity for compaction is offered by the application of compact and integrated technology offered from several manufacturers. The full overvoltage control within the entire substation under any surge condition is a key aspect of the feasibility of this type of substation. This work demonstrates that the new design option can be an attractive alternative for future substation configuration with minimum footprint. Full article
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
►▼ Show Figures

Figure 1

Figure 1
<p>Evolution of UK HV minimum clearances and the proposed one [<a href="#B7-energies-11-02492" class="html-bibr">7</a>].</p> Full article ">Figure 2
<p>Busbar dimensions.</p> Full article ">Figure 3
<p>Compact delta (<b>a</b>) and vertical (<b>b</b>) busbar arrangements and the computed electrical potential on a vertical section (<b>c</b>) and (<b>d</b>) respectively.</p> Full article ">Figure 3 Cont.
<p>Compact delta (<b>a</b>) and vertical (<b>b</b>) busbar arrangements and the computed electrical potential on a vertical section (<b>c</b>) and (<b>d</b>) respectively.</p> Full article ">Figure 4
<p>Dimensions of delta (<b>a</b>) and vertical (<b>b</b>) busbar arrangements [<a href="#B14-energies-11-02492" class="html-bibr">14</a>].</p> Full article ">Figure 5
<p>Typical line-entry bay configuration. Lateral view (<b>a</b>) and the indication of volume occupation and longitudinal clearances between equipment (<b>b</b>).</p> Full article ">Figure 5 Cont.
<p>Typical line-entry bay configuration. Lateral view (<b>a</b>) and the indication of volume occupation and longitudinal clearances between equipment (<b>b</b>).</p> Full article ">Figure 6
<p>Proposed line entry compact bay configuration.</p> Full article ">Figure 7
<p>Single line diagram of a double busbar substation (400/132 kV). V1 to V8 nodes indicating the possible location of the SA installation.</p> Full article ">
16 pages, 3632 KiB  
Article
Impact of Forest Fuels on Gas Emissions in Coal Slurry Fuel Combustion
by Galina Nyashina and Pavel Strizhak
Energies 2018, 11(9), 2491; https://doi.org/10.3390/en11092491 - 19 Sep 2018
Cited by 17 | Viewed by 3589
Abstract
Anthropogenic emissions from coal combustion pose a serious threat to human wellbeing. One prospective way to solve this problem is by using slurry fuels instead of coal. The problem is especially pressing in China and Russia, so these countries need reliable experimental data [...] Read more.
Anthropogenic emissions from coal combustion pose a serious threat to human wellbeing. One prospective way to solve this problem is by using slurry fuels instead of coal. The problem is especially pressing in China and Russia, so these countries need reliable experimental data on the SOx and NOx emissions reduction range more than others do. The experiments in this research are based on the components that are typical of Russia. Experimental research was conducted on the way typical forest fuels (ground pine needles, leaves and their mixtures, bark, sawdust, and charcoal) affect the gas emissions from the combustion of slurry fuels based on the wastes. It was established that using forest fuels as additives to coal-water slurries reduces SOx and NOx emissions by 5–91% as compared to coal or to slurries based on used turbine oil. It was revealed that even small concentrations of such additives (7–15%) could result in a several-fold reduction in SOx and NOx. The higher the temperature, the more prominent the role of forest biomass. The calculated complex criterion illustrates that forest fuels increase the performance indicator of fuel suspensions by 1.2–10 times. Full article
(This article belongs to the Special Issue Sustainability of Fossil Fuels)
►▼ Show Figures

Figure 1

Figure 1
<p>SO<sub>x</sub> concentrations at the coal-water slurry (CWS) and coal-water slurry containing petrochemicals (CWSP) (with leaves, needles, or their mixtures) combustion.</p> Full article ">Figure 2
<p>NO<sub>x</sub> concentrations at the CWS and CWSP (with leaves, needles, or their mixtures) combustion.</p> Full article ">Figure 3
<p>Relative performance indicators (<span class="html-italic">D<sub>relative</sub></span>) of burning high-potential CWSP fuels containing leaves, needles, or their mixture vs. coal at varying temperatures in the combustion chamber.</p> Full article ">Figure 4
<p>SO<sub>x</sub> concentrations at the CWS and CWSP (with bark, sawdust, charcoal) combustion.</p> Full article ">Figure 5
<p>NO<sub>x</sub> concentrations at the CWS and CWSP (with bark, sawdust, charcoal) combustion.</p> Full article ">Figure 6
<p>Relative performance indicators (<span class="html-italic">D<sub>relative</sub></span>) of burning high-potential CWSP fuels containing bark, sawdust, or charcoal vs. coal at varying temperatures in the combustion chamber.</p> Full article ">
22 pages, 8679 KiB  
Article
A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers
by Tao Lei, Weiwei Tan, Guangsi Chen and Delin Kong
Energies 2018, 11(9), 2490; https://doi.org/10.3390/en11092490 - 19 Sep 2018
Cited by 2 | Viewed by 4162
Abstract
This paper presents a novel Model Predictive Direct Power Control (MPDPC) approach for the pulse width modulation (PWM) rectifiers in the Aircraft Alternating Current Variable Frequency (ACVF) power system. The control performance of rectifiers may be largely affected by variations in the AC [...] Read more.
This paper presents a novel Model Predictive Direct Power Control (MPDPC) approach for the pulse width modulation (PWM) rectifiers in the Aircraft Alternating Current Variable Frequency (ACVF) power system. The control performance of rectifiers may be largely affected by variations in the AC side impedance, especially for systems with limited power volume system. A novel idea for estimating the impedance variation based on the Bayesian estimation, using an algorithm embedded in MPDPC is presented in this paper. The input filter inductance and its equivalent series resistance (ESR) of PWM rectifiers are estimated in this algorithm by measuring the input current and input voltage in each cycle with the probability Bayesian estimation theory. This novel estimation method can overcome the shortcomings of traditional data based estimation methods such as least square estimation (LSE), which achieves poor estimation results with the small samples data set. In ACVF systems, the effect on the parameters estimation accuracy caused by the number of sampling points in one cycle is also analyzed in detail by simulation. The validity of this method is verified by the digital and Hard-in-loop simulation compared with other estimation methods such as the least square estimation method. The experimental testing results show that the proposed estimation algorithm can improve the robustness and the control performance of the MPDPC under the condition of the uncertainty of the AC side parameters of the three-phase PWM rectifiers in aircraft electrical power system. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>The topology of three-phase pulse width modulation (PWM) rectifier.</p> Full article ">Figure 2
<p>Power prediction under the influence of model parameter errors.</p> Full article ">Figure 3
<p>The characteristics of (<b>a</b>) inductance changed with the currents and (<b>b</b>) permeability of the iron magnetic material under different temperatures.</p> Full article ">Figure 4
<p>The histogram of the inductance value (<b>a</b>) and probability density distribution function (<b>b</b>).</p> Full article ">Figure 5
<p>Block diagram of the proposed on-line parameter estimation algorithm with (model Predictive Direct Power Control) MPDPC.</p> Full article ">Figure 6
<p>The flow chart of MPDPC control algorithm with Bayesian estimation.</p> Full article ">Figure 7
<p>AC side voltage and current at inductance parameter changes (<b>a</b>) Traditional MPDPC; (<b>b</b>) Bayesian estimation algorithm with MPDPC.</p> Full article ">Figure 8
<p>The harmonic component of input current. (<b>a</b>) Traditional MPDPC; (<b>b</b>) MPDPC with Bayesian Estimation Algorithm.</p> Full article ">Figure 9
<p>The estimated value of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> </mrow> </semantics></math> from the different estimation methods. (<b>a</b>) Estimated value of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) Estimated value of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>The estimated value of and in different samples with Bayesian estimation method (<math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>2</mn> <mtext> </mtext> <mi mathvariant="sans-serif">mH</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.1</mn> <mtext> </mtext> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math>) (<b>a</b>) Estimated value of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) Estimated value of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Current harmonic content and power factor as inductance parameter changes with different MPDPC methods. (<b>a</b>) Current harmonic content under the parameter’s variation; (<b>b</b>) Power factor under the inductance value’s variation.</p> Full article ">Figure 12
<p>THD of current when adding white noise as inductance parameter changes. (<b>a</b>) THD of current after adding noise; (<b>b</b>) THD of current before and after adding noise.</p> Full article ">Figure 13
<p>Instantaneous load-unload simulation results by MPDPC with Bayesian estimation method. (<b>a</b>) active power; (<b>b</b>) reactive power; (<b>c</b>) DC bus voltage.</p> Full article ">Figure 14
<p>Typhoon semi-physical test platform diagram and practicality. (<b>a</b>) The basic theoretical diagram for the HIL system; (<b>b</b>) The hardware configuration for the HIL-platform.</p> Full article ">Figure 15
<p>AC side voltage and current at inductance parameter changes (<b>a</b>) Traditional MPC (<b>b</b>) Online parameter estimation algorithm of MPDPC.</p> Full article ">Figure 16
<p>Current harmonic content and power factor in semi-physical test platform under the variation of inductance value. (<b>a</b>) AC Current harmonic content; (<b>b</b>) AC side Power factor.</p> Full article ">Figure 17
<p>Instantaneous load-unload experiment results. (<b>a</b>) Active power; (<b>b</b>) Reactive power; (<b>c</b>) DC bus voltage.</p> Full article ">Figure 18
<p>The hardware prototype system.</p> Full article ">Figure 19
<p>The input voltage and current waveform during variable frequency conditions. (<b>a</b>) The waveform of rectifiers operated at 360 Hz; (<b>b</b>) The waveform of rectifiers operated at 800 Hz.</p> Full article ">Figure 20
<p>The output dc voltage, input voltage and current waveform underthe input voltagetransient variation by the MPDPC with the Bayesian estimation. (<b>a</b>) The waveform during input voltage drop; (<b>b</b>) The waveform during input voltage swell.</p> Full article ">Figure 21
<p>The transient waveforms under the dynamic load power variation. (<b>a</b>) The transient waveform of load power from 2.07 KW to 1.08 KW; (<b>b</b>) The transient waveform of load power from 1.38 KW to 3.45 KW.</p> Full article ">Figure 22
<p>The Bar diagram of performance index under three kinds of MPC control scheme. (<b>a</b>) The THD of input current; (<b>b</b>) the power factor of rectifiers.</p> Full article ">
25 pages, 1803 KiB  
Article
A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya
by Dragan Pamučar, Ibrahim Badi, Korica Sanja and Radojko Obradović
Energies 2018, 11(9), 2489; https://doi.org/10.3390/en11092489 - 19 Sep 2018
Cited by 86 | Viewed by 5022
Abstract
Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy [...] Read more.
Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves. This paper introduces an original multicriteria decision-making Pairwise-CODAS model in which the modification of the CODAS method was made using Linguistic Neutrosophic Numbers (LNN). The paper also suggests a new LNN Pairwise (LNN PW) model for determining the weight coefficients of the criteria developed by the authors. By integrating these models with linguistic neutrosophic numbers, it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. The LNN PW-CODAS model was tested and validated in a case study of the selection of optimal Power-Generation Technology (PGT) in Libya. Testing of the model showed that the proposed model based on linguistic neutrosophic numbers provides objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN PW-CODAS model, carried out through 68 scenarios of changes in the weight coefficients, showed a high degree of stability of the solutions obtained in the ranking of the alternatives. The results were validated by comparison with LNN extensions of four multicriteria decision-making models. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Electricity demand between 2014 and 2016.</p> Full article ">Figure 2
<p>Libyan crude-oil production from 2012 to 2017 [<a href="#B24-energies-11-02489" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>An analysis of the change in the ranking of alternatives through 68 scenarios.</p> Full article ">Figure 4
<p>Values of Spearman’s coefficient through 68 scenarios.</p> Full article ">Figure 5
<p>Comparison of ranking of alternatives according to methods.</p> Full article ">Figure 6
<p>Values of the Spearman coefficient for VKO models.</p> Full article ">
12 pages, 5151 KiB  
Article
Pseudo-Elastic Response of Gas Bearing Clastic Formations: An Italian Case Study
by Christian Coti, Vera Rocca and Quinto Sacchi
Energies 2018, 11(9), 2488; https://doi.org/10.3390/en11092488 - 19 Sep 2018
Cited by 14 | Viewed by 3031
Abstract
The research presented in this paper focuses on the analysis of land movements induced by underground gas storage operations in a depleted reservoir in Northern Italy with the aim of increasing the understanding of the deformation response of deep formations via a real [...] Read more.
The research presented in this paper focuses on the analysis of land movements induced by underground gas storage operations in a depleted reservoir in Northern Italy with the aim of increasing the understanding of the deformation response of deep formations via a real case study. The a priori knowledge of the pseudo-elastic parameters showed a substantial discrepancy between static values from triaxial lab tests and dynamic values obtained via the interpretation of sonic data at wellbore scale. The discrepancy is not surprising for the formations under investigation: a thousand meters of a silty to shaly sequence intercalated with arenaceous banks above a reservoir formation, which is basically made up of sandstone intercalated with shale intervals and conglomerates. Information collected for over more than ten years of seasonal production/injection cycles (i.e., time and space evolution of the reservoir fluid pressure and of the induced land surface movements) was then combined in a 3D numerical geomechanical model to constrain and update the a priori knowledge on the pseudo elastic model parameters via a back analysis approach. The obtained calibrated model will then be used for reliable prediction of system safety analyses, for example in terms of induced ground movements. Full article
(This article belongs to the Section D: Energy Storage and Application)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Qualitative relation between shear stress, <span class="html-italic">τ</span>, and shear strain, <span class="html-italic">γ</span> and (<b>b</b>) normalized shear modulus <span class="html-italic">G<sub>sec</sub>/G<sub>0</sub></span> (where <span class="html-italic">G<sub>0</sub></span> is at small strain) and shear strain, <span class="html-italic">γ</span>.</p> Full article ">Figure 2
<p>(<b>a</b>) Simplified structural map of the Po Valley around the modelled area. (<b>b</b>) SO-NE section through the alpine thrust structures. (<b>c</b>) SSE-NNO section intersecting the Apennine (SSE) and Alpine (NNO) thrust belts (after Reference [<a href="#B25-energies-11-02488" class="html-bibr">25</a>]).</p> Full article ">Figure 3
<p>Regional model (scale: 100–1000 s m) of studied area integrating reservoir model (scale: 1–10 s m). Reservoir is located in central zone (see well location).</p> Full article ">Figure 4
<p>(<b>a</b>) Shale specimen and (<b>b</b>) stone specimen.</p> Full article ">Figure 5
<p>(<b>a</b>) CIU on shale specimen and (<b>b</b>) CID on sandstone specimen.</p> Full article ">Figure 6
<p>Lab test results: static elastic moduli values in relation to confined pressure.</p> Full article ">Figure 7
<p>Well log analysis results: variation of the dynamic elastic modulus in relation to depth.</p> Full article ">Figure 8
<p>Cross-plot: dynamic elastic modulus and compressional wave velocity—DTCO.</p> Full article ">Figure 9
<p>Comparison between reservoir average static pressure and measured vertical displacement (<b>a</b>) at control points overlying the reservoir and (<b>b</b>) at control points outside the UGS influence area.</p> Full article ">Figure 10
<p>Areal extension of the ground surface cyclically affected by UGS activities from InSAR data analysis: (<b>a</b>) vertical displacement within a production cycle and (<b>b</b>) an injection cycle.</p> Full article ">Figure 11
<p>Comparison between simulated and measured vertical movements for three control points overlying the reservoir.</p> Full article ">
16 pages, 4651 KiB  
Article
Modified High Back-Pressure Heating System Integrated with Raw Coal Pre-Drying in Combined Heat and Power Unit
by Heng Chen, Zhen Qi, Qiao Chen, Yunyun Wu, Gang Xu and Yongping Yang
Energies 2018, 11(9), 2487; https://doi.org/10.3390/en11092487 - 19 Sep 2018
Cited by 15 | Viewed by 3721
Abstract
A conceptual high-back pressure (HBP) heating system cooperating raw coal pre-drying for combined heat and power (CHP) was proposed to improve the performance of the HBP-CHP unit. In the new design, besides of heating the supply-water of the heating network, a portion of [...] Read more.
A conceptual high-back pressure (HBP) heating system cooperating raw coal pre-drying for combined heat and power (CHP) was proposed to improve the performance of the HBP-CHP unit. In the new design, besides of heating the supply-water of the heating network, a portion of the exhaust steam from the turbine is employed to desiccate the raw coal prior to the coal pulverizer, which further recovers the waste heat of the exhaust steam and contributes to raising the overall efficiency of the unit. Thermodynamic and economic analyzes were conducted based on a typical 300 MW coal-fired HBP-CHP unit with the application of the modified configuration. The results showed that the power generation thermal efficiency promotion of the unit reaches 1.7% (absolute value) owing to suggested retrofitting, and meanwhile, the power generation standard coal consumption rate is diminished by 5.8 g/kWh. Due to the raw coal pre-drying, the energy loss of the exhaust flue gas of the boiler is reduced by 19.1% and the boiler efficiency increases from 92.7% to 95.4%. The impacts of the water content of the dried coal and the unit heating capacity on the energy-saving effect of the new concept were also examined. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of the reference coal-fired HBP-CHP unit.</p> Full article ">Figure 2
<p>Diagram of the modified HBP heating system integrated with raw coal pre-drying (based on the reference HBP-CHP unit).</p> Full article ">Figure 3
<p>Energy balance of the coal drier during the coal pre-drying process.</p> Full article ">Figure 4
<p>Diagram of the primary energy flows in the original HBP-CHP unit and proposed HBP-CHP unit: (<b>a</b>) Original unit; and (<b>b</b>) Proposed unit.</p> Full article ">Figure 4 Cont.
<p>Diagram of the primary energy flows in the original HBP-CHP unit and proposed HBP-CHP unit: (<b>a</b>) Original unit; and (<b>b</b>) Proposed unit.</p> Full article ">Figure 5
<p>Impact of the moisture content in the dried coal on the performance of the proposed design.</p> Full article ">Figure 6
<p>Impact of the unit heating capacity on the performance of the proposed design: (<b>a</b>) Total raw coal consumption of unit; and (<b>b</b>) recovery efficiency of the exhaust steam.</p> Full article ">Figure 6 Cont.
<p>Impact of the unit heating capacity on the performance of the proposed design: (<b>a</b>) Total raw coal consumption of unit; and (<b>b</b>) recovery efficiency of the exhaust steam.</p> Full article ">
15 pages, 7460 KiB  
Article
A Compensation Control Scheme of Voltage Unbalance Using a Combined Three-Phase Inverter in an Islanded Microgrid
by Biying Ren, Xiangdong Sun, Shasha Chen and Huan Liu
Energies 2018, 11(9), 2486; https://doi.org/10.3390/en11092486 - 18 Sep 2018
Cited by 17 | Viewed by 4476
Abstract
A large number of single-phase loads in an islanded microgrid have a bad influence on the alternating current (AC) bus voltage symmetry, which will further impact the power supply for the other loads. In this paper, the combined three-phase inverter is adopted as [...] Read more.
A large number of single-phase loads in an islanded microgrid have a bad influence on the alternating current (AC) bus voltage symmetry, which will further impact the power supply for the other loads. In this paper, the combined three-phase inverter is adopted as the distributed generation (DG) interface circuit for its independent control of each bridge. However, the combined three-phase inverter will generate an asymmetrical voltage with the traditional droop control. Moreover, the system impedance also effects the voltage symmetry. Therefore, the improved droop control method based on the self-adjusting P-f and Q-U droop curves and the system impedance voltage drop compensation are proposed. The system control scheme is also designed in detail. A simulation and an experiment under the conditions of the balanced, unbalanced loads are carried out, and the results verify the feasibility and effectiveness of the control strategy. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Simplified microgrid structure.</p> Full article ">Figure 2
<p>Structure architecture of the combined three-phase inverter.</p> Full article ">Figure 3
<p>General block diagram of the control scheme for the parallel inverters.</p> Full article ">Figure 4
<p>Block diagram of the voltage and current control loops.</p> Full article ">Figure 5
<p>Main circuit and the control scheme.</p> Full article ">Figure 6
<p>Block diagram of the control scheme. (<b>a</b>) Average power calculation; (<b>b</b>) Improved droop control and impedance voltage drop compensation; (<b>c</b>) Synthesis process of the reference voltage; (<b>d</b>) Voltage and current control loops.</p> Full article ">Figure 7
<p>Flowchart of the designed control scheme.</p> Full article ">Figure 8
<p><span class="html-italic">Q</span>-<span class="html-italic">U</span> droop curves. (<b>a</b>) Traditional <span class="html-italic">Q</span>-<span class="html-italic">U</span> droop curve; (<b>b</b>) Improved <span class="html-italic">Q</span>-<span class="html-italic">U</span> droop curve.</p> Full article ">Figure 9
<p>Bode diagram of the system open-loop transfer function.</p> Full article ">Figure 10
<p>Simulation waveforms under various load conditions. (<b>a</b>) Three-phase load active powers; (<b>b</b>) Three-phase load reactive powers; (<b>c</b>) load currents.</p> Full article ">Figure 11
<p>Voltage waveforms with the traditional droop control. (<b>a</b>) Frequencies of the three-phase reference voltage; (<b>b</b>) amplitudes of the three-phase reference voltage; (<b>c</b>) instantaneous output voltage.</p> Full article ">Figure 12
<p>Voltage waveforms with the proposed control method. (<b>a</b>) frequencies of the three-phase reference voltage; (<b>b</b>) amplitudes of the three-phase reference voltage; (<b>c</b>) instantaneous output voltage.</p> Full article ">Figure 12 Cont.
<p>Voltage waveforms with the proposed control method. (<b>a</b>) frequencies of the three-phase reference voltage; (<b>b</b>) amplitudes of the three-phase reference voltage; (<b>c</b>) instantaneous output voltage.</p> Full article ">Figure 13
<p>Experimental waveforms of three-phase output voltages and currents with traditional droop control in the case of a balanced load. (<b>a</b>) Voltage waveforms; (<b>b</b>) Current waveforms.</p> Full article ">Figure 14
<p>Experimental waveforms of three-phase output voltages and currents with traditional droop control in the case of an unbalanced load. (<b>a</b>) Voltage waveforms; (<b>b</b>) Current waveforms.</p> Full article ">Figure 15
<p>Three-phase output voltages and currents only with the improved droop control in the case of an unbalanced load. (<b>a</b>) Voltage waveforms; (<b>b</b>) Current waveforms.</p> Full article ">Figure 16
<p>Three-phase output voltages and currents with improved droop control and impedance-drop compensation control in the case of an unbalanced load. (<b>a</b>) Voltage waveforms; (<b>b</b>) Current waveforms.</p> Full article ">Figure 17
<p>Three-phase output voltages and c-phase current of switching from unbalanced load to balanced load.</p> Full article ">Figure 18
<p>Three-phase output voltages and c-phase current of switching from balanced load to unbalanced load.</p> Full article ">
10 pages, 2532 KiB  
Article
Improved Current Doubler Rectifier
by Mihail Antchev
Energies 2018, 11(9), 2485; https://doi.org/10.3390/en11092485 - 18 Sep 2018
Cited by 3 | Viewed by 5512
Abstract
It is widely accepted to examine and explain the functioning of the standard “Current Doubler Rectifier” as strictly symmetrical according to the electrical current through the two inductances. The present work challenges this consideration and proposes a new version of the electrical circuit [...] Read more.
It is widely accepted to examine and explain the functioning of the standard “Current Doubler Rectifier” as strictly symmetrical according to the electrical current through the two inductances. The present work challenges this consideration and proposes a new version of the electrical circuit diagram where the current symmetry is improved. The proposed circuit is called the “Improved Current Doubler Rectifier”. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram for clarifying the operation in the first cycle.</p> Full article ">Figure 2
<p>Computer simulation diagram of the standard “Current Doubler Rectifier”.</p> Full article ">Figure 3
<p>Computer simulation results of a standard “Current Doubler Rectifier” with inductances <math display="inline"><semantics> <mrow> <mn>5</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">H</mi> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Computer simulation results of standard “Current Doubler Rectifier” with inductances <math display="inline"><semantics> <mrow> <mn>10</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">H</mi> </mrow> </semantics></math></p> Full article ">Figure 5
<p>Diagram for clarifying the functioning of “Improved Current Doubler Rectifier”.</p> Full article ">Figure 6
<p>“Improved Current Doubler Rectifier” computer simulation diagram.</p> Full article ">Figure 7
<p>Computer simulation results of “Improved Current Doubler Rectifier” with inductances <math display="inline"><semantics> <mrow> <mn>5</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">H</mi> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Computer simulation results of “Improved Current Doubler Rectifier” with inductances <math display="inline"><semantics> <mrow> <mn>10</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">H</mi> </mrow> </semantics></math></p> Full article ">Figure 9
<p>Horizontal axis scale <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mn>50</mn> <mi>μ</mi> <mi>S</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>v</mi> </mrow> </mfrac> </mrow> </semantics></math>: (<b>a</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>–CH2, (<b>b</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> – CH2.</p> Full article ">Figure 10
<p>Horizontal axis scale <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mn>100</mn> <mi>μ</mi> <mi>S</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>v</mi> </mrow> </mfrac> </mrow> </semantics></math>: (<b>a</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>–CH2, (<b>b</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>–CH2.</p> Full article ">Figure 11
<p>Scale on horizontal axis <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mn>50</mn> <mi>μ</mi> <mi>S</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>v</mi> </mrow> </mfrac> </mrow> </semantics></math>: (<b>a</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>–CH2, (<b>b</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>–CH2.</p> Full article ">Figure 12
<p>Scale on horizontal axis <math display="inline"><semantics> <mrow> <mfrac> <mrow> <mn>100</mn> <mi>μ</mi> <mi>S</mi> </mrow> <mrow> <mi>d</mi> <mi>i</mi> <mi>v</mi> </mrow> </mfrac> </mrow> </semantics></math>: (<b>a</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>–CH2, (<b>b</b>) voltage <math display="inline"><semantics> <mrow> <mi>V</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>–CH1 and current <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>–CH2.</p> Full article ">
13 pages, 2097 KiB  
Article
Using Multiple Fidelity Numerical Models for Floating Offshore Wind Turbine Advanced Control Design
by Joannes Olondriz, Wei Yu, Josu Jugo, Frank Lemmer, Iker Elorza, Santiago Alonso-Quesada and Aron Pujana-Arrese
Energies 2018, 11(9), 2484; https://doi.org/10.3390/en11092484 - 18 Sep 2018
Cited by 5 | Viewed by 4551
Abstract
This paper summarises the tuning process of the Aerodynamic Platform Stabiliser control loop and its performance with Floating Offshore Wind Turbine model. Simplified Low-Order Wind turbine numerical models have been used for the system identification and control tuning process. Denmark Technical University’s 10 [...] Read more.
This paper summarises the tuning process of the Aerodynamic Platform Stabiliser control loop and its performance with Floating Offshore Wind Turbine model. Simplified Low-Order Wind turbine numerical models have been used for the system identification and control tuning process. Denmark Technical University’s 10 MW wind turbine model mounted on the TripleSpar platform concept was used for this study. Time-domain simulations were carried out in a fully coupled non-linear aero-hydro-elastic simulation tool FAST, in which wind and wave disturbances were modelled. This testing yielded significant improvements in the overall Floating Offshore Wind Turbine performance and load reduction, validating the control technique presented in this work. Full article
(This article belongs to the Collection Wind Turbines)
►▼ Show Figures

Figure 1

Figure 1
<p>DTU 10 MW TripleSpar wind turbine: conceptual model (<b>left</b>); and main parameters (<b>right</b>) [<a href="#B4-energies-11-02484" class="html-bibr">4</a>].</p> Full article ">Figure 2
<p>Bode (<b>left</b>); and Nyquist (<b>right</b>) diagrams of the SLOW and FAST linear models.</p> Full article ">Figure 3
<p>Conventional PI control scheme and the additional APS control loop.</p> Full article ">Figure 4
<p>Open loop bode diagrams of the linearised plant and the APS control loop implementation.</p> Full article ">Figure 5
<p>Close loop bode diagrams with and without the APS control loop.</p> Full article ">Figure 6
<p>Close-loop control system sensitivity peak analysis Nyquist diagram.</p> Full article ">Figure 7
<p>Blade-pitch control system gain-scheduling law.</p> Full article ">Figure 8
<p>PI controller and TripleSpar platform-pitch natural frequencies.</p> Full article ">Figure 9
<p>Time-domain simulation at 16 m/s mean wind speed and 1.37 m of wave-elevation.</p> Full article ">Figure 10
<p>Power Spectral Density analysis.</p> Full article ">Figure 11
<p>Normalised Damage Equivalent Load analysis.</p> Full article ">
15 pages, 1561 KiB  
Article
Comparative Analysis of CO2, N2, and Gas Mixture Injection on Asphaltene Deposition Pressure in Reservoir Conditions
by Peng Wang, Fenglan Zhao, Jirui Hou, Guoyong Lu, Meng Zhang and Zhixing Wang
Energies 2018, 11(9), 2483; https://doi.org/10.3390/en11092483 - 18 Sep 2018
Cited by 25 | Viewed by 3440
Abstract
CO2 and N2 injection is an effective enhanced oil recovery technology in the oilfield especially for low-permeability and extra low-permeability reservoirs. However, these processes can induce an asphaltene deposition during oil production. Asphaltene-deposition-induced formation damage is a fairly severe problem. Therefore, [...] Read more.
CO2 and N2 injection is an effective enhanced oil recovery technology in the oilfield especially for low-permeability and extra low-permeability reservoirs. However, these processes can induce an asphaltene deposition during oil production. Asphaltene-deposition-induced formation damage is a fairly severe problem. Therefore, predicting the likelihood of asphaltene deposition in reservoir conditions is crucial. This paper presents the results of flash separation experiments used to investigate the composition of crude oil in shallow and buried-hill reservoirs. Then, PVTsim Nova is used to simulate the composition change and asphaltene deposition of crude oil. Simulation tests indicate that the content of light components C1-C4 and heavy components C36+ decrease with increasing CO2 and N2 injection volumes. However, the extraction of CO2 is significantly stronger than that of N2. In shallow reservoirs, as the CO2 injection volume increases, the deposition pressure range decreases and asphaltenes are easily deposited. Conversely, the asphaltene deposition pressure of crude oil injected with N2 is higher and will not cause serious asphaltene deposition. When the CO2-N2 injection ratio reaches 1:1, the deposition pressure range shows a significant transition. In buried-hill reservoirs, asphaltene deposition is unlikely to occur with CO2, N2, and a gas mixture injection. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Device diagram of flash separation experiments.</p> Full article ">Figure 2
<p>Asphaltene deposition pressure change with different injection volumes of CO<sub>2</sub> at 65 °C in a shallow reservoir.</p> Full article ">Figure 3
<p>Asphaltene deposition pressure change with different injection volumes of N<sub>2</sub> at 65 °C in a shallow reservoir.</p> Full article ">Figure 4
<p>Asphaltene deposition pressure change with different injection ratios of gas mixture at 65 °C in a shallow reservoir.</p> Full article ">Figure 5
<p>An asphaltene deposition pressure change with different injection volumes of CO<sub>2</sub> and N<sub>2</sub> at 161.8 °C in a buried-hill reservoir.</p> Full article ">Figure 6
<p>Asphaltene deposition pressure change with different injection ratios of a gas mixture at 161.8 °C in a buried-hill reservoir.</p> Full article ">
14 pages, 2477 KiB  
Article
Laboratory Test Bed for Analyzing Fault-Detection Reaction Times of Protection Relays in Different Substation Topologies
by Goran Jurišić, Juraj Havelka, Tomislav Capuder and Stjepan Sučić
Energies 2018, 11(9), 2482; https://doi.org/10.3390/en11092482 - 18 Sep 2018
Cited by 12 | Viewed by 5353
Abstract
Visions of energy transition focus on activating end users, meaning that numerous flexible-distribution network-level devices become active participants in power-system operations. This implies a fast, reliable, and secure exchange of data, enabling the distribution-system operators to maintain, or even improve, the quality and [...] Read more.
Visions of energy transition focus on activating end users, meaning that numerous flexible-distribution network-level devices become active participants in power-system operations. This implies a fast, reliable, and secure exchange of data, enabling the distribution-system operators to maintain, or even improve, the quality and delivery of service. With the introduction of the International Electrotechnical Commission (IEC) 61850 standard, the path is set for a single communication topology covering all substation levels. The standard has the potential to change the way substations are designed, built, tested, and maintained. This means that the key segment of the substation, its protection system, will go through a transition period with the end goal of having a digitized substation where all information exchange is performed over an Ethernet communication bus. This paper analyzes the performance impact of the IEC 61850-9-2LE on the protection system. To do this, a laboratory hardware-in-the-loop test setup was developed representing traditional-, hybrid-, and digital-substation topology. The setup serves to simulate faults and create transient waveforms in an extended IEEE 123-node test system, which is then used to detect the reaction times of protection relay devices. To verify the results, a significant number of tests was performed clearly showing the benefits of digitalizing the distribution system. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Basic communication standards in substations.</p> Full article ">Figure 2
<p>Simplified physical-device communication-data structure.</p> Full article ">Figure 3
<p>(<b>A</b>) Relay testing in traditional substations; (<b>B</b>) relay testing in hybrid substation; and (<b>C</b>) relay testing in digital substations.</p> Full article ">Figure 4
<p>Simplified illustration of the IEEE 123-node test feeder topology.</p> Full article ">Figure 5
<p>Illustration of the IEC 61850 test bed.</p> Full article ">Figure 6
<p>Statistical presentation of test results for the fault simulation in different substation topologies.</p> Full article ">Figure 7
<p>Time delay caused by the merging unit.</p> Full article ">Figure 8
<p>Automated adaptive protection function testing.</p> Full article ">
17 pages, 4697 KiB  
Article
Kinetic Study on the Pyrolysis of Medium Density Fiberboard: Effects of Secondary Charring Reactions
by Longwei Pan, Yong Jiang, Lei Wang and Wu Xu
Energies 2018, 11(9), 2481; https://doi.org/10.3390/en11092481 - 18 Sep 2018
Cited by 13 | Viewed by 3577
Abstract
The reaction models employed in the kinetic studies of biomass pyrolysis generally do not include the secondary charring reactions. The aim of this work is to propose an applicable kinetic model to characterize the pyrolysis mechanism of medium density fiberboard (MDF) and to [...] Read more.
The reaction models employed in the kinetic studies of biomass pyrolysis generally do not include the secondary charring reactions. The aim of this work is to propose an applicable kinetic model to characterize the pyrolysis mechanism of medium density fiberboard (MDF) and to evaluate the effects of secondary charring reactions on estimated products yields. The kinetic study for pyrolysis of MDF was performed by a thermogravimetric analyzer over a heating rate range from 10 to 40 °C/min in a nitrogen atmosphere. Four stages related to the degradation of resin, hemicellulose, cellulose, and lignin could be distinguished from the thermogravimetric analyses (TGA). Based on the four components and multi-component parallel reaction scheme, a kinetic model considering secondary charring reactions was proposed. A comparison model was also provided. An efficient optimization algorithm, differential evolution (DE), was coupled with the two models to determine the kinetic parameters. Comparisons of the results of the two models to experiment showed that the mass fraction (TG) and mass loss rate (DTG) calculated by the model considering secondary charring reactions were in better agreement with the experimental data. Furthermore, higher product yields than the experimental values will be obtained if secondary charring reactions were not considered in the kinetic study of MDF pyrolysis. On the contrary, with the consideration of secondary charring reactions, the estimated product yield had little error with the experimental data. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Multi-component competitive scheme of biomass pyrolysis including secondary charring.</p> Full article ">Figure 2
<p>Curves of MDF at different heating rates: (<b>a</b>) TG; and (<b>b</b>) |DTG|.</p> Full article ">Figure 3
<p>Corresponding curves of MDF at 10 °C/min: (<b>a</b>) |DDTG|; and (<b>b</b>) |DTG|.</p> Full article ">Figure 4
<p>(<b>a</b>) FWO plots; (<b>b</b>) KAS plots; (<b>c</b>) Starink plots at the conversion of 0.1–0.9; and (<b>d</b>) activation energy <span class="html-italic">E</span> and temperature at 10 °C/min versus conversion α.</p> Full article ">Figure 5
<p>ln<span class="html-italic">A</span> versus <span class="html-italic">E</span> using FWO and the third order reaction model at 0.2 &lt; α ≤ 0.8.</p> Full article ">Figure 6
<p>Comparisons between experimental TG/DTG data and predictions based on optimized parameters: (<b>a</b>) β = 10 °C/min; (<b>b</b>) β = 20 °C/min; (<b>c</b>) β = 30 °C/min; and (<b>d</b>) β = 40 °C/min.</p> Full article ">Figure 7
<p>Experimental curves of |DDTG| and |DTG| of MDF at 10 °C/min with four components by: Model I is shown in dashed lines; Model II is shown in solid lines.</p> Full article ">Figure 8
<p>Comparisons of components mass between Model I and Model II at four heating rates: (<b>a</b>) 10 °C/min; (<b>b</b>) 20 °C/min; (<b>c</b>) 30 °C/min; and (<b>d</b>) 40 °C/min. Model I is shown in dashed lines, and Model II is shown in solid lines.</p> Full article ">Figure 9
<p>Comparisons of product yields between Model I and Model II at four heating rates: (<b>a</b>) 10 °C/min; (<b>b</b>) 20 °C/min; (<b>c</b>) 30 °C/min; and (<b>d</b>) 40 °C/min. Model I is shown in dashed lines, and Model II is shown in solid lines.</p> Full article ">
12 pages, 6110 KiB  
Article
Experimental Research on the Thermal Performance and Semi-Visualization of Rectangular Flat Micro-Grooved Gravity Heat Pipes
by Xiang Gou, Qiyan Zhang, Yamei Li, Yingfan Liu, Shian Liu and Saima Iram
Energies 2018, 11(9), 2480; https://doi.org/10.3390/en11092480 - 18 Sep 2018
Cited by 9 | Viewed by 3130
Abstract
To strengthen the heat dissipating capacity of a heat pipe used for integrated insulated gate bipolar transistors, as an extension of our earlier work, the effect of micro-groove dimension on the thermal performance of flat micro-grooved gravity heat pipe was studied. Nine pipes [...] Read more.
To strengthen the heat dissipating capacity of a heat pipe used for integrated insulated gate bipolar transistors, as an extension of our earlier work, the effect of micro-groove dimension on the thermal performance of flat micro-grooved gravity heat pipe was studied. Nine pipes with different depths (0.4 mm, 0.8 mm, 1.2 mm) and widths (0.4 mm, 0.8 mm, 1.2 mm) were fabricated and tested under a heating load range from 80 W to 180 W. The start-up time, temperature difference, relative thermal resistance and equivalent thermal conductivity were presented as performance indicators by comparison of flat gravity heat pipes with and without micro-grooves. Results reveal that the highest equivalent thermal conductivity of the flat micro-grooved gravity heat pipes is 2.55 times as that of the flat gravity heat pipe without micro-grooves. The flat gravity heat pipes with deeper and narrower micro-grooves show better thermal performance and the optimal rectangular micro-groove dimension among the selected options is determined to be 1.2 mm (depth) × 0.4 mm (width). Furthermore, the liquid–vapor phase behaviors were observed to verify the heat transfer effects and analyze the heat transfer mechanism of the flat micro-grooved heat pipes. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) A photo of a fabricated flat micro-grooved gravity heat pipe (FMGHP); (<b>b</b>) The A–A cross-section of the micro-grooves in a FMGHP (units: mm).</p> Full article ">Figure 2
<p>Experimental system of the semi-visualization FMGHP. AC = alternating current.</p> Full article ">Figure 3
<p>The start-up time of the FMGHPs and the flat gravity heat pipe (FGHP) at different heating loads: (<b>a</b>) FMGHPs 1–3 and FGHP; (<b>b</b>) FMGHPs 4–6 and FGHP; (<b>c</b>) FMGHPs 7–9 and FGHP; (<b>d</b>) FMGHPs 3, 6, 9 and FGHP.</p> Full article ">Figure 4
<p>The temperature difference of the FMGHPs and FGHP at different heating loads: (<b>a</b>) FMGHPs 1–3 and FGHP; (<b>b</b>) FMGHPs 4–6 and FGHP; (<b>c</b>) FMGHPs 7–9 and FGHP; (<b>d</b>) FMGHPs 3, 6, 9 and FGHP.</p> Full article ">Figure 5
<p>The relative thermal resistance of the FMGHPs and FGHP at different heating loads: (<b>a</b>) FMGHPs 1–3 and FGHP; (<b>b</b>) FMGHPs 4–6 and FGHP; (<b>c</b>) FMGHPs 7–9 and FGHP; (<b>d</b>) FMGHPs 3, 6, 9 and FGHP.</p> Full article ">Figure 6
<p>The equivalent thermal conductivity of the FMGHPs and FGHP at different heating loads: (<b>a</b>) FMGHPs 1–3 and FGHP; (<b>b</b>) FMGHPs 4–6 and FGHP; (<b>c</b>) FMGHPs 7–9 and FGHP; (<b>d</b>) FMGHPs 3, 6, 9 and FGHP.</p> Full article ">Figure 7
<p>The liquid–vapor two-phase behaviors at 300 s of FMGHP 3 (<b>a</b>–<b>c</b>) and the FGHP (<b>d</b>–<b>f</b>) at different heating loads. Note: the initial liquid level is marked with the red line.</p> Full article ">
21 pages, 5893 KiB  
Article
Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network
by Yue Wang, Zhong Liu, Jianmai Shi, Guohua Wu and Rui Wang
Energies 2018, 11(9), 2479; https://doi.org/10.3390/en11092479 - 18 Sep 2018
Cited by 8 | Viewed by 3299
Abstract
The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the [...] Read more.
The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the market share of battery electric vehicles. In this paper, we investigate the joint optimal policy for a subsidy on electric vehicles and infrastructure construction in a highway network, where the impact of siting and sizing of fast charging stations and the impact of subsidy on the potential electric vehicle flows is considered. A new specified local search (LS)-based algorithm is developed to maximize the overall number of available battery electric vehicles in the network, which can get provide better solutions in most situations when compared with existed algorithms. Moreover, we firstly combined the existing algorithms to establish a multi-stage optimization method, which can obtain better solutions than all existed algorithms. A practical case from the highway network in Hunan, China, is studied to analyze the factors that impact the choice of subsidy and the deployment of charging stations. The results prove that the joint policy for subsidy and infrastructure construction can be effectively improved with the optimization model and the algorithms we developed. The managerial analysis indicates that the improvement on the capacity of charging facility can increase the proportion of construction fees in the total budget, while the improvement in the endurance range of battery electric vehicles is more efficient in expanding battery electric vehicle adoption in the highway network. A more detailed formulation of the battery electric vehicle flow demand and equilibrium situation will be studied in the future. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>The sales of electric vehicles and battery hybrid vehicles in the world.</p> Full article ">Figure 2
<p>A simple instance of a highway network.</p> Full article ">Figure 3
<p>The flow chart of the local search algorithm.</p> Full article ">Figure 4
<p>Highway network of Hunan Province.</p> Full article ">Figure 5
<p>The results of the local search algorithm under different subsidies.</p> Full article ">Figure 6
<p>The computing times of the local search algorithm under different subsidies.</p> Full article ">Figure 7
<p>The boxplot of running the local search 20 times.</p> Full article ">Figure 8
<p>The optimal location and size of charging stations.</p> Full article ">Figure 9
<p>The results under different subsidies.</p> Full article ">Figure 10
<p>The results under different capacities of the charging pile and subsidies.</p> Full article ">Figure 11
<p>The total construction fees under different capacities of the charging piles and subsidies.</p> Full article ">Figure 12
<p>The results under different endurance ranges and subsidies.</p> Full article ">Figure 13
<p>The total construction fees under different endurance ranges and subsidies.</p> Full article ">Figure 14
<p>The results under different endurance ranges and subsidies.</p> Full article ">Figure 15
<p>The results under different endurance ranges and subsidies.</p> Full article ">Figure 16
<p>The benefit–cost rates under different budgets.</p> Full article ">Figure 17
<p>The results of the two algorithms under different subsidies.</p> Full article ">Figure 18
<p>The computing times of the two algorithms under different subsidies.</p> Full article ">
17 pages, 2407 KiB  
Article
Improved Data Center Energy Efficiency and Availability with Multilayer Node Event Processing
by Vojko Matko and Barbara Brezovec
Energies 2018, 11(9), 2478; https://doi.org/10.3390/en11092478 - 18 Sep 2018
Cited by 18 | Viewed by 3799
Abstract
This article presents an overview of a new multilayer node event processing (MNEP) method for improved data center energy efficiency and event management monitoring in its physical infrastructure. Event management is essential for the provision and maintenance of the reliability and functionality of [...] Read more.
This article presents an overview of a new multilayer node event processing (MNEP) method for improved data center energy efficiency and event management monitoring in its physical infrastructure. Event management is essential for the provision and maintenance of the reliability and functionality of the physical infrastructure. For effective maintenance, action preventing any downtime accurate event information as well as appropriate energy support is of outmost importance. The novelty of this approach lies in the enhanced availability and reliability of the physical infrastructure, improved data center infrastructure energy efficiency, as well as lower costs when designing a new physical infrastructure system. The MNEP method presents a data model based on the node tree processing. The experimental results show that the number of all potential unexpected events is reduced significantly as a result of the accurate identification of the alarm root cause. The comparison of time parameters required to evaluate the maintenance factors shows that the time needed to detect, identify, and react to alarm events decreases and has a significant impact on maintaining a defined high level of availability and energy efficiency in a data center physical infrastructure. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Data center infrastructure.</p> Full article ">Figure 2
<p>(<b>a</b>) Node tree diagram with internode; (<b>b</b>) Power Supply Block.</p> Full article ">Figure 3
<p>Node model with possible entities.</p> Full article ">Figure 4
<p>Comparison of the events numbers.</p> Full article ">Figure 5
<p>Comparison of the total of all events between July 2016 and June 2018.</p> Full article ">Figure 6
<p>Time parameter presentation.</p> Full article ">
17 pages, 1788 KiB  
Article
Pelleting Vineyard Pruning at Low Cost with a Mobile Technology
by Giuseppe Toscano, Vincenzo Alfano, Antonio Scarfone and Luigi Pari
Energies 2018, 11(9), 2477; https://doi.org/10.3390/en11092477 - 18 Sep 2018
Cited by 34 | Viewed by 3601
Abstract
The goal of this work was to test a patented pruning harvester and a mobile pelleting system specifically designed for the vineyard agripellet chain. Biomass was characterized before and after storage and after the pelleting stage. The performance, the fuel consumption, and the [...] Read more.
The goal of this work was to test a patented pruning harvester and a mobile pelleting system specifically designed for the vineyard agripellet chain. Biomass was characterized before and after storage and after the pelleting stage. The performance, the fuel consumption, and the work quality of the harvester were assessed together with the productivity and the power consumption of the mobile pelleting system. Production costs of pellet were estimated for the whole logistic chain, considering two scenarios: Storage and pelleting directly at the farm site or at a dedicated location at variable distance from the fields. For comparison, the direct production of chips without pelleting was considered. Results indicate that harvester performance was quite good and comparable with commercial solutions; the chips produced exhibited excellent storage performance, allowing direct pelleting without forced drying; the pellet quality was good comparable with that produced from forestry biomass. From an economic point of view, in-field pelleting was the most cost-effective solution, with a good margin of profit up to 57€ t−1; on the other hand, when transport to an intermediate storage center is necessary, profit margin reduces gradually and fades off at an average 50 km distance from the fields. Full article
(This article belongs to the Section A: Sustainable Energy)
►▼ Show Figures

Figure 1

Figure 1
<p>Pruning harvester manufactured by Costruzioni Nazzareno, MAREV Alba 150 model.</p> Full article ">Figure 2
<p>Mobile pelleting plant developed by Costruzioni Nazzareno.</p> Full article ">Figure 3
<p>Particle size distribution of chipped material.</p> Full article ">Figure 4
<p>Total pellet production cost according to supply basin.</p> Full article ">Figure 5
<p>Transport cost (€ t<sup>−1</sup>) according to the distance (km).</p> Full article ">Figure 6
<p>Trend of the production cost including transport in Scenario B (dashed line) and Scenario C (solid line). The crosses with the dotted line indicate the threshold distance of convenience, referring to a minimum profit margin of 30€ t<sup>−1</sup><b>.</b></p> Full article ">
16 pages, 656 KiB  
Article
SwarmGrid: Demand-Side Management with Distributed Energy Resources Based on Multifrequency Agent Coordination
by Manuel Castillo-Cagigal, Eduardo Matallanas, Estefanía Caamaño-Martín and Álvaro Gutiérrez Martín
Energies 2018, 11(9), 2476; https://doi.org/10.3390/en11092476 - 18 Sep 2018
Cited by 8 | Viewed by 2731
Abstract
This paper focuses on a multi-agent coordination for demand-side management in electrical grids with high penetration rates of distributed generation, in particular photovoltaic generation. This coordination is done by the use of swarm intelligence and coupled oscillators, proposing a novel methodology, which is [...] Read more.
This paper focuses on a multi-agent coordination for demand-side management in electrical grids with high penetration rates of distributed generation, in particular photovoltaic generation. This coordination is done by the use of swarm intelligence and coupled oscillators, proposing a novel methodology, which is implemented by the so-call SwarmGrid algorithm. SwarmGrid seeks to smooth the aggregated consumption by considering distributed and local generation by the development of a self-organized algorithm based on multifrequency agent coordination. The objective of this algorithm is to increase stability and reduce stress of the electrical grid by the aggregated consumption smoothing based on a frequency domain approach. The algorithm allows not only improvements in the electrical grid, but also increases the penetration of distributed and renewable sources. Contrary to other approaches, this objective is achieved anonymously without the need for information exchange between the users; it only takes into account the aggregated consumption of the whole grid. Full article
(This article belongs to the Special Issue Distributed Energy Resources Management 2018)
►▼ Show Figures

Figure 1

Figure 1
<p>Conceptual example of the deferrable consumption created by the virtual users. SG, SwarmGrid.</p> Full article ">Figure 2
<p>Development of the crest factors for different percentages of consumption controlled by the SwarmGrid algorithm. The solid line represents the mean of the crest factors for the 100 simulated seeds. The shaded area is between the maximum and the minimum value obtained from the 100 simulated seeds: (<b>a</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>w</mi> <mi>e</mi> <mi>e</mi> <mi>k</mi> </mrow> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>m</mi> <mi>o</mi> <mi>n</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 2 Cont.
<p>Development of the crest factors for different percentages of consumption controlled by the SwarmGrid algorithm. The solid line represents the mean of the crest factors for the 100 simulated seeds. The shaded area is between the maximum and the minimum value obtained from the 100 simulated seeds: (<b>a</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>w</mi> <mi>e</mi> <mi>e</mi> <mi>k</mi> </mrow> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>m</mi> <mi>o</mi> <mi>n</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 3
<p>Heat map representing the development of the crest factors for different combinations of <math display="inline"><semantics> <msup> <mi>ρ</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msup> </semantics></math> and <math display="inline"><semantics> <mi>β</mi> </semantics></math> with <math display="inline"><semantics> <mrow> <msup> <mi>ρ</mi> <mrow> <mi>c</mi> <mi>t</mi> <mi>r</mi> </mrow> </msup> </mrow> </semantics></math> = 100%: (<b>a</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>w</mi> <mi>e</mi> <mi>e</mi> <mi>k</mi> </mrow> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>m</mi> <mi>o</mi> <mi>n</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 4
<p>Heat map representing the development of the crest factors for different combinations of <math display="inline"><semantics> <msup> <mi>ρ</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mi>ρ</mi> <mrow> <mi>c</mi> <mi>t</mi> <mi>r</mi> </mrow> </msup> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>w</mi> <mi>e</mi> <mi>e</mi> <mi>k</mi> </mrow> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>C</mi> <mo stretchy="false">¯</mo> </mover> <mrow> <mi>m</mi> <mi>o</mi> <mi>n</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>y</mi> <mi>e</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">
15 pages, 3362 KiB  
Article
Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm
by Menglu Li, Wei Wang, Gejirifu De, Xionghua Ji and Zhongfu Tan
Energies 2018, 11(9), 2475; https://doi.org/10.3390/en11092475 - 18 Sep 2018
Cited by 55 | Viewed by 5151
Abstract
Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% [...] Read more.
Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>The topological structure of the extreme learning machine algorithm.</p> Full article ">Figure 2
<p>The flow chart of the forecasting model.</p> Full article ">Figure 3
<p>Energy consumption based on GM (1, 1): (<b>a</b>) coal consumption; (<b>b</b>) petrol consumption; (<b>c</b>) gas consumption; and (<b>d</b>) coal power consumption.</p> Full article ">Figure 3 Cont.
<p>Energy consumption based on GM (1, 1): (<b>a</b>) coal consumption; (<b>b</b>) petrol consumption; (<b>c</b>) gas consumption; and (<b>d</b>) coal power consumption.</p> Full article ">Figure 4
<p>The forecasting results: (<b>a</b>) the comparison between original amount of carbon emissions and forecasting output of SVM-ELM forecasting model; and (<b>b</b>) the comparison between original amount of carbon emissions and forecasting output of forecasting model including SVM, ELM and SVM-ELM.</p> Full article ">Figure 4 Cont.
<p>The forecasting results: (<b>a</b>) the comparison between original amount of carbon emissions and forecasting output of SVM-ELM forecasting model; and (<b>b</b>) the comparison between original amount of carbon emissions and forecasting output of forecasting model including SVM, ELM and SVM-ELM.</p> Full article ">Figure 5
<p>Forecasting values of carbon emissions: (<b>a</b>) the trend chart of carbon emissions from 2017 to 2030; and (<b>b</b>) forecasting values of carbon emissions from 2017 to 2030.</p> Full article ">Figure 6
<p>Energy structure and carbon emissions.</p> Full article ">
18 pages, 4573 KiB  
Article
An Irregular Current Injection Islanding Detection Method Based on an Improved Impedance Measurement Scheme
by Menghua Liu, Wei Zhao, Qing Wang, Songling Huang and Kunpeng Shi
Energies 2018, 11(9), 2474; https://doi.org/10.3390/en11092474 - 17 Sep 2018
Cited by 9 | Viewed by 3084
Abstract
One class of islanding detection methods, known as impedance measurement-based methods and voltage change monitoring-based methods, are implemented through injecting irregular currents into the network, for which reason they are defined in this paper as irregular current injection methods. This paper indicates that [...] Read more.
One class of islanding detection methods, known as impedance measurement-based methods and voltage change monitoring-based methods, are implemented through injecting irregular currents into the network, for which reason they are defined in this paper as irregular current injection methods. This paper indicates that such methods may be affected by distributed generation (DG) unit cut-in events. Although the network impedance change can still be used as a judgment basis for islanding detection, the general impedance measurement scheme cannot separate island events from DG unit cut-in events in multi-DG operation. In view of this, this paper proposes a new islanding detection method based on an improved impedance measurement scheme, i.e., dynamic impedance measurement, which will not be affected by DG unit cut-in events and can further assist some other equipment in islanding detection. The simulations and experiments verify the stated advantages of the new islanding detection method. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Inverter-based distributed generation (DG).</p> Full article ">Figure 2
<p>Irregular currents and irregular voltages.</p> Full article ">Figure 3
<p>Changes of <span class="html-italic">U</span><sub>ir</sub> in the improved impedance measurement scheme.</p> Full article ">Figure 4
<p>Negative sequence second harmonic voltages signal extraction. (<b>a</b>) Signal flow; (<b>b</b>) ABC to dq<sub>(−2<span class="html-italic">ω</span>)</sub> reference frame transformation.</p> Full article ">Figure 5
<p>Flow chart of the proposed islanding detection method.</p> Full article ">Figure 6
<p>Non-detection zone (NDZ) of the proposed method.</p> Full article ">Figure 7
<p>Three-phase simulation platform.</p> Full article ">Figure 8
<p>Island event occurring. (<b>a</b>) Continuously measuring |<span class="html-italic">Z</span><sub>d</sub>| after the command of increasing <span class="html-italic">I</span><sub>DG1</sub> is issued; (<b>b</b>) Measuring |<span class="html-italic">Z</span><sub>d</sub>| until <span class="html-italic">U</span><sub>ir</sub> is stable at <span class="html-italic">U</span><sub>ir2</sub>.</p> Full article ">Figure 9
<p>DG unit cut-in event occurring.</p> Full article ">Figure 10
<p>Island event occurring and the related waveforms of current and voltage. (<b>a</b>) The whole process of the islanding detection; (<b>b</b>) Waveforms of the second harmonic current and voltage during the islanding detection; (<b>c</b>) Measured current and voltage before and after the change of <span class="html-italic">I</span><sub>DG1</sub>; (<b>d</b>) Waveforms of the second harmonic current and voltage contained in the measured current and voltage shown in (<b>c</b>). CH7: the measured current; CH8: the measured phase-to-phase voltage; <span class="html-italic">i</span><sub>2h</sub>: the second harmonic current; <span class="html-italic">u</span><sub>2h</sub>: the second harmonic voltage. Theoretically, the amplitude of <span class="html-italic">u</span><sub>2h</sub> (phase-to-phase voltage) is <math display="inline"><semantics> <mrow> <msqrt> <mn>3</mn> </msqrt> </mrow> </semantics></math> times the <span class="html-italic">U</span><sub>ir</sub> (phase-to-ground voltage) in (<b>a</b>).</p> Full article ">Figure 11
<p>DG unit cut-in event occurring and the related waveforms of current and voltage. (<b>a</b>) The whole process before and after the event; (<b>b</b>) Waveforms of the second harmonic current and voltage during the whole process; (<b>c</b>) Measured current and voltage before and after the change of <span class="html-italic">I</span><sub>DG1</sub>; (<b>d</b>) Waveforms of the second harmonic current and voltage contained in the measured current and voltage shown in (<b>c</b>).</p> Full article ">
15 pages, 2454 KiB  
Article
A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions
by Xinhua Zhu, Nan Li, Yu Sun, Hongfei Zhang, Kai Wang and Sang-Bing Tsai
Energies 2018, 11(9), 2473; https://doi.org/10.3390/en11092473 - 17 Sep 2018
Cited by 9 | Viewed by 3748
Abstract
In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of [...] Read more.
In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of the number of departure aircraft on the runway utilization rate, the takeoff rate, and the departure rate of flight departures under the conditions of airport runway capacity constraints are studied. The influence of factors, such as the number of departure aircraft, the gate position of the aircraft, and the configuration of airport arrival and departure runways, on the aircraft taxiing time for departure is analyzed. Based on a multivariate linear regression equation, a time prediction model of aircraft departure taxiing time is established. The fuel consumption and pollutant emissions of aircraft are calculated. The experimental results show that, without reducing the utilization rate of the runway and the departure rate of flights, implementing a reasonable pushback number for control of departing aircraft during busy hours can reduce the departure taxiing time of aircraft by nearly 32%, effectively reducing the fuel consumption and pollutant emissions during taxiing on the airport surface. Full article
(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)
►▼ Show Figures

Figure 1

Figure 1
<p>Network diagram of the Shanghai Hongqiao Airport taxiway system.</p> Full article ">Figure 2
<p>The relationship between runway utilization and the number of departing aircraft.</p> Full article ">Figure 3
<p>The relationship between the takeoff rate and the number of departure aircraft.</p> Full article ">Figure 4
<p>The scatter diagram of the relationship between the departure taxiing time and the number of aircraft.</p> Full article ">Figure 5
<p>The scatter diagram of the relationship between the departure taxiing time and the number of departing aircraft.</p> Full article ">Figure 6
<p>The scatter diagram of the relationship between the flight taxi time and the taxi distance.</p> Full article ">Figure 7
<p>The scatter diagram of the relationship between the flight taxi time and the taxi distance.</p> Full article ">Figure 8
<p>The fuel consumption reduction graph.</p> Full article ">Figure 9
<p>The reduction in the pollutant emissions.</p> Full article ">
19 pages, 12292 KiB  
Article
Leveraging Hybrid Filter for Improving Quasi-Type-1 Phase Locked Loop Targeting Fast Transient Response
by Yunlu Li, Junyou Yang, Haixin Wang, Weichun Ge and Yiming Ma
Energies 2018, 11(9), 2472; https://doi.org/10.3390/en11092472 - 17 Sep 2018
Cited by 8 | Viewed by 3182
Abstract
In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates [...] Read more.
In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates the recent development of moving average filters (MAFs) based PLL in a quasi-type-1 system (i.e., QT1-PLL). Despite its success in certain applications, the transient response is still unsatisfactory, mainly due to the fact that the time delay caused by MAFs is still large. This has significantly limited the utilization of QT1-PLL, according to common grid codes such as German and Spanish grid codes. This challenge has been tackled in this paper. The basic idea is to develop a new hybrid filtering stage, consisting of adaptive notch filters (ANFs) and MAFs, arranged at the inner loop of QT1-PLL. Such an idea can greatly improve the transient response of QT1-PLL, owing to the fact that ANFs are utilized to remove the fundamental frequency negative voltage sequence (FFNS) component while other dominant harmonics can be removed by MAFs with a small time delay. By applying the proposed technique, the settling time is reduced to less than one cycle of grid frequency without any degradation in filtering capability. Moreover, the proposed PLL can be easily expanded to handle dc offset rejection. The effectiveness is validated by comprehensive experiments. Full article
(This article belongs to the Special Issue Power Electronics in Renewable Energy Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Block schematic of SRF-PLL.</p> Full article ">Figure 2
<p>A general classification of typical advanced PLLs.</p> Full article ">Figure 3
<p>Block scheme of QT1-PLL.</p> Full article ">Figure 4
<p>Block scheme of the proposed PLL.</p> Full article ">Figure 5
<p>Bode plot of ANF part in filtering stage. <span class="html-italic">ξ</span> = 0.5 (dotted lines), 0.7 (dashed lines), 0.9 (solid lines).</p> Full article ">Figure 6
<p>Step response of ANF(<span class="html-italic">s</span>).</p> Full article ">Figure 7
<p>The adaptive structure of ANF.</p> Full article ">Figure 8
<p>Frequency response of the entire hybrid cascaded filtering stage.</p> Full article ">Figure 9
<p>Frequency response of H<sub>dc</sub>(<span class="html-italic">s</span>).</p> Full article ">Figure 10
<p>Small-signal model of QT1-PLL.</p> Full article ">Figure 11
<p>Small-signal model of the proposed PLL.</p> Full article ">Figure 12
<p>The simplified model of the proposed structure.</p> Full article ">Figure 13
<p>2% settling time as a function of <span class="html-italic">k</span>.</p> Full article ">Figure 14
<p>Bode plot of open-loop transfer function in proposed PLL and QT1-PLL.</p> Full article ">Figure 15
<p>2% settling time of the proposed PLL (with dc rejection capability) under phase and frequency jump.</p> Full article ">Figure 16
<p>Open-loop bode plot of the proposed PLL and QT1-PLL (with dc rejection capability).</p> Full article ">Figure 17
<p>Dynamic behavior of the actual proposed PLL and its model.</p> Full article ">Figure 18
<p>Discrete-time realization of ANF.</p> Full article ">Figure 19
<p>The discretization effect of the proposed PLL.</p> Full article ">Figure 20
<p>Experimental setup.</p> Full article ">Figure 21
<p>Experimental waveforms under a +40° phase jump: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 22
<p>Experimental waveforms under a +5 Hz frequency step change: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 22 Cont.
<p>Experimental waveforms under a +5 Hz frequency step change: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 23
<p>Experimental waveforms under a +100 Hz/s frequency ramp change: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 24
<p>Experimental waveforms under a 0.5 p.u. voltage sag: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 25
<p>Experimental waveforms under distorted voltage condition with a +5 Hz frequency jump: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 25 Cont.
<p>Experimental waveforms under distorted voltage condition with a +5 Hz frequency jump: (<b>a</b>) Three-phase voltages; (<b>b</b>,<b>c</b>) Estimated frequency; (<b>d</b>,<b>e</b>) Phase error.</p> Full article ">Figure 26
<p>Experimental waveforms under a dc offset injection condition: (<b>a</b>) Three-phase voltages; (<b>b</b>) Estimated frequency; (<b>c</b>) Phase error.</p> Full article ">
23 pages, 4390 KiB  
Article
Short-Circuit Current Calculation and Harmonic Characteristic Analysis for a Doubly-Fed Induction Generator Wind Turbine under Converter Control
by Jing Li, Tao Zheng and Zengping Wang
Energies 2018, 11(9), 2471; https://doi.org/10.3390/en11092471 - 17 Sep 2018
Cited by 13 | Viewed by 4735
Abstract
An accurate calculation of short-circuit current (SCC) is very important for relay protection setting and optimization design of electrical equipment. The short-circuit current for a doubly-fed induction generator wind turbine (DFIG-WT) under excitation regulation of a converter contains the stator current and grid-side [...] Read more.
An accurate calculation of short-circuit current (SCC) is very important for relay protection setting and optimization design of electrical equipment. The short-circuit current for a doubly-fed induction generator wind turbine (DFIG-WT) under excitation regulation of a converter contains the stator current and grid-side converter (GSC) current. The transient characteristics of GSC current are controlled by double closed-loops of the converter and influenced by fluctuations of direct current (DC) bus voltage, which is characterized as high order, multiple variables, and strong coupling, resulting in great difficulty with analysis. Existing studies are mainly focused on the stator current, neglecting or only considering the steady-state short-circuit current of GSC, resulting in errors in the short-circuit calculation of DFIG-WT. This paper constructs a DFIG-WT total current analytical model involving GSC current. Based on Fourier decomposition of switch functions and the frequency domain analytical method, the fluctuation of DC bus voltage is considered and described in detail. With the proposed DFIG-WT short-circuit current analytical model, the generation mechanism and evolution law of harmonic components are revealed quantitatively, especially the second harmonic component, which has a great influence on transformer protection. The accuracies of the theoretical analysis and mathematical model are verified by comparing calculation results with simulation results and low-voltage ride-through (LVRT) field test data of a real DFIG. Full article
(This article belongs to the Special Issue Modeling of Wind Turbines and Wind Farms)
►▼ Show Figures

Figure 1

Figure 1
<p>Current regulating loop and voltage regulating loop of the grid-side converter (GSC): (<b>a</b>) current regulating loop; (<b>b</b>) voltage regulating loop.</p> Full article ">Figure 2
<p>Control framework of inner current loop of RSC.</p> Full article ">Figure 3
<p>Flow chart of the integration process of the transient short-circuit current calculation model for DFIG-WT: (<b>a</b>) simplified graphic of the relations among critical quantities; (<b>b</b>) detailed flow chart of the integration process.</p> Full article ">Figure 4
<p>Characteristics of steady-state components of DFIG-WT fault current.</p> Full article ">Figure 5
<p>Simulation system.</p> Full article ">Figure 6
<p>Comparison of DC bus voltage and decomposition of frequency components: (<b>a</b>) comparison between simulated and calculated waveform; (<b>b</b>) decomposition of frequency components.</p> Full article ">Figure 7
<p>GSC short-circuit current and its frequency spectral analysis: (<b>a</b>) comparison between simulated and calculated waveform; (<b>b</b>) frequency spectral analysis.</p> Full article ">Figure 8
<p>Proportions of the second harmonic component in short-circuit current of DFIG-WT and GSC: (<b>a</b>) supersynchronous, <span class="html-italic">s</span> = −0.2; (<b>b</b>) subsynchronous, <span class="html-italic">s</span> = 0.2.</p> Full article ">Figure 9
<p>Rotor short-circuit current and its frequency spectral analysis: (<b>a</b>) comparison between simulated and calculated waveform; (<b>b</b>) frequency spectral analysis.</p> Full article ">Figure 10
<p>Stator short-circuit current and its frequency spectral analysis: (<b>a</b>) comparison between simulated and calculated waveform; (<b>b</b>) frequency spectral analysis.</p> Full article ">Figure 11
<p>Contrast verification of key characteristic parameters of short-circuit current under different conditions, as well as proportions of steady-state GSC-SCC.</p> Full article ">Figure 12
<p>(<b>a</b>) LVRT test schematic diagram, (<b>b</b>) picture of the LVRT field test devices.</p> Full article ">Figure 13
<p>Active and reactive power under different conditions: (<b>a</b>) supersynchronous condition; (<b>b</b>) subsynchronous condition.</p> Full article ">Figure 14
<p>Comparison between recorded data and calculated data under two operating conditions: (<b>a</b>) supersynchronous condition; (<b>b</b>) subsynchronous condition.</p> Full article ">Figure A1
<p>Control diagram of the RSC.</p> Full article ">Figure A2
<p>Control diagram of the GSC.</p> Full article ">
10 pages, 4087 KiB  
Article
In Situ Studies on the Characteristics of Strata Structures and Behaviors in Mining of a Thick Coal Seam with Hard Roofs
by Yiwen Lan, Rui Gao, Bin Yu and Xiangbin Meng
Energies 2018, 11(9), 2470; https://doi.org/10.3390/en11092470 - 17 Sep 2018
Cited by 29 | Viewed by 3131
Abstract
The movements of overburden induced by mining a thick coal seam with a hard roof extend widely. The effects of breakages in the hard strata on the strata behaviors might vary with the overlying strata layers. For this reason, we applied a test [...] Read more.
The movements of overburden induced by mining a thick coal seam with a hard roof extend widely. The effects of breakages in the hard strata on the strata behaviors might vary with the overlying strata layers. For this reason, we applied a test method that integrated a borehole TV tester, borehole-based monitoring of strata movement, and monitoring of support resistance for an in situ investigation of a super-thick, 14–20 m coal seam mining in the Datong mining area in China. The results showed that the range of the overburden movement was significantly high, which could reach to more than 300 m. The key strata (KS) in the lower layer main roof were broken into a ‘cantilever beam and voussoir beam’ structure. This structure accounted for the ‘long duration and short duration’ strata behaviors in the working face. On the other hand, the hard KS in the upper layer broke into a ‘high layer structure’. The structural instability induced intensive and wide-ranging strata behaviors that lasted for a long time (two to three days). Support in the working face were over-pressured by large dynamic factors and were widely crushed, while the roadways were violently deformed. Hence, the structure of a thick coal seam with a hard roof after mining will form a ‘cantilever beam and voussoir beam and high layer structure’, which is unique to a large space stope. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Mining overview of thick coal seam.</p> Full article ">Figure 2
<p>Measurement point layout and schema of monitoring apparatus.</p> Full article ">Figure 3
<p>Variation of strata movement in borehole No. 1 with advancement of the working face.</p> Full article ">Figure 4
<p>Fracture height curve recorded by the borehole TV tester.</p> Full article ">Figure 5
<p>Resistances in No. 55 support along the breakages of key strata KS1 and KS2.</p> Full article ">Figure 6
<p>Resistances in No. 55 support along the rotations of KS3.</p> Full article ">Figure 7
<p>Roadway deformation.</p> Full article ">Figure 8
<p>Strata behaviors on supports in the working face.</p> Full article ">Figure 9
<p>Fracture height curve.</p> Full article ">Figure 10
<p>Strata structure and strata behavior characteristics.</p> Full article ">
18 pages, 5046 KiB  
Article
Synchronous Resonant Control Technique to Address Power Grid Instability Problems Due to High Renewables Penetration
by Majid Mehrasa, Edris Pouresmaeil, Bahram Pournazarian, Amir Sepehr, Mousa Marzband and João P. S. Catalão
Energies 2018, 11(9), 2469; https://doi.org/10.3390/en11092469 - 17 Sep 2018
Cited by 19 | Viewed by 4830
Abstract
This paper presents a synchronous resonant control strategy based on the inherent characteristics of permanent magnet synchronous generators (PMSG) for the control of power converters to provide stable operating conditions for the power grid under high penetration of renewable energy resources (RERs). The [...] Read more.
This paper presents a synchronous resonant control strategy based on the inherent characteristics of permanent magnet synchronous generators (PMSG) for the control of power converters to provide stable operating conditions for the power grid under high penetration of renewable energy resources (RERs). The proposed control technique is based on the small signal linearization of a dynamic model with grid specifications, load-current-based voltages, and power converter currents. A combination of the linearized dynamic model with the PMSG swing equation and resonant controller leads to a control technique with synchronous features and appropriate inertia for the control of converter-based power generators. As the main contribution of this work, an extra functionality is proposed in the control loop of the proposed model to solve the inherent inconveniences of conventional synchronous generators. Also, a comprehensive collaboration between interfaced converter specifications and PMSG features is achieved as another contribution of the proposed control technique, and this can guarantee accurate performance under various conditions. A current perturbation curve is introduced to assess the variations of the grid frequency and voltage magnitude under operation of the interfaced converters controlled by the proposed control technique. Moreover, by taking into account the load-based voltages, the effects of the current perturbation components are investigated. The proposed model is simulated in MATLAB/Simulink environment to verify the high performance of the proposed control technique over the other existing control methods. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>General structure of the proposed model.</p> Full article ">Figure 2
<p>The proposed controller: (<b>a</b>) <span class="html-italic">d</span>-component; (<b>b</b>) <span class="html-italic">q</span>-component.</p> Full article ">Figure 2 Cont.
<p>The proposed controller: (<b>a</b>) <span class="html-italic">d</span>-component; (<b>b</b>) <span class="html-italic">q</span>-component.</p> Full article ">Figure 3
<p>The Nyquist diagram of Δ<span class="html-italic">i<sub>d</sub></span> for constant values of: (<b>a</b>) Δ<span class="html-italic">P<sub>m</sub></span>; (<b>b</b>) Δ<span class="html-italic">P</span>; (<b>c</b>) Δ<span class="html-italic">v<sub>gd</sub></span>.</p> Full article ">Figure 4
<p>The Nyquist diagram of Δ<span class="html-italic">i<sub>q</sub></span> for constant values of: (<b>a</b>) Δ<span class="html-italic">P<sub>m</sub></span>; (<b>b</b>) Δ<span class="html-italic">P</span>; (<b>c</b>) Δ<span class="html-italic">Q</span>; (<b>d</b>) Δ<span class="html-italic">v<sub>gd</sub></span>.</p> Full article ">Figure 5
<p>Effects of grid frequency error variation on the current error curve: (<b>a</b>) Δ<span class="html-italic">ω</span> = 0 Hz; (<b>b</b>) Δ<span class="html-italic">ω</span> = 0.2 Hz; (<b>c</b>) for various grid frequency errors.</p> Full article ">Figure 6
<p>Effects of the variation of grid voltage magnitude error on the current error curve: (<b>a</b>) Δ<span class="html-italic">v<sub>d</sub></span> = 0 V; (<b>b</b>) Δ<span class="html-italic">v<sub>d</sub></span> = 10 V; (<b>c</b>) for various grid voltage magnitude errors.</p> Full article ">Figure 6 Cont.
<p>Effects of the variation of grid voltage magnitude error on the current error curve: (<b>a</b>) Δ<span class="html-italic">v<sub>d</sub></span> = 0 V; (<b>b</b>) Δ<span class="html-italic">v<sub>d</sub></span> = 10 V; (<b>c</b>) for various grid voltage magnitude errors.</p> Full article ">Figure 7
<p>The current perturbation components effects on: (<b>a</b>) Δ<span class="html-italic">v</span><sub>1</sub>; (<b>b</b>) Δ<span class="html-italic">v</span><sub>2</sub>.</p> Full article ">Figure 8
<p>Operation of the SAPIRC-based converter under high penetration of RERs: (<b>a</b>) under a sudden connection, and (<b>b</b>) different values of the resonant factor under sudden disconnection.</p> Full article ">Figure 9
<p>The grid voltage magnitude and frequency, the grid currents, and the active and reactive power of SAPIRC-based converter under a sudden connection of large-scale RERs.</p> Full article ">Figure 9 Cont.
<p>The grid voltage magnitude and frequency, the grid currents, and the active and reactive power of SAPIRC-based converter under a sudden connection of large-scale RERs.</p> Full article ">Figure 10
<p>Evaluation of the resonant factor variations: (<b>a</b>) active power of the SAPIRC-based converter; (<b>b</b>) reactive power of the SAPIRC-based converter; (<b>c</b>) the grid voltage magnitude; (<b>d</b>) the grid frequency.</p> Full article ">
13 pages, 2295 KiB  
Article
Using Piecewise Linearization Method to PCS Input/Output-Efficiency Curve for a Stand-Alone Microgrid Unit Commitment
by Ha-Lim Lee and Yeong-Han Chun
Energies 2018, 11(9), 2468; https://doi.org/10.3390/en11092468 - 17 Sep 2018
Cited by 4 | Viewed by 3367
Abstract
When operating a stand-alone micro grid, the battery energy storage system (BESS) and a diesel generator are key components needed in order to maintain demand-supply balance. Using Unit Commitment (UC) to calculate the optimal operation schedule of a BESS and diesel generator helps [...] Read more.
When operating a stand-alone micro grid, the battery energy storage system (BESS) and a diesel generator are key components needed in order to maintain demand-supply balance. Using Unit Commitment (UC) to calculate the optimal operation schedule of a BESS and diesel generator helps minimize the operation cost of the micro grid. While calculating the optimal operation schedule for the microgrid, it is important that it reflects the actual characteristics of the implanted devices, in order to increase the schedule result accuracy. In this paper, a piecewise linearization, on the actual power conditioning system (PCS) input/output-efficiency characteristic curve, has been considered while calculating the optimal operation schedule using UC. The optimal schedule result calculated by the proposed method has been examined by comparing the schedule calculated by a fixed input/output-efficiency case, which is conventionally used while solving UC for a stand-alone microgrid. Full article
(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Types of input/output-efficiency characteristic curve of 500 kW power conditioning system (PCS).</p> Full article ">Figure 2
<p>Power System of Ga-sa Island.</p> Full article ">Figure 3
<p>Input forecast data for case study.</p> Full article ">Figure 4
<p>Analysis process for case study.</p> Full article ">Figure 5
<p>Simulation result from Case 1.</p> Full article ">Figure 6
<p>Simulation result from Case 2.</p> Full article ">
28 pages, 14272 KiB  
Article
Nonlinear Temperature-Dependent State Model of Cylindrical LiFePO4 Battery for Open-Circuit Voltage, Terminal Voltage and State-of-Charge Estimation with Extended Kalman Filter
by Cheng Siong Chin, Zuchang Gao, Joel Hay King Chiew and Caizhi Zhang
Energies 2018, 11(9), 2467; https://doi.org/10.3390/en11092467 - 17 Sep 2018
Cited by 28 | Viewed by 5264
Abstract
Ambient temperature affects the performance of a battery power system and its accuracy in state-of-charge (SOC) estimation for electric vehicles and smart grid systems. This paper proposes a battery model that considered ambient temperature, cell temperature, hysteresis voltage and thermal aging [...] Read more.
Ambient temperature affects the performance of a battery power system and its accuracy in state-of-charge (SOC) estimation for electric vehicles and smart grid systems. This paper proposes a battery model that considered ambient temperature, cell temperature, hysteresis voltage and thermal aging on capacity due to multiple charging and discharging. The SOC is then estimated using an extended Kalman filter. Several forms of validation were tested on an actual cell battery under specific ambient temperatures to verify the battery cell model, terminal voltage and SOC estimation performance. The SOC estimation results show an improvement in root-mean-squared error as compared to Extended Kalman Filter (EKF) without considering the temperature dependency. The proposed battery temperature-dependent model gave a smaller root-mean square error in SOC and terminal voltage at 5 °C, 15 °C and 45 °C. Full article
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Proposed ambient and cell temperature-dependent ECM of battery cell; (<b>b</b>) terminal voltage of 1RC and 2RC as compared to experiment data.</p> Full article ">Figure 2
<p>Cell capacity of battery cell as function of ambient temperature.</p> Full article ">Figure 3
<p>Test bench setup for battery testing [<a href="#B51-energies-11-02467" class="html-bibr">51</a>,<a href="#B64-energies-11-02467" class="html-bibr">64</a>,<a href="#B65-energies-11-02467" class="html-bibr">65</a>,<a href="#B66-energies-11-02467" class="html-bibr">66</a>].</p> Full article ">Figure 4
<p>Discharge current pulses during discharging pulse test.</p> Full article ">Figure 5
<p>Terminal voltage during discharging pulse test.</p> Full article ">Figure 6
<p>State-of-charge during discharging pulse test at 25 °C.</p> Full article ">Figure 7
<p>(<b>a</b>) proposed OCV-SOC relationship as compared to experimental data (at ambient temperature of 25 °C); (<b>b</b>) block diagram for OCV-SOC relationship at different ambient temperature (<b>c</b>) proposed OCV-SOC relationship to experimental data at different ambient temperature; (<b>d</b>) RMSE of OCV as compared to the proposed method at a different ambient temperature.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) proposed OCV-SOC relationship as compared to experimental data (at ambient temperature of 25 °C); (<b>b</b>) block diagram for OCV-SOC relationship at different ambient temperature (<b>c</b>) proposed OCV-SOC relationship to experimental data at different ambient temperature; (<b>d</b>) RMSE of OCV as compared to the proposed method at a different ambient temperature.</p> Full article ">Figure 8
<p><span class="html-italic">R</span><sub>0</sub> as function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 8 Cont.
<p><span class="html-italic">R</span><sub>0</sub> as function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 9
<p><span class="html-italic">R</span><sub>1</sub> as function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 9 Cont.
<p><span class="html-italic">R</span><sub>1</sub> as function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 10
<p><span class="html-italic">C</span><sub>1</sub> as a function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 10 Cont.
<p><span class="html-italic">C</span><sub>1</sub> as a function of (<b>a</b>) <span class="html-italic">SOC</span>; (<b>b</b>) <span class="html-italic">SOC</span> and ambient temperature.</p> Full article ">Figure 11
<p>Schematic of experimental setup.</p> Full article ">Figure 12
<p>Thermal image of cell temperature at ambient temperature of 25 °C (for constant 2 A load current).</p> Full article ">Figure 13
<p>Cell temperature at different ambient temperature (for constant 2 A load current) for 3000 s.</p> Full article ">Figure 14
<p>Simulation block diagram for battery cell model.</p> Full article ">Figure 15
<p>Terminal voltage at different ambient temperature of 5 °C.</p> Full article ">Figure 16
<p>Terminal voltage at different ambient temperature of 15 °C.</p> Full article ">Figure 17
<p>Terminal voltage at different ambient temperature of 25 °C.</p> Full article ">Figure 18
<p>Terminal voltage at different ambient temperature of 35 °C.</p> Full article ">Figure 19
<p>Terminal voltage at different ambient temperature of 45 °C.</p> Full article ">Figure 20
<p><span class="html-italic">SOC</span> and RMSE of <span class="html-italic">SOC</span> as compared to experimental result at 25 °C.</p> Full article ">Figure 21
<p><span class="html-italic">SOC</span> and RMSE of <span class="html-italic">SOC</span> as compared to experimental result at ambient temperature of (<b>a</b>) 5 °C; (<b>b</b>) 15 °C; (<b>c</b>) 35 °C; (<b>d</b>) 45 °C.</p> Full article ">Figure 21 Cont.
<p><span class="html-italic">SOC</span> and RMSE of <span class="html-italic">SOC</span> as compared to experimental result at ambient temperature of (<b>a</b>) 5 °C; (<b>b</b>) 15 °C; (<b>c</b>) 35 °C; (<b>d</b>) 45 °C.</p> Full article ">Figure 22
<p><span class="html-italic">SOC</span> as compared to experimental result at different ambient temperature (5 °C, 15 °C and 25 °C).</p> Full article ">Figure 23
<p><span class="html-italic">SOC</span> as compared to experimental result at different ambient temperature (35 °C and 45 °C).</p> Full article ">Figure 24
<p>Terminal voltage at different ambient temperature (5 °C, 15 °C and 25 °C).</p> Full article ">Figure 25
<p>Terminal voltage at different ambient temperature (35 °C and 45 °C).</p> Full article ">
17 pages, 5765 KiB  
Article
An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering
by Rongheng Lin, Budan Wu and Yun Su
Energies 2018, 11(9), 2466; https://doi.org/10.3390/en11092466 - 17 Sep 2018
Cited by 16 | Viewed by 3831
Abstract
Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the [...] Read more.
Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering. Full article
(This article belongs to the Collection Smart Grid)
►▼ Show Figures

Figure 1

Figure 1
<p>Daily LCTAP and ILCTAP.</p> Full article ">Figure 2
<p>The overall flowchart of the weighted Pearson-based hierarchy clustering algorithm.</p> Full article ">Figure 3
<p>The experimental procedure.</p> Full article ">Figure 4
<p>Clustering results (the left sub-figure includes low level load samples, the right sub-figure includes high level load samples).</p> Full article ">Figure 5
<p>Typical load curve from <a href="#energies-11-02466-f004" class="html-fig">Figure 4</a> (blue for cluster 1, and red for cluster 2).</p> Full article ">Figure 6
<p>Month distribution of cluster 1 and cluster 2 (axis x for the months and axis y for the numbers of users).</p> Full article ">Figure 7
<p>Original Pearson results.</p> Full article ">Figure 8
<p>Weighted Pearson results.</p> Full article ">Figure 9
<p>Weekday distribution of the original Pearson results.</p> Full article ">Figure 10
<p>Weekdays distribution of Weighted Pearson Results.</p> Full article ">Figure 11
<p>Typical load curves of clusters 10, 11, 12.</p> Full article ">Figure 12
<p>Two typical days’ load curves.</p> Full article ">Figure 13
<p>Load curve comparison (Predict 2 is our method).</p> Full article ">Figure 14
<p>Deviation analysis of real, original Pearson and weighted Pearson.</p> Full article ">
14 pages, 2972 KiB  
Article
Enhancing Green Building Rating of a School under the Hot Climate of UAE; Renewable Energy Application and System Integration
by Joud Al Dakheel, Kheira Tabet Aoul and Ahmed Hassan
Energies 2018, 11(9), 2465; https://doi.org/10.3390/en11092465 - 17 Sep 2018
Cited by 19 | Viewed by 4578
Abstract
Similar to many fast growing countries, the United Arab Emirates (UAE) witnessed fast population and urbanization growth. The building sector accounts for a major share of its electricity consumption, reaching up to 70%. To encourage sustainable development and reduce energy consumption and emissions, [...] Read more.
Similar to many fast growing countries, the United Arab Emirates (UAE) witnessed fast population and urbanization growth. The building sector accounts for a major share of its electricity consumption, reaching up to 70%. To encourage sustainable development and reduce energy consumption and emissions, the government introduced a sustainability initiative called “Estidama”, which employs the use of the Pearl Building Rating System (PBRS). Government buildings, which constitute 20% of the built environment, aim to lead the way, and are therefore required to attain a high level of achievement, based on their PBRS ranking (minimum of two out of five pearls). Schools, led by Abu Dhabi Educational Council (ADEC), are governmental buildings and aim to attain a higher level of achievement (three out of five pearls). The ADEC plans to build one hundred schools to be built by 2020, through its Future Schools Program. Over half of the schools have been completed, but only 20% reached the targeted rating (of three out of five pearls). The Renewable Energy (RE) application in the UAE is minimal, although it represents 25% of the local rating code. The objective of this paper is to explore the sustainable performance of one representative school that did not reach the desired green rating level, with the objective to assess opportunities for an enhanced performance. This is done through testing the performance and the application of three RE systems comprising of photovoltaics (PV) array, an absorption cooling system and a geothermal cooling system through Transient Systems Simulation (TRNSYS) software. Cumulatively, implementation of these options results in RE potentially contributing to 19% of the school’s annual energy consumption, enhancing the school’s performance by up to 14 additional credit points, and reaching the target level of achievement (a three pearl rating). Furthermore, system integration of RE into the existing school were also considered. Results indicate the significant potential of integrating RE systems in future schools in hot climatic contexts, for an improved energy performance. Full article
(This article belongs to the Section A: Sustainable Energy)
►▼ Show Figures

Figure 1

Figure 1
<p>Total electricity power production in the Abu Dhabi emirate [<a href="#B1-energies-11-02465" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>External view of the school [<a href="#B17-energies-11-02465" class="html-bibr">17</a>].</p> Full article ">Figure 3
<p>Monthly energy produced per panel at various Photovoltaic (PV) slopes representative of different building integration schemes.</p> Full article ">Figure 4
<p>Annual power production per panel (kWh) for different static slope angles, and an optimized adjustable slope design.</p> Full article ">Figure 5
<p>Total annual power production (kWh) for different PV panel azimuth angles for at latitude angle of 24°.</p> Full article ">Figure 6
<p>Energy and collector outlet temperature produced by the simulated absorption cooling system under various concentration ratios (<b>a</b>) and fluid flow rates (<b>b</b>) in 2016.</p> Full article ">Figure 7
<p>Peak ambient air temperature and ground temperature at a depth of 5 m.</p> Full article ">Figure 8
<p>Delivered energy for various pipe flow rates.</p> Full article ">Figure 9
<p>The parametric influence of pipe material (<b>a</b>) pipe depth (<b>b</b>) pipe spacing (<b>c</b>) and pipe diameter (<b>d</b>) on the delivered energy.</p> Full article ">Figure 10
<p>Renewable energy system integration.</p> Full article ">
20 pages, 6418 KiB  
Article
Prediction of Combustion and Heat Release Rates in Non-Premixed Syngas Jet Flames Using Finite-Rate Scale Similarity Based Combustion Models
by Ali Shamooni, Alberto Cuoci, Tiziano Faravelli and Amsini Sadiki
Energies 2018, 11(9), 2464; https://doi.org/10.3390/en11092464 - 17 Sep 2018
Cited by 5 | Viewed by 4346
Abstract
Generating energy from combustion is prone to pollutant formation. In energy systems working under non-premixed combustion mode, rapid mixing is required to increase the heat release rates. However, local extinction and re-ignition may occur, resulting from strong turbulence–chemistry interaction, especially when rates of [...] Read more.
Generating energy from combustion is prone to pollutant formation. In energy systems working under non-premixed combustion mode, rapid mixing is required to increase the heat release rates. However, local extinction and re-ignition may occur, resulting from strong turbulence–chemistry interaction, especially when rates of mixing exceed combustion rates, causing harmful emissions and flame instability. Since the physical mechanisms for such processes are not well understood, there are not yet combustion models in large eddy simulation (LES) context capable of accurately predicting them. In the present study, finite-rate scale similarity (SS) combustion models were applied to evaluate both heat release and combustion rates. The performance of three SS models was a priori assessed based on the direct numerical simulation of a temporally evolving syngas jet flame experiencing high level of local extinction and re-ignition. The results show that SS models following the Bardina’s “grid filtering” approach (A and B) have lower errors than the model based on the Germano’s “test filtering” approach (C), in terms of mean, root mean square (RMS), and local errors. In mean, both Bardina’s based models capture well the filtered combustion and heat release rates. Locally, Model A captures better major species, while Model B retrieves radicals more accurately. Full article
(This article belongs to the Special Issue Computational Fluid Dynamics (CFD) 2018)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The schematic of the temporal jet direct numerical simulation (DNS) case, including the contours of heat release rate (colored from black to white) and the mass fraction of OH radical (colored from blue to red) at the maximum extinction time (<math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>). Local extinction events on the shear layers are observed with low OH mass fraction and the corresponding low heat release rate; (<b>b</b>) favre averaged mixture fraction (<math display="inline"><semantics> <mrow> <msub> <munder accentunder="true"> <mi>Z</mi> <mo>_</mo> </munder> <mi>f</mi> </msub> </mrow> </semantics></math>) in the whole simulation time colored by Favre averaged temperature. The vertical red lines are the time instants analyzed in the current study; and (<b>c</b>) normalized energy spectrum on the center plane (black line) and −5/3 scaling law shown by dashed red line; <math display="inline"><semantics> <mi>κ</mi> </semantics></math> is the wave number, <math display="inline"><semantics> <mrow> <msub> <munder accentunder="true"> <mi>η</mi> <mo>_</mo> </munder> <mi>f</mi> </msub> </mrow> </semantics></math>, Favre averaged Kolmogorov length scale and <math display="inline"><semantics> <mrow> <msub> <munder accentunder="true"> <mi>ε</mi> <mo>_</mo> </munder> <mi>f</mi> </msub> </mrow> </semantics></math> Favre averaged turbulent dissipation rate.</p> Full article ">Figure 2
<p>(<b>a</b>) Locations of the cutoff filter with the corresponding filter widths on log-log plot of compensated energy spectrum computed on the central plane; and (<b>b</b>) fraction of the Favre averaged resolved TKE using different filter widths at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Production/consumption rate of H<sub>2</sub> with units (kg/(m<sup>3</sup>s)), (<span style="color:#A9E2F3">-</span>) filtered DNS, (<span style="color:red">∗</span>) “no model”, (<span style="color:#A9F5BC">◁</span>) SS Model A, (+) SS Model B, (◇) SS Model C: (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) root mean square (RMS). Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. vertical dashed-blue lines: planes of maximum mean turbulent kinetic energy (TKE); vertical dot-dashed green lines: planes of mean stoichiometric mixture fraction; and vertical red lines: planes of maximum mean temperature fluctuations.</p> Full article ">Figure 4
<p>Production/consumption rate of H with units (kg/(m<sup>3</sup>s)): (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) RMS. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. Vertical lines are the same as in <a href="#energies-11-02464-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Cumulative local errors incurred using different models in prediction of different species net formation rates: (<b>a</b>–<b>c</b>) logarithm of errors for all models; and (<b>d</b>–<b>f</b>) the errors divided by the “no model” approach error. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Heat release rate with units (kg/(ms<sup>3</sup>)): (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) RMS. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. Vertical lines are the same as in <a href="#energies-11-02464-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 7
<p>Production/consumption rate of H<sub>2</sub> with units (kg/(m<sup>3</sup>s)): (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) RMS. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>35</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. Vertical lines are the same as in <a href="#energies-11-02464-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 8
<p>Production/consumption rate of H with units (kg/(m<sup>3</sup>s)): (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) RMS. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>35</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. Vertical lines are the same as in <a href="#energies-11-02464-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 9
<p>Cumulative local errors incurred using different models in prediction of different species net formation rates: (<b>a</b>–<b>c</b>) logarithm of errors for all models; and (<b>d</b>–<b>f</b>) the errors are divided by the “no model” approach error. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> = 18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>35</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Heat release rate with units (kg/(ms<sup>3</sup>)): (<b>a</b>–<b>c</b>) Mean; and (<b>d</b>–<b>f</b>) RMS. Different filter widths are applied: (<b>a</b>,<b>d</b>) ∆/∆<sub>DNS</sub> = 8; (<b>b</b>,<b>e</b>) ∆/∆<sub>DNS</sub> = 12; and (<b>c</b>,<b>f</b>) ∆/∆<sub>DNS</sub> =18. The data are extracted at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>35</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>. Vertical lines are the same as in <a href="#energies-11-02464-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 11
<p>Residual <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ω</mi> <mo>˙</mo> </mover> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>2</mn> </msub> <mi mathvariant="normal">O</mi> </mrow> </msub> </mrow> </semantics></math> with units (kg/(m<sup>3</sup>s)) when DNS is filtered by using <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mo>/</mo> <msub> <mo>Δ</mo> <mrow> <mi>DNS</mi> </mrow> </msub> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> <msub> <mi>t</mi> <mi>j</mi> </msub> </mrow> </semantics></math>: (<b>a</b>) Exact; and (<b>b</b>) Predicted by Model A. Cut-off plane is the central <span class="html-italic">xy</span> plane.</p> Full article ">
20 pages, 2892 KiB  
Review
An Overview of Direct Current Distribution System Architectures & Benefits
by Venkata Anand Prabhala, Bhanu Prashant Baddipadiga, Poria Fajri and Mehdi Ferdowsi
Energies 2018, 11(9), 2463; https://doi.org/10.3390/en11092463 - 17 Sep 2018
Cited by 64 | Viewed by 9364
Abstract
This paper examines existing and future direct current (DC) distribution systems with a wide range of applications in data centers, telecommunication systems, commercial buildings, residential homes, electric vehicles, spacecraft, and aircrafts. DC distribution systems have many advantages and disadvantages over their alternating current [...] Read more.
This paper examines existing and future direct current (DC) distribution systems with a wide range of applications in data centers, telecommunication systems, commercial buildings, residential homes, electric vehicles, spacecraft, and aircrafts. DC distribution systems have many advantages and disadvantages over their alternating current (AC) counterparts. There are a few surviving examples of DC distribution systems; among them are the telecommunication systems and data centers that use the low-voltage 48 Vdc systems. However, recently, there has been a move towards higher DC bus voltages. In this paper, a comparative study of different DC distribution architectures and bus structures is presented and voltage level selection is discussed for maximizing system efficiency and reliability, reducing system costs, and increasing the flexibility of the system for future expansion. Furthermore, DC distribution systems are investigated from a safety standpoint and the current global market for these distribution systems is also discussed. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>DC bus structures: (<b>a</b>) radial; (<b>b</b>) ring; (<b>c</b>) ladder; (<b>d</b>) meshed.</p> Full article ">Figure 2
<p>Power architectures: (<b>a</b>) centralized; (<b>b</b>) distributed. Distribution configurations: (<b>c</b>) parallel; (<b>d</b>) cascading; (<b>e</b>) source splitting; (<b>f</b>) load splitting; (<b>g</b>) sum stacking module; (<b>h</b>) difference stacking module.</p> Full article ">Figure 3
<p>Power architectures in telecom and data centers: (<b>a</b>) conventional AC architecture; (<b>b</b>) rack-level DC architecture; (<b>c</b>) facility-level DC architecture [<a href="#B4-energies-11-02463" class="html-bibr">4</a>].</p> Full article ">Figure 4
<p>Characteristic curve of body current vs. duration of flow, IEC/TR 60479-5 [<a href="#B37-energies-11-02463" class="html-bibr">37</a>].</p> Full article ">Figure 5
<p>(<b>a</b>) Design features of Carling Tech DC circuit breaker (© of Carling Tech) [<a href="#B45-energies-11-02463" class="html-bibr">45</a>]; (<b>b</b>) configuration of DC plug and socket outlet system (© of NTT Facilities) [<a href="#B38-energies-11-02463" class="html-bibr">38</a>].</p> Full article ">Figure 6
<p>Grounding methods: (<b>a</b>) positive line; (<b>b</b>) negative line; (<b>c</b>) mid-point; (<b>d</b>) mid-point high resistance; (<b>e</b>) one-end high resistance; (<b>f</b>) floating system.</p> Full article ">Figure 7
<p>Reliability field data of power systems for 10,000 UPSs and 23,000 DC systems (© of NTT Facilities) [<a href="#B47-energies-11-02463" class="html-bibr">47</a>].</p> Full article ">Figure 8
<p>Cost breakdown of 48 Vdc and 270 Vdc systems.</p> Full article ">Figure 9
<p>DC bus voltage level selection [<a href="#B47-energies-11-02463" class="html-bibr">47</a>].</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-313
Previous Issue
Next Issue
Back to TopTop