Nothing Special   »   [go: up one dir, main page]
Next Issue
Volume 11, January
Previous Issue
Volume 10, November
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Volume 10
Issue 12
energies-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energies, Volume 10, Issue 12 (December 2017) – 252 articles

Cover Story (view full-size image): A control-oriented NOx model has been developed for an FPT Euro VI 3.0 L diesel engine for light-duty applications. The model is based on the estimation of the deviations of NOx emissions, with respect to the baseline values, as a function of the deviations of the intake O2 concentration and MFB50, as well as of the engine load and speed. The model was tested on a rapid prototyping device, and it was found that it is suitable for implementation on the ECU for real-time NOx control tasks. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
145 KiB  
Correction
Correction: Halil, B.; Gökhan, S. Numerical Investigation of the Effect of Variable Baffle Spacing on the Thermal Performance of a Shell and Tube Heat Exchanger. Energies 2017, 10, 1156
by Halil Bayram and Gökhan Sevilgen
Energies 2017, 10(12), 2181; https://doi.org/10.3390/en10122181 - 20 Dec 2017
Viewed by 2623
Abstract
The authors wish to make the following corrections to this paper [1][...] Full article
4970 KiB  
Article
Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting
by Ming-Wei Li, Jing Geng, Shumei Wang and Wei-Chiang Hong
Energies 2017, 10(12), 2180; https://doi.org/10.3390/en10122180 - 19 Dec 2017
Cited by 37 | Viewed by 5179
Abstract
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy [...] Read more.
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy. Full article
(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)
►▼ Show Figures

Figure 1

Figure 1
<p>Chaotic quantum bat algorithm flowchart.</p> Full article ">Figure 2
<p>Forecasting values of SVRCQBA and other alternative compared models.</p> Full article ">
1960 KiB  
Article
A Probabilistically Constrained Approach for the Energy Procurement Problem
by Patrizia Beraldi, Antonio Violi, Maria Elena Bruni and Gianluca Carrozzino
Energies 2017, 10(12), 2179; https://doi.org/10.3390/en10122179 - 19 Dec 2017
Cited by 15 | Viewed by 3862
Abstract
The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given [...] Read more.
The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given time horizon at the minimum cost. The problem is made more complex by the presence of uncertainty, mainly related to the energy requirements and electricity market prices. Ignoring the uncertain nature of these elements can lead to the definition of procurement plans which are infeasible or overly expensive in a real setting. In this paper, we deal with the procurement problem under uncertainty by adopting the paradigm of joint chance constraints to define reliable plans that are feasible with a high probability level. Moreover, the proposed model includes in the objective function a risk measure to control undesirable effects caused by the random variations of the electricity market prices. The proposed model is applied to a real test case. The results show the benefit deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion. Full article
(This article belongs to the Special Issue Selected Papers from ICEER 2017: 2017 the 4th International Conference on Energy and Environment Research)
►▼ Show Figures

Figure 1

Figure 1
<p>CVaR defined in terms of costs.</p> Full article ">Figure 2
<p>Scenarios for the market electricity price.</p> Full article ">Figure 3
<p>Scenarios for the overall energy demand.</p> Full article ">Figure 4
<p>Optimal procurement plan.</p> Full article ">Figure 5
<p>Cost distribution.</p> Full article ">Figure 6
<p>Trade-off between expected cost and CVaR.</p> Full article ">Figure 7
<p>Procurement costs.</p> Full article ">
4466 KiB  
Article
Extension of Operating Range in Pump-Turbines. Influence of Head and Load
by Carme Valero, Mònica Egusquiza, Eduard Egusquiza, Alexandre Presas, David Valentin and Matias Bossio
Energies 2017, 10(12), 2178; https://doi.org/10.3390/en10122178 - 19 Dec 2017
Cited by 32 | Viewed by 5406
Abstract
Due to the increasing share of new renewable energies like wind and solar in the generation of electricity the need for power regulation and energy storage is becoming of paramount importance. One of the systems to store huge amounts of energy is pumped [...] Read more.
Due to the increasing share of new renewable energies like wind and solar in the generation of electricity the need for power regulation and energy storage is becoming of paramount importance. One of the systems to store huge amounts of energy is pumped storage using reversible hydropower units. The machines used in these power plants are pump-turbines, which can operate as a pump and as a turbine. The surplus of electrical energy during low consumption hours can be converted into potential hydraulic energy by pumping water to a higher level. The stored energy can be converted into electricity again by operating the runner as a turbine. Due to new regulation requirements machines have to extend the operating range in order to match energy generation with consumption for the grid stability. In this paper the consequences of extending the operating range in existing pump-turbines have been studied. For that purpose, the data obtained after two years of condition monitoring were analyzed. Vibrations and pressure fluctuations of two pump-turbines of 85 MW each have been studied during pump and turbine operation. For turbine operation the effects of extending the operating range from the standard range of 45–85 MW to and increased range of 20–85 MW were analyzed. The change in vibration levels and signatures at very low load are presented with the identification of the phenomena that occur under these conditions. The influence of head in the vibration behavior is also presented. The appearance of fluid instabilities generated at part load that may produce power swing is also presented. Finally, the effect of head on the vibration levels for pump operation is shown and analyzed. Full article
(This article belongs to the Special Issue Hydropower 2017)
►▼ Show Figures

Figure 1

Figure 1
<p>Sketch of a hydroelectric reversible power plant.</p> Full article ">Figure 2
<p>Sensors installed in the pump turbine. Accelerometers are displayed in grey and pressure transducers in black.</p> Full article ">Figure 3
<p>Monitoring system installed in the pump turbine.</p> Full article ">Figure 4
<p>Variation of head level in pumping operation (blue marker) and in turbine operation (red marker).</p> Full article ">Figure 5
<p>Vibration levels of turbine bearing (<b>a</b>); guide vane opening (<b>b</b>).</p> Full article ">Figure 5 Cont.
<p>Vibration levels of turbine bearing (<b>a</b>); guide vane opening (<b>b</b>).</p> Full article ">Figure 6
<p>Zoom of vibration levels of turbine bearing (<b>a</b>) and guide vane opening (<b>b</b>).</p> Full article ">Figure 7
<p>(<b>a</b>)Vibration levels of generator and (<b>b</b>) turbine bearing in mm/s RMS. Different heads and loads. Unit 1.</p> Full article ">Figure 8
<p>(<b>a</b>)Vibration levels of generator and (<b>b</b>) turbine bearing in mm/s RMS. Different heads and loads. Unit 2.</p> Full article ">Figure 9
<p>Variation levels related with operation (similar head, different load) (<b>a</b>) Unit 1; (<b>b</b>): Unit 2. The possible reasons for these changes are explained in <a href="#sec3dot2-energies-10-02178" class="html-sec">Section 3.2</a>.</p> Full article ">Figure 10
<p>Variation in vibration levels related with head (similar load).</p> Full article ">Figure 11
<p>Spectral signatures of the accelerometer located at turbine bearing.</p> Full article ">Figure 12
<p>Spectral signatures for maximum load (<b>bottom</b>) and minimum load (<b>top</b>) at high (green) and low head (red).</p> Full article ">Figure 13
<p>(<b>a</b>) Spectral pressure signature in draft tube and (<b>b</b>) spectral vibration signature in turbine bearing and (<b>c</b>) axial bearing at part load.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Spectral pressure signature in draft tube and (<b>b</b>) spectral vibration signature in turbine bearing and (<b>c</b>) axial bearing at part load.</p> Full article ">Figure 14
<p>(<b>a</b>) Variation of gross head versus time; (<b>b</b>) variation of overall vibration versus time.</p> Full article ">Figure 15
<p>(<b>a</b>) Vibration levels of generator bearing; (<b>b</b>) Vibration levels of turbine bearing. Both in pumping mode. Unit 1.</p> Full article ">Figure 16
<p>Spectral vibration signature in turbine bearing at low (<b>a</b>) and high head (<b>b</b>) both in pump operation.</p> Full article ">Figure 17
<p>Spectral vibration signature in turbine bearing operating (<b>a</b>) at high head and (<b>b</b>) at low head during pump operation.</p> Full article ">
4993 KiB  
Article
Analysis of the Influence of Compensation Capacitance Errors of a Wireless Power Transfer System with SS Topology
by Yi Wang, Fei Lin, Zhongping Yang and Zhiyuan Liu
Energies 2017, 10(12), 2177; https://doi.org/10.3390/en10122177 - 19 Dec 2017
Cited by 8 | Viewed by 4717
Abstract
In this study, in order to determine the reasonable accuracy of the compensation capacitances satisfying the requirements on the output characteristics for a wireless power transfer (WPT) system, taking the series-series (SS) compensation structure as an example, the calculation formulas of the output [...] Read more.
In this study, in order to determine the reasonable accuracy of the compensation capacitances satisfying the requirements on the output characteristics for a wireless power transfer (WPT) system, taking the series-series (SS) compensation structure as an example, the calculation formulas of the output characteristics, such as the power factor, output power, coil transfer efficiency, and capacitors’ voltage stress, are given under the condition of incomplete compensation according to circuit theory. The influence of compensation capacitance errors on the output characteristics of the system is then analyzed. The Taylor expansions of the theoretical formulas are carried out to simplify the formulas. The influence degrees of compensation capacitance errors on the output characteristics are calculated according to the simplified formulas. The reasonable error ranges of the compensation capacitances are then determined according to the requirements of the output characteristics of the system in the system design. Finally, the validity of the theoretical analysis and the simplified processing is verified through experiments. The proposed method has a certain guiding role for practical engineering design, especially in mass production. Full article
(This article belongs to the Special Issue Wireless Power Transfer and Energy Harvesting Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p>Typical structure of wireless power transfer (WPT) system with series-series (SS) compensation topology.</p> Full article ">Figure 2
<p>SS compensation topology equivalent circuit diagram.</p> Full article ">Figure 3
<p>The variation of output characteristics with capacitance errors calculated using the original and simplified formulas, and allowable capacitance error ranges calculated using the simplified formulas: (<b>a</b>) The power factor varying with capacitance errors and allowable capacitance error range when the power factor is greater than 0.9; (<b>b</b>) The output power varying with capacitance errors and allowable capacitance error range when the output power change rate is smaller than 0.1; (<b>c</b>) The coil transfer efficiency varying with capacitance errors.</p> Full article ">Figure 4
<p>The variation of the capacitors voltage stress with capacitance errors calculated using the original and simplified formulas, and allowable capacitance error ranges calculated using the simplified formulas: (<b>a</b>) The voltage stress of <span class="html-italic">C</span><sub>1</sub> varying with capacitance errors and allowable capacitance error range when the rise rate of the voltage stress of <span class="html-italic">C</span><sub>1</sub> is smaller than 0.1; (<b>b</b>) The voltage stress of <span class="html-italic">C</span><sub>2</sub> varying with capacitance errors and allowable capacitance error range when the rise rate of the voltage stress of <span class="html-italic">C</span><sub>2</sub> is smaller than 0.1.</p> Full article ">Figure 5
<p>Intersection of allowable capacitance errors satisfying the system requirements on the output characteristics.</p> Full article ">Figure 6
<p>Experimental platform device.</p> Full article ">Figure 7
<p>Experiment waveforms: (<b>a</b>) <span class="html-italic">e</span><sub>1</sub> = −0.3%, <span class="html-italic">e</span><sub>2</sub> = +0.3%; (<b>b</b>) <span class="html-italic">e</span><sub>1</sub> = −10.1%, <span class="html-italic">e</span><sub>2</sub> = −10.0%; (<b>c</b>) <span class="html-italic">e</span><sub>1</sub> = −5.2%, <span class="html-italic">e</span><sub>2</sub> = +0.3%; and (<b>d</b>) <span class="html-italic">e</span><sub>1</sub> = +9.8%, <span class="html-italic">e</span><sub>2</sub> = +4.4%.</p> Full article ">Figure 8
<p>The variation of output characteristics with capacitance errors and the variation of output characteristics fitted by the experimental data: (<b>a</b>) The power factor varying with capacitance errors; (<b>b</b>) the output power varying with capacitance errors; (<b>c</b>) The coil transfer efficiency varying with capacitance errors; (<b>d</b>) The voltage stress of <span class="html-italic">C</span><sub>1</sub> varying with capacitance errors; (<b>e</b>) The voltage stress of <span class="html-italic">C</span><sub>2</sub> varying with capacitance errors.</p> Full article ">Figure 9
<p>The calculated allowable range and experimental allowable range.</p> Full article ">
2807 KiB  
Article
Modeling and Stability Analysis of a Single-Phase Two-Stage Grid-Connected Photovoltaic System
by Liying Huang, Dongyuan Qiu, Fan Xie, Yanfeng Chen and Bo Zhang
Energies 2017, 10(12), 2176; https://doi.org/10.3390/en10122176 - 19 Dec 2017
Cited by 21 | Viewed by 6129
Abstract
The stability issue of a single-phase two-stage grid-connected photovoltaic system is complicated due to the nonlinear v-i characteristic of the photovoltaic array as well as the interaction between power converters. Besides, even though linear system theory is widely used in stability [...] Read more.
The stability issue of a single-phase two-stage grid-connected photovoltaic system is complicated due to the nonlinear v-i characteristic of the photovoltaic array as well as the interaction between power converters. Besides, even though linear system theory is widely used in stability analysis of balanced three-phase systems, the application of the same theory to single-phase systems meets serious challenges, since single-phase systems cannot be transformed into linear time-invariant systems simply using Park transformation as balanced three-phase systems. In this paper, (1) the integrated mathematical model of a single-phase two-stage grid-connected photovoltaic system is established, in which both DC-DC converter and DC-AC converter are included also the characteristic of the PV array is considered; (2) an observer-pattern modeling method is used to eliminate the time-varying variables; and (3) the stability of the system is studied using eigenvalue sensitivity and eigenvalue loci plots. Finally, simulation results are given to validate the proposed model and stability analysis. Full article
(This article belongs to the Special Issue PV System Design and Performance)
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of a single-phase two-stage grid-connected photovoltaic system: (<b>a</b>) Power stage circuit; (<b>b</b>) MPPT controller; (<b>c</b>) double loop controller.</p> Full article ">Figure 1 Cont.
<p>Diagram of a single-phase two-stage grid-connected photovoltaic system: (<b>a</b>) Power stage circuit; (<b>b</b>) MPPT controller; (<b>c</b>) double loop controller.</p> Full article ">Figure 2
<p>DC bus voltage waveform.</p> Full article ">Figure 3
<p>Output current waveform.</p> Full article ">Figure 4
<p>Loci of the eigenvalues with respect to various PI controller parameters: (<b>a</b>) <span class="html-italic">λ</span><sub>1,2</sub> when <span class="html-italic">K<sub>p</sub></span><sub>3</sub> is varied within the range (0.2, 1.8); (<b>b</b>) <span class="html-italic">λ</span><sub>3,4</sub> when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> is varied within the range (0.01, 0.19); (<b>c</b>) <span class="html-italic">λ</span><sub>3,4</sub> when <span class="html-italic">K<sub>p</sub></span><sub>1</sub> is varied within the range (0.005, 0.095); (<b>d</b>) <span class="html-italic">λ</span><sub>5</sub> when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> is varied within the range (0.01, 0.19); (<b>e</b>) <span class="html-italic">λ</span><sub>6,7</sub> when <span class="html-italic">K<sub>p</sub></span><sub>2</sub> is varied within the range (0.002, 0.038); (<b>f</b>) <span class="html-italic">λ</span><sub>8,9</sub> when <span class="html-italic">T<sub>i</sub></span><sub>3</sub> is varied within the range (0.02, 0.38).</p> Full article ">Figure 5
<p>Simulation results of <span class="html-italic">u<sub>dc</sub></span>: (<b>a</b>) Time domain waveforms when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> = 0.03; (<b>b</b>) Fast Fourier Transformation (FFT) analysis when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> = 0.03; (<b>c</b>) Time domain waveforms when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> = 0.01; (<b>d</b>) FFT analysis when <span class="html-italic">T<sub>i</sub></span><sub>1</sub> = 0.01.</p> Full article ">
8155 KiB  
Article
Experimental and Finite Element Analysis to Investigate the Vibration of Oblique-Stud Stator Frame in a Large Hydropower Generator Unit
by Jianzhong Zhou, Xuanlin Peng, Ruhai Li, Yanhe Xu, Han Liu and Diyi Chen
Energies 2017, 10(12), 2175; https://doi.org/10.3390/en10122175 - 19 Dec 2017
Cited by 13 | Viewed by 6154
Abstract
This paper presents an investigation on the undesirable vibration of an oblique-stud stator frame in a large hydropower generator by means of experimental and finite element (FE) analysis. First, field experimental tests were performed, and the results indicate that the main vibration component [...] Read more.
This paper presents an investigation on the undesirable vibration of an oblique-stud stator frame in a large hydropower generator by means of experimental and finite element (FE) analysis. First, field experimental tests were performed, and the results indicate that the main vibration component comes from electromagnetic factors. Then, a 2D-magnetic and 3D-mechanical FE model was developed to investigate the vibration of the stator frame under the action of electromagnetic forces. A set of contrast models was established to study the effects of different kinds of eccentricity and different structures. Based on the comparative analysis between the results of simulations and experimental tests, it can be inferred that the abnormal vibration is generated because of the lack of stiffness in the upper part of structure and the existence of dynamic eccentricity in the rotor–stator system. In addition, the structural simulation analysis shows that the flexible designed oblique-stud stator frame is relatively vulnerable against the electromagnetic forces. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Structure diagram of the stator frame.</p> Full article ">Figure 2
<p>Vibration shape of the tested stator frame.</p> Full article ">Figure 3
<p>Diagram of monitoring point.</p> Full article ">Figure 4
<p>Vibration peak value of stator frame of experimental tests: (<b>a</b>) excitation test at no-load and rated speed condition; (<b>b</b>) rotation speed test at no-load and 100% excitation condition; and (<b>c</b>) load test at rated speed and 100% excitation condition.</p> Full article ">Figure 4 Cont.
<p>Vibration peak value of stator frame of experimental tests: (<b>a</b>) excitation test at no-load and rated speed condition; (<b>b</b>) rotation speed test at no-load and 100% excitation condition; and (<b>c</b>) load test at rated speed and 100% excitation condition.</p> Full article ">Figure 5
<p>Frequency spectrums of vibration at the mid of stator frame.</p> Full article ">Figure 6
<p>Schematic procedure of finite element (FE) analysis.</p> Full article ">Figure 7
<p>2D model of electromagnetic analysis.</p> Full article ">Figure 8
<p>B-H (B stands for magnetic field intensity, while H represent magnetic induction intensity) curve of silicon steel 50W250.</p> Full article ">Figure 9
<p>External circuit of hydro-generator.</p> Full article ">Figure 10
<p>Improved mesh of generator model.</p> Full article ">Figure 11
<p>The first order mode-shape of the stator pack at 28.8 Hz.</p> Full article ">Figure 12
<p>Magnetic flux density at the center of air-gap with different eccentric models: (<b>a</b>) concentric model; (<b>b</b>) static eccentric model; and (<b>c</b>) dynamic eccentric model.</p> Full article ">Figure 13
<p>Electromagnetic force densities at the center of air-gap with different eccentricity: (<b>a</b>) concentric model; (<b>b</b>) static eccentric model; and (<b>c</b>) dynamic eccentric model.</p> Full article ">Figure 14
<p>Fast fourier transformation (FFT) of electromagnetic force density with different eccentric models.</p> Full article ">Figure 15
<p>Comparison between concentric model and dynamic eccentric model.</p> Full article ">Figure 16
<p>The transient analysis results of concentric model.</p> Full article ">Figure 17
<p>The harmonic response of the studied generator.</p> Full article ">Figure 18
<p>Models of oblique-stud structure and radial-stud structure.</p> Full article ">Figure 19
<p>The first order mode-shape of the contrast model at 32.2 Hz.</p> Full article ">
4140 KiB  
Article
Comparison of Lithium-Ion Anode Materials Using an Experimentally Verified Physics-Based Electrochemical Model
by Rujian Fu, Xuan Zhou, Hengbin Fan, Douglas Blaisdell, Ajay Jagadale, Xi Zhang and Rui Xiong
Energies 2017, 10(12), 2174; https://doi.org/10.3390/en10122174 - 19 Dec 2017
Cited by 23 | Viewed by 6099
Abstract
Researchers are in search of parameters inside Li-ion batteries that can be utilized to control their external behavior. Physics-based electrochemical model could bridge the gap between Li+ transportation and distribution inside battery and battery performance outside. In this paper, two commercially available Li-ion [...] Read more.
Researchers are in search of parameters inside Li-ion batteries that can be utilized to control their external behavior. Physics-based electrochemical model could bridge the gap between Li+ transportation and distribution inside battery and battery performance outside. In this paper, two commercially available Li-ion anode materials: graphite and Lithium titanate (Li4Ti5O12 or LTO) were selected and a physics-based electrochemical model was developed based on half-cell assembly and testing. It is found that LTO has a smaller diffusion coefficient (Ds) than graphite, which causes a larger overpotential, leading to a smaller capacity utilization and, correspondingly, a shorter duration of constant current charge or discharge. However, in large current applications, LTO performs better than graphite because its effective particle radius decreases with increasing current, leading to enhanced diffusion. In addition, LTO has a higher activation overpotential in its side reactions; its degradation rate is expected to be much smaller than graphite, indicating a longer life span. Full article
(This article belongs to the Section D: Energy Storage and Application)
►▼ Show Figures

Figure 1

Figure 1
<p>OCV or <math display="inline"> <semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msub> </mrow> </semantics> </math> measurement vs. state of charge (SOC) for graphite and Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub> anode.</p> Full article ">Figure 2
<p>Experimental validation of the terminal voltage of a graphite half-cell at 0.05 C, 0.1 C and 0.15 C cycles.</p> Full article ">Figure 3
<p>Experimental validation of terminal voltage of an LTO half-cell at 0.05 C, 0.1 C and 0.15 C cycles.</p> Full article ">Figure 4
<p>Terminal voltage comparison of simulated graphite and LTO half-cells cycled with constant current at 0.045 C, 0.089 C and 0.13 C.</p> Full article ">Figure 5
<p>State of charge (SOC) comparison of graphite and LTO half-cell simulations of 0.045 C, 0.089 C, and 0.13 C cycles.</p> Full article ">Figure 6
<p>Ion concentrations in solid particles of graphite and LTO half-cells during 0.045 C, 0.089 C, and 0.13 C cycles.</p> Full article ">Figure 7
<p>Ion concentrations in the electrolyte of graphite and LTO half-cells while cycling at 0.045 C, 0.089 C, and 0.13 C.</p> Full article ">Figure 8
<p>Geometry setup for a half-cell model with a graphite or LTO anode (single particle schematics).</p> Full article ">Figure 9
<p>Activation overpotential of side reactions in graphite and LTO half-cells cycled at 0.045 C, 0.089 C, and 0.13 C.</p> Full article ">Figure A1
<p>SEM images of: graphite particles (<b>left</b>); and LTO particles (<b>right</b>).</p> Full article ">Figure A2
<p>Terminal voltage curves of discharges at a very small current rate.</p> Full article ">Figure A3
<p>Experimental validation of terminal voltage of LTO half-cell without considering the current dependency of <span class="html-italic">D<sub>s</sub></span>.</p> Full article ">
22729 KiB  
Article
Investigation of the Magnetic Circuit and Performance of Less-Rare-Earth Interior Permanent-Magnet Synchronous Machines Used for Electric Vehicles
by Ping Zheng, Weinan Wang, Mingqiao Wang, Yong Liu and Zhenxing Fu
Energies 2017, 10(12), 2173; https://doi.org/10.3390/en10122173 - 19 Dec 2017
Cited by 8 | Viewed by 4497
Abstract
The less-rare-earth interior permanent-magnet synchronous machines (LRE-IPMSMs), which have the advantages of high power density, high efficiency, and low cost, are promising candidates for electric vehicles (EVs). In this paper, the equivalent magnetic circuit (EMC) of LRE-IPMSM is established and analyzed to investigate [...] Read more.
The less-rare-earth interior permanent-magnet synchronous machines (LRE-IPMSMs), which have the advantages of high power density, high efficiency, and low cost, are promising candidates for electric vehicles (EVs). In this paper, the equivalent magnetic circuit (EMC) of LRE-IPMSM is established and analyzed to investigate the machine design principles, and then the performance of an optimized machine is analyzed. Firstly, the equivalent magnetic circuits of the LRE-IPMSM are established by taking the saturation effect into consideration. Secondly, the effects of geometric parameters, such as the permanent-magnet (PM) width, the PM thickness, the flux barrier thickness, the flux barrier span angle, and the bridge width, on no-load flux, q-axis flux, and d-axis flux are investigated, respectively. The results calculated by the EMC method and finite-element analysis (FEA) are analyzed and compared, which proves the effectiveness of the EMC method. Finally, an optimized design of LRE-IPMSM obtained by the magnetic circuit analyses is proposed. The electromagnetic performances and mechanical strength of the optimized LRE-IPMSM are analyzed and verified, respectively. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Structure of the preliminary less-rare-earth interior permanent-magnet synchronous machine (LRE-IPMSM); (<b>b</b>) Sketches of flux barriers and bridges.</p> Full article ">Figure 2
<p>Magnetization curve of silicon steel.</p> Full article ">Figure 3
<p>No-load magnetic circuit model.</p> Full article ">Figure 4
<p>Rewriting of the no-load magnetic circuit model.</p> Full article ">Figure 5
<p>Equivalent of the flux barrier, permanent-magnet (PM) and bridge parallel circuit.</p> Full article ">Figure 6
<p>Sketches of geometric parameters: (<b>a</b>) the flux barrier span angle (<span class="html-italic">θ<sub>Bai</sub></span>) and the flux barrier thickness (<span class="html-italic">h<sub>Bai</sub></span>); (<b>b</b>) the PM thickness (<span class="html-italic">h<sub>PMi</sub></span>), the PM width (<span class="html-italic">b<sub>PMi</sub></span>), and the bridge width (<span class="html-italic">b<sub>Br</sub></span>).</p> Full article ">Figure 7
<p>No-load flux (Φ<span class="html-italic"><sub>δ</sub></span>) versus the PM thickness (<span class="html-italic">h<sub>PMi</sub></span>). EMC: equivalent magnetic circuit. FEA: finite-element analysis.</p> Full article ">Figure 8
<p>No-load flux (Φ<span class="html-italic"><sub>δ</sub></span>) versus the PM width (<span class="html-italic">b<sub>PMi</sub></span>).</p> Full article ">Figure 9
<p>No-load flux (Φ<span class="html-italic"><sub>δ</sub></span>) versus the flux barrier thickness (<span class="html-italic">h<sub>Bai</sub></span>).</p> Full article ">Figure 10
<p>No-load flux (Φ<span class="html-italic"><sub>δ</sub></span>) versus the flux barrier span angle (<span class="html-italic">θ<sub>Bai</sub></span>).</p> Full article ">Figure 11
<p>No-load flux (Φ<span class="html-italic"><sub>δ</sub></span>) versus the bridge width (<span class="html-italic">b<sub>Br</sub></span>).</p> Full article ">Figure 12
<p><span class="html-italic">q</span>-axis magnetic circuit model.</p> Full article ">Figure 13
<p>Rewriting of <span class="html-italic">q</span>-axis magnetic circuit model.</p> Full article ">Figure 14
<p>Equivalent of the flux barrier and bridge parallel circuit.</p> Full article ">Figure 15
<p><span class="html-italic">q</span>-axis flux (Φ<span class="html-italic"><sub>q</sub></span>) versus the PM thickness (<span class="html-italic">h<sub>PMi</sub></span>).</p> Full article ">Figure 16
<p><span class="html-italic">q</span>-axis flux (Φ<span class="html-italic"><sub>q</sub></span>) versus the PM width (<span class="html-italic">b<sub>PMi</sub></span>).</p> Full article ">Figure 17
<p><span class="html-italic">q</span>-axis flux (Φ<span class="html-italic"><sub>q</sub></span>) versus the flux barrier thickness (<span class="html-italic">h<sub>Bai</sub></span>).</p> Full article ">Figure 18
<p><span class="html-italic">q</span>-axis flux (Φ<span class="html-italic"><sub>q</sub></span>) versus the flux barrier span angle (<span class="html-italic">θ<sub>Bai</sub></span>).</p> Full article ">Figure 19
<p><span class="html-italic">q</span>-axis flux (Φ<span class="html-italic"><sub>q</sub></span>) versus the bridge width (<span class="html-italic">b<sub>Br</sub></span>).</p> Full article ">Figure 20
<p>Sketch of <span class="html-italic">d</span>-axis flux distribution.</p> Full article ">Figure 21
<p><span class="html-italic">d</span>-axis magnetic circuit model.</p> Full article ">Figure 22
<p>Rewriting of <span class="html-italic">d</span>-axis magnetic circuit model.</p> Full article ">Figure 23
<p>Equivalent of the flux barrier and bridges parallel circuit.</p> Full article ">Figure 24
<p><span class="html-italic">d</span>-axis flux (Φ<span class="html-italic"><sub>d</sub></span>) versus the PM thickness (<span class="html-italic">h<sub>PMi</sub></span>).</p> Full article ">Figure 25
<p><span class="html-italic">d</span>-axis flux (Φ<span class="html-italic"><sub>d</sub></span>) versus the PM width (<span class="html-italic">b<sub>PMi</sub></span>).</p> Full article ">Figure 26
<p><span class="html-italic">d</span>-axis flux (Φ<span class="html-italic"><sub>d</sub></span>) versus the flux barrier thickness (<span class="html-italic">h<sub>Bai</sub></span>).</p> Full article ">Figure 27
<p><span class="html-italic">d</span>-axis flux (Φ<span class="html-italic"><sub>d</sub></span>) versus the flux barrier span angle (<span class="html-italic">θ<sub>Bai</sub></span>).</p> Full article ">Figure 28
<p><span class="html-italic">d</span>-axis flux (Φ<span class="html-italic"><sub>d</sub></span>) versus the bridge width (<span class="html-italic">b<sub>Br</sub></span>).</p> Full article ">Figure 29
<p>Sketch of the optimized LRE-IPMSM.</p> Full article ">Figure 30
<p>Various torques calculated by the EMC method and FEA.</p> Full article ">Figure 31
<p>Waveforms of electromagnetic torque and reluctance torque.</p> Full article ">Figure 32
<p>Loss and efficiency over the whole speed range.</p> Full article ">Figure 33
<p>Stress distribution at maximum speed.</p> Full article ">
2742 KiB  
Article
Effects of Mixture Stratification on Combustion and Emissions of Boosted Controlled Auto-Ignition Engines
by Jacek Hunicz, Aymen Tmar and Paweł Krzaczek
Energies 2017, 10(12), 2172; https://doi.org/10.3390/en10122172 - 19 Dec 2017
Cited by 16 | Viewed by 6175
Abstract
The stratification of in-cylinder mixtures appears to be an effective method for managing the combustion process in controlled auto-ignition (CAI) engines. Stratification can be achieved and controlled using various injection strategies such as split fuel injection and the introduction of a portion of [...] Read more.
The stratification of in-cylinder mixtures appears to be an effective method for managing the combustion process in controlled auto-ignition (CAI) engines. Stratification can be achieved and controlled using various injection strategies such as split fuel injection and the introduction of a portion of fuel directly before the start of combustion. This study investigates the effect of injection timing and the amount of fuel injected for stratification on the combustion and emissions in CAI engine. The experimental research was performed on a single cylinder engine with direct gasoline injection. CAI combustion was achieved using negative valve overlap and exhaust gas trapping. The experiments were performed at constant engine fueling. Intake boost was applied to control the excess air ratio. The results show that the application of the late injection strategy has a significant effect on the heat release process. In general, the later the injection is and the more fuel is injected for stratification, the earlier the auto-ignition occurs. However, the experimental findings reveal that the effect of stratification on combustion duration is much more complex. Changes in combustion are reflected in NOX emissions. The attainable level of stratification is limited by the excessive emission of unburned hydrocarbons, CO and soot. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Cross-section of the valvetrain (<b>a</b>) and combustion system with mixture stratification idea (<b>b</b>).</p> Full article ">Figure 2
<p>Example of in-cylinder pressure, profiles of valve lifts and injection timings.</p> Full article ">Figure 3
<p>In-cylinder pressure: (<b>a</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>b</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg; (<b>c</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>d</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg.</p> Full article ">Figure 4
<p>Calculated in-cylinder temperature: (<b>a</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>b</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg; (<b>c</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>d</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg.</p> Full article ">Figure 5
<p>Calculated heat release rate: (<b>a</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>b</b>) <span class="html-italic">λ</span> = 1.3, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg; (<b>c</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 2.4 mg; (<b>d</b>) <span class="html-italic">λ</span> = 1.6, <span class="html-italic">m</span><sub>F3</sub> = 5.2 mg.</p> Full article ">Figure 6
<p>Location of 5% mass fraction burned (MFB) (<b>a</b>) and combustion duration (5–95% MFB) (<b>b</b>) versus SOI<sub>3</sub> timing for all investigated conditions.</p> Full article ">Figure 7
<p>Pressure rise rate versus SOI<sub>3</sub> timing for all investigated conditions.</p> Full article ">Figure 8
<p>Indicated specific emissions of exhaust toxic compounds versus SOI<sub>3</sub> timing for all investigated conditions: (<b>a</b>) CO; (<b>b</b>) unburned hydrocarbons (HC); (<b>c</b>) NO<sub>X</sub>; (<b>d</b>) soot.</p> Full article ">Figure 9
<p>Net indicated thermal efficiency versus SOI<sub>3</sub> timing for all investigated conditions.</p> Full article ">
2011 KiB  
Article
Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System
by Luca Massidda and Marino Marrocu
Energies 2017, 10(12), 2171; https://doi.org/10.3390/en10122171 - 19 Dec 2017
Cited by 15 | Viewed by 3660
Abstract
The balance between production and consumption in a smart grid with high penetration of renewable sources and in the presence of energy storage systems benefits from an accurate load prediction. A general approach to load forecasting is not possible because of the additional [...] Read more.
The balance between production and consumption in a smart grid with high penetration of renewable sources and in the presence of energy storage systems benefits from an accurate load prediction. A general approach to load forecasting is not possible because of the additional complication due to the increasing presence of distributed and usually unmeasured photovoltaic production. Various methods are proposed in the literature that can be classified into two classes: those that predict by separating the portion of load due to consumption habits from the part of production due to local weather conditions, and those that attempt to predict the load as a whole. The characteristic that should lead to a preference for one approach over another is obviously the percentage of penetration of distributed production. The study site discussed in this document is the grid of Borkum, an island located in the North Sea. The advantages in terms of reducing forecasting errors for the electrical load, which can be obtained by using weather information, are explained. In particular, when comparing the results of different approaches gradually introducing weather forecasts, it is clear that the correct functional dependency of production has to be taken into account in order to obtain maximum yield from the available information. Where possible, this approach can significantly improve the quality of the forecasts, which in turn can improve the balance of a network—especially if energy storage systems are in place. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Scheme of the grid in Borkum. A photovoltaic (PV) power plant and two wind turbines are connected to the medium-voltage (MV) line. An energy storage system will be connected to the MV line in the future. The low-voltage (LV) line distributes power to the town, where several PV devices are connected. The available power measurements are relative to the total power exchange with the mainland (<math display="inline"> <semantics> <msub> <mi>p</mi> <mi>e</mi> </msub> </semantics> </math>) and to the LV net load (<math display="inline"> <semantics> <msub> <mi>p</mi> <mi>n</mi> </msub> </semantics> </math>). BESS: battery energy storage system.</p> Full article ">Figure 2
<p>Box plots of the net load and of the power exchange of the microgrid. The box represents the interquartile range, the line in the box is the median, and the whiskers are set at the 5 and 95 percentile of the data range.</p> Full article ">Figure 3
<p>Error in the LV load forecast as a function of the temperature 2 m above ground forecast. Coefficient of determination <math display="inline"> <semantics> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> </semantics> </math> = <math display="inline"> <semantics> <mrow> <mo>−</mo> <mn>1.67</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>Root mean squared error (RMSE) and mean absolute error (MAE) for load forecast using the approach <span class="html-italic">NL3</span> as a function of the distributed PV capacity assumed. The vertical line corresponds to the estimated real value obtained by summing up the single utility capacities.</p> Full article ">Figure 5
<p>Boxplot of the net load forecast error as a function of the hour of the day. The box represents the interquartile range, the line in the box is the median, and the whiskers are set at the 5 and 95 percentile of the data range. The uncertainty follows the same daily pattern of the PV generation. The improvement in the forecast accuracy obtained with the model <span class="html-italic">NL3</span> is apparent.</p> Full article ">Figure 6
<p>Boxplot of the PE forecast error as a function of the hour of the day. The box represents the interquartile range, the line in the box is the median, and the whiskers are set at the 5 and 95 percentile of the data range. The improvement in the forecast accuracy is evident. The uncertainty is still related to the PV generation, but the pattern is less apparent compared to the net load forecasting case, due to the influence of the wind power forecast uncertainty.</p> Full article ">
3360 KiB  
Article
Spatial Distribution of the Baltic Sea Near-Shore Wave Power Potential along the Coast of Klaipėda, Lithuania
by Egidijus Kasiulis, Jens Peter Kofoed, Arvydas Povilaitis and Algirdas Radzevičius
Energies 2017, 10(12), 2170; https://doi.org/10.3390/en10122170 - 19 Dec 2017
Cited by 5 | Viewed by 3967
Abstract
Wave power is an abundant source of energy that can be utilized to produce electricity. Therefore, assessments of wave power resources are being carried out worldwide. An overview of the recent assessments is presented in this paper, revealing the global distribution of these [...] Read more.
Wave power is an abundant source of energy that can be utilized to produce electricity. Therefore, assessments of wave power resources are being carried out worldwide. An overview of the recent assessments is presented in this paper, revealing the global distribution of these resources. Additionally, a study, which aims to assess the spatial distribution of the Baltic Sea near-shore wave power potential along the coast of Klaipėda (Lithuania), is introduced in this paper. The impacts of the wave propagation direction and decreasing depth on wave power resources were examined using the numerical wind-wave model MIKE 21 NSW. The wave height loss of the design waves propagating to shore was modelled, and the wave power fluxes in the studied depths were calculated using the JONSWAP wave spectrum modified for the Baltic Sea. The results revealed that all waves that propagate to the shore in the Baltic Sea near-shore area along the coast of Klaipėda from 30 m depth to 5 m depth lose at least 30% of their power. Still, most common waves in this area are low, and therefore, they start to lose their power while propagating to the shore at relatively low (10–14 m) depths. To turn this into an advantage the wave power converter would have to work efficiently under low power conditions. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Location of the study area in the southeastern part of the Baltic Seal; (<b>b</b>) model area together with the available wave data points used for model calibration; (<b>c</b>) bathymetry of the model area and calculation points.</p> Full article ">Figure 2
<p>Comparison between measured and modelled significant wave heights in the Baltic Sea near-shore area along the coast of Klaipėda for a Nikuradse roughness parameter value of 0.005.</p> Full article ">Figure 3
<p>The change in height of waves propagating to the shore.</p> Full article ">Figure 4
<p>Wave rose for year 2012 (significant wave heights) at ERA-Interim grid point.</p> Full article ">Figure 5
<p>The change in power flux of waves propagating to the shore.</p> Full article ">Figure 6
<p>The impact of decreasing depth on wave power flux for a 1.25 m wave.</p> Full article ">Figure 7
<p>The impact of decreasing depth on wave power flux for a 0.67 m wave.</p> Full article ">Figure 8
<p>The impact of decreasing depth on wave power flux for a 0.48 m wave.</p> Full article ">
3102 KiB  
Article
Nuclear Power Learning and Deployment Rates; Disruption and Global Benefits Forgone
by Peter A. Lang
Energies 2017, 10(12), 2169; https://doi.org/10.3390/en10122169 - 18 Dec 2017
Cited by 13 | Viewed by 49166
Abstract
This paper presents evidence of the disruption of a transition from fossil fuels to nuclear power, and finds the benefits forgone as a consequence are substantial. Learning rates are presented for nuclear power in seven countries, comprising 58% of all power reactors ever [...] Read more.
This paper presents evidence of the disruption of a transition from fossil fuels to nuclear power, and finds the benefits forgone as a consequence are substantial. Learning rates are presented for nuclear power in seven countries, comprising 58% of all power reactors ever built globally. Learning rates and deployment rates changed in the late-1960s and 1970s from rapidly falling costs and accelerating deployment to rapidly rising costs and stalled deployment. Historical nuclear global capacity, electricity generation and overnight construction costs are compared with the counterfactual that pre-disruption learning and deployment rates had continued to 2015. Had the early rates continued, nuclear power could now be around 10% of its current cost. The additional nuclear power could have substituted for 69,000–186,000 TWh of coal and gas generation, thereby avoiding up to 9.5 million deaths and 174 Gt CO2 emissions. In 2015 alone, nuclear power could have replaced up to 100% of coal-generated and 76% of gas-generated electricity, thereby avoiding up to 540,000 deaths and 11 Gt CO2. Rapid progress was achieved in the past and could be again, with appropriate policies. Research is needed to identify impediments to progress, and policy is needed to remove them. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Overnight construction cost (in 2010 US <span>$</span>/kW) plotted against cumulative global capacity (GW), based on construction start dates, of nuclear power reactors for seven countries, including regression lines for US before and after 32 GW cumulative global capacity.</p> Full article ">Figure 2
<p>OCC (2010 US <span>$</span>/kW) plotted against cumulative global capacity (GW) of nuclear power reactors, based on construction start dates; regression lines fitted to points before and after trend reversals.</p> Full article ">Figure 3
<p>Regression lines for seven countries: OCC plotted against cumulative global capacity of construction starts.</p> Full article ">Figure 4
<p>Learning rates pre- and post-reversal points vs. time span of construction starts.</p> Full article ">Figure 5
<p>Annual global capacity of construction starts and commercial operation starts, 1954–2015.</p> Full article ">Figure 6
<p>(<b>Top</b>) Cumulative global capacity of construction starts and of commercial operation starts (sorted by construction start date); (<b>Bottom</b>) Cumulative global capacity of construction starts (red and green data points); accelerating projection of 1960–1976 data points (dotted green line); Linear and Accelerating projections of capacity in commercial operation (dashed pink and green lines).</p> Full article ">Figure 6 Cont.
<p>(<b>Top</b>) Cumulative global capacity of construction starts and of commercial operation starts (sorted by construction start date); (<b>Bottom</b>) Cumulative global capacity of construction starts (red and green data points); accelerating projection of 1960–1976 data points (dotted green line); Linear and Accelerating projections of capacity in commercial operation (dashed pink and green lines).</p> Full article ">Figure 7
<p>Electricity generated by fuel type by the Actual (<b>top</b>); and by the projected capacity in Linear (<b>middle</b>) and Accelerating (<b>bottom</b>) deployment scenarios (TWh).</p> Full article ">
2323 KiB  
Article
Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method
by Christos A. Christodoulou, Vasiliki Vita, Georgios Perantzakis, Lambros Ekonomou and George Milushev
Energies 2017, 10(12), 2168; https://doi.org/10.3390/en10122168 - 18 Dec 2017
Cited by 11 | Viewed by 4623
Abstract
The appropriate circuit modeling of metal oxide gapless surge arresters is critical for insulation coordination studies. Metal oxide arresters present a dynamic behavior for fast front surges; namely, their residual voltage is dependent on the peak value, as well as the duration of [...] Read more.
The appropriate circuit modeling of metal oxide gapless surge arresters is critical for insulation coordination studies. Metal oxide arresters present a dynamic behavior for fast front surges; namely, their residual voltage is dependent on the peak value, as well as the duration of the injected impulse current, and should therefore not only be represented by non-linear elements. The aim of the current work is to adjust the parameters of the most frequently used surge arresters’ circuit models by considering the magnitude of the residual voltage, as well as the dissipated energy for given pulses. In this aim, the harmony search method is implemented to adjust parameter values of the arrester equivalent circuit models. This functions by minimizing a defined objective function that compares the simulation outcomes with the manufacturer’s data and the results obtained from previous methodologies. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>V–I characteristic of a typical metal oxide arrester (I: capacitive linear area, current ≤ 1 mA; II: knee point, transition from the almost-insulating to the conducting condition; III: intensely non-linear area; IV: ohmic area, high current area) [<a href="#B23-energies-10-02168" class="html-bibr">23</a>,<a href="#B24-energies-10-02168" class="html-bibr">24</a>].</p> Full article ">Figure 2
<p>Basic ZnO element model [<a href="#B1-energies-10-02168" class="html-bibr">1</a>,<a href="#B9-energies-10-02168" class="html-bibr">9</a>].</p> Full article ">Figure 3
<p>The IEEE model [<a href="#B8-energies-10-02168" class="html-bibr">8</a>] (d is the height of the arrester in m, n is the number of varistor columns).</p> Full article ">Figure 4
<p>Voltage–current characteristics of the non-linear resistances A<sub>0</sub> and A<sub>1</sub> [<a href="#B8-energies-10-02168" class="html-bibr">8</a>].</p> Full article ">Figure 5
<p>The Pinceti–Giannettoni model (V<sub>n</sub> is the arrester’s rated voltage, V<sub>r8/20</sub> is the residual voltage for an 8/20 10 kA impulse current and V<sub>r1/T2</sub> is the residual voltage for a 1/T<sub>2</sub> 10 kA impulse current) [<a href="#B9-energies-10-02168" class="html-bibr">9</a>].</p> Full article ">Figure 6
<p>The Fernandez–Diaz model [<a href="#B13-energies-10-02168" class="html-bibr">13</a>].</p> Full article ">Figure 7
<p>Results for the IEEE model.</p> Full article ">Figure 8
<p>Results for the Pinceti–Giannettoni model.</p> Full article ">Figure 9
<p>Results for the Fernandez–Diaz model.</p> Full article ">Figure 10
<p>Residual voltage (in kV) and dissipated energy (in kJ/kV) for the three examined models (case 3) (the symbol * corresponds to the absorbed energy by the arresters).</p> Full article ">
15112 KiB  
Article
Influence of the Periodicity of Sinusoidal Boundary Condition on the Unsteady Mixed Convection within a Square Enclosure Using an Ag–Water Nanofluid
by Azharul Karim, M. Masum Billah, M. T. Talukder Newton and M. Mustafizur Rahman
Energies 2017, 10(12), 2167; https://doi.org/10.3390/en10122167 - 18 Dec 2017
Cited by 12 | Viewed by 5149
Abstract
A numerical study of the unsteady mixed convection heat transfer characteristics of an Ag–water nanofluid confined within a square shape lid-driven cavity has been carried out. The Galerkin weighted residual of the finite element method has been employed to investigate the effects of [...] Read more.
A numerical study of the unsteady mixed convection heat transfer characteristics of an Ag–water nanofluid confined within a square shape lid-driven cavity has been carried out. The Galerkin weighted residual of the finite element method has been employed to investigate the effects of the periodicity of sinusoidal boundary condition for a wide range of Grashof numbers (Gr) (105 to 107) with the parametric variation of sinusoidal even and odd frequency, N, from 1 to 6 at different instants (for τ = 0.1 and 1). It has been observed that both the Grashof number and the sinusoidal even and odd frequency have a significant influence on the streamlines and isotherms inside the cavity. The heat transfer rate enhanced by 90% from the heated surface as the Grashof number (Gr) increased from 105 to 107 at sinusoidal frequency N = 1 and τ = 1. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic view of the cavity with the boundary conditions.</p> Full article ">Figure 2
<p>Grid independency study with <span class="html-italic">δ</span> = 0.04, <span class="html-italic">N</span> = 1, and <span class="html-italic">Gr</span> = 10<sup>7</sup>.</p> Full article ">Figure 3
<p>Influence of the odd values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>5</sup>.</p> Full article ">Figure 4
<p>Influence of the even values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>5</sup>.</p> Full article ">Figure 5
<p>Influence of the odd values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>6</sup>.</p> Full article ">Figure 6
<p>Influence of the even values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>6</sup>.</p> Full article ">Figure 7
<p>Influence of the odd values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>7</sup>.</p> Full article ">Figure 8
<p>Influence of the even values of <span class="html-italic">N</span> on streamlines for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>7</sup>.</p> Full article ">Figure 9
<p>Influence of the odd values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>5</sup>.</p> Full article ">Figure 10
<p>Influence of the even values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>5</sup>.</p> Full article ">Figure 11
<p>Influence of the odd values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>6</sup>.</p> Full article ">Figure 12
<p>Influence of the even values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>6</sup>.</p> Full article ">Figure 13
<p>Influence of the odd values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>7</sup>.</p> Full article ">Figure 14
<p>Influence of the even values of <span class="html-italic">N</span> on isotherms for the selected values of <span class="html-italic">τ</span> with <span class="html-italic">Gr</span> = 10<sup>7</sup>.</p> Full article ">Figure 15
<p>Variation of the local Nusselt number for (<b>a</b>) even values of <span class="html-italic">N</span> and (<b>b</b>) odd values of N, when <span class="html-italic">Gr</span> = 10<sup>7</sup> and <span class="html-italic">τ</span> = 1.</p> Full article ">Figure 16
<p>Variation of the overall Nusselt number for (<b>a</b>) odd values of <span class="html-italic">N</span> and (<b>b</b>) even values of <span class="html-italic">N</span>.</p> Full article ">
8816 KiB  
Article
Performance Recovery of Natural Draft Dry Cooling Systems by Combined Air Leading Strategies
by Weijia Wang, Lei Chen, Xianwei Huang, Lijun Yang and Xiaoze Du
Energies 2017, 10(12), 2166; https://doi.org/10.3390/en10122166 - 18 Dec 2017
Cited by 11 | Viewed by 4651
Abstract
The cooling efficiency of natural draft dry cooling system (NDDCS) are vulnerable to ambient winds, so the implementation of measures against the wind effects is of great importance. This work presents the combined air leading strategies to recover the flow and heat transfer [...] Read more.
The cooling efficiency of natural draft dry cooling system (NDDCS) are vulnerable to ambient winds, so the implementation of measures against the wind effects is of great importance. This work presents the combined air leading strategies to recover the flow and heat transfer performances of NDDCS. Following the energy balance among the exhaust steam, circulating water, and cooling air, numerical models of natural draft dry cooling systems with the combined air leading strategies are developed. The cooling air streamlines, volume effectiveness, thermal efficiency and outlet water temperature for each cooling delta of the large-scale heat exchanger are obtained. The overall volume effectiveness, average outlet water temperature of NDDCS and steam turbine back pressure are calculated. The results show that with the air leading strategies inside or outside the dry-cooling tower, the thermo-flow performances of natural draft dry cooling system are improved under all wind conditions. The combined inner and outer air leading strategies are superior to other single strategy in the performance recovery, thus can be recommended for NDDCS in power generating units. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic of cold end with natural draft dry cooling system and surface condenser.</p> Full article ">Figure 2
<p>Geometry of natural draft dry cooling system.</p> Full article ">Figure 3
<p>Schematics of combined air leading strategies. (<b>a</b>) Case A without air leading; (<b>b</b>) Case B with inner air leading baffles and rounded frustum; (<b>c</b>) Case C with outer air leading baffles; (<b>d</b>) Case D with both inner and outer air leading measures; (<b>e</b>) Geometric parameters.</p> Full article ">Figure 4
<p>Numerical domain and boundaries.</p> Full article ">Figure 5
<p>Local grids for dry-cooling tower and finned tube bundles.</p> Full article ">Figure 6
<p>Numerical heat transfer process of cold end system.</p> Full article ">Figure 7
<p>Experimental validation for numerical simulation. (<b>a</b>) Measuring points in vertical and horizontal views; (<b>b</b>) Ascending velocity without winds; (<b>c</b>) Ascending velocity at 4 m/s.</p> Full article ">Figure 8
<p>Streamlines colored by temperature for case A at various wind speeds. (<b>a</b>) at 4 m/s; (<b>b</b>) at 12 m/s; (<b>c</b>) at 20 m/s.</p> Full article ">Figure 9
<p>Flow and temperature fields through lateral cooling deltas for case A at various wind speeds. (<b>a</b>) at 4 m/s; (<b>b</b>) at 12 m/s; (<b>c</b>) at 20 m/s.</p> Full article ">Figure 10
<p>Performance distributions for case A. (<b>a</b>) Volume effectiveness; (<b>b</b>) Thermal efficiency; (<b>c</b>) Outlet water temperature.</p> Full article ">Figure 11
<p>Streamlines colored by temperature for case D at various wind speeds. (<b>a</b>) at 4 m/s; (<b>b</b>) at 12 m/s; (<b>c</b>) at 20 m/s.</p> Full article ">Figure 12
<p>Flow and temperature fields through lateral cooling deltas of case D at various wind speeds. (<b>a</b>) at 4 m/s; (<b>b</b>) at 12 m/s; (<b>c</b>) at 20 m/s.</p> Full article ">Figure 13
<p>Performance distributions for case B. (<b>a</b>) Volume effectiveness; (<b>b</b>) Thermal efficiency; (<b>c</b>) Outlet water temperature.</p> Full article ">Figure 14
<p>Performance distributions for case C. (<b>a</b>) Volume effectiveness; (<b>b</b>) Thermal efficiency; (<b>c</b>) Outlet water temperature.</p> Full article ">Figure 15
<p>Performance distributions for case D. (<b>a</b>) Volume effectiveness; (<b>b</b>) Thermal efficiency; (<b>c</b>) Outlet water temperature.</p> Full article ">Figure 16
<p>Overall thermo-flow performances of natural draft dry cooling system with air leading strategies. (<b>a</b>) Overall volume effectiveness; (<b>b</b>) Average outlet water temperature; (<b>c</b>) Turbine back pressure.</p> Full article ">
926 KiB  
Article
Hydro Power Reservoir Aggregation via Genetic Algorithms
by Markus Löschenbrand and Magnus Korpås
Energies 2017, 10(12), 2165; https://doi.org/10.3390/en10122165 - 18 Dec 2017
Cited by 14 | Viewed by 3796
Abstract
Electrical power systems with a high share of hydro power in their generation portfolio tend to display distinct behavior. Low generation cost and the possibility of peak shaving create a high amount of flexibility. However, stochastic influences such as precipitation and external market [...] Read more.
Electrical power systems with a high share of hydro power in their generation portfolio tend to display distinct behavior. Low generation cost and the possibility of peak shaving create a high amount of flexibility. However, stochastic influences such as precipitation and external market effects create uncertainty and thus establish a wide range of potential outcomes. Therefore, optimal generation scheduling is a key factor to successful operation of hydro power dominated systems. This paper aims to bridge the gap between scheduling on large-scale (e.g., national) and small scale (e.g., a single river basin) levels, by applying a multi-objective master/sub-problem framework supported by genetic algorithms. A real-life case study from southern Norway is used to assess the validity of the method and give a proof of concept. The introduced method can be applied to efficiently integrate complex stochastic sub-models into Virtual Power Plants and thus reduce the computational complexity of large-scale models whilst minimizing the loss of information. Full article
(This article belongs to the Special Issue Hydropower 2017)
►▼ Show Figures

Figure 1

Figure 1
<p>Aggregates for different Reservoir Types.</p> Full article ">Figure 2
<p>Genetic algorithm flowchart.</p> Full article ">Figure 3
<p>Sira Kvina river basin.</p> Full article ">Figure 4
<p>Simplified version of the Sira Kvina river basin.</p> Full article ">Figure 5
<p>Result comparison of the original and simplified systems.</p> Full article ">Figure A1
<p>Generated test scenarios.</p> Full article ">
23119 KiB  
Article
Flow Adjustment Inside and Around Large Finite-Size Wind Farms
by Ka Ling Wu and Fernando Porté-Agel
Energies 2017, 10(12), 2164; https://doi.org/10.3390/en10122164 - 18 Dec 2017
Cited by 74 | Viewed by 10486
Abstract
In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and [...] Read more.
In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and farm wake regions. The wind farms extend 20 km in the streamwise direction and comprise 36 wind turbine rows arranged in aligned and staggered configurations. Results show that, under weak free-atmosphere stratification ( Γ = 1 K/km), the flow inside and above both wind farms, and thus the turbine power, do not reach the fully-developed regime even though the farm length is two orders of magnitude larger than the boundary layer height. In that case, the wind farm induction region, affected by flow blockage, extends upwind about 0.8 km and leads to a power reduction of 1.3% and 3% at the first row of turbines for the aligned and staggered layouts, respectively. The wind farm wake leads to velocity deficits at hub height of around 3.5% at a downwind distance of 10 km for both farm layouts. Under stronger stratification ( Γ = 5 K/km), the vertical deflection of the subcritical flow induced by the wind farm at its entrance and exit regions triggers standing gravity waves whose effects propagate upwind. They, in turn, induce a large decelerating induction region upwind of the farm leading edge, and an accelerating exit region upwind of the trailing edge, both extending about 7 km. As a result, the turbine power output in the entrance region decreases more than 35% with respect to the weakly stratified case. It increases downwind as the flow adjusts, reaching the fully-developed regime only for the staggered layout at a distance of about 8.5 km from the farm edge. The flow acceleration in the exit region leads to an increase of the turbine power with downwind distance in that region, and a relatively fast (compared with the weakly stratified case) recovery of the farm wake, which attains its inflow hub height speed at a downwind distance of 5 km. Full article
(This article belongs to the Collection Wind Turbines)
►▼ Show Figures

Figure 1

Figure 1
<p>Vertical profiles of the horizontally-averaged ABL flow characteristics in the no-farm cases: (<b>a</b>) mean velocity magnitude <math display="inline"> <semantics> <mover> <mi>M</mi> <mo>¯</mo> </mover> </semantics> </math>; (<b>b</b>) wind direction; (<b>c</b>) potential temperature; (<b>d</b>) total Reynolds shear stress <math display="inline"> <semantics> <msup> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <mover> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo>¯</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mover> <mrow> <msup> <mi>v</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo>¯</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msup> </semantics> </math>; and (<b>e</b>) TKE. The black solid lines represent the top-tip, hub, and bottom-tip heights.</p> Full article ">Figure 2
<p>Computational domain and layout of the large finite-size wind farms simulated.</p> Full article ">Figure 3
<p>Flow adjustment regions in large finite-size wind farms in CNBLs with (<b>a</b>) weak and (<b>b</b>) strong free-atmosphere stratification. The flow can be divided into the following regions: (i) induction region; (ii) entrance and development region; (iii) fully-developed region; (iv) exit region; (v) wind farm wake.</p> Full article ">Figure 4
<p>Contours of time-averaged horizontal velocity magnitude <span class="html-italic">M</span> on the xz plane through the center of a wind turbine column for the case (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The IBL height (thick blue line), the CNBL height (white line), the CNBL height of the inflow (dashed black line), Elliot’s 0.8 power law (bright green line) and the CNBL height with an infinite wind farm (solid black line) are also included. The thin blue lines are the velocity streamlines (i.e., the vertical component is made larger by a scale factor of 5). The black vertical solid lines indicate the start and end of different flow regions.</p> Full article ">Figure 5
<p>Time- and spanwise-averaged velocity magnitude at the hub height in the wind farm induction region, normalized by the inflow velocity magnitude at hub height.</p> Full article ">Figure 6
<p>Contours of time- and spanwise-averaged vertical potential temperature gradient <math display="inline"> <semantics> <mrow> <mi>d</mi> <mi>θ</mi> <mo>/</mo> <mi>d</mi> <mi>z</mi> </mrow> </semantics> </math> for the case (<b>a</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The dotted black lines indicate the bottom and the top heights of the capping inversion. Contours of time- and spanwise-averaged modified pressure for the case (<b>c</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The vertical solid black lines indicate the streamwise positions of the beginning and the end of the wind farm. Results are not shown for the aligned farms as they are similar to the staggered farms.</p> Full article ">Figure 7
<p>Vertical profiles of time- and spanwise-averaged velocity magnitude at the 1st, 5th, 12th and 36th turbine rows for the cases: (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; and (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The black solid profile represents the velocity magnitude of the ABL inflow, and the black dashed profile represents the time- and horizontal-averaged velocity magnitude of the infinite wind farm with the same wind farm configuration. The black horizontal solid lines represent the turbine top-tip, hub and bottom-tip heights.</p> Full article ">Figure 8
<p>Time- and horizontally-averaged (the distance of streamwise averaging is 7D, with the turbine row placed at the center) velocity magnitude at the hub height at different wind turbine rows for the wind farm cases under (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> K/km; and (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> K/km. The black horizontal dotted and solid lines are the time- and horizontally-averaged velocity magnitude at the hub height for the corresponding aligned and staggered infinite wind farm cases, respectively</p> Full article ">Figure 9
<p>Vertical profiles of time- and spanwise-averaged total shear stress at the 1st, 5th, 12th and 36th turbine rows for the cases: (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; and (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The black solid profile represents the total shear stress of the ABL inflow, and the black dashed profile represents the time- and horizontal-averaged total shear stress of the infinite wind farm with the same wind farm configuration. The black horizontal solid lines represents the top-tip, hub and bottom-tip heights.</p> Full article ">Figure 10
<p>Vertical profiles of time- and spanwise-averaged TKE at the 1st, 2nd, 7th, and 36th turbine rows for the cases: (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; and (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The black solid profile represents the TKE of the ABL inflow, and the black dashed profile represents the time- and horizontal-averaged TKE of the infinite wind farm with the same wind farm configuration. The black horizontal solid lines represents the top-tip, hub and bottom-tip heights.</p> Full article ">Figure 11
<p>Power production at different turbine rows inside the wind farms for the (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> K/km cases and (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> K/km cases, normalized by the power production of a single turbine operating under the same lapse rate. The black horizontal dotted and solid lines are the power production of a wind turbine for the corresponding aligned and staggered infinite wind farm cases, respectively.</p> Full article ">Figure 12
<p>Vertical profiles of time- and spanwise-averaged velocity magnitude at 1 km, 5 km and 10 km downwind of the wind farm, and at the 36th (last) turbine row for the cases: (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; and (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The solid black profile represents the velocity magnitude of the ABL inflow, and the black dashed profile represents the time- and horizontal-averaged velocity magnitude of the infinite wind farm with the same wind farm configuration. The black horizontal solid lines represent the top-tip, hub and bottom-tip heights.</p> Full article ">Figure 13
<p>Vertical profiles of time- and spanwise-averaged TKE at 1 km, 5 km and 10 km downwind of the wind farm, and at the 36th (last) turbine row for the cases: (<b>a</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>b</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>1</mn> </mrow> </semantics> </math>; (<b>c</b>) FS−a<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>; and (<b>d</b>) FS−s<math display="inline"> <semantics> <mrow> <mo>Γ</mo> <mn>5</mn> </mrow> </semantics> </math>. The solid black profile represents the TKE of the ABL inflow, and the black dashed profile represents the time- and horizontal-averaged total shear stress of the infinite wind farm with the same turbine configuration. The black horizontal solid lines represent the top-tip, hub and bottom-tip heights.</p> Full article ">
13085 KiB  
Article
Numerical Investigation of the Air-Steam Biomass Gasification Process Based on Thermodynamic Equilibrium Model
by Qitai Eri, Wenzhen Wu and Xinjun Zhao
Energies 2017, 10(12), 2163; https://doi.org/10.3390/en10122163 - 18 Dec 2017
Cited by 19 | Viewed by 4820
Abstract
In the present work, the air-steam biomass gasification model with tar has been developed based on the equilibrium constants. The simulation results based on two different models (with and without tar) have been validated by the experimental data. The model with tar can [...] Read more.
In the present work, the air-steam biomass gasification model with tar has been developed based on the equilibrium constants. The simulation results based on two different models (with and without tar) have been validated by the experimental data. The model with tar can well predict the tar content in gasification; meanwhile, the predicted gas yield (GY), based on the model with tar, is much closer to the experimental data. The energy exchange between the gasifier and the surrounding has been studied based on the dimensionless heat transfer ratio (DHTR), and the relationship between DHTR and the process parameters is given by a formula. The influence of process parameters on the syngas composition, tar content, GY, lower heating value (LHV), and exergy efficiency have been researched. Full article
(This article belongs to the Special Issue Biofuel and Bioenergy Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>Comparison between experimental data and predicted data for syngas composition.</p> Full article ">Figure 2
<p>Comparisons between experimental data and predicted data for lower heating value (LHV) and gas yield (GY).</p> Full article ">Figure 3
<p>RMS errors from comparisons between experimental data and predicted results.</p> Full article ">Figure 4
<p>Comparison between experimental data and predicted date for tar content.</p> Full article ">Figure 5
<p>Comparison between experimental data and predicted data based on different models for GY.</p> Full article ">Figure 6
<p>Comparison between experimental data and predicted data based on different models for syngas composition (E-experimental data, M1-model with tar, M2-model without tar).</p> Full article ">Figure 7
<p>Effects of gasification temperature on syngas composition and tar content at ER = 0.2 and SBR = 0.3.</p> Full article ">Figure 8
<p>Effects of gasification temperature on LHV and GY at ER = 0.2 and SBR = 0.3.</p> Full article ">Figure 9
<p>Effect of gasification temperature on syngas composition at ER = 0.4 and SBR = 0.5.</p> Full article ">Figure 10
<p>Effects of gasification temperature on LHV and GY at ER = 0.4 and SBR = 0.5.</p> Full article ">Figure 11
<p>Effects of ER on syngas composition and tar content at <span class="html-italic">T</span> = 950 K and SBR = 0.3.</p> Full article ">Figure 12
<p>Effects of ER on LHV and GY at <span class="html-italic">T</span> = 950 K and SBR = 0.3.</p> Full article ">Figure 13
<p>Effect of ER on syngas composition at <span class="html-italic">T</span> = 1100 K and SBR = 0.5.</p> Full article ">Figure 14
<p>Effects of ER on LHV and GY at <span class="html-italic">T</span> = 1100 K and SBR = 0.5.</p> Full article ">Figure 15
<p>Effects of SBR on syngas composition and tar content at <span class="html-italic">T</span> = 950 K and ER = 0.3.</p> Full article ">Figure 16
<p>Effects of SBR on LHV and GY at <span class="html-italic">T</span> = 950 K and ER = 0.3.</p> Full article ">Figure 17
<p>Effect of SBR on syngas composition at <span class="html-italic">T</span> = 1100 K and ER = 0.2.</p> Full article ">Figure 18
<p>Effects of SBR on LHV and GY at <span class="html-italic">T</span> = 1100 K and ER = 0.2.</p> Full article ">Figure 19
<p>Dimensionless heat transfer ratio (DHTR) distribution at <span class="html-italic">T</span> = 900 K.</p> Full article ">Figure 20
<p>DHTR distribution at <span class="html-italic">T</span> = 950 K.</p> Full article ">Figure 21
<p>DHTR distribution at <span class="html-italic">T</span> = 1050 K.</p> Full article ">Figure 22
<p>DHTR distribution at <span class="html-italic">T</span> = 1100 K.</p> Full article ">Figure 23
<p>Exergy efficiency distribution at <span class="html-italic">T</span> = 900 K.</p> Full article ">Figure 24
<p>Exergy efficiency distribution at <span class="html-italic">T</span> = 1000 K.</p> Full article ">Figure 25
<p>Exergy efficiency distribution at <span class="html-italic">T</span> = 1100 K.</p> Full article ">
4619 KiB  
Article
A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement
by Dan Wang, Qing’e Hu, Jia Tang, Hongjie Jia, Yun Li, Shuang Gao and Menghua Fan
Energies 2017, 10(12), 2162; https://doi.org/10.3390/en10122162 - 18 Dec 2017
Cited by 7 | Viewed by 4170
Abstract
Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the [...] Read more.
Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the optimal operation model of the ADN considering a diversity of controllable resources including energy storage devices, distributed generators, voltage regulators and switchable capacitor banks. The objective functions contain reducing the power losses and improving the voltage profiles. To solve the optimization problem, the Kriging model based Improved Surrogate Optimization-Mixed-Integer (ISO-MI) algorithm is proposed in this paper. The Kriging model is applied to approximate the complicated distribution networks, which speeds up the solving process. Finally, the accuracy of the Kriging model is validated and the efficiency among the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) is compared in an unbalanced IEEE-123 nodes test feeder. The results demonstrate that the proposed method has better performance than GA and PSO. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of optimal scheduling and operation of active distribution network (ADN).</p> Full article ">Figure 2
<p>General framework of Kriging model based optimization algorithm.</p> Full article ">Figure 3
<p>Flowchart of the proposed algorithm.</p> Full article ">Figure 4
<p>Interaction method between GridLAB-D and MATLAB.</p> Full article ">Figure 5
<p>IEEE-123 node test feeder single line diagram.</p> Full article ">Figure 6
<p>Outputs of DGs.</p> Full article ">Figure 7
<p>Load profiles of the test system.</p> Full article ">Figure 8
<p>Convergence characteristic by Improved Surrogate Optimization-Mixed-Integer (ISO-MI), genetic algorithm (GA) and particle swarm optimization (PSO).</p> Full article ">Figure 9
<p>Different power loss of optimization results between ISO-MI, GA and PSO.</p> Full article ">Figure 10
<p>Different optimization results of voltage profiles of all nodes during 24 h between ISO-MI, GA and PSO.</p> Full article ">Figure 11
<p>Optimization results of tap positions of Reg1–Reg4 obtained by ISO-MI.</p> Full article ">Figure 12
<p>Charge power and SOC of BAT1 of ISO-MI.</p> Full article ">
1248 KiB  
Article
Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation
by Bernhard Faessler, Michael Schuler, Markus Preißinger and Peter Kepplinger
Energies 2017, 10(12), 2161; https://doi.org/10.3390/en10122161 - 18 Dec 2017
Cited by 30 | Viewed by 5218
Abstract
Due to the promoted integration of renewable sources, a further growth of strongly transient, distributed generation is expected. Thus, the existing electrical grid may reach its physical limits. To counteract this, and to fully exploit the viable potential of renewables, grid-balancing measures are [...] Read more.
Due to the promoted integration of renewable sources, a further growth of strongly transient, distributed generation is expected. Thus, the existing electrical grid may reach its physical limits. To counteract this, and to fully exploit the viable potential of renewables, grid-balancing measures are crucial. In this work, battery storage systems are embedded in a grid simulation to evaluate their potential for grid balancing. The overall setup is based on a real, low-voltage distribution grid topology, real smart meter household load profiles, and real photovoltaics load data. An autonomous optimization routine, driven by a one-way communicated incentive, determines the prospective battery operation mode. Different battery positions and incentives are compared to evaluate their impact. The configurations incorporate a baseline simulation without storage, a single, central battery storage or multiple, distributed battery storages which together have the same power and capacity. The incentives address either market conditions, grid balancing, optimal photovoltaic utilization, load shifting, or self-consumption. Simulations show that grid-balancing incentives result in lowest peak-to-average power ratios, while maintaining negligible voltage changes in comparison to a reference case. Incentives reflecting market conditions for electricity generation, such as real-time pricing, negatively influence the power quality, especially with respect to the peak-to-average power ratio. A central, feed-in-tied storage performs better in terms of minimizing the voltage drop/rise and shows lower distribution losses, while distributed storages attached at nodes with electricity generation by photovoltaics achieve lower peak-to-average power ratios. Full article
(This article belongs to the Section D: Energy Storage and Application)
►▼ Show Figures

Figure 1

Figure 1
<p>Low-voltage distribution grid from local system operator.</p> Full article ">Figure 2
<p>Used and normalized incentives to drive the optimization of the BESS for a one-week period: EXAA day-ahead spot-market price for electricity (RTP); total grid load at the slack node (GRID); photovoltaic generation (PV); individual household loads (LOAD) for household at node 21, 24, and 37 comprising a distributed storage system; individual total household consumption including load and photovoltaic generation (SELF) for household at node 21, 24, and 37 comprising a distributed storage system.</p> Full article ">Figure 3
<p>Peak-to-average power ratio, voltages and cumulative distribution losses for all configurations for a single, central storage (c) and multiple, distributed storages (d). The superscript * refers to normed quantities with respect to the reference case, i.e., <math display="inline"> <semantics> <mrow> <msubsup> <mi>E</mi> <mrow> <mi>losses</mi> </mrow> <mo>*</mo> </msubsup> <mo>=</mo> <mfrac> <mrow> <msub> <mi>E</mi> <mrow> <mi>losses</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>E</mi> <mrow> <mi>losses</mi> <mo>,</mo> <mtext> </mtext> <mi>REF</mi> </mrow> </msub> </mrow> </mfrac> </mrow> </semantics> </math>, analogously for <math display="inline"> <semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi mathvariant="normal">d</mi> <mo>/</mo> <mi mathvariant="normal">r</mi> </mrow> </msub> </mrow> </semantics> </math> and PAPR.</p> Full article ">Figure 4
<p>Power duration curve for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives.</p> Full article ">Figure 5
<p>Voltage duration curve for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives.</p> Full article ">
2951 KiB  
Article
A Combined Electro-Thermal Breakdown Model for Oil-Impregnated Paper
by Meng Huang, Yuanxiang Zhou, Zhongliu Zhou and Bo Qi
Energies 2017, 10(12), 2160; https://doi.org/10.3390/en10122160 - 18 Dec 2017
Cited by 13 | Viewed by 4625
Abstract
The breakdown property of oil-impregnated paper is a key factor for converter transformer design and operation, but it is not well understood. In this paper, breakdown voltages of oil-impregnated paper were measured at different temperatures. The results showed that with the increase of [...] Read more.
The breakdown property of oil-impregnated paper is a key factor for converter transformer design and operation, but it is not well understood. In this paper, breakdown voltages of oil-impregnated paper were measured at different temperatures. The results showed that with the increase of temperature, electrical, electro-thermal and thermal breakdown occurred successively. An electro-thermal breakdown model was proposed based on the heat equilibrium and space charge transport, and negative differential mobility was introduced to the model. It was shown that carrier mobility determined whether it was electrical or thermal breakdown, and the model can effectively explain the temperature-dependent breakdown. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Breakdown system for oil-impregnated paper under different temperatures.</p> Full article ">Figure 2
<p>Temperature-dependent breakdown voltage of oil-impregnated paper insulation. (<b>a</b>) Weibull distribution of breakdown voltages; (<b>b</b>) Temperature-dependent shape and scale parameters.</p> Full article ">Figure 2 Cont.
<p>Temperature-dependent breakdown voltage of oil-impregnated paper insulation. (<b>a</b>) Weibull distribution of breakdown voltages; (<b>b</b>) Temperature-dependent shape and scale parameters.</p> Full article ">Figure 3
<p>Thermal gravimetric analysis results of insulating paper.</p> Full article ">Figure 4
<p>Temperature-dependent breakdown of insulating polymers [<a href="#B16-energies-10-02160" class="html-bibr">16</a>]. (I) <span class="html-italic">T</span> &lt; <span class="html-italic">T</span><sub>c1</sub>, electric breakdown; (II) <span class="html-italic">T</span><sub>c1</sub> &lt; <span class="html-italic">T</span> &lt; <span class="html-italic">T</span><sub>c2</sub>, thermal or electro-thermal breakdown; (III) <span class="html-italic">T</span> &gt; <span class="html-italic">T</span><sub>c2</sub>, electro-mechanical breakdown.</p> Full article ">Figure 5
<p>Comparison of thermal breakdown simulations and breakdown experiments.</p> Full article ">Figure 6
<p>Simulated variation of temperature within the sample during thermal breakdown.</p> Full article ">Figure 7
<p>Comparison of electro-thermal breakdown simulations and breakdown experiments. Temperature- and electric field-dependent mobility was considered.</p> Full article ">Figure 8
<p>Comparison of electro-thermal breakdown simulations and breakdown experiments. Temperature- and electric field-dependent mobility was considered, and so was negative differential mobility.</p> Full article ">Figure 9
<p>Comparison of calculated hopping and Kelvin conductivity under different temperatures and electric fields.</p> Full article ">Figure 10
<p>Variations in the highest electric field and temperature before breakdown. (<b>a</b>) Variation in the highest electric field, (<b>b</b>) Variation in the highest temperature.</p> Full article ">
3042 KiB  
Article
Power Decoupling Method Based on the Diagonal Compensating Matrix for VSG-Controlled Parallel Inverters in the Microgrid
by Bin Li and Lin Zhou
Energies 2017, 10(12), 2159; https://doi.org/10.3390/en10122159 - 17 Dec 2017
Cited by 41 | Viewed by 5032
Abstract
The thought of the virtual synchronous generator (VSG) for controlling the grid-connected inverters and providing virtual inertia to the microgrid is emerging as a wide extension of the droop control, power coupling that always exists in the low-voltage microgrid, which may deteriorate the [...] Read more.
The thought of the virtual synchronous generator (VSG) for controlling the grid-connected inverters and providing virtual inertia to the microgrid is emerging as a wide extension of the droop control, power coupling that always exists in the low-voltage microgrid, which may deteriorate the dynamic response and the stability of the system. In this paper, the principle of VSG control is introduced first. As an important issue of VSG control, the mechanism of the power coupling in the low-voltage microgrid is analyzed and the small-signal equivalent model of the power transmission loop is established. Subsequently, a power decoupling method based on the diagonal compensating matrix for VSG is proposed, which can realize the power decoupling with no impact on the original control channel. Meanwhile, the feasibility analysis of the decoupling method and the improved approach for reactive power sharing are also discussed. Simulation results verify the effectiveness of the decoupling strategy for VSGs. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Control diagram of the inverter based on virtual synchronous generator (VSG).</p> Full article ">Figure 2
<p>Equivalent circuit of an inverter under the grid-connected mode.</p> Full article ">Figure 3
<p>Small-signal equivalent model of VSG.</p> Full article ">Figure 4
<p>Control diagram of the power loop in series with the decoupling link.</p> Full article ">Figure 5
<p>Improved <span class="html-italic">Q</span>–<span class="html-italic">V</span> control loop.</p> Full article ">Figure 6
<p>(<b>a</b>) The frequency waveform of the output voltage; (<b>b</b>) The PCC voltage waveform.</p> Full article ">Figure 7
<p>(<b>a</b>) The power output response to the frequency fluctuation; (<b>b</b>) The power output response to the voltage amplitude fluctuation.</p> Full article ">Figure 8
<p>Microgrid simulation platform.</p> Full article ">Figure 9
<p>Output waveforms of VSG control to frequency deviation. (<b>a</b>) Without decoupling link; (<b>b</b>) With decoupling link; (<b>c</b>) Output current of VSG1 with decoupling link.</p> Full article ">Figure 9 Cont.
<p>Output waveforms of VSG control to frequency deviation. (<b>a</b>) Without decoupling link; (<b>b</b>) With decoupling link; (<b>c</b>) Output current of VSG1 with decoupling link.</p> Full article ">Figure 10
<p>Output waveforms of VSG control to voltage deviation. (<b>a</b>) Without decoupling link; (<b>b</b>) With decoupling link; (<b>c</b>) Output current of VSG2 with decoupling link.</p> Full article ">Figure 10 Cont.
<p>Output waveforms of VSG control to voltage deviation. (<b>a</b>) Without decoupling link; (<b>b</b>) With decoupling link; (<b>c</b>) Output current of VSG2 with decoupling link.</p> Full article ">
3192 KiB  
Article
Renewable Energy Potential by the Application of a Building Integrated Photovoltaic and Wind Turbine System in Global Urban Areas
by Jaewook Lee, Jeongsu Park, Hyung-Jo Jung and Jiyoung Park
Energies 2017, 10(12), 2158; https://doi.org/10.3390/en10122158 - 17 Dec 2017
Cited by 15 | Viewed by 6898
Abstract
Globally, maintaining equilibrium between energy supply and demand is critical in urban areas facing increasing energy consumption and high-speed economic development. As an alternative, the large-scale application of renewable energy, such as solar and wind power, might be a long-term solution in an [...] Read more.
Globally, maintaining equilibrium between energy supply and demand is critical in urban areas facing increasing energy consumption and high-speed economic development. As an alternative, the large-scale application of renewable energy, such as solar and wind power, might be a long-term solution in an urban context. This study assessed the overall utilization potential of a building-integrated photovoltaic and wind turbine (BIPvWt) system, which can be applied to a building skin in global urban areas. The first step of this study was to reorganize the large volume of global annual climate data. The data were analyzed by computational fluid dynamic analysis and an energy simulation applicable to the BIPvWt system, which can generate a Pmax 300 Wp/module with a 15% conversion efficiency from a photovoltaic (PV) system and a 0.149 power coefficient/module from wind turbines in categorized urban contexts and office buildings in specific cities; it was constructed to evaluate and optimize the ratio that can cover the current energy consumption. A diagram of the distribution of the solar and wind energy potential and design guidelines for a building skin were developed. The perspective of balancing the increasing energy consumption using renewable energy in urban areas can be visualized positively in the near future. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Histogram of 143 cities according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) classification.</p> Full article ">Figure 2
<p>Building module prototype for the energy simulation. (<b>A</b>) Detailed unit drawing; (<b>B</b>) basic module (b1); (<b>C</b>) basic module2 (b2); (<b>D</b>) building-integrated photovoltaic and wind turbine (BIPvWt) module (BI).</p> Full article ">Figure 3
<p>Diagram of the proposed BIPvWt system. (<b>A</b>) Detailed BIPvWt system drawing; (<b>B</b>) building envelope installation of BIPvWt.</p> Full article ">Figure 4
<p>Energy potential according to the ASHRAE classification. (<b>A</b>) Solar energy potential; (<b>B</b>) wind energy potential.</p> Full article ">Figure 5
<p>Total energy potential of the solar and wind source in global regions according to the population (see <a href="#app1-energies-10-02158" class="html-app">Appendix A</a> for a more comprehensive list of abbreviations).</p> Full article ">Figure 6
<p>Solar and wind energy potential in global regions (see <a href="#app1-energies-10-02158" class="html-app">Appendix A</a> for a more comprehensive list of abbreviations).</p> Full article ">Figure 7
<p>Photovoltaics (PV) energy output according to the envelope angle.</p> Full article ">Figure 8
<p>Annual average PV energy output as a function of the solar irradiation.</p> Full article ">Figure 9
<p>Power coefficient according to the wind turbine types [<a href="#B43-energies-10-02158" class="html-bibr">43</a>]. (<b>A</b>) Sectional diagram of the wind turbine; (<b>B</b>) The generated power output of the wind turbine.</p> Full article ">Figure 10
<p>Annual average wind energy output as a function of the wind potential.</p> Full article ">Figure 11
<p>Chart of the energy consumption and generation according to the orientation. (<b>A</b>) New York City, (<b>B</b>) San Francisco, (<b>C</b>) Tokyo-Yokohama, (<b>D</b>) Seoul-Incheon, (<b>E</b>) Copenhagen, and (<b>F</b>) Amsterdam.</p> Full article ">Figure 11 Cont.
<p>Chart of the energy consumption and generation according to the orientation. (<b>A</b>) New York City, (<b>B</b>) San Francisco, (<b>C</b>) Tokyo-Yokohama, (<b>D</b>) Seoul-Incheon, (<b>E</b>) Copenhagen, and (<b>F</b>) Amsterdam.</p> Full article ">
6149 KiB  
Article
Design and Analysis of a New Torque Vectoring System with a Ravigneaux Gearset for Vehicle Applications
by Yu-Fan Chen, I-Ming Chen, Joshua Chang and Tyng Liu
Energies 2017, 10(12), 2157; https://doi.org/10.3390/en10122157 - 17 Dec 2017
Cited by 12 | Viewed by 6484
Abstract
The purpose of this research is to develop a new torque vectoring differential (TVD) for vehicle applications and investigate its effect on vehicle dynamic control. TVD is a technology that is able to distribute the engine torque to the left and right driving [...] Read more.
The purpose of this research is to develop a new torque vectoring differential (TVD) for vehicle applications and investigate its effect on vehicle dynamic control. TVD is a technology that is able to distribute the engine torque to the left and right driving wheels at different ratios so that the yaw motion control can be realized. Attention has been paid to this technology in recent years because of its potential to improve the vehicle performance and driving safety. In this study, a new TVD design with a Ravigneaux gearset was developed. This new design is able to use only one pair of gearsets to generate two different speed ratios, and the weight and volume of the system can be reduced. To execute the research, current TVD designs were analyzed and their design principles were clarified. Next, a new TVD design with Ravigneaux gearset was proposed. Then the connecting manner and the gear ratio of the Ravigneaux gearset were discussed. The dynamic equation of the system was then derived and the operation of the system was simulated in a MATLAB program. Further simulation was performed with a vehicle dynamic model in SimulationX to demonstrate the effect of the new system. The results of this study show the potential of building a new TVD with a Ravigneaux gearset and can be helpful for further system development. Full article
(This article belongs to the Special Issue Methods to Improve Energy Use in Road Vehicles)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagrams of current torque vectoring differential (TVD) designs: (<b>a</b>) superposition-clutch (SPC)-TVD and (<b>b</b>) stationary-clutch (STC)-TVD [<a href="#B15-energies-10-02157" class="html-bibr">15</a>].</p> Full article ">Figure 2
<p>System configurations of current TVD designs: (<b>a</b>) SPC-TVD and (<b>b</b>) STC-TVD.</p> Full article ">Figure 3
<p>(<b>a</b>) System configuration and (<b>b</b>) Schematic diagram of the Rav-TVD.</p> Full article ">Figure 4
<p>Lever diagram of a Ravigneaux gearset.</p> Full article ">Figure 5
<p>Operation of the Rav-TVD while (<b>a</b>) <span class="html-italic">B</span><sub>1</sub> is engaged and (<b>b</b>) <span class="html-italic">B</span><sub>2</sub> is engaged.</p> Full article ">Figure 6
<p>The interface and vehicle dynamic model for the open differential (OD) in SimulationX.</p> Full article ">Figure 7
<p>The influence to vehicle turning of the solid axle (SA) and OD models, (<b>a</b>) tire traction; (<b>b</b>) tire normal force; (<b>c</b>) tire speed; (<b>d</b>) vehicle trajectory.</p> Full article ">Figure 8
<p>The vehicle dynamic model for the SPC-TVD in SimulationX.</p> Full article ">Figure 9
<p>Torque vectoring effect and influence of the vehicle turning of the SPC-TVD model, (<b>a</b>) no engagement; (<b>b</b>) <span class="html-italic">C</span><sub>1</sub> is engaged; (<b>c</b>) <span class="html-italic">C</span><sub>2</sub> is engaged; (<b>d</b>) without steering; (<b>e</b>) with a 5-degree constant steering angle.</p> Full article ">Figure 9 Cont.
<p>Torque vectoring effect and influence of the vehicle turning of the SPC-TVD model, (<b>a</b>) no engagement; (<b>b</b>) <span class="html-italic">C</span><sub>1</sub> is engaged; (<b>c</b>) <span class="html-italic">C</span><sub>2</sub> is engaged; (<b>d</b>) without steering; (<b>e</b>) with a 5-degree constant steering angle.</p> Full article ">Figure 10
<p>The vehicle dynamic model for the Rav-TVD in SimulationX.</p> Full article ">Figure 11
<p>Torque vectoring effect and influence of vehicle turning of the Rav-TVD model, (<b>a</b>) no engagement; (<b>b</b>) <span class="html-italic">B</span><sub>1</sub> is engaged; (<b>c</b>) <span class="html-italic">B</span><sub>2</sub> is engaged; (<b>d</b>) without steering; (<b>e</b>) with a 5-degree constant steering angle.</p> Full article ">Figure 12
<p>Comparison of cornering for the SA, OD, SPC-TVD, and Rav-TVD models.</p> Full article ">Figure 13
<p>Sensitivity analysis of the Rav-TVD model for different gear ratios: (<b>a</b>) <span class="html-italic">i<sub>sl</sub></span> and (<b>b</b>) <span class="html-italic">i<sub>ss</sub></span>.</p> Full article ">Figure 14
<p>Sensitivity analysis of the Rav-TVD model for different Max. press-on forces: (<b>a</b>) brake <span class="html-italic">B</span><sub>1</sub> and (<b>b</b>) brake <span class="html-italic">B</span><sub>2</sub>.</p> Full article ">Figure 15
<p>Vehicle dynamics of the Rav-TVD model for different vehicle speeds while (<b>a</b>) <span class="html-italic">B</span><sub>1</sub> is engaged and (<b>b</b>) <span class="html-italic">B</span><sub>2</sub> is engaged.</p> Full article ">Figure 16
<p>Vehicle dynamics of the Rav-TVD model for different steering angle: (<b>a</b>) 3 degrees and (<b>b</b>) 1 degree.</p> Full article ">
9993 KiB  
Article
Research on Energy-Saving Operation Strategy for Multiple Trains on the Urban Subway Line
by Jianqiang Liu and Nan Zhao
Energies 2017, 10(12), 2156; https://doi.org/10.3390/en10122156 - 17 Dec 2017
Cited by 19 | Viewed by 4723
Abstract
Energy consumption for multiple trains on the urban subway line is predominantly affected by the operation strategy. This paper proposed an energy-saving operation strategy for multiple trains, which is suitable for various line conditions and complex train schedules. The model and operation modes [...] Read more.
Energy consumption for multiple trains on the urban subway line is predominantly affected by the operation strategy. This paper proposed an energy-saving operation strategy for multiple trains, which is suitable for various line conditions and complex train schedules. The model and operation modes of the strategy are illustrated in detail, aiming to take full use of regenerative braking energy in complex multi-train systems with different departure intervals and dwell times. The computing method is proposed by means of the heuristic algorithm to obtain the optimum operation curve for each train. The simulation result based on a real urban subway line is provided to verify the correctness and effectiveness of the proposed energy-saving operation strategy. Full article
(This article belongs to the Section F: Electrical Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Configurations of the 100% low floor train.</p> Full article ">Figure 2
<p>The structure of the traction substation system.</p> Full article ">Figure 3
<p>Energy-saving operation curve and limiting velocity curve.</p> Full article ">Figure 4
<p>Optimal energy-saving operation curve of two trains.</p> Full article ">Figure 5
<p>The driving curves for multi-train system when the 3rd train operates under four-mode operation.</p> Full article ">Figure 6
<p>The driving curves for multi-train system when the 3rd train operates under five-mode operation.</p> Full article ">Figure 7
<p>The traction curves of the 1st train when the 3rd train operates in four-mode operation.</p> Full article ">Figure 8
<p>The traction curves of the 1st train when the 3rd train operates in five-mode operation.</p> Full article ">Figure 9
<p>The whole algorithm of the proposed computing method.</p> Full article ">Figure 10
<p>The calculation process of the 3rd train under four-mode operation.</p> Full article ">Figure 11
<p>The calculation process of the 3rd train under five-mode operation.</p> Full article ">Figure 12
<p>Diagram of absolute braking distance in moving automatic block system.</p> Full article ">Figure 13
<p>Process of modifying speed in multi-train system.</p> Full article ">Figure 14
<p>The conclusion of proposed energy-saving operation strategy.</p> Full article ">Figure 15
<p>Results under five-mode operation: (<b>a</b>) energy consumption of the 1st train and the 3rd train; and (<b>b</b>) total energy consumption and energy-saving efficiency.</p> Full article ">Figure 16
<p>Results under four-mode operation: (<b>a</b>) energy consumption of the 1st train and the 3rd train; and (<b>b</b>) total energy consumption and energy-saving efficiency.</p> Full article ">
6070 KiB  
Article
Electromagnetic Vibration Simulation of a 250-MW Large Hydropower Generator with Rotor Eccentricity and Rotor Deformation
by Ruhai Li, Chaoshun Li, Xuanlin Peng and Wei Wei
Energies 2017, 10(12), 2155; https://doi.org/10.3390/en10122155 - 17 Dec 2017
Cited by 29 | Viewed by 6521
Abstract
The electromagnetic vibration caused by electromagnetic force on the stator has threatened large hydro generators operating safely and stably. At the Zhexi hydropower station, the hydro generator was beset by electromagnetic vibration for a long time. Therefore, the paper provided a new method [...] Read more.
The electromagnetic vibration caused by electromagnetic force on the stator has threatened large hydro generators operating safely and stably. At the Zhexi hydropower station, the hydro generator was beset by electromagnetic vibration for a long time. Therefore, the paper provided a new method to help to find the vibration source and detect the hydro generator fault, through the combination of simulation and experiments. In this paper, the 3D stator pack structure model and the 2D hydro generator electromagnetic models under rotor eccentricity and rotor ellipse deformation conditions were built. Then, electromagnetism simulations were conducted to study the characteristics of the electromagnetic flux and electromagnetic force under different conditions by using the finite element method (FEM). Lastly, the vibration testing experiments and harmonic response simulations of stator frame were performed to present the characteristics of vibration distribution in frequency conditions. The simulation results were compared with the generator measured data to try to find out the main vibration source and guide the overhaul. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the simulation.</p> Full article ">Figure 2
<p>Magnetization curve of the material 50W250.</p> Full article ">Figure 3
<p>The 2D electromagnetic finite element method (FEM) model and mesh of the hydropower generator.</p> Full article ">Figure 4
<p>External circuit of hydropower generator stator windings.</p> Full article ">Figure 5
<p>Static Eccentricity model.</p> Full article ">Figure 6
<p>Dynamic eccentricity model.</p> Full article ">Figure 7
<p>Rotor ellipse deformation model.</p> Full article ">Figure 8
<p>The flux distribution at the rated load (<b>a</b>) flux lines (<b>b</b>) flux distribution nephogram.</p> Full article ">Figure 9
<p>The radial flux density on the surface of stator over one pair of poles in space.</p> Full article ">Figure 10
<p>The radial flux densities across the air gap under a no-load field current in space: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 10 Cont.
<p>The radial flux densities across the air gap under a no-load field current in space: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 11
<p>The radial electromagnetic force densities across the air gap under no-load field current in space: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 11 Cont.
<p>The radial electromagnetic force densities across the air gap under no-load field current in space: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 12
<p>Radial electromagnetic force densities at the point of stator tooth end under no-load field current in time: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition; (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 12 Cont.
<p>Radial electromagnetic force densities at the point of stator tooth end under no-load field current in time: (<b>a</b>) centric rotor; (<b>b</b>) static eccentricity condition; (<b>c</b>) dynamic eccentricity condition (<b>d</b>) rotor ellipse deformation condition.</p> Full article ">Figure 13
<p>Fast Fourier transformation (FFT) of radial electromagnetic force densities with different conditions.</p> Full article ">Figure 14
<p>Radial electromagnetic force density following the increase of field current.</p> Full article ">Figure 15
<p>The radial electromagnetic force density at the point of the stator tooth end under rated load in time.</p> Full article ">Figure 16
<p>FFT of radial electromagnetic force density with dynamic eccentricity conditions.</p> Full article ">Figure 17
<p>Radial electromagnetic force density following the increase of load.</p> Full article ">Figure 18
<p>The installation position of experiment apparatus for vibration measurement: (<b>a</b>) The distribution of vibration sensors; (<b>b</b>) The installation position of vibration sensors.</p> Full article ">Figure 19
<p>The three-dimensional FEM model of the hydropower generator.</p> Full article ">Figure 20
<p>The first-order vibration model of the hydropower generator stator.</p> Full article ">Figure 21
<p>The parts that are assigned exciting forces.</p> Full article ">Figure 22
<p>Amplitude frequency of stator deformation under various conditions at no load current.</p> Full article ">Figure 23
<p>Amplitude frequency of stator deformation under the rotor elliptical deformation condition with various loads: (<b>a</b>) The vibrations with various field current; (<b>b</b>) The vibrations with various load.</p> Full article ">
1846 KiB  
Article
Gas Hydrate and Free Gas Concentrations in Two Sites inside the Chilean Margin (Itata and Valdivia Offshores)
by Vargas-Cordero Iván, Tinivella Umberta and Villar-Muñoz Lucía
Energies 2017, 10(12), 2154; https://doi.org/10.3390/en10122154 - 16 Dec 2017
Cited by 19 | Viewed by 4424
Abstract
Two sectors, Itata and Valdivia, which are located in the Chilean margin were analysed by using seismic data with the main purpose to characterize the gas hydrate concentration. Strong lateral velocity variations are recognised, showing a maximum value in Valdivia offshore (2380 ms [...] Read more.
Two sectors, Itata and Valdivia, which are located in the Chilean margin were analysed by using seismic data with the main purpose to characterize the gas hydrate concentration. Strong lateral velocity variations are recognised, showing a maximum value in Valdivia offshore (2380 ms−1 above the BSR) and a minimum value in the Itata offshore (1380 m·s−1 below the BSR). In both of the sectors, the maximum hydrate concentration reaches 17% of total volume, while the maximum free gas concentration is located Valdivia offshore (0.6% of total volume) in correspondence of an uplift sector. In the Itata offshore, the geothermal gradient that is estimated is variable and ranges from 32 °C·km−1 to 87 °C·km−1, while in Valdivia offshore it is uniform and about 35 °C·km−1. When considering both sites, the highest hydrate concentration is located in the accretionary prism (Valdivia offshore) and highest free gas concentration is distributed upwards, which may be considered as a natural pathway for lateral fluid migration. The results that are presented here contribute to the global knowledge of the relationship between hydrate/free gas presence and tectonic features, such as faults and folds, and furnishes a piece of the regional hydrate potentiality Chile offshore. Full article
(This article belongs to the Section L: Energy Sources)
►▼ Show Figures

Figure 1

Figure 1
<p>Location map of the two analysed seismic sections (black lines). The red segments indicate the analysed parts.</p> Full article ">Figure 2
<p>Velocity model superimposed to the Kirchhoff pre-stack depth migration (PreSDM) sections of the northern (<b>upper</b> panel) and southern (<b>lower</b> panel) sectors. The rectangles in the sections indicate the position of the zooms (a and b), in which the bottom simulating reflector (BSR) and the free gas reflector (BGR) (if present) are indicated by red arrows. Faults and fractures are indicated by dotted lines.</p> Full article ">Figure 3
<p>Gas hydrate (positive values) and free gas (negative values) concentration models. The BSR is indicated by dotted lines. GG (red lines) indicates the geothermal gradient estimated by BSR depth. See text for details. The numbers reported across the red line indicate the upper and lower GG values.</p> Full article ">Figure 3 Cont.
<p>Gas hydrate (positive values) and free gas (negative values) concentration models. The BSR is indicated by dotted lines. GG (red lines) indicates the geothermal gradient estimated by BSR depth. See text for details. The numbers reported across the red line indicate the upper and lower GG values.</p> Full article ">
353 KiB  
Article
The (R)evolution of China: Offshore Wind Diffusion
by Thomas Poulsen and Charlotte Bay Hasager
Energies 2017, 10(12), 2153; https://doi.org/10.3390/en10122153 - 16 Dec 2017
Cited by 11 | Viewed by 12842
Abstract
This research presents an industry level gap analysis for Chinese offshore wind, which serves as a way to illuminate how China may fast track industry evolution. The research findings provide insight into how the Chinese government strongly and systematically decrees state-owned Chinese firms [...] Read more.
This research presents an industry level gap analysis for Chinese offshore wind, which serves as a way to illuminate how China may fast track industry evolution. The research findings provide insight into how the Chinese government strongly and systematically decrees state-owned Chinese firms to expand into overseas markets to speed up learning efforts. Insights are offered regarding the nation-level strategic plans and institutional support policies mobilized by China in order to be able to conquer market shares internationally by building a strong home market and then facilitating an end-to-end and fully financed export solution. This is interesting in itself and in particular so because it now also includes complex billion-dollar megaprojects such as turnkey offshore wind farm assets with an expected lifespan of 30+ years. Research findings are provided on how European and Chinese firms may successfully forge long-term alliances also for future Chinese wind energy export projects. Examples of past efforts of collaboration not yielding desired results have been included as well. At policy level, recommendations are provided on how the evolution of the Chinese offshore wind power industry can be fast-tracked to mirror the revolutionary pace, volume, and velocity which the Chinese onshore wind power industry has mustered. Full article
(This article belongs to the Section L: Energy Sources)
6080 KiB  
Review
LNG Regasification Terminals: The Role of Geography and Meteorology on Technology Choices
by Randeep Agarwal, Thomas J. Rainey, S. M. Ashrafur Rahman, Ted Steinberg, Robert K. Perrons and Richard J. Brown
Energies 2017, 10(12), 2152; https://doi.org/10.3390/en10122152 - 16 Dec 2017
Cited by 40 | Viewed by 9497
Abstract
Liquefied natural gas (LNG) projects are regulated by host countries, but policy and regulation should depend on geography and meteorology. Without considering the role of geography and meteorology, sub-optimal design choices can result, leading to energy conversion efficiency and capital investment decisions that [...] Read more.
Liquefied natural gas (LNG) projects are regulated by host countries, but policy and regulation should depend on geography and meteorology. Without considering the role of geography and meteorology, sub-optimal design choices can result, leading to energy conversion efficiency and capital investment decisions that are less than ideal. A key step in LNG is regasification, which transforms LNG back from liquid to the gaseous state and requires substantial heat input. This study investigated different LNG regasification technologies used around the world and benchmarked location and meteorology-related factors, such as seawater temperatures, ambient air temperatures, wind speeds and relative humidity. Seawater vaporizers are used for more than 95% of locations subject to water quality. Ambient air conditions are relatively better for South America, India, Spain and other Asian countries (Singapore, Taiwan, Indonesia, and Thailand) and provide a much cleaner regasification technology option for natural and forced draft systems and air-based intermediate fluid vaporizers. On a global basis, cold energy utilization currently represents <1% of the total potential, but this approach could deliver nearly 12 Gigawatt (GW) per annum. Overall, climate change is expected to have a positive financial impact on the LNG regasification industry, but the improvement could be unevenly distributed. Full article
(This article belongs to the Section L: Energy Sources)
►▼ Show Figures

Figure 1

Figure 1
<p>Global liquefied natural gas (LNG) importing countries and quantity 2015.</p> Full article ">Figure 2
<p>Global LNG exporting countries and quantity 2015.</p> Full article ">Figure 3
<p>Ambient air vaporizer.</p> Full article ">Figure 4
<p>Open rack vaporizer.</p> Full article ">Figure 5
<p>Submerged combustion vaporizer.</p> Full article ">Figure 6
<p>Intermediate fluid vaporizer.</p> Full article ">Figure 7
<p>Intermediate fluid vaporizer (air IFV).</p> Full article ">Figure 8
<p>LNG cold power generation.</p> Full article ">Figure 9
<p>Maximum seawater temperatures around the world.</p> Full article ">Figure 10
<p>Minimum seawater temperatures around the world.</p> Full article ">Figure 11
<p>Maximum and minimum ambient air temperatures around the world.</p> Full article ">Figure 12
<p>Maximum and minimum relative humidity around the world.</p> Full article ">Figure 13
<p>Effect of humidity on fan forced vaporizer performance [<a href="#B18-energies-10-02152" class="html-bibr">18</a>].</p> Full article ">Figure 14
<p>Effect of temperature on fan forced vaporizer performance [<a href="#B18-energies-10-02152" class="html-bibr">18</a>].</p> Full article ">Figure 15
<p>Effect of humidity on fan forced vaporizer performance [<a href="#B18-energies-10-02152" class="html-bibr">18</a>].</p> Full article ">Figure 16
<p>Maximum wind speeds around the world.</p> Full article ">Figure 17
<p>Minimum wind speeds around the world.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-252
Previous Issue
Next Issue
Back to TopTop