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Energies, Volume 9, Issue 3 (March 2016) – 104 articles

Cover Story (view full-size image): Rubber-Tyred Gantry (RTG) cranes demand high power when lifting containers and generate energy when lowering, offering opportunities for reducing the energy consumption by introducing an energy storage device. A supervisory control system can be used to control the storage in order to increase the efficiency. By modelling the crane activity as a stochastic process, it was possible to develop an optimal supervisor for on-board energy storage that greatly reduces energy consumption and peak power demand. Benefits include lower energy costs, lower CO2 emissions and reduced maintenance costs. View this paper
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12033 KiB  
Article
Reduced-Capacity Inrush Current Suppressor Using a Matrix Converter in a Wind Power Generation System with Squirrel-Cage Induction Machines
by Sho Shibata, Hiroaki Yamada, Toshihiko Tanaka and Masayuki Okamoto
Energies 2016, 9(3), 223; https://doi.org/10.3390/en9030223 - 21 Mar 2016
Cited by 6 | Viewed by 5938
Abstract
This paper describes the reduced capacity of the inrush current suppressor using a matrix converter (MC) in a large-capacity wind power generation system (WPGS) with two squirrel-cage induction machines (SCIMs). These SCIMs are switched over depending on the wind speed. The input side [...] Read more.
This paper describes the reduced capacity of the inrush current suppressor using a matrix converter (MC) in a large-capacity wind power generation system (WPGS) with two squirrel-cage induction machines (SCIMs). These SCIMs are switched over depending on the wind speed. The input side of the MC is connected to the source in parallel. The output side of the MC is connected in series with the SCIM through matching transformers. The modulation method of the MC used is direct duty ratio pulse width modulation. The reference output voltage of the MC is decided by multiplying the SCIM current with the variable control gain. Therefore, the MC performs as resistors for the inrush current. Digital computer simulation is implemented to confirm the validity and practicability of the proposed inrush current suppressor using PSCAD/EMTDC (power system computer-aided design/electromagnetic transients including DC). Furthermore, the equivalent resistance of the MC is decided by the relationship between the equivalent resistance and the capacity of the MC. Simulation results demonstrate that the proposed inrush current suppressor can suppress the inrush current perfectly. Full article
(This article belongs to the Special Issue Selected Papers from 5th Asia-Pacific Forum on Renewable Energy)
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Figure 1
<p>System configuration with the proposed inrush current suppressor using matrix converter (MC) in a large-capacity wind power generation system (WPGS).</p>
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<p>Switching states in Pattern I and Pattern II in the direct duty ratio pulse width modulation (DDRPWM) method.</p>
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<p>Simulation condition by the wind speed.</p>
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<p>Simulated waveforms of the direct connection for the 100-kW SCIM.</p>
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<p>Simulated waveforms of the direct connection for the 400-kW SCIM.</p>
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<p>System configuration with the soft-starter in a large-capacity WPGS.</p>
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<p>Simulated waveforms with a soft-starter for the 100-kW SCIM.</p>
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<p>Source current total harmonic distortion (THD) of the soft-starter for the 100-kW SCIM in <a href="#energies-09-00223-f007" class="html-fig">Figure 7</a>.</p>
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<p>Simulated waveforms with a soft-starter for the 400-kW SCIM.</p>
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<p>Source current THD of the soft-starter for the 400-kW SCIM in <a href="#energies-09-00223-f009" class="html-fig">Figure 9</a>.</p>
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<p>System configuration with external resistors.</p>
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<p>Source current for the 100-kW SCIM with the external resistors.</p>
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<p>Source current for the 400-kW SCIM with the external resistors.</p>
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<p>Simulation waveforms with the proposed inrush current suppressor for the 100-kW SCIM.</p>
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<p>Simulation waveforms with the proposed inrush current suppressor for the 400-kW SCIM.</p>
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<p>Relationship between the number of turns on the secondary side of the matching transformer and the proposed inrush current suppressor. (<b>a</b>) For the 100-kW SCIM; (<b>b</b>) For the 400-kW SCIM.</p>
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<p>Simulation results with the proposed inrush current suppressor for the 100-kW SCIM when turn ratio is 1:4.</p>
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<p>Simulation results with the proposed inrush current suppressor for the 400-kW SCIM when the turn ratio is 1:4.</p>
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8514 KiB  
Article
Evaluation of Gas Production from Marine Hydrate Deposits at the GMGS2-Site 8, Pearl River Mouth Basin, South China Sea
by Yi Wang, Jing-Chun Feng, Xiao-Sen Li, Yu Zhang and Gang Li
Energies 2016, 9(3), 222; https://doi.org/10.3390/en9030222 - 21 Mar 2016
Cited by 37 | Viewed by 7132
Abstract
Natural gas hydrate accumulations were confirmed in the Dongsha Area of the South China Sea by the Guangzhou Marine Geological Survey 2 (GMGS2) scientific drilling expedition in 2013. The drilling sites of GMGS2-01, -04, -05, -07, -08, -09, -11, -12, and -16 verified [...] Read more.
Natural gas hydrate accumulations were confirmed in the Dongsha Area of the South China Sea by the Guangzhou Marine Geological Survey 2 (GMGS2) scientific drilling expedition in 2013. The drilling sites of GMGS2-01, -04, -05, -07, -08, -09, -11, -12, and -16 verified the existence of a hydrate-bearing layer. In this work gas production behavior was evaluated at GMGS2-8 by numerical simulation. The hydrate reservoir in the GMGS2-8 was characterized by dual hydrate layers and a massive hydrate layer. A single vertical well was considered as the well configuration, and depressurization was employed as the dissociation method. Analyses of gas production sensitivity to the production pressure, the thermal conductivity, and the intrinsic permeability were investigated as well. Simulation results indicated that the total gas production from the reference case is approximately 7.3 × 107 ST m3 in 30 years. The average gas production rate in 30 years is 6.7 × 103 ST m3/day, which is much higher than the previous study in the Shenhu Area of the South China Sea performed by the GMGS-1. Moreover, the maximum gas production rate (9.5 × 103 ST m3/day) has the same order of magnitude of the first offshore methane hydrate production test in the Nankai Trough. When production pressure decreases from 4.5 to 3.4 MPa, the volume of gas production increases by 20.5%, and when production pressure decreases from 3.4 to 2.3 MPa, the volume of gas production increases by 13.6%. Production behaviors are not sensitive to the thermal conductivity. In the initial 10 years, the higher permeability leads to a larger rate of gas production, however, the final volume of gas production in the case with the lowest permeability is the highest. Full article
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<p>Map of northeastern part of South China Sea. The rectangle shows the location of the drilling area.</p>
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<p>Bathymetric map of gas hydrate drilling area and sites.</p>
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<p>Gas hydrate morphologies in core samples recovered from GMGS2 drilling sites, South China Sea. <b>1</b> and <b>2</b> (Site GMGS2-08F) massive; <b>3</b> and <b>4</b> (Site GMGS2-08E) laminated; <b>5</b> (Site GMGS2-08E) and <b>6</b> (Site GMGS2-08C) nodular; <b>7</b> (Site GMGS2-08E) vein; <b>8</b> (Site GMGS2-16D) disseminated.</p>
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<p>Description of the cylindrical system used in the simulations.</p>
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<p>Domain discretization of the hydrate reservoir in the simulations.</p>
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<p>Spatial distributions of hydrate saturation, pressure, and temperatureat <span class="html-italic">t</span> = 0 day.</p>
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<p>Reference case: evolution of cumulative volume of produced gas (<span class="html-italic">V<sub>p</sub></span>), cumulative volume of dissociated gas (<span class="html-italic">V<sub>R</sub></span>), volumetric flow rate of produced gas (<span class="html-italic">Q<sub>p</sub></span>), and volumetric flow rate of dissociated gas (<span class="html-italic">Q<sub>R</sub></span>) overtime.</p>
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<p>Reference case: evolution of the cumulative mass of produced water (<span class="html-italic">M<sub>w</sub></span>) and gas to water ratio (<span class="html-italic">R<sub>GW</sub></span>) overtime.</p>
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<p>Reference case: evolution of the pore pressure (<span class="html-italic">P</span>) distribution overtime.</p>
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<p>Reference case: evolution of the Temperature (<span class="html-italic">T</span>) distribution overtime.</p>
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<p>Reference case: evolution of the hydrate saturation (<span class="html-italic">S<sub>H</sub></span>) distribution overtime.</p>
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<p>Sensitivity to the production pressure (<span class="html-italic">P<sub>W</sub></span>): evolution of <span class="html-italic">V<sub>P</sub></span> and <span class="html-italic">V<sub>R</sub></span> overtime.</p>
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<p>Sensitivity to the production pressure (<span class="html-italic">P<sub>W</sub></span>): evolution of <span class="html-italic">M<sub>W</sub></span> and <span class="html-italic">R<sub>GW</sub></span> overtime.</p>
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<p>Sensitivity to the production pressure (<span class="html-italic">P<sub>W</sub></span>): evolution of <span class="html-italic">P</span>, <span class="html-italic">S<sub>H</sub></span>, and <span class="html-italic">T</span> distribution at <span class="html-italic">t</span> = 30 years.</p>
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<p>Sensitivity to the thermal conductivity (<span class="html-italic">k</span><sub>Θ</sub>): evolution of <span class="html-italic">V<sub>P</sub></span> and <span class="html-italic">V<sub>R</sub></span> overtime.</p>
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<p>Sensitivity to the thermal conductivity (<span class="html-italic">k</span><sub>Θ</sub>): evolution of <span class="html-italic">M<sub>W</sub></span> and <span class="html-italic">R<sub>GW</sub></span> overtime.</p>
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<p>Sensitivity to the thermal conductivity (<span class="html-italic">k</span><sub>Θ</sub>): evolution of <span class="html-italic">P</span>, <span class="html-italic">S<sub>H</sub></span>, and <span class="html-italic">T</span> distribution at <span class="html-italic">t</span> = 30 years.</p>
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<p>Sensitivity to the intrinsic permeability (<span class="html-italic">k</span>): evolution of <span class="html-italic">V<sub>P</sub></span> and <span class="html-italic">V<sub>R</sub></span> overtime.</p>
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<p>Sensitivity to the intrinsic permeability (<span class="html-italic">k</span>): evolution of <span class="html-italic">M<sub>W</sub></span> and <span class="html-italic">R<sub>GW</sub></span> overtime.</p>
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<p>Sensitivity to the intrinsic permeability (<span class="html-italic">k</span>): evolution of <span class="html-italic">P</span>, <span class="html-italic">S<sub>H</sub></span>, and <span class="html-italic">T</span> distribution at <span class="html-italic">t</span> = 30 years.</p>
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4108 KiB  
Article
Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting
by Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang and Wei-Chiang Hong
Energies 2016, 9(3), 221; https://doi.org/10.3390/en9030221 - 19 Mar 2016
Cited by 42 | Viewed by 6802
Abstract
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an [...] Read more.
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability. Full article
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<p>Differential EMD algorithm flowchart.</p>
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<p>QPSO algorithm flowchart.</p>
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<p>The full flowchart of the DEMD-QPSO-SVR-AR model flowchart.</p>
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<p>Comparison the forecasted electric load of train and test by the QPSO-SVR model for the data-I of sample data: (<b>a</b>) One-day ahead prediction of May 8, 2007 are performed by the model; (<b>b</b>) One-week ahead prediction from May 18, 2007 May 24, 2007 are performed by the model.</p>
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<p>Comparison the forecasted electric load of training and test data by the QPSO-SVR model for the data-I of sample data in Case 2: (<b>a</b>) One-day ahead prediction from 13 to 14 January 2015 are performed by the model; (<b>b</b>) One-week ahead prediction from 2 to 15 February 2015 are performed by the model.</p>
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<p>Comparison of the original data and the forecasted electric load by the DEMD-QPSO-SVR-AR Model, the SVR model and the PSO-SVR model for (<b>a</b>) the small sample size (One-day ahead prediction of 8 May 2007 are performed by the models); (<b>b</b>) the large sample size (One-week ahead prediction from 18 May 2007 to 24 May 2007 are performed by the models).</p>
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<p>Comparison of the original data and the forecasted electric load by the DEMD-QPSO-SVR-AR Model, the ARIMA model, the BPNN model and the GA-ANN model for: (<b>a</b>) the small sample size (One-day ahead prediction from 13 to 14 January 2015 are performed by the models); (<b>b</b>) the large sample size (One-week ahead prediction from 2 to15 February 2015 are performed by the models).</p>
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<p>The local enlargement (peak) comparison of the DEMDQPSOSVRAR Model, the SVR model and the PSO-SVR model for (<b>a</b>) the small sample size <span class="html-italic">(<b>1</b>)</span>; (<b>b</b>) the large sample size <span class="html-italic">(<b>2</b>).</span></p>
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<p>The definition of shape factor.</p>
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2930 KiB  
Article
Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission
by Wenchen Shen, Huilong Yu, Yuhui Hu and Junqiang Xi
Energies 2016, 9(3), 220; https://doi.org/10.3390/en9030220 - 19 Mar 2016
Cited by 29 | Viewed by 8273
Abstract
Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV [...] Read more.
Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV. Full article
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<p>Simulation configuration of the researched HEV.</p>
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<p>Simplified battery quasi-static circuitry.</p>
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<p>Schematic of the solving algorithm at time step <span class="html-italic">k</span>.</p>
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<p>Results of optimized shift schedule with different penalty factors. (<b>a</b>) Gear shift schedule when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">ϕ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>; (<b>b</b>) Gear shift schedule when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">ϕ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math>; (<b>c</b>) Gear shift schedule when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">ϕ</mi> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics> </math>.</p>
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<p>The extracted gear shift schedule.</p>
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<p>The optimized extracted two-parameter gear shift schedule.</p>
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<p>Speed following curve of the test.</p>
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<p>Simulation result of battery State Of Charge (SOC).</p>
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<p>Real vehicle platform.</p>
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9609 KiB  
Article
An Embedded System in Smart Inverters for Power Quality and Safety Functionality
by Rafael Real-Calvo, Antonio Moreno-Munoz, Juan J. Gonzalez-De-La-Rosa, Victor Pallares-Lopez, Miguel J. Gonzalez-Redondo and Isabel M. Moreno-Garcia
Energies 2016, 9(3), 219; https://doi.org/10.3390/en9030219 - 18 Mar 2016
Cited by 11 | Viewed by 8587
Abstract
The electricity sector is undergoing an evolution that demands the development of a network model with a high level of intelligence, known as a Smart Grid. One of the factors accelerating these changes is the development and implementation of renewable energy. In particular, [...] Read more.
The electricity sector is undergoing an evolution that demands the development of a network model with a high level of intelligence, known as a Smart Grid. One of the factors accelerating these changes is the development and implementation of renewable energy. In particular, increased photovoltaic generation can affect the network’s stability. One line of action is to provide inverters with a management capacity that enables them to act upon the grid in order to compensate for these problems. This paper describes the design and development of a prototype embedded system able to integrate with a photovoltaic inverter and provide it with multifunctional ability in order to analyze power quality and operate with protection. The most important subsystems of this prototype are described, indicating their operating fundamentals. This prototype has been tested with class A protocols according to IEC 61000-4-30 and IEC 62586-2. Tests have also been carried out to validate the response time in generating orders and alarm signals for protections. The highlights of these experimental results are discussed. Some descriptive aspects of the integration of the prototype in an experimental smart inverter are also commented upon. Full article
(This article belongs to the Special Issue Microgrids)
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<p>Required Inverter operating ranges at the PCC according to the Spanish regulations (Operational Procedure 12.3) [<xref ref-type="bibr" rid="B7-energies-09-00219">7</xref>]: (<bold>a</bold>) Voltage-time curve that defines the operating ranges of the inverter on a fault; (<bold>b</bold>) Injecting or absorbing reactive power depending on the voltage at the PCC.</p>
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<p>Context of an embedded system in a smart inverter.</p>
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<p>Functional architecture for the developed ES.</p>
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<p>Embedded system block diagram.</p>
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<p>Alarms generation algorithm for exceeding operating ranges of voltage, frequency and DC current injection.</p>
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<p>Functional diagram of the anti-islanding subsystem.</p>
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<p>Event detection algorithm based on HOS.</p>
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<p>Block diagram of event detection subsystem.</p>
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<p>Test context and equipment.</p>
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<p>Tests of uncertainty in frequency measures. Histograms, probability density functions and box plots, corresponding to: (<bold>a1</bold>) (<bold>a2</bold>) (<bold>a3</bold>) Test point P1 (42.5 Hz); (<bold>b1</bold>) (<bold>b2</bold>) (<bold>b3</bold>) Test point P2 (50.05 Hz); (<bold>c1</bold>) (<bold>c2</bold>) (<bold>c3</bold>) Test point P3 (57.5 Hz).</p>
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<p>Tests of uncertainty in frequency measurements under influence quantities. Histograms, probability density functions and box plots, corresponding to: (<bold>a1</bold>) (<bold>a2</bold>) (<bold>a3</bold>) Influence of voltage (S1: 23 V) in the frequency measurement (P2: 50.05 Hz); (<bold>b1</bold>) (<bold>b2</bold>) (<bold>b3</bold>) Influence of voltage harmonics (S1) in the frequency measurement (P2: 50.05 Hz).</p>
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<p>Tests of uncertainty in voltage measures. Histograms, probability density functions and box plots, corresponding to: (<bold>a1</bold>) (<bold>a2</bold>) (<bold>a3</bold>) Test point P1 (23 V); (<bold>b1</bold>) (<bold>b2</bold>) (<bold>b3</bold>) Test point P3 (184 V); (<bold>c1</bold>) (<bold>c2</bold>) (<bold>c3</bold>) Test point 300 V.</p>
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<p>Tests of uncertainty in voltage measurements (P3: 184 V) under influence quantities. Histograms, probability density functions and box plots, corresponding to: (<bold>a1</bold>) (<bold>a2</bold>) (<bold>a3</bold>) Influence of frequency (S1: 42.5 Hz); (<bold>b1</bold>) (<bold>b2</bold>) (<bold>b3</bold>) Influence of frequency (S3: 55.75 Hz); (<bold>c1</bold>) (<bold>c2</bold>) (<bold>c3</bold>) Influence of voltage harmonics (S1).</p>
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<p>Comparison between probability density functions of the data sets of the three phases measured P1 (10% V<sub>nom</sub> = 23 V) with the prototype and the Yokogawa DL850E ScopeCorder.</p>
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<p>Comparison of harmonic readings with both the prototype and the Fluke 434-II analyzer for testing at point P2 (150 Hz, 10% V<sub>nom</sub>): (<bold>a</bold>) Harmonic reading with the prototype; (<bold>b</bold>) Harmonic reading with the analyzer.</p>
Full article ">Figure 15 Cont.
<p>Comparison of harmonic readings with both the prototype and the Fluke 434-II analyzer for testing at point P2 (150 Hz, 10% V<sub>nom</sub>): (<bold>a</bold>) Harmonic reading with the prototype; (<bold>b</bold>) Harmonic reading with the analyzer.</p>
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<p>Comparison of harmonic readings with both the prototype and the Fluke 434-II analyzer for testing at point P3 (2500 Hz, 1% V<sub>nom</sub>): (<bold>a</bold>) Harmonic reading with the prototype; (<bold>b</bold>) Harmonic reading with the analyzer.</p>
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<p>Time response tests: (<bold>a</bold>) Tests context; (<bold>b</bold>) Signals for delay measurement.</p>
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<p>Tests of delay on the alarm generation for different protections. Probability density functions and box plots, corresponding to: (<bold>a1</bold>) (<bold>a2</bold>) Exceeding frequency range; (<bold>b1</bold>) (<bold>b2</bold>) Exceeding voltage range; (<bold>c1</bold>) (<bold>c2</bold>) Exceeding DC injection range; (<bold>d1</bold>) (<bold>d2</bold>) Anti-Islanding (Test A); (<bold>e1</bold>) (<bold>e2</bold>) Anti-Islanding (Test B).</p>
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<p>Comparison between central values of delay obtained in the tests and response times referred to in the standards.</p>
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<p>Test context for SIDER smart inverter.</p>
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<p>Experimental environment for the SIDER smart inverter.</p>
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<p>Console of operating ranges for voltage and frequency.</p>
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<p>Screen for anti-islanding operation.</p>
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3679 KiB  
Article
Advances in Thin-Film Si Solar Cells by Means of SiOx Alloys
by Lucia V. Mercaldo, Iurie Usatii and Paola Delli Veneri
Energies 2016, 9(3), 218; https://doi.org/10.3390/en9030218 - 18 Mar 2016
Cited by 13 | Viewed by 5406
Abstract
The conversion efficiency of thin-film silicon solar cells needs to be improved to be competitive with respect to other technologies. For a more efficient use of light across the solar spectrum, multi-junction architectures are being considered. Light-management considerations are also crucial in order [...] Read more.
The conversion efficiency of thin-film silicon solar cells needs to be improved to be competitive with respect to other technologies. For a more efficient use of light across the solar spectrum, multi-junction architectures are being considered. Light-management considerations are also crucial in order to maximize light absorption in the active regions with a minimum of parasitic optical losses in the supportive layers. Intrinsic and doped silicon oxide alloys can be advantageously applied within thin-film Si solar cells for these purposes. Intrinsic a-SiOx:H films have been fabricated and characterized as a promising wide gap absorber for application in triple-junction solar cells. Single-junction test devices with open circuit voltage up to 950 mV and ~1 V have been demonstrated, in case of rough and flat front electrodes, respectively. Doped silicon oxide alloys with mixed-phase structure have been developed, characterized by considerably lower absorption and refractive index with respect to standard Si-based films, accompanied by electrical conductivity above 10−5 S/cm. These layers have been successfully applied both into single-junction and micromorph tandem solar cells as superior doped layers with additional functionalities. Full article
(This article belongs to the Special Issue Key Developments in Thin Film Solar Cells)
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<p>Bandgap evolution for two sample series: variable CO<sub>2</sub> flow rate at H<sub>2</sub> = 120 sccm (black symbols, bottom axis) and variable H<sub>2</sub> flow rate at CO<sub>2</sub> = 3 sccm (blue symbols, top axis).</p>
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<p>External quantum efficiency (EQE) of a-SiO<span class="html-italic"><sub>x</sub></span>:H solar cells: (<b>a</b>) series deposited with different CO<sub>2</sub> values, setting H<sub>2</sub> =120 sccm; (<b>b</b>) series deposited with different H<sub>2</sub> flow rates, setting CO<sub>2</sub> = 3 sccm. In (<b>a</b>) the black curve corresponds to the reference cell that uses our standard a-Si:H, with no H<sub>2</sub> dilution, as absorber layer; the inset shows the effect of increased discharge power density for CO<sub>2</sub> = 3 sccm.</p>
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<p>Evolution of open circuit voltage (left axis) and short circuit current density (right axis) for the two cell series of <a href="#energies-09-00218-f002" class="html-fig">Figure 2</a>: (<b>a</b>) series deposited with different CO<sub>2</sub> values, setting H<sub>2</sub> =120 sccm; (<b>b</b>) series deposited with different H<sub>2</sub> flow rates, setting CO<sub>2</sub> = 3 sccm. The open symbols in (<b>a</b>) correspond to the cell with absorber layer grown at 40 mW/cm<sup>2</sup>, while in all the other cases the power density was 28 mW/cm<sup>2</sup>. The values for the a-Si:H reference cell in (<b>a</b>) are shown in slightly modified color to remind that the H<sub>2</sub> dilution is different in this case.</p>
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<p>J-V characteristic under AM1.5 illumination for the same a-SiO<span class="html-italic"><sub>x</sub></span>:H solar cell grown at 28 mW/cm<sup>2</sup> with CO<sub>2</sub> = 3 sccm and H<sub>2</sub> = 120 sccm on commercial rough substrate (black line) and on in-house flat ZnO:Al on glass (red line).</p>
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<p>Optical, electrical and structural properties of n-type nc-SiO<span class="html-italic"><sub>x</sub></span>:H samples grown with different CO<sub>2</sub>/SiH<sub>4</sub> flow rate ratio: (<b>a</b>) refractive index at ~500 nm (black symbols, left axis) and E<sub>04</sub> parameter (blues symbols, right axis); (<b>b</b>) planar electrical conductivity and energy filtered transmission electron microscopy (EFTEM) plan-view image of the sample deposited with CO<sub>2</sub>/SiH<sub>4</sub> = 3 (the Si phase appears as white and the O-rich phase as dark in the image).</p>
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<p>EQE of (<b>a</b>) p-i-n a-Si:H solar cells with Si- or SiO<span class="html-italic"><sub>x</sub></span>-based n-layer and (<b>b</b>) micromorph tandem cells with Si- or SiO<span class="html-italic"><sub>x</sub></span>-based n-layer in both the top and bottom junction, completed with ZnO/Ag or simple Ag back reflecting contact.</p>
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<p>J-V characteristic under AM1.5 illumination for a micromorph tandem solar cell with n-SiO<span class="html-italic"><sub>x</sub></span> layers and no ZnO buffer.</p>
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<p>Optical and electrical properties of p-type mixed phase SiO<span class="html-italic"><sub>x</sub></span> samples grown with different CO<sub>2</sub>/SiH<sub>4</sub> flow rate ratio: (<b>a</b>) in-plane electrical conductivity for two sample series grown at two discharge power levels; (<b>b</b>) refractive index at 635 nm (black symbols, left axis) and E<sub>04</sub> parameter (blues symbols, right axis) for the samples grown at lower discharge power.</p>
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<p>Evolution of lateral conductivity with increasing bandgap (estimated in terms of <span class="html-italic">E</span><sub>04</sub> parameter) obtained with increasing CO<sub>2</sub>/SiH<sub>4</sub> flow rate ratio for three sample series grown at different discharge power.</p>
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<p>EQE (<b>a</b>) and reflectance (<b>b</b>) of 1 μm thick μc-Si:H solar cells on Asahi/ZnO(20 nm) with p-μc-Si:H (p-Si in the legend, black line) and p-SiO<span class="html-italic"><sub>x</sub></span> window layer (red line).</p>
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2691 KiB  
Article
Modeling and Control of the Distributed Power Converters in a Standalone DC Microgrid
by Xiaodong Lu and Jiangwen Wan
Energies 2016, 9(3), 217; https://doi.org/10.3390/en9030217 - 18 Mar 2016
Cited by 13 | Viewed by 6484
Abstract
A standalone DC microgrid integrated with distributed renewable energy sources, energy storage devices and loads is analyzed. To mitigate the interaction among distributed power modules, this paper describes a modeling and control design procedure for the distributed converters. The system configuration and steady-state [...] Read more.
A standalone DC microgrid integrated with distributed renewable energy sources, energy storage devices and loads is analyzed. To mitigate the interaction among distributed power modules, this paper describes a modeling and control design procedure for the distributed converters. The system configuration and steady-state analysis of the standalone DC microgrid under study are discussed first. The dynamic models of the distributed converters are then developed from two aspects corresponding to their two operating modes, device-regulating mode and bus-regulating mode. Average current mode control and linear compensators are designed accordingly for each operating mode. The stability of the designed system is analyzed at last. The operation and control design of the system are verified by simulation results. Full article
(This article belongs to the Special Issue Distributed Renewable Generation)
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<p>(<b>a</b>) Architecture of the standalone DC microgrid. (<b>b</b>) Power stage of the adopted bidirectional non-inverting buck-boost converter.</p>
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<p>Diagram of the transitions between bus-regulating mode and device-regulating mode.</p>
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<p>(<b>a</b>) Circuit diagram of the DC microgrid where <span class="html-italic">m</span> converters work in the bus-regulating mode and <span class="html-italic">n</span> converters work in the device-regulating mode. (<b>b</b>) Equivalent circuit diagram.</p>
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<p>Averaged small-signal circuit of a device-regulating converter when it operates in (<b>a</b>) the buck mode or in (<b>b</b>) the boost mode.</p>
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<p>The small-signal control block diagram of a device-regulating converter.</p>
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<p>Comparison between the closed-loop input impedance <math display="inline"> <msub> <mi>Z</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </math> and incremental resistance <math display="inline"> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </math> of a device-regulating converter when (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> </mrow> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Circuit diagram of the bus-regulating converters.</p>
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<p>Averaged small-signal circuit of a bus-regulating converter when it works in (<b>a</b>) continuous conduction mode (CCM) or in (<b>b</b>) discontinuous conduction mode (DCM).</p>
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<p>Linearized resistance <math display="inline"> <msub> <mi>R</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </math> of a PV array consisting of two series-connected Conergy P 175M PV modules when the solar irradiance is 1000 W/m<math display="inline"> <msup> <mrow/> <mn>2</mn> </msup> </math> and the temperature is 25 <math display="inline"> <msup> <mrow/> <mo>∘</mo> </msup> </math>C.</p>
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<p>The control block diagram of bus-regulating converters.</p>
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<p>Bode plots of (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>v</mi> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> when <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> <mo>,</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Bode plots of (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>v</mi> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> when <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> <mo>,</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Measurement of the loop gains <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math>.</p>
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<p>Nyquist plots of (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> when <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> <mo>,</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Bode plots of (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> with gain and phase margins when <math display="inline"> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>e</mi> <mi>v</mi> <mo>,</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Bode plots of (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> and (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </math> when the bus-regulating converter works at different steady-state operating points.</p>
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<p>Small-signal equivalent circuit of the standalone DC microgrid.</p>
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<p>Comparison between <math display="inline"> <mrow> <mrow> <mo>∥</mo> </mrow> <msub> <mi>Z</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mo>,</mo> <mi>t</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>∥</mo> </mrow> </mrow> </math> and <math display="inline"> <mrow> <mrow> <mo>∥</mo> </mrow> <msub> <mi>Z</mi> <mi>N</mi> </msub> <mrow> <mo>∥</mo> </mrow> </mrow> </math>, <math display="inline"> <mrow> <mrow> <mo>∥</mo> </mrow> <msub> <mi>Z</mi> <mi>D</mi> </msub> <mrow> <mo>∥</mo> </mrow> </mrow> </math> when (<b>a</b>) the load converter or (<b>b</b>) the PV converter is analyzed.</p>
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<p>Schematic diagram of the simulated standalone DC microgrid.</p>
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<p>Simulation results. (<b>a</b>) Solar irradiance variation. (<b>b</b>) DC bus voltage <math display="inline"> <msub> <mi>V</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>s</mi> </mrow> </msub> </math>. (<b>c</b>) Total charging/discharging power <math display="inline"> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </math> of the two battery packs. (<b>d</b>) Battery voltage <math display="inline"> <msub> <mi>V</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </math>. (<b>e</b>) Total power generation <math display="inline"> <msub> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </math> of the three PV arrays. (<b>f</b>) PV array voltage <math display="inline"> <msub> <mi>V</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </math>. (<b>g</b>) Total load power <math display="inline"> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> </math>. (<b>h</b>) Voltages <math display="inline"> <msub> <mi>V</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <msub> <mi>d</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </msub> </mrow> </msub> </math> at three load points.</p>
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7441 KiB  
Article
A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures
by Silvio Rodrigues, Carlos Restrepo, George Katsouris, Rodrigo Teixeira Pinto, Maryam Soleimanzadeh, Peter Bosman and Pavol Bauer
Energies 2016, 9(3), 216; https://doi.org/10.3390/en9030216 - 18 Mar 2016
Cited by 41 | Viewed by 17075
Abstract
Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design [...] Read more.
Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases. Full article
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<p>Cost of power (COP) and capital expenditure (CAPEX) breakdown of offshore wind farms (OWFs). (<b>a</b>) COP of the European OWFs composed of five or more turbines [<a href="#B10-energies-09-00216" class="html-bibr">10</a>,<a href="#B11-energies-09-00216" class="html-bibr">11</a>]. Circle size represents the installed capacity of the OWFs. The monetary values were updated considering a Eurozone inflation of 1.85% [<a href="#B12-energies-09-00216" class="html-bibr">12</a>]; (<b>b</b>) Typical CAPEX breakdown of an OWF [<a href="#B13-energies-09-00216" class="html-bibr">13</a>].</p>
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<p>Lifecycle of an OWF and location of the FEED phase [<a href="#B14-energies-09-00216" class="html-bibr">14</a>,<a href="#B17-energies-09-00216" class="html-bibr">17</a>].</p>
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<p>Differences in topology and design complexity between two OWFs. (<b>a</b>) Vindeby wind farm [<a href="#B10-energies-09-00216" class="html-bibr">10</a>]; (<b>b</b>) Gwynt y Môr wind farm [<a href="#B23-energies-09-00216" class="html-bibr">23</a>].</p>
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<p>Flowchart of current single-objective optimization strategies. Dotted-line arrows represent input from the designer, solid-line arrows represent algorithm flow and dotted-point-line arrows represent component use.</p>
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<p>Proposed MO optimization framework for the design of OWFs and their electrical infrastructure. Dotted-line arrows represent input from the designer, solid-line arrows represent algorithm flow and dotted-point-line arrows represent component use.</p>
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<p>Main components of an OWF: (<b>a</b>) Wind turbines; (<b>b</b>) Collection cables; (<b>c</b>) Export cables; (<b>d</b>) Transformer station; (<b>e</b>) Converter station; (<b>f</b>) Meteorological mast; (<b>g</b>) Onshore stations.</p>
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<p>Hub height, rated power and rotor diameter of several wind turbine models and their commission year [<a href="#B10-energies-09-00216" class="html-bibr">10</a>,<a href="#B11-energies-09-00216" class="html-bibr">11</a>].</p>
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<p>Early and total statistics for commissioned and under construction European OWFs [<a href="#B10-energies-09-00216" class="html-bibr">10</a>,<a href="#B11-energies-09-00216" class="html-bibr">11</a>,<a href="#B54-energies-09-00216" class="html-bibr">54</a>]; (<b>a</b>) Turbine rated power; (<b>b</b>) Turbine electrical configuration; (<b>c</b>) Turbine support structure; (<b>d</b>) Transmission technology.</p>
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<p>Most common grounded turbine support structures and several existing floating solutions [<a href="#B61-energies-09-00216" class="html-bibr">61</a>,<a href="#B62-energies-09-00216" class="html-bibr">62</a>,<a href="#B63-energies-09-00216" class="html-bibr">63</a>,<a href="#B64-energies-09-00216" class="html-bibr">64</a>].</p>
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<p>Statistics for commissioned and under construction European OWFs [<a href="#B10-energies-09-00216" class="html-bibr">10</a>,<a href="#B11-energies-09-00216" class="html-bibr">11</a>,<a href="#B54-energies-09-00216" class="html-bibr">54</a>]. Circle size represents the installed capacity of the wind farms; (<b>a</b>) Number and location of offshore substations; (<b>b</b>) Number of different collection cables.</p>
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<p>Dutch commissioned, under construction, planned and future OWF areas [<a href="#B81-energies-09-00216" class="html-bibr">81</a>,<a href="#B82-energies-09-00216" class="html-bibr">82</a>].</p>
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<p>Description of the Borssele wind farm area, existing pipeline and telecom cables and water depth [<a href="#B84-energies-09-00216" class="html-bibr">84</a>]. The color bar of the left figure represents the water depth in meters and the legend of the right figure presents the wind bins in m/s. (<b>a</b>) Wind farm water depth [<a href="#B84-energies-09-00216" class="html-bibr">84</a>]; (<b>b</b>) Annual wind rose at height of 90 m [<a href="#B86-energies-09-00216" class="html-bibr">86</a>].</p>
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<p>Power and thrust curves of the turbines used in the case study. (<b>a</b>) Power curves; (<b>b</b>) Thrust curves.</p>
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<p>Transformer model [<a href="#B76-energies-09-00216" class="html-bibr">76</a>,<a href="#B99-energies-09-00216" class="html-bibr">99</a>].</p>
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<p>Electrical infrastructures considered in the optimization framework.</p>
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<p>Optimized trade-off obtained between the optimization goals for areas I and II and standard layouts. The optimized layouts will be numbered from left to right, <span class="html-italic">i.e.</span>, with increasing annual energy delivered (AED) and CAPEX.</p>
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<p>Layouts obtained with standard design philosophies and restricted to the design specifications [<a href="#B134-energies-09-00216" class="html-bibr">134</a>]. Red and blue circles represent the 5 MW and 8 MW turbines, respectively. Gray lines are existing pipelines and telecom cables, blue and red lines are the collection system cables and green lines are the exporting HVac cables. The purple circles represent the offshore substations. (<b>a</b>) Layout with 140 turbines (5 MW); (<b>b</b>) Layout with 88 turbines (8 MW).</p>
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<p>Results obtained for the different economic functions of the layouts of the optimized trade-off. The values obtained with the standard layouts are also shown. (<b>a</b>) levelized cost of energy (LCOE); (<b>b</b>) discounted payback time (DPT); (<b>c</b>) Internal rate of return (IRR); (<b>d</b>) return on investment (ROI); (<b>e</b>) utilization factor (UF); (<b>f</b>) annualized value (AV); (<b>g</b>) benefit to cost ratio (BCR); (<b>h</b>) cost of power (COP); (<b>i</b>) Installed capacity; (<b>j</b>) net present value (NPV).</p>
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<p>Layouts obtained with proposed optimization framework. Blue, purple and black circles represent, respectively, the 8 MW turbines, HVac and HVdc substations. Gray lines are existing pipelines and telecom cables, blue and red lines are the collection system cables and green and yellow lines are the exporting HVac and HVdc cables, respectively. (<b>a</b>) Layout number 3; (<b>b</b>) Layout number 5; (<b>c</b>) Layout number 18; (<b>d</b>) Layout number 248; (<b>e</b>) Layout number 322; (<b>f</b>) Layout number 358.</p>
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<p>LCOE and NPV values for different economic factors. (<b>a</b>) LCOE; (<b>b</b>) NPV.</p>
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<p>Layouts with the best NPV values. Blue, purple and black circles represent, respectively, the 8 MW turbines, HVac and HVdc substations. Gray lines are existing pipelines and telecom cables, blue and red lines are the collection system cables and yellow lines are the exporting HVdc cables. (<b>a</b>) Layout number 57; (<b>b</b>) Layout number 115; (<b>c</b>) Layout number 248; (<b>d</b>) Layout number 301.</p>
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<p>Layout with similar annual energy delivered (AED) and CAPEX values as the standard layout with Vestas 8 MW turbines.</p>
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8761 KiB  
Article
Evaluation of a Mixed Method Approach for Studying User Interaction with Novel Building Control Technology
by Birgit Painter, Katherine N. Irvine, Ruth Kelly Waskett and John Mardaljevic
Energies 2016, 9(3), 215; https://doi.org/10.3390/en9030215 - 17 Mar 2016
Cited by 6 | Viewed by 5899
Abstract
Energy-efficient building performance requires sophisticated control systems that are based on realistic occupant behaviour models. To provide robust data for the development of these models, research studies in real-world settings are needed. Yet, such studies are challenging and necessitate careful design in terms [...] Read more.
Energy-efficient building performance requires sophisticated control systems that are based on realistic occupant behaviour models. To provide robust data for the development of these models, research studies in real-world settings are needed. Yet, such studies are challenging and necessitate careful design in terms of data collection methods and procedures. This paper describes and critiques the design of a mixed methods approach for occupant behaviour research. It reviews the methodology developed for a longitudinal study in a real-world office environment where occupants’ experience with a novel facade technology (electrochromic glazing) was investigated. The methodology integrates objective physical measurements, observational data and self-reported experience data. Using data from one day of the study, this paper illustrates how the different sources can be combined in order to derive an in-depth understanding of the interplay between external daylight conditions, characteristics of the facade technology, occupant interaction with the technology and the resulting occupant experience. It was found that whilst the individual methods may be affected by practical limitations, these can be partially offset by combining physical measurements and observations with self-reported data. The paper critically evaluates the individual techniques, as well as the benefits of their integration and makes recommendations for the design of future occupant behaviour studies in real-world settings. Full article
(This article belongs to the Special Issue Multi-Disciplinary Perspectives on Energy and Sustainable Development)
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<p>Photographs of the electrochromic (EC) windows in the two offices with corresponding schematics showing the control zones.</p>
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<p>Room layout showing desks, participant seating positions (A1, B1, B2 and B3) and luminance cameras (HDR1–HDR3; HDR, high dynamic range).</p>
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<p>Control system summary sheet showing selected data extracted from the EC glazing control log for (<b>a</b>) 23 January 2014 and, for comparison; (<b>b</b>) 11 July 2013, including illuminance data from external sensor, window tint states and manual overrides. Compare <a href="#energies-09-00215-f001" class="html-fig">Figure 1</a> for window/zone arrangements. Note, sun azimuth and altitude are not part of the log, but were calculated separately.</p>
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<p>(<b>a</b>) Blinds diary sheet for January 2014 (the example day is highlighted, and the original entries of participant initials have been replaced with their study IDs); (<b>b</b>) Facade photo showing both case study offices from the outside on the example day at 12:32.</p>
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<p>Selected false-colour luminance images from around the time of visual comfort actions (VCAs), morning (top two images) and afternoon (lower two images). Note, the scale of luminance (cd/m<math display="inline"> <msup> <mrow/> <mn>2</mn> </msup> </math>) and time stamps are in the corner of each image.</p>
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<p>Example of a daily experience sheet, showing responses from Participant B1 in January 2014 (example day highlighted with a blue border).</p>
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4912 KiB  
Article
Application of Breathing Architectural Members to the Natural Ventilation of a Passive Solar House
by Kyung-Soon Park, Sang-Woo Kim and Seong-Hwan Yoon
Energies 2016, 9(3), 214; https://doi.org/10.3390/en9030214 - 17 Mar 2016
Cited by 10 | Viewed by 5878
Abstract
The efficient operation of a passive solar house requires an efficient ventilation system to prevent the loss of energy and provide the required ventilation rates. This paper proposes the use of “breathing architectural members” (BAMs) as passive natural ventilation devices to achieve much [...] Read more.
The efficient operation of a passive solar house requires an efficient ventilation system to prevent the loss of energy and provide the required ventilation rates. This paper proposes the use of “breathing architectural members” (BAMs) as passive natural ventilation devices to achieve much improved ventilation and insulation performance compared to mechanical ventilation. Considering the importance of evaluating the ventilation and insulation performances of the members, we also propose numerical models for predicting the heat and air movements afforded by the members. The numerical model was validated by comparison with experimental results. The effectiveness of the BAMs was also verified by installation in houses located in an area with warm climate. For this purpose, chamber experiments were performed using samples of the BAMs, as well as numerical simulations to assess natural ventilation and heat load. The main findings of the study are as follows: (1) the one-dimensional chamber experiments confirmed the validity of the numerical models for predicting the heat and air movements afforded by the BAMs. Comparison of the experimental and calculated values for the temperature of air that flowed into the room from outside revealed a difference of less than 5%; (2) observations of the case studies in which BAMs were installed in the ceilings and exterior walls of Tokyo model houses revealed good annual ventilation and energy-saving effects. When BAMs with an opening area per unit area of A = 0.002 m2/m2 were applied to three surfaces, the required ventilation rate was 0.5 ACH (air changes per hour), and this was achieved consistently. Compared to a house with general insulation and conventional mechanical ventilation, heating load was reduced by 15.3%–40.2% depending on the BAM installation points and the differing areas of the house models. Full article
(This article belongs to the Special Issue Energy Efficient Actuators and Systems)
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<p>Basic structure of a BAM.</p>
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<p>Schematic of the heat transfer numerical model.</p>
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<p>BAM test sample.</p>
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<p>Experimental setup for simulating the effects of wind variation [<a href="#B5-energies-09-00214" class="html-bibr">5</a>].</p>
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<p>Measurement parts and items.</p>
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<p>Infiltration and opening area per unit area of the BAM.</p>
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<p>Effect of temperature increase on air infiltration.</p>
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<p>Overall heat transfer coefficient under long-wave emissivity.</p>
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<p>Combination of BAM design and ventilation rate prediction.</p>
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<p>House models used for the simulations.</p>
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<p>Annual ventilation rate change in a passive house with BAMs.</p>
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<p>Total heat load between Dec. and Feb.</p>
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3105 KiB  
Article
An Actuator Control Unit for Safety-Critical Mechatronic Applications with Embedded Energy Storage Backup
by Sergio Saponara
Energies 2016, 9(3), 213; https://doi.org/10.3390/en9030213 - 17 Mar 2016
Cited by 4 | Viewed by 6477
Abstract
This paper presents an actuator control unit (ACU) with a 450-J embedded energy storage backup to face safety critical mechatronic applications. The idea is to ensure full operation of electric actuators, even in the case of battery failure, by using supercapacitors as a [...] Read more.
This paper presents an actuator control unit (ACU) with a 450-J embedded energy storage backup to face safety critical mechatronic applications. The idea is to ensure full operation of electric actuators, even in the case of battery failure, by using supercapacitors as a local energy tank. Thanks to integrated switching converter circuitry, the supercapacitors provide the required voltage and current levels for the required time to guarantee actuator operation until the system enters into safety mode. Experimental results are presented for a target application related to the control of servomotors for a robotized prosthetic arm. Mechatronic devices for rehabilitation or assisted living of injured and/or elderly people are available today. In most cases, they are battery powered with lithium-based cells, providing high energy density and low weight, but at the expense of a reduced robustness compared to lead-acid- or nickel-based battery cells. The ACU of this work ensures full operation of the wearable robotized arm, controlled through acceleration and electromyography (EMG) sensor signals, even in the case of battery failure, thanks to the embedded energy backup unit. To prove the configurability and scalability of the proposed solution, experimental results related to the electric actuation of the car door latch and of a robotized gearbox in vehicles are also shown. The reliability of the energy backup device has been assessed in a wide temperature range, from −40 to 130 °C, and in a durability test campaign of more than 10,000 cycles. Achieved results prove the suitability of the proposed approach for ACUs requiring a burst of power of hundreds of watts for only a few seconds in safety-critical applications. Alternatively, the aging and temperature characterizations of energy backup units is limited to supercapacitors of thousands of farads for high power applications (e.g., electric/hybrid propulsion) and with a temperature range limited to 70 °C. Full article
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<p>Architecture of the integrated mechatronic actuator control unit: low-voltage processing domain, high voltage power management, and H-bridge domain.</p>
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<p>(<b>A</b>) Simplified schematic diagram and (<b>B</b>) detailed control loops, of the DC DC converter in boost mode, with programmable digital output value.</p>
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<p>Thermal dependence of the ESR on temperature.</p>
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<p>Thermal dependence of the voltage slope in the charge-discharge test.</p>
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<p>(<b>A</b>) Ag/AgCl electrodes; (<b>B</b>) the accelerometer; (<b>C</b>) the electromyography (EMG) sensor.</p>
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<p>Example response of (<b>A</b>) the 3-axis accelerometer; (<b>B</b>) the acquired EMG sensor signal.</p>
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<p>Example application of the actuator control unit (ACU) for robotized gearbox control.</p>
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<p>Experimental results of the gearbox actuation; electric (<b>top</b>) <span class="html-italic">vs.</span> pneumatic (<b>bottom</b>) actuators.</p>
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<p>Motor torque during a latch release at different operating conditions of the energy backup source.</p>
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2610 KiB  
Article
Exergy Flows inside a One Phase Ejector for Refrigeration Systems
by Mohammed Khennich, Mikhail Sorin and Nicolas Galanis
Energies 2016, 9(3), 212; https://doi.org/10.3390/en9030212 - 17 Mar 2016
Cited by 6 | Viewed by 5347
Abstract
The evaluation of the thermodynamic performance of the mutual transformation of different kinds of exergy linked to the intensive thermodynamic parameters of the flow inside the ejector of a refrigeration system is undertaken. Two thermodynamic metrics, exergy produced and exergy consumed, are introduced [...] Read more.
The evaluation of the thermodynamic performance of the mutual transformation of different kinds of exergy linked to the intensive thermodynamic parameters of the flow inside the ejector of a refrigeration system is undertaken. Two thermodynamic metrics, exergy produced and exergy consumed, are introduced to assess these transformations. Their calculation is based on the evaluation of the transiting exergy within different ejector sections taking into account the temperature, pressure and velocity variations. The analysis based on these metrics has allowed pinpointing the most important factors affecting the ejector’s performance. A new result, namely the temperature rise in the sub-environmental region of the mixing section is detected as an important factor responsible for the ejector’s thermodynamic irreversibility. The overall exergy efficiency of the ejector as well as the efficiencies of its sections are evaluated based on the proposed thermodynamic metrics. Full article
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<p>Grassmann diagram with transiting exergy.</p>
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<p>Throttling process on a specific exergy-enthalpy diagram.</p>
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<p>An ejector model with constant mixing pressure.</p>
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<p>Temperature (<b>a</b>), Pressure (<b>b</b>) and Velocity (<b>c</b>) profiles along the ejector.</p>
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<p>Diagram of Grassman illustrating transiting exergy in the mixing zone.</p>
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4184 KiB  
Article
Indices to Study the Electrical Power Signals in Active and Passive Distribution Lines: A Combined Analysis with Empirical Mode Decomposition
by Silvano Vergura, Roberto Zivieri and Mario Carpentieri
Energies 2016, 9(3), 211; https://doi.org/10.3390/en9030211 - 17 Mar 2016
Cited by 10 | Viewed by 4827
Abstract
The broad diffusion of renewable energy-based technologies has introduced several open issues in the design and operation of smart grids (SGs) when distributed generators (DGs) inject a large amount of power into the grid. In this paper, a theoretical investigation on active and [...] Read more.
The broad diffusion of renewable energy-based technologies has introduced several open issues in the design and operation of smart grids (SGs) when distributed generators (DGs) inject a large amount of power into the grid. In this paper, a theoretical investigation on active and reactive power data is performed for one active line characterized by several photovoltaic (PV) plants with a great amount of injectable power and two passive lines, one of them having a small peak power PV plant and the other one having no PV power. The frequencies calculated via the empirical mode decomposition (EMD) method based on the Hilbert-Huang transform (HHT) are compared to the ones obtained via the fast Fourier transform (FFT) and the wavelet transform (WT), showing a wider spectrum of significant modes mainly due to the non-periodical behavior of the power signals. The results obtained according to the HHT-EMD analysis are corroborated by the calculation of three new indices that are computed starting from the electrical signal itself and not from the Hilbert spectrum. These indices give the quantitative deviation from the periodicity and the coherence degree of the power signals, which typically deviate from the stationary regime and have a nonlinear behavior in terms of amplitude and phase. This information allows to extract intrinsic features of power lines belonging to SGs and this is useful for their optimal operation and planning. Full article
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<p>(<b>a</b>) Active power P<sub>1</sub>; (<b>b</b>) active power P<sub>2</sub>; (<b>c</b>) active power P<sub>3</sub>; (<b>d</b>) reactive power Q<sub>1</sub>; (<b>e</b>) reactive power Q<sub>2</sub>; and (<b>f</b>) reactive power Q<sub>3</sub>.</p>
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<p>(<b>a</b>) Active power P<sub>1</sub>; (<b>b</b>) active power P<sub>2</sub>; (<b>c</b>) active power P<sub>3</sub>; (<b>d</b>) reactive power Q<sub>1</sub>; (<b>e</b>) reactive power Q<sub>2</sub>; and (<b>f</b>) reactive power Q<sub>3</sub>.</p>
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<p>FFT spectra. (<b>a</b>) FFT of the P<sub>1</sub> active power; (<b>b</b>) FFT of the P<sub>2</sub> active power; (<b>c</b>) FFT of the P<sub>3</sub> active power; (<b>d</b>) FFT of the Q<sub>1</sub> reactive power; (<b>e</b>) FFT of the Q<sub>2</sub> reactive power and (<b>f</b>) FFT of the Q<sub>3</sub> reactive power.</p>
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<p>Time-frequency domain plot computed according to the WT-based method. (<b>a</b>) WT of the P<sub>1</sub> active power; (<b>b</b>) WT of the P<sub>2</sub> active power; (<b>c</b>) WT of the P<sub>3</sub> active power; (<b>d</b>) WT of the Q<sub>1</sub> reactive power; (<b>e</b>) WT of the Q<sub>2</sub> reactive power and (<b>f</b>) WT of the Q<sub>3</sub> reactive power.</p>
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<p>Amplitude of the 14 IMFs as a function of time related to the P<sub>3</sub> active power. An arrow indicates the amplitudes of the IMFs having frequencies close to the ones of the harmonics obtained via the FFT analysis (see the text for a discussion).</p>
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<p>(<b>a</b>) HHT-EMD frequencies for the active power P of L<sub>1</sub>, L<sub>2</sub> and L<sub>3</sub> subdivided into the three frequency regions (upper, central and lower) of the HHT spectrum. Green circles: HHT frequencies for P<sub>1</sub>. Red up triangles: HHT frequencies for P<sub>2</sub>. Blue down triangles: HHT-EMD frequencies for P<sub>3</sub>; (<b>b</b>) main FFT frequencies compared to the HHT-EMD frequencies of the central region for the active power of L<sub>1</sub>, L<sub>2</sub> and L<sub>3</sub>. Black squares: FFT harmonics H<sub>1</sub>, H<sub>2</sub>, H<sub>3</sub> and H<sub>4</sub>. The meaning of the other symbols is the same as in panel (<b>a</b>).</p>
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<p>(<b>a</b>) HHT-EMD frequencies for the reactive power of L<sub>1</sub>, L<sub>2</sub> and L<sub>3</sub> subdivided into the three frequency regions (upper, central and lower) of the HHT spectrum. Green squares: HHT frequencies for Q<sub>1</sub>. Red diamonds: HHT-EMD frequencies for Q<sub>2</sub>. Blue up triangles: HHT frequencies for Q<sub>3</sub>. (<b>b</b>) Main FFT frequencies compared to the HHT-EMD frequencies of the central region for the reactive power of L<sub>1</sub>, L<sub>2</sub> and L<sub>3</sub>. Black circles: FFT harmonics H<sub>1</sub>, H<sub>2</sub>, H<sub>3</sub> and H<sub>4</sub>. The meaning of the other symbols is the same as in panel (<b>a</b>).</p>
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<p><span class="html-italic">DP</span> index for the three active powers and for the cosine function. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>) <span class="html-italic">DP</span><sub>P3</sub>; (<b>e</b>) <span class="html-italic">DP</span><sub>P1</sub> in a temporal window; (<b>f</b>) <span class="html-italic">DP</span><sub>P2</sub> in a temporal window; (<b>g</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> in a temporal window; (<b>d</b>) Coherence <sub>cos-cos</sub> and (<b>h</b>) Coherence <sub>cos-cos</sub> in a temporal window.</p>
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<p><span class="html-italic">DP</span> index for the three active powers and for the cosine function. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>) <span class="html-italic">DP</span><sub>P3</sub>; (<b>e</b>) <span class="html-italic">DP</span><sub>P1</sub> in a temporal window; (<b>f</b>) <span class="html-italic">DP</span><sub>P2</sub> in a temporal window; (<b>g</b>) <math display="inline"> <semantics> <mrow> <mi>D</mi> <msub> <mi>P</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> in a temporal window; (<b>d</b>) Coherence <sub>cos-cos</sub> and (<b>h</b>) Coherence <sub>cos-cos</sub> in a temporal window.</p>
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<p><span class="html-italic">CI</span> between couples of active powers. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>; (<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> in a temporal window; (<b>e</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> in a temporal window; and (<b>f</b>) <math display="inline"> <semantics> <mrow> <mi>C</mi> <msub> <mi>I</mi> <mrow> <msub> <mi mathvariant="normal">P</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="normal">P</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> in a temporal window.</p>
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<p><span class="html-italic">PC</span> between couples of active powers (full circles) and between a given active power and the cosine function (empty circles).</p>
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1542 KiB  
Article
Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid
by J. Richard Snape
Energies 2016, 9(3), 210; https://doi.org/10.3390/en9030210 - 17 Mar 2016
Cited by 34 | Viewed by 6402
Abstract
Distributed renewable electricity generators facilitate decarbonising the electricity network, and the smart grid allows higher renewable penetration while improving efficiency. Smart grid scenarios often emphasise localised control, balancing small renewable generation with consumer electricity demand. This research investigates the applicability of proposed decentralised [...] Read more.
Distributed renewable electricity generators facilitate decarbonising the electricity network, and the smart grid allows higher renewable penetration while improving efficiency. Smart grid scenarios often emphasise localised control, balancing small renewable generation with consumer electricity demand. This research investigates the applicability of proposed decentralised smart grid scenarios utilising a mixed strategy: quantitative analysis of PV adoption data and qualitative policy analysis focusing on policy design, apparent drivers for adoption of the deviation of observed data from the feed-in tariff impact assessment predictions. Analysis reveals that areas of similar installed PV capacity are clustered, indicating a strong dependence on local conditions for PV adoption. Analysing time series of PV adoption finds that it fits neither neo-classical predictions, nor diffusion of innovation S-curves of adoption cleanly. This suggests the influence of external factors on the decision making process. It is shown that clusters of low installed PV capacity coincide with areas of high population density and vice versa, implying that while visions of locally-balanced smart grids may be viable in certain rural and suburban areas, applicability to urban centres may be limited. Taken in combination, the data analysis, policy impact and socio-psychological drivers of adoption demonstrate the need for a multi-disciplinary approach to understanding and modelling the adoption of technology necessary to enable the future smart grid. Full article
(This article belongs to the Special Issue Multi-Disciplinary Perspectives on Energy and Sustainable Development)
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<p>Cost of an installed 2.6-kWp PV system, feed-in tariff rate and rate of return for the installed system 2010–2015. (<b>a</b>) Installation cost of 2.6 kWp PV system; (<b>b</b>) Indicative rate of return for the 2.6 kWp reference system (assumed 1280 kWh/m<math display="inline"> <msup> <mrow/> <mn>2</mn> </msup> </math> insolation, panel area 6 m<math display="inline"> <msup> <mrow/> <mn>2</mn> </msup> </math>, load factor 10%). Data sources: cost [<a href="#B26-energies-09-00210" class="html-bibr">26</a>] (2010 figure), [<a href="#B27-energies-09-00210" class="html-bibr">27</a>] (2011 and 2012), [<a href="#B28-energies-09-00210" class="html-bibr">28</a>] (2013–2015); feed-in tariffs, [<a href="#B29-energies-09-00210" class="html-bibr">29</a>].</p>
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<p>Spatial distribution of domestic PV installations on 30 September 2015. (<b>a</b>) Installation count (per (A) in <a href="#sec3-energies-09-00210" class="html-sec">Section 3</a>); (<b>b</b>) Capacity per thousand population per (D) in <a href="#sec3-energies-09-00210" class="html-sec">Section 3</a>.</p>
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<p>Temporal distribution of domestic PV installations under the feed-in tariff incentive. (<b>a</b>) Domestic PV capacity commissioned per week; (<b>b</b>) Cumulative domestic PV capacity commissioned.</p>
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<p>Rate of PV adoption with spikes highlighted.</p>
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<p>Snapshots of the distribution of the absolute number of domestic PV installations per postcode district (PCD) (per (A) in <a href="#sec3-energies-09-00210" class="html-sec">Section 3</a>). (<b>a</b>) Snapshot of 1 April 2010; (<b>b</b>) Snapshot of 6 January 2011; (<b>c</b>) Snapshot of 30 June 2011; (<b>d</b>) Snapshot of 5 January 2012; (<b>e</b>) Snapshot of 3 January 2013; (<b>f</b>) Snapshot of 2 January 2014; (<b>g</b>) Snapshot of 1 January 2015; (<b>h</b>) Snapshot of 30 September 2015; (<b>i</b>) Key: number of installations per PCD.</p>
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<p>Snapshots of the distribution of the absolute number of domestic PV installations per postcode district (PCD) (per (A) in <a href="#sec3-energies-09-00210" class="html-sec">Section 3</a>). (<b>a</b>) Snapshot of 1 April 2010; (<b>b</b>) Snapshot of 6 January 2011; (<b>c</b>) Snapshot of 30 June 2011; (<b>d</b>) Snapshot of 5 January 2012; (<b>e</b>) Snapshot of 3 January 2013; (<b>f</b>) Snapshot of 2 January 2014; (<b>g</b>) Snapshot of 1 January 2015; (<b>h</b>) Snapshot of 30 September 2015; (<b>i</b>) Key: number of installations per PCD.</p>
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<p>Moran’s <span class="html-italic">I</span> for the capacity of domestic PV installations per PCD over time.</p>
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<p>Indicators of local clustering of capacity (per (B) in <a href="#sec3-energies-09-00210" class="html-sec">Section 3</a>). (<b>a</b>) Distribution of capacity by percentile (8 April 2010); (<b>b</b>) Clusters of capacity by type (8 April 2010); (<b>c</b>) Significance level of clusters (8 April 2010); (<b>d</b>) Distribution of capacity by percentile (30 September 2015); (<b>e</b>) Clusters of capacity by type (30 September 2015); (<b>f</b>) Significance level of clusters (30 September 2015).</p>
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4460 KiB  
Article
Pulse-Based Fast Battery IoT Charger Using Dynamic Frequency and Duty Control Techniques Based on Multi-Sensing of Polarization Curve
by Meng Di Yin, Jeonghun Cho and Daejin Park
Energies 2016, 9(3), 209; https://doi.org/10.3390/en9030209 - 17 Mar 2016
Cited by 45 | Viewed by 11650
Abstract
The pulse-based charging method for battery cells has been recognized as a fast and efficient way to overcome the shortcoming of a slow charging time in distributed battery cells, which is regarded as a connection of cells such as the Internet of Things [...] Read more.
The pulse-based charging method for battery cells has been recognized as a fast and efficient way to overcome the shortcoming of a slow charging time in distributed battery cells, which is regarded as a connection of cells such as the Internet of Things (IoT). The pulse frequency for controlling the battery charge duration is dynamically controlled within a certain range in order to inject the maximum charge current into the battery cells. The optimal frequency is determined in order to minimize battery impedance. The adaptation of the proposed pulse duty and frequency decreases the concentration of the polarization by sensing the runtime characteristics of battery cells so that it guarantees a certain level of safety in charging the distributed battery cells within the operating temperature range of 5–45 °C. The sensed terminal voltage and temperature of battery cells are dynamically monitored while the battery is charging so as to adjust the frequency and duty of the proposed charging pulse method, thereby preventing battery degradation. The evaluation results show that a newly designed charging algorithm for the implemented charger system is about 18.6% faster than the conventional constant-current (CC) charging method with the temperature rise within a reasonable range. The implemented charger system, which is based on the proposed dynamic frequency and duty control by considering the cell polarization, charges to about 80% of its maximum capacity in less than 56 min and involves a 13 °C maximum temperature rise without damaging the battery. Full article
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<p>Large scale battery cells (internet of cell things) and battery management system (BMS). MCU: Microcontroller.</p>
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<p>Charger voltage and cell terminal voltage.</p>
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<p>The constant current, constant voltage (CC-CV) charging of Li-Ion batteries (model: Samsung INR18650-25R) (lithium type).</p>
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<p>The profile of five stages of current charging algorithm.</p>
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<p>Relaxation after charging pulse. (<b>a</b>) battery charging current under 4.2 volts of charging pulse; (<b>b</b>) text result of diffusion after pulse on INR18650-25R.</p>
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<p>The open circuit voltage-state of charge (OCV-SOC) profiles of the INR18650-25R battery.</p>
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<p>(<b>a</b>) Equivalent circuit model, and (<b>b</b>) lookup table (LUT) for statically sensed characteristics of the lithium battery cell (X axis: SOC).</p>
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<p>Acceptable current based on polarization voltage. (<b>a</b>) Polarization voltage according to charging rate and SOC; (<b>b</b>) adjusting charging current based on polarization boundary (limit ) curve.</p>
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<p>The sensed impedance of <math display="inline"> <msub> <mi>Z</mi> <mn>1</mn> </msub> </math> varies with SOC and the pulse charging frequency at 20 <math display="inline"> <msup> <mrow/> <mo>∘</mo> </msup> </math>C.</p>
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<p>Proposed algorithm for controlling the optimal frequency and charging current.</p>
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<p>Controlling the acceptable charging current using dynamic pulse duty cycle searching method based on cell polarization.</p>
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<p>Proposed algorithm for the optimal duty search mode.</p>
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<p>Flow chart of the efficient frequency and duty cycle control method.</p>
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<p>MATLAB/Simulink-based model to evaluate the feasibility of the proposed charger system.</p>
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<p>The overall control flow of the proposed charger system. (<b>a</b>) Conceptual flow; (<b>b</b>) an implementation using Matlab stateflow<sup>TM</sup>.</p>
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<p>Schematic diagram for the charging test.</p>
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<p>Experiment environment for the charging test. (<b>a</b>) Experiment environment for the charging text; (<b>b</b>) waveform captured by Oscilloscope.</p>
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<p>Timed measurement result of the optimal pulse-based charging process during one cycle using the proposed frequency and duty searching mode.</p>
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<p>Results of proposed charging algorithm. (<b>a</b>) duty and frequency search operation; (<b>b</b>) comparison of SOC and temperature rise; (<b>c</b>) dynamic adjustment result of voltage and current by the proposed method compared to conventional approaches.</p>
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<p>Fabricated chip-based BMS Microcontroller and evaluation environment. (<b>a</b>) Evaluation environment of battery charge-algorithm and BMS; (<b>b</b>) fabricated BMS IC (stacked on multiple batteries).</p>
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8288 KiB  
Article
Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm
by Fei Lin, Shihui Liu, Zhihong Yang, Yingying Zhao, Zhongping Yang and Hu Sun
Energies 2016, 9(3), 208; https://doi.org/10.3390/en9030208 - 17 Mar 2016
Cited by 38 | Viewed by 8881
Abstract
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the [...] Read more.
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the optimization of train dwell time and builds a multiple train operation model for energy conservation of a power supply system. By changing the dwell time, the braking energy can be absorbed and utilized by other traction trains as efficiently as possible. The application of genetic algorithms is proposed for the optimization, based on the current schedule. Next, to validate the correctness and effectiveness of the optimization, a real case is studied. Actual data from the Beijing subway Yizhuang Line are employed to perform the simulation, and the results indicate that the optimization method of the dwell time is effective. Full article
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<p>Schematic diagram of the urban rail transit power supply system. TSS, traction substation.</p>
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<p>Tractive and regenerative braking curve under different net voltages.</p>
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<p>Squeezing curve of regenerative braking.</p>
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<p>Train power curves before dwell time modification.</p>
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<p>Train power curves after dwell time modification.</p>
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<p>Definition of control parameter u.</p>
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<p>Schematic diagram of rectifier units.</p>
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<p>Equivalent circuit of the DC power supply system.</p>
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<p>Flowchart of multiple train operation calculation.</p>
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<p>Schematic diagram of dwell time modification.</p>
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<p>Schematic diagram of dwell time encoding.</p>
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<p>Initial population of dwell time.</p>
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<p>Flowchart of the dwell time optimization algorithm.</p>
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<p>Evolution of searching for the optimal solution.</p>
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<p>Total output power of traction substation and absorption power of braking resistance before dwell time optimization.</p>
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<p>Total output power of traction substation and absorption power of braking resistance after dwell time optimization.</p>
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<p>Energy consumption of each substation before and after dwell time optimization.</p>
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<p>Power of Yizhuang Railway Station’s substation before and after dwell time optimization.</p>
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<p>Power of Yizhuang Railway Station’s braking resistance before and after dwell time optimization.</p>
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<p>Power of Songjiazhuang’s substation before and after dwell time optimization.</p>
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<p>Power of Songjiazhuang’s braking resistance before and after dwell time optimization.</p>
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2103 KiB  
Article
The Evaluation of Energy Conservation Performance on Electricity: A Case Study of the TFT-LCD Optronics Industry
by Ven-Shing Wang, Cheng-Fong Sie, Ta-Yuan Chang and Keh-Ping Chao
Energies 2016, 9(3), 206; https://doi.org/10.3390/en9030206 - 17 Mar 2016
Cited by 13 | Viewed by 5385
Abstract
This study describes the performance evaluation of an energy management system, based on electricity consumption, for a Gen 6 Thin Film Transistor Liquid Crystal Display (TFT-LCD) panel plant. Of the various production lines and facility systems, the array system and the compressed dry [...] Read more.
This study describes the performance evaluation of an energy management system, based on electricity consumption, for a Gen 6 Thin Film Transistor Liquid Crystal Display (TFT-LCD) panel plant. Of the various production lines and facility systems, the array system and the compressed dry air consumed the most electricity of 21.8% and 19.8%, respectively, while the public utility used only 1.6% of the total electricity. The baseline electricity consumptions were correlated well (R2 ≥ 0.77) to the monthly average wet-bulb temperatures of ambient air and the panel yield rates, which were determined by the product yield over the equipment available time index. After implementing the energy saving projects, the energy conservation performance was determined using a three-parameter change-point regression model incorporated with the panel yield rates. The post-retrofit monthly savings of the total electricity consumption for the panel manufacture were 5.35%–10.36%, with the efficiency of the electricity performance revealing an upswing trend following the implementation of the energy management system. Full article
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<p>Energy management structure of the factory.</p>
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<p>Distribution of electricity utilization in 2010.</p>
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<p>Correlation between chillers electricity usage and wet-bulb temperature. (<b>a</b>) Linear correlation model; (<b>b</b>) Three-parameter change-point model.</p>
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<p>Comparisons of measured and predicted values of electricity consumption in 2010.</p>
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<p>Monthly electricity consumptions and calculations using Equation (4) from January 2011 to June 2014.</p>
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3437 KiB  
Article
Progress on Low-Temperature Pulsed Electron Deposition of CuInGaSe2 Solar Cells
by Massimo Mazzer, Stefano Rampino, Enos Gombia, Matteo Bronzoni, Francesco Bissoli, Francesco Pattini, Marco Calicchio, Aldo Kingma, Filippo Annoni, Davide Calestani, Nicholas Cavallari, Vimalkumar Thottapurath Vijayan, Mauro Lomascolo, Arianna Cretì and Edmondo Gilioli
Energies 2016, 9(3), 207; https://doi.org/10.3390/en9030207 - 16 Mar 2016
Cited by 24 | Viewed by 7909
Abstract
The quest for single-stage deposition of CuInGaSe2 (CIGS) is an open race to replace very effective but capital intensive thin film solar cell manufacturing processes like multiple-stage coevaporation or sputtering combined with high pressure selenisation treatments. In this paper the most recent [...] Read more.
The quest for single-stage deposition of CuInGaSe2 (CIGS) is an open race to replace very effective but capital intensive thin film solar cell manufacturing processes like multiple-stage coevaporation or sputtering combined with high pressure selenisation treatments. In this paper the most recent achievements of Low Temperature Pulsed Electron Deposition (LTPED), a novel single stage deposition process by which CIGS can be deposited at 250 °C, are presented and discussed. We show that selenium loss during the film deposition is not a problem with LTPED as good crystalline films are formed very close to the melting temperature of selenium. The mechanism of formation of good ohmic contacts between CIGS and Mo in the absence of any MoSe2 transition layers is also illustrated, followed by a brief summary of the measured characteristics of test solar cells grown by LTPED. The 17% efficiency target achieved by lab-scale CIGS devices without bandgap modulation, antireflection coating or K-doping is considered to be a crucial milestone along the path to the industrial scale-up of LTPED. The paper ends with a brief review of the open scientific and technological issues related to the scale-up and the possible future applications of the new technology. Full article
(This article belongs to the Special Issue Key Developments in Thin Film Solar Cells)
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<p>(<b>a</b>) Bar chart reporting the total deposition rate as a function of the electron beam energy. The blue and the orange sections of the bars represent the evaporated and the ablated fraction of the target material; (<b>b</b>) Scanning Electron Microscope images of the CIGS surface obtained with the electron source operating at 14 kV, 16 kV and 18 kV.</p>
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<p>Dark (red circles) and illuminated (blue squares) characteristics of a CIGS solar cell where the absorber layer is deposited at 500 °C. The corresponding cell parameters are: Voc = 444 mV, FF = 58.6%, Jsc = 30.1 mA/cm<sup>2</sup>, η = 7.8%.</p>
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<p>Low-temperature photoluminescence spectra of two PED-grown CIGS samples. Sample D<sub>1</sub> was deposited at a substrate temperature of 500 °C, sample D<sub>2</sub> at 270 °C (LTPED).</p>
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<p>IV characteristics in the dark (red curves) and under illumination (blue curves) of the (<b>a</b>) Au/CIGS/Mo and (<b>b</b>) Au/CIGS/NaF/Mo structures deposited on a glass substrate. The curves clearly indicate the CIGS/Mo contacts to be rectifying and ohmic in the sample without and with NaF, respectively.</p>
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<p>Capacitance-voltage profiles obtained at 300 K and 120 K on typical CIGS solar cell with intermediate NaF buffer-layer. The test signal frequency is 1 MHz.</p>
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<p>Temperature dependence of Voc of a typical CIGS solar cell fabricated by LTPED. The extrapolated value of Voc as T → 0 corresponds to Eg/q, where Eg is the absorber bandgap and q is the electron charge.</p>
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<p>(<b>a</b>) Current-voltage characteristics of a test solar cell under the illumination of a class-A solar simulator; (<b>b</b>) External quantum efficiency spectrum of the same cell.</p>
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<p>Schematic representation of the Pulsed Electron Deposition technique.</p>
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5014 KiB  
Article
Transient Numerical Simulation of the Melting and Solidification Behavior of NaNO3 Using a Wire Matrix for Enhancing the Heat Transfer
by Martin Koller, Heimo Walter and Michael Hameter
Energies 2016, 9(3), 205; https://doi.org/10.3390/en9030205 - 16 Mar 2016
Cited by 18 | Viewed by 7221
Abstract
The paper presents the results of a transient numerical investigation of the melting and solidification process of sodium nitrate (NaNO3), which is used as phase change material. For enhancing the heat transfer to the sodium nitrate an aluminum wire matrix is [...] Read more.
The paper presents the results of a transient numerical investigation of the melting and solidification process of sodium nitrate (NaNO3), which is used as phase change material. For enhancing the heat transfer to the sodium nitrate an aluminum wire matrix is used. The numerical simulation of the melting and solidification process was done with the enthalpy-porosity approach. The numerical analysis of the melting process has shown that apart from the first period of the charging process, where heat conduction is the main heat transfer mechanism, natural convection is the dominant heat transfer mechanism. The numerical investigation of the solidification process has shown that the dominant heat transfer mechanism is heat conduction. Based on the numerical results, the discharging process has been slower than the charging process. The performance of the charged and discharged power has shown that the wire matrix is an alternative method to enhance the heat transfer into the phase change material. Full article
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<p>Heat exchanger tube with wire.</p>
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<p>Computational domain and geometry data of the analyzed heat exchanger tube with wired matrix.</p>
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<p>Boundary conditions used for the analyzed heat exchanger configuration.</p>
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<p>Chronological sequence of the volume averaged temperature and liquid fraction for the phase change material during the melting process.</p>
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<p>Contours of the liquid fraction of the PCM during the melting process at different time steps.</p>
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<p>Vector plot of the liquid velocity of the molten PCM at different time steps.</p>
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<p>Vector plot of the liquid velocity of the PCM 225 s after simulation start.</p>
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<p>Chronological sequence of the volume averaged velocity during the charging process.</p>
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<p>Chronological sequence of the volume averaged charged power during the melting process.</p>
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<p>Chronological sequence of the volume averaged velocity during the discharging process.</p>
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<p>Contours of the liquid fraction of the PCM during the discharging process at different time steps.</p>
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<p>Chronological sequence of the volume averaged temperature and liquid fraction for the PCM during the discharging process.</p>
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<p>Chronological sequence of the volume averaged discharged power per m<sup>3</sup> NaNO<sub>3</sub> during the solidification process.</p>
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3486 KiB  
Article
Tunneling Horizontal IEC 61850 Traffic through Audio Video Bridging Streams for Flexible Microgrid Control and Protection
by Michael Short, Fathi Abugchem and Muneeb Dawood
Energies 2016, 9(3), 204; https://doi.org/10.3390/en9030204 - 16 Mar 2016
Cited by 13 | Viewed by 8043
Abstract
In this paper, it is argued that some low-level aspects of the usual IEC 61850 mapping to Ethernet are not well suited to microgrids due to their dynamic nature and geographical distribution as compared to substations. It is proposed that the integration of [...] Read more.
In this paper, it is argued that some low-level aspects of the usual IEC 61850 mapping to Ethernet are not well suited to microgrids due to their dynamic nature and geographical distribution as compared to substations. It is proposed that the integration of IEEE time-sensitive networking (TSN) concepts (which are currently implemented as audio video bridging (AVB) technologies) within an IEC 61850 / Manufacturing Message Specification framework provides a flexible and reconfigurable platform capable of overcoming such issues. A prototype test platform and bump-in-the-wire device for tunneling horizontal traffic through AVB are described. Experimental results are presented for sending IEC 61850 GOOSE (generic object oriented substation events) and SV (sampled values) messages through AVB tunnels. The obtained results verify that IEC 61850 event and sampled data may be reliably transported within the proposed framework with very low latency, even over a congested network. It is argued that since AVB streams can be flexibly configured from one or more central locations, and bandwidth reserved for their data ensuring predictability of delivery, this gives a solution which seems significantly more reliable than a pure MMS-based solution. Full article
(This article belongs to the Special Issue Microgrids 2016)
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<p>Typical layout of a microgrid and its constituent components.</p>
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<p>Layout of bridged local area networks (LANs) in a substation, showing the station LAN and multiple process LANs.</p>
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<p>Typical IEC 61850 Ethernet-based communication stack.</p>
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<p>Audio video bridging (AVB) streaming in an IEC 61850 based microgrid communications infrastructure.</p>
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<p>Implementation of proposed reconfigurable architecture using current-generation AVB technology and bump-in-the-wire (Bit-W) devices at the S-Node interfaces.</p>
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<p>Photograph of testbed setup.</p>
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<p>Schematic of experiment one configuration.</p>
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<p>Schematic of experiment two configuration.</p>
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<p>(<bold>a</bold>) AVB-enabled (blue/solid) and UDP-enabled (red/dashed) frequency regulation performance over a 20-min test period. (<bold>b</bold>) Load change profile.</p>
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4858 KiB  
Article
Quantifying the Impact of Feedstock Quality on the Design of Bioenergy Supply Chain Networks
by Krystel K. Castillo-Villar, Hertwin Minor-Popocatl and Erin Webb
Energies 2016, 9(3), 203; https://doi.org/10.3390/en9030203 - 16 Mar 2016
Cited by 17 | Viewed by 6144
Abstract
Logging residues, which refer to the unused portions of trees cut during logging, are important sources of biomass for the emerging biofuel industry and are critical feedstocks for the first-type biofuel facilities (e.g., corn-ethanol facilities). Logging residues are under-utilized sources of biomass for [...] Read more.
Logging residues, which refer to the unused portions of trees cut during logging, are important sources of biomass for the emerging biofuel industry and are critical feedstocks for the first-type biofuel facilities (e.g., corn-ethanol facilities). Logging residues are under-utilized sources of biomass for energetic purposes. To support the scaling-up of the bioenergy industry, it is essential to design cost-effective biofuel supply chains that not only minimize costs, but also consider the biomass quality characteristics. The biomass quality is heavily dependent upon the moisture and the ash contents. Ignoring the biomass quality characteristics and its intrinsic costs may yield substantial economic losses that will only be discovered after operations at a biorefinery have begun. This paper proposes a novel bioenergy supply chain network design model that minimizes operational costs and includes the biomass quality-related costs. The proposed model is unique in the sense that it supports decisions where quality is not unrealistically assumed to be perfect. The effectiveness of the proposed methodology is proven by assessing a case study in the state of Tennessee, USA. The results demonstrate that the ash and moisture contents of logging residues affect the performance of the supply chain (in monetary terms). Higher-than-target moisture and ash contents incur in additional quality-related costs. The quality-related costs in the optimal solution (with final ash content of 1% and final moisture of 50%) account for 27% of overall supply chain cost. Based on the numeral experimentation, the total supply chain cost increased 7%, on average, for each additional percent in the final ash content. Full article
(This article belongs to the Special Issue Applied Energy System Modeling 2015)
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<p>Averaged Spatial Distribution of Available Logging Residues at $60/dry ton [<a href="#B10-energies-09-00203" class="html-bibr">10</a>].</p>
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<p>Classical view on the left and the modern view on the right.</p>
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<p>Logging residue collection options.</p>
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<p>Schematic Diagram of a Bioenergy Supply Chain Network.</p>
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<p>Ash quality-related costs.</p>
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<p>Comparison between the collection and mechanical drying costs.</p>
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<p>Percentage of quality-related costs with respect to total cost (for a moisture content of 50%).</p>
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<p>Break down of percentage of quality-related costs with respect to total cost (for moisture content of 50%).</p>
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<p>Total cost obtained from different interest rates and customer demands.</p>
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<p>Ash penalty cost (per ton of biomass) obtained from different prices of a gallon of oil and different final ash concentrations.</p>
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3132 KiB  
Article
Realistic Scheduling Mechanism for Smart Homes
by Danish Mahmood, Nadeem Javaid, Nabil Alrajeh, Zahoor Ali Khan, Umar Qasim, Imran Ahmed and Manzoor Ilahi
Energies 2016, 9(3), 202; https://doi.org/10.3390/en9030202 - 15 Mar 2016
Cited by 86 | Viewed by 8989
Abstract
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot [...] Read more.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility. Full article
(This article belongs to the Special Issue Energy Efficient Building Design 2016)
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<p>System Model: Realistic Scheduling Mechanism (RSM) Block Diagram.</p>
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<p>Advertisement packet format: ADA, ODA and OIA classes.</p>
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<p>RSM: flow chart.</p>
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<p>Appliance functioning algorithms. (<b>a</b>) ADA class; (<b>b</b>) ODA class; (<b>c</b>) OIA class.</p>
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<p>Comparative analysis: RSM and unscheduled cases during <math display="inline"> <mrow> <mi>T</mi> <mn>1</mn> </mrow> </math>. (<b>a</b>) Hourly Price During <math display="inline"> <mrow> <mi>T</mi> <mn>1</mn> </mrow> </math>; (<b>b</b>) Consumption Comparison; (<b>c</b>) Cost Comparison.</p>
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<p>Comparative analysis: Realistic Scheduling Mechanism (RSM) and unscheduled cases during <math display="inline"> <mrow> <mi>T</mi> <mn>2</mn> </mrow> </math>. (<b>a</b>) Hourly Price During <math display="inline"> <mrow> <mi>T</mi> <mn>2</mn> </mrow> </math>; (<b>b</b>) Consumption Comparison; (<b>c</b>) Cost Comparison.</p>
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<p>Comparative analysis: RSM and unscheduled cases during <math display="inline"> <mrow> <mi>T</mi> <mn>3</mn> </mrow> </math>. (<b>a</b>) Hourly Price During <math display="inline"> <mrow> <mi>T</mi> <mn>3</mn> </mrow> </math>; (<b>b</b>) Consumption Comparison; (<b>c</b>) Cost Comparison.</p>
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<p>Comparative analysis: RSM and unscheduled cases during <math display="inline"> <mrow> <mi>T</mi> <mn>4</mn> </mrow> </math>. (<b>a</b>) Hourly Price During <math display="inline"> <mrow> <mi>T</mi> <mn>4</mn> </mrow> </math>; (<b>b</b>) Consumption Comparison; (<b>c</b>) Cost Comparison.</p>
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<p>Analysing appliance utility (<b>a</b>) Desired Ops Time; (<b>b</b>) OPS time using BPSO; (<b>c</b>) OPS time using RSM.</p>
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<p>Financial aspects: Unscheduled, RSM and BPSO. (<b>a</b>) Savings in <math display="inline"> <mrow> <mi>T</mi> <mn>1</mn> </mrow> </math>; (<b>b</b>) Savings in <math display="inline"> <mrow> <mi>T</mi> <mn>2</mn> </mrow> </math>; (<b>c</b>) Savings in <math display="inline"> <mrow> <mi>T</mi> <mn>3</mn> </mrow> </math>; (<b>d</b>) Savings in <math display="inline"> <mrow> <mi>T</mi> <mn>4</mn> </mrow> </math>; (<b>e</b>) Cost Comparison: 24 h Time.</p>
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<p>Power consumption comparison: RSM, BPSO and unscheduled.</p>
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<p>User Comfort Gain: Unscheduled, BPSO, RSM and RSM with PB scheduling.</p>
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<p>Impact of variation in sub-time slot size on electricity cost. (<b>a</b>) Cost analysis of RSM: two, three, four and six sub-time slots; (<b>b</b>) Cost analysis of RSM with PB: two, three, four and six sub-time slots.</p>
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3903 KiB  
Article
Catalytic Pyrolysis of Wild Reed over a Zeolite-Based Waste Catalyst
by Myung Lang Yoo, Yong Ho Park, Young-Kwon Park and Sung Hoon Park
Energies 2016, 9(3), 201; https://doi.org/10.3390/en9030201 - 15 Mar 2016
Cited by 10 | Viewed by 5449
Abstract
Fast catalytic pyrolysis of wild reed was carried out at 500 °C. Waste fluidized catalytic cracking (FCC) catalyst disposed from a petroleum refinery process was activated through acetone-washing and calcination and used as catalyst for pyrolysis. In order to evaluate the catalytic activity [...] Read more.
Fast catalytic pyrolysis of wild reed was carried out at 500 °C. Waste fluidized catalytic cracking (FCC) catalyst disposed from a petroleum refinery process was activated through acetone-washing and calcination and used as catalyst for pyrolysis. In order to evaluate the catalytic activity of waste FCC catalyst, commercial HY zeolite catalyst with a SiO2/Al2O3 ratio of 5.1 was also used. The bio-oil produced from pyrolysis was analyzed using gas chromatography/mass spectrometry (GC/MS). When the biomass-to-catalyst ratio was 1:1, the production of phenolics and aromatics was promoted considerably by catalysis, whereas the content of oxygenates was affected little. Significant conversion of oxygenates to furans and aromatics was observed when the biomass-to-catalyst ratio of 1:10 was used. Activated waste FCC catalyst showed comparable catalytic activity for biomass pyrolysis to HY in terms of the promotion of valuable chemicals, such as furans, phenolics and aromatics. The results of this study imply that waste FCC catalyst can be an important economical resource for producing high-value-added chemicals from biomass. Full article
(This article belongs to the Special Issue Selected Papers from 5th Asia-Pacific Forum on Renewable Energy)
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Graphical abstract
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<p>Thermo-gravimetric analysis (TGA) curves of catalysts.</p>
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<p>Derivative thermogravimetry (DTG) curves of catalysts.</p>
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<p>Yields of solid, liquid, and gas products of pyrolysis obtained under different catalytic conditions.</p>
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<p>Pyrolysis product distribution obtained under different catalytic conditions at 500 °C: (<b>a</b>) biomass:catalyst = 1:1; and (<b>b</b>) biomass:catalyst = 1:10.</p>
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<p>Detailed species distributions of oxygenates obtained from non-catalytic and catalytic pyrolyses of wild reed: (<b>a</b>) biomass:catalyst = 1:1; and (<b>b</b>) biomass:catalyst = 1:10.</p>
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<p>Detailed phenolic species distributions obtained from non-catalytic and catalytic pyrolyses with biomass:catalyst = 1:1: (<b>a</b>) small-molecular-mass phenolics; and (<b>b</b>) large-molecular-mass phenolics.</p>
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<p>Detailed phenolic species distributions obtained from non-catalytic and catalytic pyrolyses with biomass:catalyst = 1:10: (<b>a</b>) small-molecular-mass phenolics; and (<b>b</b>) large-molecular-mass phenolics.</p>
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<p>Comparison of the benzene, toluene, ethylbenzene, and xylene (BTEX) fractions obtained from non-catalytic and catalytic pyrolyses of wild reed.</p>
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<p>Detailed species distributions of gas obtained from non-catalytic and catalytic pyrolyses of wild reed.</p>
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4911 KiB  
Article
Solar Radiation Forecasting, Accounting for Daily Variability
by Roberto Langella, Daniela Proto and Alfredo Testa
Energies 2016, 9(3), 200; https://doi.org/10.3390/en9030200 - 15 Mar 2016
Cited by 4 | Viewed by 6454
Abstract
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical [...] Read more.
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons. Full article
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<p>Profile of solar irradiance and its components, according to Equation (6), on (<b>a</b>) 11 August 2004; and (<b>b</b>) 12 August 2004.</p>
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<p>Flow chart of the “Statistical Analysis” stage.</p>
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<p>Flow chart of the “Simulation of solar irradiance” stage.</p>
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<p>Flow chart of the “Forecasting of solar irradiance including weather predictions” stage.</p>
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<p>Discrete probability distributions of <math display="inline"> <semantics> <mrow> <msub> <mi>K</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> and their fitting probability density functions <span class="html-italic">(pdfs)</span> with reference to the months of (<b>a</b>) January; (<b>b</b>) April; (<b>c</b>) July; and (<b>d</b>) October.</p>
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<p>Discrete probability distributions of <math display="inline"> <semantics> <mrow> <msub> <mi>K</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> and their fitting probability density functions <span class="html-italic">(pdfs)</span> with reference to the months of (<b>a</b>) January; (<b>b</b>) April; (<b>c</b>) July; and (<b>d</b>) October.</p>
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<p>Discrete probability distributions of <math display="inline"> <semantics> <mrow> <msubsup> <mi>E</mi> <mi>m</mi> <mrow> <mo>′</mo> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics> </math>, and their fitting <span class="html-italic">pdfs</span> with reference to the months of (<b>a</b>) January; (<b>b</b>) April; (<b>c</b>) July; and (<b>d</b>) October.</p>
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<p>MAE<span class="html-italic"><sub>j,m</sub></span> values <span class="html-italic">versus</span> the day of the month for the two different forecast methods: EXPERIMENTAL and PARAMETRIC. (<b>a</b>) January, (<b>b</b>) April, (<b>c</b>) July, and (<b>d</b>) October.</p>
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<p>MAE<span class="html-italic"><sub>j,m</sub></span> values <span class="html-italic">versus</span> the day of the month for the two different forecast methods: EXPERIMENTAL and PARAMETRIC. (<b>a</b>) January, (<b>b</b>) April, (<b>c</b>) July, and (<b>d</b>) October.</p>
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<p>22nd October: Measured solar irradiance, <span class="html-italic">R</span><sub>22,10</sub>, forecasted solar irradiance, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>R</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>22</mn> <mo>,</mo> <mn>10</mn> </mrow> </msub> </mrow> </semantics> </math>, clear sky theoretical solar irradiance, <span class="html-italic">S</span><sub>LJ22,10</sub>, and product of the factor , <span class="html-italic">k</span><sub>22,10</sub> by <span class="html-italic">S</span><sub>W22,10</sub> <span class="html-italic">versus</span> the time: (<b>a</b>) pure statistical forecasting; and (<b>b</b>) forecasting accounting for weather predictions.</p>
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706 KiB  
Article
Measuring the Dynamic Characteristics of a Low Specific Speed Pump—Turbine Model
by Eve Cathrin Walseth, Torbjørn K. Nielsen and Bjørnar Svingen
Energies 2016, 9(3), 199; https://doi.org/10.3390/en9030199 - 15 Mar 2016
Cited by 29 | Viewed by 5747
Abstract
This paper presents results from an experiment performed to obtain the dynamic characteristics of a reversible pump-turbine model. The characteristics were measured in an open loop system where the turbine initially was run on low rotational speed before the generator was disconnected allowing [...] Read more.
This paper presents results from an experiment performed to obtain the dynamic characteristics of a reversible pump-turbine model. The characteristics were measured in an open loop system where the turbine initially was run on low rotational speed before the generator was disconnected allowing the turbine to go towards runaway. The measurements show that the turbine experience damped oscillations in pressure, speed and flow rate around runaway corresponding with presented stability criterion in published literature. Results from the experiment is reproduced by means of transient simulations. A one dimensional analytical turbine model for representation of the pump-turbine is used in the calculations. The simulations show that it is possible to reproduce the physics in the measurement by using a simple analytical model for the pump-turbine as long as the inertia of the water masses in the turbine are modeled correctly. Full article
(This article belongs to the Special Issue Hydropower)
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<p>Open loop test rig.</p>
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<p><math display="inline"> <msub> <mi>Q</mi> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<math display="inline"> <msub> <mi>N</mi> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> characteristics at <math display="inline"> <msup> <mn>10</mn> <mo>∘</mo> </msup> </math> guide vane opening.</p>
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<p>Flow rate during dynamic sequence at <math display="inline"> <msup> <mn>10</mn> <mo>∘</mo> </msup> </math> guide vane opening.</p>
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<p>Repeatability at <math display="inline"> <msup> <mn>10</mn> <mo>∘</mo> </msup> </math> guide vane opening.</p>
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<p>Steady state <span class="html-italic">T</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<span class="html-italic">N</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> characteristics.</p>
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<p>Steady state <span class="html-italic">Q</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<span class="html-italic">N</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> characteristics.</p>
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<p>Integration of streamline.</p>
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<p>Dynamic characteristics with redefined net head.</p>
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<p><span class="html-italic">Q</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<span class="html-italic">N</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> Characteristic.</p>
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<p>Rotational speed.</p>
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<p>Flow rate.</p>
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<p>Steady state <span class="html-italic">T</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<span class="html-italic">N</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> characteristics.</p>
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<p>Steady state <span class="html-italic">Q</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math>-<span class="html-italic">N</span><math display="inline"> <msub> <mrow/> <mrow> <mi>e</mi> <mi>d</mi> </mrow> </msub> </math> characteristics.</p>
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3293 KiB  
Article
Comparing the Bio-Hydrogen Production Potential of Pretreated Rice Straw Co-Digested with Seeded Sludge Using an Anaerobic Bioreactor under Mesophilic Thermophilic Conditions
by Asma Sattar, Chaudhry Arslan, Changying Ji, Sumiyya Sattar, Irshad Ali Mari, Haroon Rashid and Fariha Ilyas
Energies 2016, 9(3), 198; https://doi.org/10.3390/en9030198 - 15 Mar 2016
Cited by 22 | Viewed by 6515
Abstract
Three common pretreatments (mechanical, steam explosion and chemical) used to enhance the biodegradability of rice straw were compared on the basis of bio-hydrogen production potential while co-digesting rice straw with sludge under mesophilic (37 °C) and thermophilic (55 °C) temperatures. The results showed [...] Read more.
Three common pretreatments (mechanical, steam explosion and chemical) used to enhance the biodegradability of rice straw were compared on the basis of bio-hydrogen production potential while co-digesting rice straw with sludge under mesophilic (37 °C) and thermophilic (55 °C) temperatures. The results showed that the solid state NaOH pretreatment returned the highest experimental reduction of LCH (lignin, cellulose and hemi-cellulose) content and bio-hydrogen production from rice straw. The increase in incubation temperature from 37 °C to 55 °C increased the bio-hydrogen yield, and the highest experimental yield of 60.6 mL/g VSremoved was obtained under chemical pretreatment at 55 °C. The time required for maximum bio-hydrogen production was found on the basis of kinetic parameters as 36 h–47 h of incubation, which can be used as a hydraulic retention time for continuous bio-hydrogen production from rice straw. The optimum pH range of bio-hydrogen production was observed to be 6.7 ± 0.1–5.8 ± 0.1 and 7.1 ± 0.1–5.8 ± 0.1 under mesophilic and thermophilic conditions, respectively. The increase in temperature was found useful for controlling the volatile fatty acids (VFA) under mechanical and steam explosion pretreatments. The comparison of pretreatment methods under the same set of experimental conditions in the present study provided a baseline for future research in order to select an appropriate pretreatment method. Full article
(This article belongs to the Special Issue Advances in Biomass for Energy Technology)
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Graphical abstract

Graphical abstract
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<p>Schematic diagram for the double jacket anaerobic bio-reactor.</p>
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<p>Cumulative bio-hydrogen production under tested treatments.</p>
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<p>Modeled bio-hydrogen production: (<b>a</b>) mechanical pretreatment; (<b>b</b>) steam explosion; (<b>c</b>) chemical pretreatment.</p>
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<p>Drop in pH during incubation.</p>
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<p>Modeled drop in pH: (<b>a</b>) mechanical pretreatment; (<b>b</b>) steam explosion; (<b>c</b>) chemical pretreatment.</p>
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<p>Volatile fatty acids (VFA) trend under various treatments.</p>
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<p>Modeled VFA: (<b>a</b>) mechanical pretreatment; (<b>b</b>) steam explosion; (<b>c</b>) chemical retreatment.</p>
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4601 KiB  
Article
Asymmetrical Fault Correction for the Sensitive Loads Using a Current Regulated Voltage Source Inverter
by Syed Sabir Hussain Bukhari, Shahid Atiq, Thomas A. Lipo and Byung-il Kwon
Energies 2016, 9(3), 196; https://doi.org/10.3390/en9030196 - 15 Mar 2016
Cited by 9 | Viewed by 6223
Abstract
Numerous industrial applications involve loads that are very sensitive to electrical supply instabilities. These instances involve various types of voltage imbalances as well as more serious disturbances such as symmetrical and asymmetrical faults. This paper proposes a cost-effective voltage imbalance and asymmetrical fault [...] Read more.
Numerous industrial applications involve loads that are very sensitive to electrical supply instabilities. These instances involve various types of voltage imbalances as well as more serious disturbances such as symmetrical and asymmetrical faults. This paper proposes a cost-effective voltage imbalance and asymmetrical fault correction solution for the three phase sensitive loads utilizing an industry-standard current regulated voltage source inverter by connecting it in parallel to the grid mains powering to the sensitive load. The inverter regulates the current for the load and never permits it to go beyond a prescribed value under any type of asymmetrical fault condition, which ensures high power regulating and conditioning capacities. Experimental results are obtained from a small laboratory size prototype to validate the operation of the proposed technique. Full article
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<p>Load currents during: (<b>a</b>) single-line-to-ground (S-L-G), (<b>b</b>) double-line-to-ground (D-L-G), (<b>c</b>) single-line-to-line (S-L-L) faults (X-axis: 0.01 s/div; Y-axis: 0.5 p.u/div).</p>
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<p>Simple diagram of the proposed asymmetrical fault correction technique for sensitive loads.</p>
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<p>Complete diagram of the proposed asymmetrical fault correction technique for a sensitive load.</p>
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<p>Average value model block diagram of the proposed asymmetrical fault correction technique.</p>
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<p>Bode plot of Open Loop Forward Path Gain for implemented controller with effects of transport and sampling delays.</p>
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<p>Experimental waveforms of the: (<b>a</b>) grid currents (<span class="html-italic">I<sub>Grid</sub></span>); and (<b>b</b>) load currents (<span class="html-italic">I<sub>Load</sub></span>) with the proposed asymmetrical fault correction technique during a S-L-G fault (X-axis: 20 ms/div; Y-axis: 1 A/div).</p>
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<p>Experimental waveforms of the: (<b>a</b>) grid currents (<span class="html-italic">I<sub>Grid</sub></span>); and (<b>b</b>) load currents (<span class="html-italic">I<sub>Load</sub></span>) with the proposed asymmetrical fault correction technique during S-L-L and D-L-G faults (X-axis: 20 ms/div; Y-axis: 1 A/div).</p>
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<p>Experimental waveforms of inverter currents (<span class="html-italic">I<sub>Inverter</sub></span>) with the proposed asymmetrical fault correction technique during: (<b>a</b>) S-L-G; (<b>b</b>) S-L-L; and D-L-G faults (X-axis: 20 ms/div; Y-axis: 1 A/div).</p>
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<p>Experimental waveforms of inverter currents (<span class="html-italic">I<sub>Inverter</sub></span>) with the proposed asymmetrical fault correction technique during: (<b>a</b>) S-L-G; (<b>b</b>) S-L-L; and D-L-G faults (X-axis: 20 ms/div; Y-axis: 1 A/div).</p>
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<p>Experimental waveforms of: (<b>a</b>) grid currents (<span class="html-italic">I<sub>Grid</sub></span>); (<b>b</b>) inverter currents (<span class="html-italic">I<sub>Inverter</sub></span>); and (<b>c</b>) load currents (<span class="html-italic">I<sub>Load</sub></span>) with the proposed asymmetrical fault correction technique during a sag for 8 ms after every half cycle (X-axis: 20 ms/div; Y-axis: 1 A/div).</p>
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<p>DC link voltage (V) when the grid experiences: (<b>a</b>) S-L-G; (<b>b</b>) S-L-L; and (<b>c</b>) D-L-G faults (X-axis: 0.1 s/div; Y-axis: 5 V/div).</p>
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2542 KiB  
Article
Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case
by Antonio Bello, Javier Reneses and Antonio Muñoz
Energies 2016, 9(3), 193; https://doi.org/10.3390/en9030193 - 15 Mar 2016
Cited by 21 | Viewed by 6077
Abstract
One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel [...] Read more.
One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
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<p>Global overview of Monte Carlo simulation with spatial interpolation techniques.</p>
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<p>Density estimation.</p>
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<p>Decision tree: Model 1.</p>
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<p>Decision tree: Saturdays.</p>
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<p>Decision tree: Sundays and holidays.</p>
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<p>Decision tree: working days.</p>
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<p>Decomposition of the price series using an additive model.</p>
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<p>Calibration results for the MRS model.</p>
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<p>Global overview of the out-of-sample simulation with the hybrid approach.</p>
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<p>Probability of the appearance of extremely low prices predicted by the Logistic Regression M1 for January 2012.</p>
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<p>Probability density function for the number of hours with extremely low prices that has been predicted by the hybrid model for February 2012.</p>
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1739 KiB  
Article
Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster
by Alberto Cocaña-Fernández, Luciano Sánchez and José Ranilla
Energies 2016, 9(3), 197; https://doi.org/10.3390/en9030197 - 14 Mar 2016
Cited by 6 | Viewed by 4835
Abstract
As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up [...] Read more.
As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters. Full article
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<p>Resource Management System (RMS) components.</p>
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<p>EECluster Tool: System components overview. DBMS: Database Management System.</p>
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<p>EECluster learning process. QoS: Quality of Service.</p>
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<p>Cluster simulation trace for the test set of scenario 1. GFS: Genetic Fuzzy System.</p>
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<p>Cluster simulation trace for the test set of scenario 2.</p>
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<p>Cluster simulation trace for the test set of scenario 3.</p>
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<p>Cluster simulation trace for the test set of scenario 4.</p>
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<p>Cluster simulation trace for the test set of the CMS cluster workload records.</p>
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1302 KiB  
Article
On Scalability and Replicability of Smart Grid Projects—A Case Study
by Lukas Sigrist, Kristof May, Andrei Morch, Peter Verboven, Pieter Vingerhoets and Luis Rouco
Energies 2016, 9(3), 195; https://doi.org/10.3390/en9030195 - 14 Mar 2016
Cited by 30 | Viewed by 7494
Abstract
This paper studies the scalability and replicability of smart grid projects. Currently, most smart grid projects are still in the R&D or demonstration phases. The full roll-out of the tested solutions requires a suitable degree of scalability and replicability to prevent project demonstrators [...] Read more.
This paper studies the scalability and replicability of smart grid projects. Currently, most smart grid projects are still in the R&D or demonstration phases. The full roll-out of the tested solutions requires a suitable degree of scalability and replicability to prevent project demonstrators from remaining local experimental exercises. Scalability and replicability are the preliminary requisites to perform scaling-up and replication successfully; therefore, scalability and replicability allow for or at least reduce barriers for the growth and reuse of the results of project demonstrators. The paper proposes factors that influence and condition a project’s scalability and replicability. These factors involve technical, economic, regulatory and stakeholder acceptance related aspects, and they describe requirements for scalability and replicability. In order to assess and evaluate the identified scalability and replicability factors, data has been collected from European and national smart grid projects by means of a survey, reflecting the projects’ view and results. The evaluation of the factors allows quantifying the status quo of on-going projects with respect to the scalability and replicability, i.e., they provide a feedback on to what extent projects take into account these factors and on whether the projects’ results and solutions are actually scalable and replicable. Full article
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<p>Overview of the methodology for assessing the factors.</p>
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<p>Average scores and average importance of (<b>a</b>) scalability and (<b>b</b>) replicability factors.</p>
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<p>Comparison of scores: (<b>a</b>) scalability and (<b>b</b>) replicability factors of distribution projects, and (<b>c</b>) scalability and (<b>d</b>) replicability factors of transmission projects.</p>
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