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Energies, Volume 10, Issue 10 (October 2017) – 236 articles

Cover Story (view full-size image): Medium-sized commercial buildings account for approximately 50% of Australia’s commercial office building stock. There are significant opportunities to achieve energy and greenhouse gas emission reductions by using solar-assisted cooling technologies. This study models the performance of solar desiccant-evaporative cooling, solar absorption cooling and hybrid solar desiccant-compression cooling for all eight Australian capital cities in five climate zones. The technical, environmental, and economic performance of each system is compared. View the paper here.
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1116 KiB  
Correction
Correction: Hu, Z.; et al. Transport and Deposition of Carbon Nanoparticles in Saturated Porous Media. Energies 2017, 10, 1151
by Zhongliang Hu, Jin Zhao, Hui Gao, Ehsan Nourafkan and Dongsheng Wen
Energies 2017, 10(10), 1681; https://doi.org/10.3390/en10101681 - 24 Oct 2017
Viewed by 2982
Abstract
The author wishes to correct Figure 1b in this paper [1][...] Full article
(This article belongs to the Special Issue Nanotechnology for Oil and Gas Applications)
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<p>(<b>b</b>) TEM picture for carbon dots at ~5 nm.</p> Full article ">
7121 KiB  
Article
Numerical Simulation of Hydraulic Fracture Propagation Guided by Single Radial Boreholes
by Tiankui Guo, Zhanqing Qu, Facheng Gong and Xiaozhi Wang
Energies 2017, 10(10), 1680; https://doi.org/10.3390/en10101680 - 23 Oct 2017
Cited by 26 | Viewed by 5127
Abstract
Conventional hydraulic fracturing is not effective in target oil development zones with available wellbores located in the azimuth of the non-maximum horizontal in-situ stress. To some extent, we think that the radial hydraulic jet drilling has the function of guiding hydraulic fracture propagation [...] Read more.
Conventional hydraulic fracturing is not effective in target oil development zones with available wellbores located in the azimuth of the non-maximum horizontal in-situ stress. To some extent, we think that the radial hydraulic jet drilling has the function of guiding hydraulic fracture propagation direction and promoting deep penetration, but this notion currently lacks an effective theoretical support for fracture propagation. In order to verify the technology, a 3D extended finite element numerical model of hydraulic fracturing promoted by the single radial borehole was established, and the influences of nine factors on propagation of hydraulic fracture guided by the single radial borehole were comprehensively analyzed. Moreover, the term ‘Guidance factor (Gf)’ was introduced for the first time to effectively quantify the radial borehole guidance. The guidance of nine factors was evaluated through gray correlation analysis. The experimental results were consistent with the numerical simulation results to a certain extent. The study provides theoretical evidence for the artificial control technology of directional propagation of hydraulic fracture promoted by the single radial borehole, and it predicts the guidance effect of a single radial borehole on hydraulic fracture to a certain extent, which is helpful for planning well-completion and fracturing operation parameters in radial borehole-promoted hydraulic fracturing technology. Full article
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<p>Schematic diagram of hydraulic fracture-directed propagation guided by a single radial well.</p> Full article ">Figure 2
<p>Schematic diagram of fracture surface simulation and level set function on the calculation point.</p> Full article ">Figure 3
<p>Wireframe view: (<b>a</b>) A model without radial well and (<b>b</b>) B model with the single radial well.</p> Full article ">Figure 4
<p>Simulation result of hydraulic fracture propagation without the guidance of radial well.</p> Full article ">Figure 5
<p>Simulation results of hydraulic fracture propagation guided by the single radial well in different azimuths: (<b>a</b>) 15°; (<b>b</b>) 30°; and (<b>c</b>) 45°.</p> Full article ">Figure 6
<p>Schematic diagram of the guiding factor “G<sub>f</sub>”.</p> Full article ">Figure 7
<p>Simulation results of hydraulic fracture propagation guided by the single radial well under the different horizontal principal stress differences: (<b>a</b>) σh = 39 MPa; (<b>b</b>) σh = 36 MPa; and (<b>c</b>) σh = 33 MPa.</p> Full article ">Figure 8
<p>Simulation results of hydraulic fracture propagation guided by a single radial well under radial well different diameters: (<b>a</b>) Φ = 0.03 m; (<b>b</b>) Φ = 0.05 m; and (<b>c</b>) Φ = 0.07 m.</p> Full article ">Figure 9
<p>Simulation results of hydraulic fracture propagation guided by the single radial well under the different radial well lengths: (<b>a</b>) 10 m; (<b>b</b>) 15 m; and (<b>c</b>) 20 m.</p> Full article ">Figure 10
<p>Simulation results of hydraulic fracture propagation guided by a single radial well under different Young’s modulus of reservoir rock: (<b>a</b>) 13 GPa; (<b>b</b>) 23 GPa; and (<b>c</b>) 33 GPa.</p> Full article ">Figure 11
<p>Simulation results of hydraulic fracture propagation guided by a single radial well under different Poisson ratios of reservoir rock: (<b>a</b>) 0.15; (<b>b</b>) 0.2; and (<b>c</b>) 0.25.</p> Full article ">Figure 12
<p>Simulation results of hydraulic fracture propagation guided by a single radial well under different reservoir permeabilities: (<b>a</b>) 1 × 10<sup>−3</sup> μm<sup>2</sup>; (<b>b</b>) 10 × 10<sup>−3</sup> μm<sup>2</sup>; and (<b>c</b>) 100 × 10<sup>−3</sup> μm<sup>2</sup>.</p> Full article ">Figure 13
<p>Simulation results of hydraulic fracture propagation guided by a single radial well under different fracturing fluid viscosities: (<b>a</b>) 1 mPa·s; (<b>b</b>) 50 mPa·s; (<b>c</b>) 100 mPa·s; and (<b>d</b>,<b>b</b>) 150 mPa·s.</p> Full article ">Figure 14
<p>Simulation results of hydraulic fracture propagation guided by the single radial well under the different fracturing fluid injection rates: (<b>a</b>) 1 m<sup>3</sup>/min; (<b>b</b>) 3 m<sup>3</sup>/min; (<b>c</b>) 6 m<sup>3</sup>/min; and (<b>d</b>) 9 m<sup>3</sup>/min.</p> Full article ">Figure 15
<p>Fracture morphology after hydraulic fracturing of sample 1#: (<b>a</b>) the overall picture before opening; (<b>b</b>) the inner picture after opening.</p> Full article ">Figure 16
<p>Fracture morphology after hydraulic fracturing of sample 2#: (<b>a</b>) the overall picture before opening; (<b>b</b>) the fracture initiates in the heel of radial borehole; (<b>c</b>–<b>e</b>) the multi-branch fractures occur in the core.</p> Full article ">
4377 KiB  
Article
Flat Optical Fiber Daylighting System with Lateral Displacement Sun-Tracking Mechanism for Indoor Lighting
by Ngoc Hai Vu and Seoyong Shin
Energies 2017, 10(10), 1679; https://doi.org/10.3390/en10101679 - 23 Oct 2017
Cited by 10 | Viewed by 6236
Abstract
An essential impact which can improve the indoor environment and save on power consumption for artificial lighting is utilization of daylight. Optical fiber daylighting technology offers a way to use direct daylight for remote spaces in a building. However, the existing paradigm based [...] Read more.
An essential impact which can improve the indoor environment and save on power consumption for artificial lighting is utilization of daylight. Optical fiber daylighting technology offers a way to use direct daylight for remote spaces in a building. However, the existing paradigm based on the precise orientation of sunlight concentrator toward the Sun is very costly and difficult to install on the roof of buildings. Here, we explore an alternative approach using mirror-coated lens array and planar waveguide to develop a flat optical fiber daylighting system (optical fiber daylighting panel) with lateral displacement Sun-tracking mechanism. Sunlight collected and reflected by each mirror-coated lens in a rectangular lens array is coupled into a planar waveguide using cone prisms placed at each lens focus. This geometry yields a thin, flat profile for Sunlight concentrator. Our proposed concentrating panel can be achieved with 35 mm thickness while the concentrator’s width and length are 500 mm × 500 mm. The commercial optical simulation tool (LightToolsTM) was used to develop the simulation models and analyze the system performance. Simulation results based on the designed system demonstrated an optical efficiency of 51.4% at a concentration ratio of 125. The system can support utilizing a lateral displacement Sun-tracking system, which allows for replacing bulky and robust conventional rotational Sun-tracking systems. This study shows a feasibility of a compact and inexpensive optical fiber daylighting system to be installed on the roof of buildings. Full article
(This article belongs to the Special Issue Solar Energy Application in Buildings)
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<p>(<b>a</b>) Principles of a conventional planar waveguide concentrator; (<b>b</b>) Our proposed flat optical fiber daylighting systems (OFDS) which allows using lateral displacement Sun-tracking mechanism.</p> Full article ">Figure 2
<p>Flow chart of the proposed flat OFDS.</p> Full article ">Figure 3
<p>A mirror-coated spherical lens array and its associated parameter and Ray-tracing analysis of a singlet lens.</p> Full article ">Figure 4
<p>(<b>a</b>) Distribution of concentrated sunlight on the focal plane of mirror-coated lens array; (<b>b</b>) the dependence of focal area on the sunlight incident angle.</p> Full article ">Figure 5
<p>(<b>a</b>) A planar waveguide with mirror-coated prisms placed on the top of the lens array. A prism is exaggerated for convenient interpretation and ray-tracing analysis is applied to illustrate how the sunlight decouples inside the waveguide; (<b>b</b>) Ray tracing on a planar waveguide combined with a mirror-coated spherical lens array to illustrate planar waveguide collects light from different lenses and transports it to the exit port.</p> Full article ">Figure 6
<p>Lateral view of the waveguide with single ray-tracing to illustrate the theoretical calculation of optical efficiency.</p> Full article ">Figure 7
<p>A ribbon arrangement of optical fiber is coupled with planar waveguide.</p> Full article ">Figure 8
<p>(<b>a</b>) Alignment of the system along the N–S and E–W directions; (<b>b</b>) Sun position at the different time of a day.</p> Full article ">Figure 9
<p>The dependence of concentrator optical efficiency and concentration ratio.</p> Full article ">Figure 10
<p>Flat OFDS performance over the course of the day including POF coupling loss. (<b>a</b>) The optical efficiency and output luminous flux changes depending on the position of the sun in the sky; (<b>b</b>) The dependence of luminous flux on the surface of the sunlight concentrator and the output at the exit port at different times during a sunny day.</p> Full article ">Figure 11
<p>(<b>a</b>) Illustration of focal point displacement when sunlight incident angle changes from 0° to 45°; (<b>b</b>) Dependence of displacement of focal point on the course of the day.</p> Full article ">Figure 12
<p>(<b>a</b>) Commercial flat POF with different sizes; (<b>b</b>) Alternative approach for optical fiber–planar waveguide coupling using a bundle of flat POFs.</p> Full article ">
9405 KiB  
Article
Characterization and Prediction of the Gas Hydrate Reservoir at the Second Offshore Gas Production Test Site in the Eastern Nankai Trough, Japan
by Machiko Tamaki, Tetsuya Fujii and Kiyofumi Suzuki
Energies 2017, 10(10), 1678; https://doi.org/10.3390/en10101678 - 23 Oct 2017
Cited by 72 | Viewed by 7321
Abstract
Following the world’s first offshore production test that was conducted from a gas hydrate reservoir by a depressurization technique in 2013, the second offshore production test has been planned in the eastern Nankai Trough. In 2016, the drilling survey was performed ahead of [...] Read more.
Following the world’s first offshore production test that was conducted from a gas hydrate reservoir by a depressurization technique in 2013, the second offshore production test has been planned in the eastern Nankai Trough. In 2016, the drilling survey was performed ahead of the production test, and logging data that covers the reservoir interval were newly obtained from three wells around the test site: one well for geological survey, and two wells for monitoring surveys, during the production test. The formation evaluation using the well log data suggested that our target reservoir has a more significant heterogeneity in the gas hydrate saturation distribution than we expected, although lateral continuity of sand layers is relatively good. To evaluate the spatial distribution of gas hydrate, the integration analysis using well and seismic data was performed. The seismic amplitude analysis supports the lateral reservoir heterogeneity that has a significant positive correlation with the resistivity log data at the well locations. The spatial distribution of the apparent low-resistivity interval within the reservoir observed from log data was investigated by the P-velocity volume derived from seismic inversion. The integrated results were utilized for the pre-drill prediction of the reservoir quality at the producing wells. These approaches will reduce the risk of future commercial production from the gas hydrate reservoir. Full article
(This article belongs to the Special Issue Methane Hydrate Research and Development)
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<p>(<b>a</b>) Index map of the Tokai-Atsumi-Kumano forearc basin around the eastern Nankai Trough; and (<b>b</b>) the seabed structure map (vertical scale is two-way-time (ms)) and well location map around the first and second offshore gas hydrate production test sites.</p> Full article ">Figure 2
<p>Seismic cross-section through the AT1-MC well and logging data obtained in the AT1-MC well (modified from [<a href="#B9-energies-10-01678" class="html-bibr">9</a>]). The location of the cross-section profile is shown in <a href="#energies-10-01678-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>(<b>a</b>) Well log results at the first production test site. Log data at each well shows the gamma ray (<b>left</b>) and resistivity (<b>right</b>) measurements; and (<b>b</b>) the geological cross-sections pass through the candidate locations and the first production test site. The location of the cross-section profile is shown in <a href="#energies-10-01678-f004" class="html-fig">Figure 4</a>. The geological unit boundary defined in this figure is in accordance with [<a href="#B16-energies-10-01678" class="html-bibr">16</a>].</p> Full article ">Figure 4
<p>Candidate locations for the second production test (planned) (modified from [<a href="#B6-energies-10-01678" class="html-bibr">6</a>]). The colored area shows the thickness of the upper MHCZ (Unit IV-1 and Unit IV-2 within the methane hydrate-concentrated zone).</p> Full article ">Figure 5
<p>Logging data at AT1-UD, MT2, and MT3 wells, and the results of well-to-well correlations together with the existing wells (MC and MT1) at the first production test. Each well shows the log data of the gamma ray (<b>left</b>) and resistivity (<b>right</b>) measurements. Cross-section profiles pass through the MT2 well (<a href="#energies-10-01678-f004" class="html-fig">Figure 4</a>). The wells, except for the MT2 well, are plotted by the vertical projection to the cross-section. The geological unit boundary defined in this figure is in accordance with [<a href="#B16-energies-10-01678" class="html-bibr">16</a>].</p> Full article ">Figure 6
<p>RMS amplitude map generated from the top of the MHCZ horizon around the production test site.</p> Full article ">Figure 7
<p>Cross-plot between the integrated resistivity for the upper unit (Unit IV-1 and Unit IV-2) and the RMS amplitude for the top of the MHCZ at each location using the existing seven wells.</p> Full article ">Figure 8
<p>The well log data and seismic inversion results: (<b>a</b>) two-way-time scale (TWT); (<b>b</b>) geological unit; (<b>c</b>) natural gamma ray log (GR); (<b>d</b>) resistivity log (Res); (<b>e</b>) P-velocity from sonic logs (Vp); (<b>f</b>) filtered Vp log applied by a high cut filter of 87.5 Hz; (<b>g</b>) Vp, low-frequency model (LFM) used for the seismic inversion; and (<b>h</b>) Vp, result of the seismic inversion. The lines (<b>g</b>) and (<b>h</b>) are extracted at the location of the UD well.</p> Full article ">Figure 9
<p>The results of the Vp estimated by the seismic inversion. The cross-sections are through the wells: (<b>a</b>) MC-MT3-P3-UD wells for the west side; (<b>b</b>) MC-P2-MT2-UD wells for the east side; and (<b>c</b>) P2-P3 wells for the pre-drill prediction of the reservoir quality. The projected distribution of the low Vp in the lower unit is shown by 2D plane (upper right side in <a href="#energies-10-01678-f009" class="html-fig">Figure 9</a>c). The vertical scale in the cross-section is two-way-time (ms).</p> Full article ">Figure 9 Cont.
<p>The results of the Vp estimated by the seismic inversion. The cross-sections are through the wells: (<b>a</b>) MC-MT3-P3-UD wells for the west side; (<b>b</b>) MC-P2-MT2-UD wells for the east side; and (<b>c</b>) P2-P3 wells for the pre-drill prediction of the reservoir quality. The projected distribution of the low Vp in the lower unit is shown by 2D plane (upper right side in <a href="#energies-10-01678-f009" class="html-fig">Figure 9</a>c). The vertical scale in the cross-section is two-way-time (ms).</p> Full article ">
819 KiB  
Article
Design of Parallel Air-Cooled Battery Thermal Management System through Numerical Study
by Kai Chen, Zeyu Li, Yiming Chen, Shuming Long, Junsheng Hou, Mengxuan Song and Shuangfeng Wang
Energies 2017, 10(10), 1677; https://doi.org/10.3390/en10101677 - 23 Oct 2017
Cited by 71 | Viewed by 8674
Abstract
In electric vehicles, the battery pack is one of the most important components that strongly influence the system performance. The battery thermal management system (BTMS) is critical to remove the heat generated by the battery pack, which guarantees the appropriate working temperature for [...] Read more.
In electric vehicles, the battery pack is one of the most important components that strongly influence the system performance. The battery thermal management system (BTMS) is critical to remove the heat generated by the battery pack, which guarantees the appropriate working temperature for the battery pack. Air cooling is one of the most commonly-used solutions among various battery thermal management technologies. In this paper, the cooling performance of the parallel air-cooled BTMS is improved through choosing appropriate system parameters. The flow field and the temperature field of the system are calculated using the computational fluid dynamics method. Typical numerical cases are introduced to study the influences of the operation parameters and the structure parameters on the performance of the BTMS. The operation parameters include the discharge rate of the battery pack, the inlet air temperature and the inlet airflow rate. The structure parameters include the cell spacing and the angles of the divergence plenum and the convergence plenum. The results show that the temperature rise and the temperature difference of the batter pack are not affected by the inlet air flow temperature and are increased as the discharge rate increases. Increasing the inlet airflow rate can reduce the maximum temperature, but meanwhile significantly increase the power consumption for driving the airflow. Adopting smaller cell spacing can reduce the temperature and the temperature difference of the battery pack, but it consumes much more power. Designing the angles of the divergence plenum and the convergence plenum is an effective way to improve the performance of the BTMS without occupying more system volume. An optimization strategy is used to obtain the optimal values of the plenum angles. For the numerical cases with fixed power consumption, the maximum temperature and the maximum temperature difference at the end of the five-current discharge process for the optimized BTMS are respectively reduced by 2.1 K and 4.3 K, compared to the original system. Full article
(This article belongs to the Special Issue Thermal Energy Storage and Thermal Management (TESM2017))
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<p>Schematic of the battery pack.</p> Full article ">Figure 2
<p>Schematic of the parallel air-cooled battery thermal management system (BTMS). (<b>a</b>) Orthographic view of the BTMS; (<b>b</b>) Side view of the BTMS.</p> Full article ">Figure 3
<p>Grid dependence analysis result.</p> Full article ">Figure 4
<p>Schematic of the grid system at a certain <span class="html-italic">x</span> cross-section.</p> Full article ">Figure 5
<p>Comparison of the numerical results by 2D calculation and 3D calculation. (<b>a</b>) Maximum temperature; (<b>b</b>) maximum temperature difference.</p> Full article ">Figure 6
<p>Comparison of the numerical results of the present study and the experiment data in the reference.</p> Full article ">Figure 7
<p>Comparison of numerical results for various inlet air temperatures. (<b>a</b>) Maximum temperature; (<b>b</b>) maximum temperature difference.</p> Full article ">Figure 8
<p>Comparison of results for various inlet airflow rates. (<b>a</b>) Flow rate in the cooling channel; (<b>b</b>) maximum temperature; (<b>c</b>) maximum temperature difference.</p> Full article ">Figure 8 Cont.
<p>Comparison of results for various inlet airflow rates. (<b>a</b>) Flow rate in the cooling channel; (<b>b</b>) maximum temperature; (<b>c</b>) maximum temperature difference.</p> Full article ">Figure 9
<p>Comparison of the results for various cell spacings. (<b>a</b>) Flow rate in the cooling channel; (<b>b</b>) maximum temperature; (<b>c</b>) maximum temperature difference.</p> Full article ">Figure 9 Cont.
<p>Comparison of the results for various cell spacings. (<b>a</b>) Flow rate in the cooling channel; (<b>b</b>) maximum temperature; (<b>c</b>) maximum temperature difference.</p> Full article ">Figure 10
<p>Comparison of numerical results for various angles of the plenums. (<b>a</b>) Pressure; (<b>b</b>) pressure drop.</p> Full article ">Figure 11
<p>Schematic of the characteristic points in the air-cooled BTMS.</p> Full article ">Figure 12
<p>Comparison of the maximum temperature and the maximum temperature difference of the battery pack with time with fixed power consumption. (<b>a</b>) Maximum temperature; (<b>b</b>) maximum temperature difference.</p> Full article ">Figure 12 Cont.
<p>Comparison of the maximum temperature and the maximum temperature difference of the battery pack with time with fixed power consumption. (<b>a</b>) Maximum temperature; (<b>b</b>) maximum temperature difference.</p> Full article ">
1316 KiB  
Article
Solar-Enhanced Air-Cooled Heat Exchangers for Geothermal Power Plants
by Kamel Hooman, Xiaoxue Huang and Fangming Jiang
Energies 2017, 10(10), 1676; https://doi.org/10.3390/en10101676 - 23 Oct 2017
Cited by 10 | Viewed by 4003
Abstract
This paper focuses on the optimization of a Solar-Enhanced Natural-Draft Dry-Cooling Tower (SENDDCT), originally designed by the Queensland Geothermal Energy Centre of Excellence (QGECE), as the air-cooled condenser of a geothermal power plant. The conventional method of heat transfer augmentation through fin-assisted area [...] Read more.
This paper focuses on the optimization of a Solar-Enhanced Natural-Draft Dry-Cooling Tower (SENDDCT), originally designed by the Queensland Geothermal Energy Centre of Excellence (QGECE), as the air-cooled condenser of a geothermal power plant. The conventional method of heat transfer augmentation through fin-assisted area extension is compared with a metal foam-wrapped tube bundle. Both lead to heat-transfer enhancement, albeit at the expense of a higher pressure drop when compared to the bare tube bundle as our reference case. An optimal design is obtained through the use of a simplified analytical model and existing correlations by maximizing the heat transfer rate with a minimum pressure drop goal as the constraint. Sensitivity analysis was conducted to investigate the effect of sunroof diameter, as well as tube bundle layouts and tube spacing, on the overall performance of the system. Aiming to minimize the flow and thermal resistances for a SENDDCT, an optimum design is presented for an existing tower to be equipped with solar panels to afterheat the air leaving the heat exchanger bundles, which are arranged vertically around the tower skirt. Finally, correlations are proposed to predict the total pressure drop and heat transfer of the extended surfaces considered here. Full article
(This article belongs to the Special Issue Selected Papers from Sixth Annual Conference for the Development and Utilization of Deep Geothermal Energy)
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<p>(<b>a</b>) The SENDDCT concept investigated by Zou [<a href="#B5-energies-10-01676" class="html-bibr">5</a>] and (<b>b</b>) Schematic view of half of the tower (due to symmetry).</p> Full article ">Figure 2
<p>Close-up of an aluminium foam sample attached to a model condenser tube where the cells’ structures induce a tortuous flow path.</p> Full article ">Figure 3
<p>Heat transfer from the current design versus that of [<a href="#B5-energies-10-01676" class="html-bibr">5</a>] for an identical tower size and height.</p> Full article ">Figure 4
<p>Heat transfer versus height for the current design with the tower details being the same as those of [<a href="#B5-energies-10-01676" class="html-bibr">5</a>] except for H.</p> Full article ">Figure 5
<p>Required tower height for given sunroof diameters; other tower geometrical constraints are as those of [<a href="#B5-energies-10-01676" class="html-bibr">5</a>].</p> Full article ">Figure 6
<p>Solar heating ratio defined as ∆<span class="html-italic">T<sub>s</sub></span>/∆<span class="html-italic">T<sub>hx</sub></span> for different air velocity values.</p> Full article ">
2951 KiB  
Article
A Novel Topology of Hybrid HVDC Circuit Breaker for VSC-HVDC Application
by Van-Vinh Nguyen, Ho-Ik Son, Thai-Thanh Nguyen, Hak-Man Kim and Chan-Ki Kim
Energies 2017, 10(10), 1675; https://doi.org/10.3390/en10101675 - 23 Oct 2017
Cited by 19 | Viewed by 6647
Abstract
The use of high voltage direct current (HVDC) circuit breakers (CBs) with the capabilities of bidirectional fault interruption, reclosing, and rebreaking can improve the reliable and safe operation of HVDC grids. Although several topologies of CBs have been proposed to perform these capabilities, [...] Read more.
The use of high voltage direct current (HVDC) circuit breakers (CBs) with the capabilities of bidirectional fault interruption, reclosing, and rebreaking can improve the reliable and safe operation of HVDC grids. Although several topologies of CBs have been proposed to perform these capabilities, the limitation of these topologies is either high on-state losses or long time interruption in the case bidirectional fault current interruption. Long time interruption results in the large magnitude of the fault current in the voltage source converter based HVDC (VSC-HVDC) system due to the high rate of rise of fault current. This paper proposes a new topology of hybrid CB (HCB) with lower conduction loss and lower interruption time to solve the problems. The proposed topology is based on the inverse current injection method, which uses the capacitor to enforce the fault current to zero. In the case of the bidirectional fault current interruption, the capacitor does not change its polarity after identifying the direction of fault current, which can reduce the interruption time accordingly. A switching control algorithm for the proposed topology is presented in detail. Different operation modes of proposed HCB, such as normal current mode, breaking fault current mode, discharging, and reversing capacitor voltage modes after clearing the fault, are considered in the proposed algorithm. The proposed topology with the switching control algorithm is tested in a simulation-based system. Different simulation scenarios such as temporary and permanent faults are carried out to verify the performance of the proposed topology. The simulation is performed in the Matlab/Simulink environment. Full article
(This article belongs to the Special Issue Emerging Power Electronics Technologies for Power Systems and Machine Drives)
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<p>The proposed bidirectional hybrid circuit breaker (HCB) topology.</p> Full article ">Figure 2
<p>Current waveforms of the proposed HCB topology.</p> Full article ">Figure 3
<p>Operating modes of the proposed HCB topology with DC fault at the B-side.</p> Full article ">Figure 4
<p>Operating modes of the proposed HCB topology with DC fault at the A-side.</p> Full article ">Figure 5
<p>Operation mode of proposed HCB topology.</p> Full article ">Figure 6
<p>Flowchart of the switching control algorithm for the proposed HCB topology.</p> Full article ">Figure 7
<p>Configuration of the voltage source converter-based high voltage direct current (VSC-HVDC) model.</p> Full article ">Figure 8
<p>Overall currents on the switches of HCB 1.</p> Full article ">Figure 9
<p>Overall currents on the switches of HCB 2.</p> Full article ">Figure 10
<p>Overall currents on the switches of HCB 3.</p> Full article ">Figure 11
<p>Overall currents on the switches of HCB 4.</p> Full article ">Figure 12
<p>Capacitor voltage in the proposed HCB.</p> Full article ">Figure 13
<p>Overall currents on the switches of HCB 1.</p> Full article ">Figure 14
<p>Overall currents on the switches of HCB 2.</p> Full article ">Figure 15
<p>Overall currents on the switches of HCB 3.</p> Full article ">Figure 16
<p>Overall currents on the switches of HCB 4.</p> Full article ">Figure 17
<p>Capacitor voltage in the proposed HCB.</p> Full article ">
3504 KiB  
Article
A Comparative Study of CFD Models of a Real Wind Turbine in Solar Chimney Power Plants
by Ehsan Gholamalizadeh and Jae Dong Chung
Energies 2017, 10(10), 1674; https://doi.org/10.3390/en10101674 - 23 Oct 2017
Cited by 13 | Viewed by 5611
Abstract
A solar chimney power plant consists of four main parts, a solar collector, a chimney, an energy storage layer, and a wind turbine. So far, several investigations on the performance of the solar chimney power plant have been conducted. Among them, different approaches [...] Read more.
A solar chimney power plant consists of four main parts, a solar collector, a chimney, an energy storage layer, and a wind turbine. So far, several investigations on the performance of the solar chimney power plant have been conducted. Among them, different approaches have been applied to model the turbine inside the system. In particular, a real wind turbine coupled to the system was simulated using computational fluid dynamics (CFD) in three investigations. Gholamalizadeh et al. simulated a wind turbine with the same blade profile as the Manzanares SCPP’s turbine (FX W-151-A blade profile), while a CLARK Y blade profile was modelled by Guo et al. and Ming et al. In this study, simulations of the Manzanares prototype were carried out using the CFD model developed by Gholamalizadeh et al. Then, results obtained by modelling different turbine blade profiles at different turbine rotational speeds were compared. The results showed that a turbine with the CLARK Y blade profile significantly overestimates the value of the pressure drop across the Manzanares prototype turbine as compared to the FX W-151-A blade profile. In addition, modelling of both blade profiles led to very similar trends in changes in turbine efficiency and power output with respect to rotational speed. Full article
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<p>Schematic of a Solar Chimney Power Plant and Boundary Conditions.</p> Full article ">Figure 2
<p>Grids of the turbine zone.</p> Full article ">Figure 3
<p>Mass flow rate at different turbine rotational speeds.</p> Full article ">Figure 4
<p>Pressure drop across the turbine at different turbine rotational speeds.</p> Full article ">Figure 5
<p>Turbine efficiency at different turbine rotational speeds.</p> Full article ">Figure 6
<p>Power output at different turbine rotational speeds.</p> Full article ">
1177 KiB  
Article
Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application
by Pamela MacDougall, Bob Ran, George B. Huitema and Geert Deconinck
Energies 2017, 10(10), 1673; https://doi.org/10.3390/en10101673 - 23 Oct 2017
Cited by 8 | Viewed by 4154
Abstract
With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration [...] Read more.
With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration of aggregator services into market participation is knowledge of the current and future flexible capacity. To calculate the available flexibility, the current aggregator pilot and simulation implementations use lower level measurements and device specifications. This type of implementation is not scalable due to computational constraints, as well as it could conflict with end user privacy rights. Black box machine learning approaches have been proven to accurately estimate the available capacity of a cluster of heating devices using only aggregated data. This study will investigate the accuracy of this approach when applied to a heterogeneous virtual power plant (VPP). Firstly, a sensitivity analysis of the machine learning model is performed when varying the underlying device makeup of the VPP. Further, the forecasted flexible capacity of a heterogeneous residential VPP was applied to a trade strategy, which maintains a day ahead schedule, as well as offers flexibility to the imbalance market. This performance is then compared when using the same strategy with no capacity forecasting, as well as perfect knowledge. It was shown that at most, the highest average error, regardless of the VPP makeup, was still less than 9%. Further, when applying the forecasted capacity to a trading strategy, 89% of the optimal performance can be met. This resulted in a reduction of monthly costs by approximately 20%. Full article
(This article belongs to the Special Issue Selected Papers from International Workshop of Energy-Open)
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<p>Schematic of Dutch control reserve bidding ladder [<a href="#B16-energies-10-01673" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>Data input and output of the predictive model.</p> Full article ">Figure 3
<p>Aggregated bid for the PowerMatcher technology [<a href="#B10-energies-10-01673" class="html-bibr">10</a>].</p> Full article ">Figure 4
<p>Information exchange between intelligence (red), electricity markets (green) and aggregator (blue).</p> Full article ">Figure 5
<p>DAM price with associated day ahead schedule of 1000 households for one Week.</p> Full article ">Figure 6
<p>Imbalance and day ahead price for one Week.</p> Full article ">Figure 7
<p>Mean average error of the forecasting model for each virtual power plant (VPP) when the penetration of EV and washing machines is varied. Each VPP composition’s error is the result of averaging the MAE over a varying number of heat pumps.</p> Full article ">Figure 8
<p>Mean average error of the forecasting model for each VPP when the penetration of heat pumps and washing machines is varied. Each VPP composition’s error is the result of averaging the MAE over a varying number of electric vehicles.</p> Full article ">Figure 9
<p>Mean average error of the forecasting model for each VPP when the penetration of EV and heat pumps is varied. Each VPP composition’s error is the result of averaging the MAE over a varying number of washing machines.</p> Full article ">Figure 10
<p>Percent error and ramp power of <math display="inline"> <semantics> <mi>τ</mi> </semantics> </math> prediction for the ANN algorithm using a 500 run validation set.</p> Full article ">Figure 11
<p>Flexibility degradation with no forecasting.</p> Full article ">Figure 12
<p>Flexibility degradation with machine learning.</p> Full article ">Figure 13
<p>Flexibility degradation with perfect knowledge.</p> Full article ">Figure 14
<p>Total allocations for all trade strategy approaches and the imbalance price.</p> Full article ">
8785 KiB  
Review
Building Applications, Opportunities and Challenges of Active Shading Systems: A State-of-the-Art Review
by Joud Al Dakheel and Kheira Tabet Aoul
Energies 2017, 10(10), 1672; https://doi.org/10.3390/en10101672 - 23 Oct 2017
Cited by 87 | Viewed by 12560
Abstract
Active shading systems in buildings have emerged as a high performing shading solution that selectively and optimally controls daylight and heat gains. Active shading systems are increasingly used in buildings, due to their ability to mainly improve the building environment, reduce energy consumption [...] Read more.
Active shading systems in buildings have emerged as a high performing shading solution that selectively and optimally controls daylight and heat gains. Active shading systems are increasingly used in buildings, due to their ability to mainly improve the building environment, reduce energy consumption and in some cases generate energy. They may be categorized into three classes: smart glazing, kinetic shading and integrated renewable energy shading. This paper reviews the current status of the different types in terms of design principle and working mechanism of the systems, performance, control strategies and building applications. Challenges, limitations and future opportunities of the systems are then discussed. The review highlights that despite its high initial cost, the electrochromic (EC) glazing is the most applied smart glazing due to the extensive use of glass in buildings under all climatic conditions. In terms of external shadings, the rotating shading type is the predominantly used one in buildings due to its low initial cost. Algae façades and folding shading systems are still emerging types, with high initial and maintenance costs and requiring specialist installers. The algae façade systems and PV integrated shading systems are a promising solution due to their dual benefits of providing shading and generating electricity. Active shading systems were found to save 12 to 50% of the building cooling electricity consumption. Full article
(This article belongs to the Special Issue Selected Papers from ZEMCH 2016: Building Energy Performance Evaluation/Simulation)
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<p>Active shading systems classification diagram</p> Full article ">Figure 2
<p>Smart materials types for folding shading systems.</p> Full article ">Figure 3
<p>Algae façade system details [<a href="#B42-energies-10-01672" class="html-bibr">42</a>]. Reproduced with the permission from author Kyoung-Hee Kim.</p> Full article ">
2496 KiB  
Article
An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting
by Qiang Ni, Shengxian Zhuang, Hanmin Sheng, Song Wang and Jian Xiao
Energies 2017, 10(10), 1669; https://doi.org/10.3390/en10101669 - 23 Oct 2017
Cited by 22 | Viewed by 3691
Abstract
High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are [...] Read more.
High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance. Full article
(This article belongs to the Section D: Energy Storage and Application)
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<p>Diagram of ELM.</p> Full article ">Figure 2
<p>Diagram of proposed approach.</p> Full article ">Figure 3
<p>Experimental setup.</p> Full article ">Figure 4
<p>The 5-min ahead PV power forecasting results of four seasons in Singapore: (<b>a</b>) northeast monsoon sunny conditions; (<b>b</b>) Inter monsoon (NS); (<b>c</b>) southwest monsoon; and, (<b>d</b>) Inter monsoon (SN) (PIs constructed by proposed model, double bootstrap, MLE-bootstrap and PeEn respectively) with 90% confidence level.</p> Full article ">Figure 5
<p>The 5-min ahead photovoltaic (PV) power forecasting results of typical weather conditions: (<b>a</b>) sunny conditions; (<b>b</b>) cloudy conditions; and (<b>c</b>) thunderstorm (PIs constructed by proposed model, double bootstrap, MLK bootstrap and PeEn, respectively) with 90% confidence level.</p> Full article ">
3233 KiB  
Article
Laser Radiation Induces Growth and Lipid Accumulation in the Seawater Microalga Chlorella pacifica
by Haonan Zhang, Zhengquan Gao, Zhe Li, Huanmin Du, Bin Lin, Meng Cui, Yonghao Yin, Fengming Lei, Chunyu Yu and Chunxiao Meng
Energies 2017, 10(10), 1671; https://doi.org/10.3390/en10101671 - 22 Oct 2017
Cited by 11 | Viewed by 3948
Abstract
The impacts of laser radiation (Nd: YAG laser, 1064 nm at 800 mW, He–Ne laser 808 nm at 6 W, semiconductor laser 632.8 nm at 40 mW) on growth and lipid accumulation of Chlorella pacifica were investigated in this study. The results showed [...] Read more.
The impacts of laser radiation (Nd: YAG laser, 1064 nm at 800 mW, He–Ne laser 808 nm at 6 W, semiconductor laser 632.8 nm at 40 mW) on growth and lipid accumulation of Chlorella pacifica were investigated in this study. The results showed growth rates increased 1.23, 1.41, and 1.40-fold over controls by 4 min Nd: YAG, 4 min He–Ne, and 8 min semiconductor laser treatments, respectively, whereas the corresponding nitrate reductase observed increased 1.25, 1.63, and 2.08-fold over controls. Moreover, total chlorophyll concentration was increased to 1.09, 1.29, and 1.33-fold over controls, respectively. After 20 days cultivation, the highest lipid content was 35.99%, 18.46%, and 31.00% after 2 min Nd: YAG, 4 min He–Ne, and 4 min semiconductor laser treatments, corresponding to 2.86, 1.50, and 2.46-fold increase over controls, respectively. Furthermore, the lipid productivity of the above 3 treatments were 15.25 ± 2.56, 16.25 ± 2.45, and 14.75 ± 2.11 mg L−1 d−1. However, the highest lipid productivity was 22.00 ± 3.28, 16.25 ± 2.45, and 19.25 ± 1.78 mg L−1 d−1, in response to treatment for 2 min Nd: YAG, 1 min He–Ne, and 4 min semiconductor laser treatments, with 2.67, 1.97, and 2.33-fold increase over controls, respectively. These results indicated that lipid accumulation efficiency of C. pacifica could be significantly improved by laser irradiation using Nd: YAG, He–Ne, and semiconductor laser treatments. Full article
(This article belongs to the Section L: Energy Sources)
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<p>The calibration curve between OD540 and cell density.</p> Full article ">Figure 2
<p>Microalgal cell growth curves after irradiated by Nd: YAG Laser with different time.</p> Full article ">Figure 3
<p>Microalgal cell growth curves after irradiated by He–Ne Laser with different time.</p> Full article ">Figure 4
<p>Microalgal cell growth curves after irradiated by semiconductor Laser with different time.</p> Full article ">Figure 5
<p>Total microalgal chlorophyll content after irradiated for different time by three lasers.</p> Full article ">Figure 6
<p>The nitrate reductase activity of microalgae sample after irradiated for different time by three type of lasers.</p> Full article ">Figure 7
<p>Fluorescence (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and light microscopy (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) images of <span class="html-italic">C. pacifica</span> after laser radiation. Bar = 10 μm.</p> Full article ">Figure 8
<p>Total microalgal lipid content after irradiated for different time by three lasers.</p> Full article ">
6263 KiB  
Article
An Improved Coordinated Control Strategy for PV System Integration with VSC-MVDC Technology
by Yanbo Che, Wenxun Li, Xialin Li, Jinhuan Zhou, Shengnan Li and Xinze Xi
Energies 2017, 10(10), 1670; https://doi.org/10.3390/en10101670 - 22 Oct 2017
Cited by 11 | Viewed by 4688
Abstract
The rapid development of renewable energy calls for feasible and reliable technologies to transmit and integrate power into grids. Voltage Source Converter (VSC)- Direct Current (DC) technology is considered as a promising solution for its independent control of active and reactive power. Modeling [...] Read more.
The rapid development of renewable energy calls for feasible and reliable technologies to transmit and integrate power into grids. Voltage Source Converter (VSC)- Direct Current (DC) technology is considered as a promising solution for its independent control of active and reactive power. Modeling and coordinated control of a large-scale concentrating photovoltaic integration system with VSC-MVDC (Voltage Source Converter-Medium Voltage Direct Current) technology have been investigated in this paper. The average controlled-source model of PhotoVoltaic (PV) integration system is firstly established. Then, a novel control strategy without fast communication is proposed to improve the reliability of the coordinated control system. An extra voltage loop is added to the basic control block, which is able to assure stable operation of the PV system in various conditions. Finally, the proposed control strategy is verified with simulation results. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>PhotoVoltaic (PV) integration system with VSC-MVDC technology.</p> Full article ">Figure 2
<p>(<b>a</b>) PV array characteristic; (<b>b</b>) PV array model.</p> Full article ">Figure 3
<p>(<b>a</b>) Boost converter topology; (<b>b</b>) Average model of boost converter; and, (<b>c</b>) Controller design of boost converter.</p> Full article ">Figure 4
<p>(<b>a</b>) Full-bridge isolated Direct Current (DC)/DC converter topology; (<b>b</b>) Average model of full-bridge isolated DC/DC converter; and, (<b>c</b>) Controller design of full-bridge isolated DC/DC converter.</p> Full article ">Figure 5
<p>Steady-state operating waveforms of full-bridge isolated DC/DC converter.</p> Full article ">Figure 6
<p>(<b>a</b>) DC/Alternating Current (AC) topology; (<b>b</b>) Average model of DC/AC; and, (<b>c</b>) Controller design of DC/AC converter.</p> Full article ">Figure 7
<p>(<b>a</b>) AC grid; (<b>b</b>) Equivalent model of AC grid.</p> Full article ">Figure 8
<p>Conventional control scheme of PV integration system.</p> Full article ">Figure 9
<p>Improved control scheme of PV integration system.</p> Full article ">Figure 10
<p>(<b>a</b>) Improved controller design of boost converter <span class="html-italic">ij</span>; (<b>b</b>) Improved controller design of full-bridge isolated DC/DC converter <span class="html-italic">i</span>.</p> Full article ">Figure 11
<p>The PV integration system model in MATLAB/Simulink.</p> Full article ">Figure 12
<p>DC voltage, active power and reactive power of DC/AC converter.</p> Full article ">Figure 13
<p>MVDC bus voltage and enable signal of the original voltage loop in isolated DC/DC converter 1.</p> Full article ">Figure 14
<p>DC voltage and power of isolated DC/DC converter 1~4.</p> Full article ">Figure 15
<p>Primary side DC voltage and enable signal of original voltage loop and MPPT controller in boost converter 11.</p> Full article ">Figure 16
<p>DC voltage and power of boost converter 11~12.</p> Full article ">Figure 17
<p>Comparison of the two control strategies.</p> Full article ">
888 KiB  
Article
Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data
by Minkyung Kim, Sangdon Park, Joohyung Lee, Yongjae Joo and Jun Kyun Choi
Energies 2017, 10(10), 1668; https://doi.org/10.3390/en10101668 - 21 Oct 2017
Cited by 54 | Viewed by 5809
Abstract
This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from [...] Read more.
This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods. Full article
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<p>Example of collected power data and missing data.</p> Full article ">Figure 2
<p>Example of a missing situation and the first and the last past situation.</p> Full article ">Figure 3
<p>The procedure of extended LAI method (eLAI). The shaded areas represent selected past situations.</p> Full article ">Figure 4
<p>Histogram of cumulative frequency distribution of missing data.</p> Full article ">Figure 5
<p>Example of data used in the performance evaluation. (<b>a</b>) DOE; (<b>b</b>) KEPCO-high voltage; (<b>c</b>) KEPCO-low voltage.</p> Full article ">Figure 6
<p>Example of data used in performance evaluation (every Monday for one year). (<b>a</b>) DOE; (<b>b</b>) KEPCO-high voltage.</p> Full article ">Figure 7
<p>Example of training dataset when missing interval is three.</p> Full article ">Figure 8
<p>Average MAPE and 95% confidence interval of each method. (<b>a</b>) DOE; (<b>b</b>) KEPCO-high voltage; (<b>c</b>) KEPCO-low voltage.</p> Full article ">Figure 9
<p>Average RMSE and 95% confidence interval of each method. (<b>a</b>) DOE; (<b>b</b>) KEPCO-high voltage; (<b>c</b>) KEPCO-low voltage.</p> Full article ">Figure 9 Cont.
<p>Average RMSE and 95% confidence interval of each method. (<b>a</b>) DOE; (<b>b</b>) KEPCO-high voltage; (<b>c</b>) KEPCO-low voltage.</p> Full article ">Figure 10
<p>Example of missing power data imputation.</p> Full article ">Figure 11
<p>Setting for performance evaluation according to the missing ratio.</p> Full article ">Figure 12
<p>The average MAPE (%) of the proposed eLAI with various missing ratios.</p> Full article ">Figure 13
<p>Average MAPE according to <span class="html-italic">p</span>.</p> Full article ">Figure 14
<p>Average MAPE according to <math display="inline"> <semantics> <msub> <mi>t</mi> <mi>max</mi> </msub> </semantics> </math>.</p> Full article ">Figure 15
<p>Example of difference between imputed data and missing data. (<b>a</b>) Unexpected variation in past situations for imputation, normal case; (<b>b</b>) unexpected variation in past situations for imputation, extreme case; (<b>c</b>) unexpected variation in current missing situation; (<b>d</b>) an example of imputation using SVR.</p> Full article ">
4603 KiB  
Article
Dynamic Modeling and Simulation of Deep Geothermal Electric Submersible Pumping Systems
by Julian Kullick and Christoph M. Hackl
Energies 2017, 10(10), 1659; https://doi.org/10.3390/en10101659 - 21 Oct 2017
Cited by 6 | Viewed by 7162
Abstract
Deep geothermal energy systems employ electric submersible pumps (ESPs) in order to lift geothermal fluid from the production well to the surface. However, rough downhole conditions and high flow rates impose heavy strain on the components, leading to frequent failures of the pump [...] Read more.
Deep geothermal energy systems employ electric submersible pumps (ESPs) in order to lift geothermal fluid from the production well to the surface. However, rough downhole conditions and high flow rates impose heavy strain on the components, leading to frequent failures of the pump system. As downhole sensor data is limited and often unrealible, a detailed and dynamical model system will serve as basis for deeper understanding and analysis of the overall system behavior. Furthermore, it allows to design model-based condition monitoring and fault detection systems, and to improve controls leading to a more robust and efficient operation. In this paper, a detailed state-space model of the complete ESP system is derived, covering the electrical, mechanical and hydraulic subsystems. Based on the derived model, the start-up phase of an exemplary yet realistic ESP system in the Megawatt range—located at a setting depth of 950 m and producing geothermal fluid of 140 ∘ C temperature at a rate of 0.145 m 3 s − 1 —is simulated in MATLAB/Simulink. The simulation results show that the system reaches a stable operating point with realistic values. Furthermore, the effect of self-excitation between the filter capacitor and the motor inductor can clearly be observed. A full set of parameters is provided, allowing for direct model implementation and reproduction of the presented results. Full article
(This article belongs to the Special Issue Low Enthalpy Geothermal Energy)
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<p>Subsystems and components of an electric submersible pump (ESP) in deep geothermal energy applications (GR = geothermal reservoir).</p> Full article ">Figure 2
<p>Equivalent circuit for a single phase <inline-formula> <mml:math id="mm686" display="block"> <mml:semantics> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>b</mml:mi> <mml:mo>,</mml:mo> <mml:mi>c</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> of a 5-level active neutral point clamped (ANPC-5L) inverter. The current paths (colored lines) depend on the inverter switching levels.</p> Full article ">Figure 3
<p>Normalized voltage hexagon (with respect to <inline-formula> <mml:math id="mm687" display="block"> <mml:semantics> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>dc</mml:mi> </mml:msub> </mml:semantics> </mml:math> </inline-formula>) of a 5-level inverter.</p> Full article ">Figure 4
<p>Equivalent circuit of a non-ideal LC-filter including copper losses.</p> Full article ">Figure 5
<p>Equivalent circuit of the power cable <inline-formula> <mml:math id="mm688" display="block"> <mml:semantics> <mml:mi>τ</mml:mi> </mml:semantics> </mml:math> </inline-formula>-segment.</p> Full article ">Figure 6
<p>Equivalent circuit of the power cable <inline-formula> <mml:math id="mm689" display="block"> <mml:semantics> <mml:mi>π</mml:mi> </mml:semantics> </mml:math> </inline-formula>-segment.</p> Full article ">Figure 7
<p>Three-phase equivalent circuit of a squirrel-cage induction motor.</p> Full article ">Figure 8
<p>(<bold>a</bold>) 2D impeller cross section (top view) defining the control volume <inline-formula> <mml:math id="mm690" display="block"> <mml:semantics> <mml:mi mathvariant="script">V</mml:mi> </mml:semantics> </mml:math> </inline-formula> and (<bold>b</bold>) exemplary velocity triangle of the fluid contained in the impeller.</p> Full article ">Figure 9
<p>Qualitative H-Q curve of a pump stage, with theoretical head, slip losses, friction losses and shock (incidence) losses.</p> Full article ">Figure 10
<p>Hydraulic system of the geothermal production well.</p> Full article ">Figure 11
<p>Free body diagram of a rotational two mass system.</p> Full article ">Figure 12
<p>Pump curves of the simulated pump system with trajectories <inline-formula> <mml:math id="mm691" display="block"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>H</mml:mi> <mml:mi mathvariant="normal">p</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo>·</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>Q</mml:mi> <mml:mi mathvariant="normal">p</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo>·</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (<bold>a</bold>) and <inline-formula> <mml:math id="mm692" display="block"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo>·</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>Q</mml:mi> <mml:mi>P</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo>·</mml:mo> <mml:mspace width="0.166667em"/> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (<bold>b</bold>) of operating points taken from the simulation data shown in <xref ref-type="fig" rid="energies-10-01659-f013">Figure 13</xref>.</p> Full article ">Figure 13
<p>Simulation results (I): Overview of the results from all subsystems.</p> Full article ">Figure 14
<p>Simulation results (II): Power and efficiency related results.</p> Full article ">Figure 15
<p>Simulation results (III): Detailed views of the electrical (<bold>a</bold>,<bold>b</bold>) and mechanical (<bold>c</bold>) subsystems.</p> Full article ">Figure 16
<p>Open loop Bode diagrams of (<bold>a</bold>) LC filter + RL-load transfer functions <inline-formula> <mml:math id="mm693" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">f</mml:mi> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mi>i</mml:mi> <mml:mrow> <mml:msub> <mml:mi mathvariant="normal">f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msubsup> <mml:mi>u</mml:mi> <mml:mrow> <mml:msub> <mml:mi mathvariant="normal">f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> [<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="energies-10-01659-i013.tif"/>] and <inline-formula> <mml:math id="mm694" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">f</mml:mi> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mi>i</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msubsup> <mml:mi>u</mml:mi> <mml:mrow> <mml:msub> <mml:mi mathvariant="normal">f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> [<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="energies-10-01659-i014.tif"/>]; (<bold>b</bold>) cable transfer function <inline-formula> <mml:math id="mm695" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mi mathvariant="normal">c</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mi>u</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">c</mml:mi> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>π</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msubsup> <mml:mi>u</mml:mi> <mml:mrow> <mml:msub> <mml:mi mathvariant="normal">f</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mi>α</mml:mi> </mml:msubsup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> [<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="energies-10-01659-i013.tif"/>] and (<bold>c</bold>) two-mass system transfer functions <inline-formula> <mml:math id="mm696" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">m</mml:mi> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>ω</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>e</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> [<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="energies-10-01659-i013.tif"/>] and <inline-formula> <mml:math id="mm697" display="block"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">m</mml:mi> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>ω</mml:mi> <mml:mi mathvariant="normal">p</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>e</mml:mi> </mml:msub> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> [<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="energies-10-01659-i014.tif"/>].</p> Full article ">Figure 17
<p>Isolated capacitance network of the <inline-formula> <mml:math id="mm673" display="block"> <mml:semantics> <mml:mi>π</mml:mi> </mml:semantics> </mml:math> </inline-formula>- and <inline-formula> <mml:math id="mm674" display="block"> <mml:semantics> <mml:mi>τ</mml:mi> </mml:semantics> </mml:math> </inline-formula>-cable equivalent circuits: (<bold>a</bold>) Voltage mesh for phase <italic>k</italic> over phase <italic>j</italic> to ground, <inline-formula> <mml:math id="mm675" display="block"> <mml:semantics> <mml:mrow> <mml:mi>j</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>b</mml:mi> <mml:mo>,</mml:mo> <mml:mi>c</mml:mi> <mml:mo>}</mml:mo> <mml:mo>,</mml:mo> <mml:mi>j</mml:mi> <mml:mo>≠</mml:mo> <mml:mi>k</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> and (<bold>b</bold>) currents flowing from and to phase <italic>k</italic>.</p> Full article ">
2724 KiB  
Article
A Mobile Battery Swapping Service for Electric Vehicles Based on a Battery Swapping Van
by Sujie Shao, Shaoyong Guo and Xuesong Qiu
Energies 2017, 10(10), 1667; https://doi.org/10.3390/en10101667 - 20 Oct 2017
Cited by 56 | Viewed by 11291
Abstract
This paper presents a novel approach for providing a mobile battery swapping service for electric vehicles (EVs) that is provided by a mobile battery swapping van. This battery swapping van can carry many fully charged batteries and drive up to an EV to [...] Read more.
This paper presents a novel approach for providing a mobile battery swapping service for electric vehicles (EVs) that is provided by a mobile battery swapping van. This battery swapping van can carry many fully charged batteries and drive up to an EV to swap a battery within a few minutes. First, a reasonable EV battery swapping architecture based on a battery swapping van is established in this paper. The function and role of each participant and the relationships between each participant are determined, especially their changes compared with the battery charging service. Second, the battery swapping service is described, including the service request priority and service request queuing model. To provide the battery swapping service efficiently and effectively, the battery swapping service request scheduling is analyzed well, and a minimum waiting time based on priority and satisfaction scheduling strategy (MWT-PS) is proposed. Finally, the battery swapping service is simulated, and the performance of MWT-PS is evaluated in simulation scenarios. The simulation results show that this novel approach can be used as a reference for a future system that provides reasonable and satisfying battery swapping service for EVs. Full article
(This article belongs to the Special Issue Battery Energy Storage Applications in Smart Grid)
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<p>EV battery swapping structure based on battery swapping van.</p> Full article ">Figure 2
<p>The general battery swapping service request queuing model.</p> Full article ">Figure 3
<p>General queuing model for a single service area.</p> Full article ">Figure 4
<p>New queuing model for a single service area.</p> Full article ">Figure 5
<p>Scheduling model for the single battery swapping van.</p> Full article ">Figure 6
<p>MWT-PS scheduling strategy.</p> Full article ">Figure 7
<p>performance evaluation with respect to the number of battery swapping vans and fully charged battery capacity (<b>a</b>) miss ratio; (<b>b</b>) average response time.</p> Full article ">Figure 8
<p>Performance evaluation with respect to the number of requests (<b>a</b>) miss ratio; (<b>b</b>) average response time; (<b>c</b>) satisfaction ratio with priority 1 and 2.</p> Full article ">
9604 KiB  
Article
Energy Production by Means of Pumps As Turbines in Water Distribution Networks
by Mauro Venturini, Stefano Alvisi, Silvio Simani and Lucrezia Manservigi
Energies 2017, 10(10), 1666; https://doi.org/10.3390/en10101666 - 20 Oct 2017
Cited by 29 | Viewed by 4489
Abstract
This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different [...] Read more.
This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different characteristics are investigated to estimate the producible yearly electric energy. The performance curves of Pumps As Turbines (PATs), which relate head, power, and efficiency to the volume flow rate over the entire PAT operation range, were derived by using published experimental data. The four considered water distribution networks, for which experimental data taken during one year were available, are characterized by significantly different hydraulic features (average flow rate in the range 10–116 L/s; average pressure reduction in the range 12–53 m). Therefore, energy production accounts for actual flow rate and head variability over the year. The conversion efficiency is also estimated, for both the whole water distribution network and the PAT alone. Full article
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<p>(<b>a</b>) Non-dimensional head vs. non-dimensional volume flow rate; (<b>b</b>) Non-dimensional power vs. non-dimensional volume flow rate; and, (<b>c</b>) Efficiency vs. non-dimensional volume flow rate (symbols: experimental data reported in [<a href="#B11-energies-10-01666" class="html-bibr">11</a>]; lines: interpolation curves).</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Non-dimensional head vs. non-dimensional volume flow rate; (<b>b</b>) Non-dimensional power vs. non-dimensional volume flow rate; and, (<b>c</b>) Efficiency vs. non-dimensional volume flow rate (symbols: experimental data reported in [<a href="#B11-energies-10-01666" class="html-bibr">11</a>]; lines: interpolation curves).</p> Full article ">Figure 2
<p>RMSE<span class="html-italic"><sub>Yk</sub></span> values for (<b>a</b>) non-dimensional head; (<b>b</b>) non-dimensional power and (<b>c</b>) efficiency.</p> Full article ">Figure 2 Cont.
<p>RMSE<span class="html-italic"><sub>Yk</sub></span> values for (<b>a</b>) non-dimensional head; (<b>b</b>) non-dimensional power and (<b>c</b>) efficiency.</p> Full article ">Figure 3
<p>Layout of the four water distribution networks (WDNs).</p> Full article ">Figure 4
<p>Available head drop vs. volume flow rate (measured values). (<b>a</b>) WDN A and B; (<b>b</b>) WDN C and D.</p> Full article ">Figure 5
<p>Summary of (<b>a</b>) volume flow rate and (<b>b</b>) head drop for WDN A, B, C and D.</p> Full article ">Figure 6
<p>Producible yearly electric energy.</p> Full article ">Figure 7
<p>Unexploited flow rate and head drop for (<b>a</b>) WDN A and PAT #1; (<b>b</b>) WDN B and PAT #2; (<b>c</b>) WDN C and PAT #4 and (<b>d</b>) WDN D and PAT #4.</p> Full article ">Figure 8
<p>Average yearly conversion efficiency: (<b>a</b>) overall and (<b>b</b>) PAT.</p> Full article ">
3031 KiB  
Article
A Maximum Power Transfer Tracking Method for WPT Systems with Coupling Coefficient Identification Considering Two-Value Problem
by Xin Dai, Xiaofei Li, Yanling Li, Pengqi Deng and Chunsen Tang
Energies 2017, 10(10), 1665; https://doi.org/10.3390/en10101665 - 20 Oct 2017
Cited by 17 | Viewed by 4474
Abstract
Maximum power transfer tracking (MPTT) is meant to track the maximum power point during the system operation of wireless power transfer (WPT) systems. Traditionally, MPTT is achieved by impedance matching at the secondary side when the load resistance is varied. However, due to [...] Read more.
Maximum power transfer tracking (MPTT) is meant to track the maximum power point during the system operation of wireless power transfer (WPT) systems. Traditionally, MPTT is achieved by impedance matching at the secondary side when the load resistance is varied. However, due to a loosely coupling characteristic, the variation of coupling coefficient will certainly affect the performance of impedance matching, therefore MPTT will fail accordingly. This paper presents an identification method of coupling coefficient for MPTT in WPT systems. Especially, the two-value issue during the identification is considered. The identification approach is easy to implement because it does not require additional circuit. Furthermore, MPTT is easy to realize because only two easily measured DC parameters are needed. The detailed identification procedure corresponding to the two-value issue and the maximum power transfer tracking process are presented, and both the simulation analysis and experimental results verified the identification method and MPTT. Full article
(This article belongs to the Special Issue Wireless Power Transfer and Energy Harvesting Technologies)
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<p>Schematic circuit of the tracking topology.</p> Full article ">Figure 2
<p>Continuous current condition waveforms of the system.</p> Full article ">Figure 3
<p>Flowchart of the identification process when considering the two-value issue.</p> Full article ">Figure 4
<p>Primary and secondary series-series (SS) resonant wireless power transfer (WPT) topology.</p> Full article ">Figure 5
<p>Flowchart of the maximum power transfer tracking.</p> Full article ">Figure 6
<p>The control structure of the proposed method.</p> Full article ">Figure 7
<p>Identification accuracies of coupling coefficient when <span class="html-italic">d</span> varies from 0.2 to 0.9.</p> Full article ">Figure 8
<p>Maximum power tracking when load <span class="html-italic">R<sub>b</sub></span> changes with different <span class="html-italic">k</span>: (<b>a</b>) <span class="html-italic">k</span> = 0.0811; (<b>b</b>) <span class="html-italic">k</span> = 0.0448.</p> Full article ">Figure 9
<p>Simulation analysis of system efficiencies under maximum power transfer tracking (MPTT) condition.</p> Full article ">Figure 10
<p>The experimental setup.</p> Full article ">Figure 11
<p>Experimental MPTT results when load <span class="html-italic">R<sub>b</sub></span> changes with different <span class="html-italic">k</span>: (<b>a</b>) <span class="html-italic">k</span> = 0.0811; (<b>b</b>) <span class="html-italic">k</span> = 0.0448.</p> Full article ">Figure 11 Cont.
<p>Experimental MPTT results when load <span class="html-italic">R<sub>b</sub></span> changes with different <span class="html-italic">k</span>: (<b>a</b>) <span class="html-italic">k</span> = 0.0811; (<b>b</b>) <span class="html-italic">k</span> = 0.0448.</p> Full article ">Figure 12
<p>Experimental analysis of system efficiencies under MPTT condition.</p> Full article ">
2848 KiB  
Article
Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER
by Jiaxin Lu, Weijun Wang, Yingchao Zhang and Song Cheng
Energies 2017, 10(10), 1664; https://doi.org/10.3390/en10101664 - 20 Oct 2017
Cited by 80 | Viewed by 5829
Abstract
Implementation of hybrid energy system (HES) is generally considered as a promising way to satisfy the electrification requirements for remote areas. In the present study, a novel decision making methodology is proposed to identify the best compromise configuration of HES from a set [...] Read more.
Implementation of hybrid energy system (HES) is generally considered as a promising way to satisfy the electrification requirements for remote areas. In the present study, a novel decision making methodology is proposed to identify the best compromise configuration of HES from a set of feasible combinations obtained from HOMER. For this purpose, a multi-objective function, which comprises four crucial and representative indices, is formulated by applying the weighted sum method. The entropy weight method is employed as a quantitative methodology for weighting factors calculation to enhance the objectivity of decision-making. Moreover, the optimal design of a stand-alone PV/wind/battery/diesel HES in Yongxing Island, China, is conducted as a case study to validate the effectiveness of the proposed method. Both the simulation and optimization results indicate that, the optimization method is able to identify the best trade-off configuration among system reliability, economy, practicability and environmental sustainability. Several useful conclusions are given by analyzing the operation of the best configuration. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Schematic configuration of a PV/wind/battery/diesel stand-alone HES.</p> Full article ">Figure 2
<p>The power curve of FD16-30.</p> Full article ">Figure 3
<p>The flowchart of control strategy.</p> Full article ">Figure 4
<p>Typical daily load profile for Yongxing Island.</p> Full article ">Figure 5
<p><span class="html-italic">LCOE</span> for different sizes of PV panels and wind turbines.</p> Full article ">Figure 6
<p><span class="html-italic">RF</span> for different sizes of PV panels and autonomy-hour.</p> Full article ">Figure 7
<p>HES power distribution for the best configuration.</p> Full article ">Figure 8
<p>HES cost distribution for the best configuration.</p> Full article ">Figure 9
<p>Economic sensitivity analysis.</p> Full article ">Figure 10
<p>Most economical system type with different wind speed and solar irradiation.</p> Full article ">
4273 KiB  
Article
A Multi-Energy System Expansion Planning Method Using a Linearized Load-Energy Curve: A Case Study in South Korea
by Woong Ko, Jong-Keun Park, Mun-Kyeom Kim and Jae-Haeng Heo
Energies 2017, 10(10), 1663; https://doi.org/10.3390/en10101663 - 20 Oct 2017
Cited by 15 | Viewed by 5143
Abstract
Multi-energy systems can integrate heat and electrical energy efficiently, using resources such as cogeneration. In order to meet energy demand cost-effectively in a multi-energy system, adopting appropriate energy resources at the right time is of great importance. In this paper, we propose an [...] Read more.
Multi-energy systems can integrate heat and electrical energy efficiently, using resources such as cogeneration. In order to meet energy demand cost-effectively in a multi-energy system, adopting appropriate energy resources at the right time is of great importance. In this paper, we propose an expansion planning method for a multi-energy system that supplies heat and electrical energy. The proposed approach formulates expansion planning as a mixed integer linear programming (MILP) problem. The objective is to minimize the sum of the annualized cost of the multi-energy system. The candidate resources that constitute the cost of the multi-energy system are fuel-based power generators, heat-only boilers, a combined heat and power (CHP) unit, energy storage resources, and a renewable electrical power source. We use a load-energy curve, instead of a load-duration curve, for constructing the optimization model, which is subsequently linearized using a Douglas-Peucker algorithm. The residual load-energy curve, for utilizing the renewable electrical power source, is also linearized. This study demonstrates the effectiveness of the proposed method through a comparison with a conventional linearization method. In addition, we evaluate the cost and planning schedules of different case studies, according to the configuration of resources in the multi-energy system. Full article
(This article belongs to the Special Issue Integration of Renewable Technologies in Water, Electricity, Heating and Cooling Networks)
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<p>Model of the multi-energy system.</p> Full article ">Figure 2
<p>Illustrative LDC.</p> Full article ">Figure 3
<p>Illustrative load-energy curve.</p> Full article ">Figure 4
<p>Piecewise linear load-energy curve constructed using the Douglas-Peucker algorithm.</p> Full article ">Figure 5
<p>Optimization process for determining the candidate resource: (<b>a</b>) Determining the desired point using the SOS2 method; (<b>b</b>) Allocating the capacity and utilized energy of a candidate resource.</p> Full article ">Figure 6
<p>Load curves used in analysis: (<b>a</b>) LDC and stepwise representation of LDC; (<b>b</b>) Piecewise linear load-energy curve.</p> Full article ">Figure 7
<p>Load profiles for the first project year: (<b>a</b>) Electricity load; (<b>b</b>) Heat load; (<b>c</b>) Load duration curve for electricity load; (<b>d</b>) Load duration curve for heat load.</p> Full article ">Figure 8
<p>Piecewise linear load-energy curves for the first project year: (<b>a</b>) Electricity load; (<b>b</b>) Heat load.</p> Full article ">Figure 9
<p>Output pattern of the renewable electrical power source.</p> Full article ">Figure 10
<p>Estimated costs by case. (Note that, in the Case 3 and 4, (a) means a case with forced allocation of RES and (b) means a case without forced allocation of RES).</p> Full article ">Figure 11
<p>Results of planning schedules for Case 4: (<b>a</b>) Installed capacity of electricity resources without forced allocation of RES; (<b>b</b>) Installed capacity of electricity resources with forced allocation of RES; (<b>c</b>) Utilized energy of electricity resources without forced allocation of RES; (<b>d</b>) Utilized energy of electricity resources with forced allocation of RES; (<b>e</b>) Installed capacity of heat resources without forced allocation of RES; (<b>f</b>) Installed capacity of heat resources with forced allocation of RES; (<b>g</b>) Utilized energy of heat resources without forced allocation of RES; (<b>h</b>) Utilized energy of heat resources with forced allocation of RES.</p> Full article ">
2265 KiB  
Article
The Heat Transfer of Microencapsulated Phase Change Material Slurry and Its Thermal Energy Storage Performance of Combined Heat and Power Generating Units
by Yonghong Guo, Xinglong Zhang, Lijun Yang, Chao Xu and Xiaoze Du
Energies 2017, 10(10), 1662; https://doi.org/10.3390/en10101662 - 20 Oct 2017
Cited by 9 | Viewed by 4269
Abstract
The application of thermal energy storage (TES) is an effective way of improving the power load regulation capability of combined heat and power (CHP) generating units. In this paper, a theoretical investigation on the thermal energy storage system of a CHP unit that [...] Read more.
The application of thermal energy storage (TES) is an effective way of improving the power load regulation capability of combined heat and power (CHP) generating units. In this paper, a theoretical investigation on the thermal energy storage system of a CHP unit that employs the microencapsulated phase change material slurry (MPCMS) as the working fluid is carried out. The results indicate that the microcapsule particle internal melting rate is progressively small; 90% latent heat can be absorbed in 63% total melting time. The melting time of particles in micron is very short, and the diameter is an important factor for microcapsule melting. For the MPCMS flow in a circular tube, the temperature distribution between laminar flows and turbulent flows is different. In a turbulent flow, there is an approximate isothermal section along the tube, which cannot be found in a laminar flow. Additionally, a thermal storage system with MPCMS as heat transfer fluid for a CHP unit is proposed. A case study for a 300 MW CHP unit found that the use of an MPSMS thermal energy storage system increases the power peak shaving capacity by 81.4%. This indicates that the thermal storage system increases the peak shaving capacity of cogeneration units. Full article
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<p>Schematic of a single microencapsulated phase change material (MPCM) particle melting process. (<b>a</b>) An MPCM particle structure. (<b>b</b>) The phase change inside an MPCM particle.</p> Full article ">Figure 2
<p>Schematic of MPCMS in a circular tube flow.</p> Full article ">Figure 3
<p>Melting process at different diameters. (<b>a</b>) Melting process; (<b>b</b>) Total melting time.</p> Full article ">Figure 4
<p>Melting process at different wall temperatures. (<b>a</b>) Melting process; (<b>b</b>) Total melting time.</p> Full article ">Figure 5
<p>Average temperature distribution along the tube. (<b>a</b>) Laminar flow; (<b>b</b>) Turbulent flow.</p> Full article ">Figure 6
<p>Schematic of MPCMS storage system.</p> Full article ">Figure 7
<p>Peak shaving capacity with MPCMS storage system.</p> Full article ">
3389 KiB  
Article
Risk Assessment of Failure of Outdoor High Voltage Polluted Insulators under Combined Stresses Near Shoreline
by Muhammad Majid Hussain, Shahab Farokhi, Scott G. McMeekin and Masoud Farzaneh
Energies 2017, 10(10), 1661; https://doi.org/10.3390/en10101661 - 20 Oct 2017
Cited by 22 | Viewed by 4540
Abstract
The aim of this paper is to investigate the various effects of climate conditions on outdoor insulators in coastal areas as a result of saline contamination under acidic and normal cold fog, determining significant electrical and physico-chemical changes on the insulator surface and [...] Read more.
The aim of this paper is to investigate the various effects of climate conditions on outdoor insulators in coastal areas as a result of saline contamination under acidic and normal cold fog, determining significant electrical and physico-chemical changes on the insulator surface and considering the effect of discharge current, electric field distribution and surface roughness. To replicate similar conditions near the shoreline, experimental investigations have been carried out on insulation materials with the combined application of saline contamination and acidic or normal cold fog. The test samples included silicone rubber (SiR), ethylene propylene diene monomer (EPDM) and high-density polyethylene (HDPE), which were used as reference. The materials are of the same composition as those used in real-life outdoor high voltage insulators. All samples were aged separately in an environmental chamber for 150 h for various saline contaminations combined with acidic and normal cold fog, and were generated by means of the adopted experimental setup. This analysis represented conditions similar to those existing near the shoreline exposed to saline and acid spray during winter and early spring. Electric field and discharge current along polymeric samples were examined under acidic and normal cold fog. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopic (SEM) were used to probe the physico-chemical changes on the samples surface and investigate the hydrophobicity recovery property after aging tests. Finally, a comparative study was carried out on polymeric samples before and after being exposed to the acidic and normal cold fog based on the results obtained from the experiment. Research data may provide references for the better prediction of surface degradation as well as for the better material coating and design of external insulation. Full article
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<p>Experimental samples of SiR, HPDE and EPDM.</p> Full article ">Figure 2
<p>Experimental setup.</p> Full article ">Figure 3
<p>Discharge current: (<b>a</b>) Under combined stress of saline contamination and acid cold fog; (<b>b</b>) under combined stress of saline contamination and normal cold fog.</p> Full article ">Figure 4
<p>E-field distribution along creepage distance: (<b>a</b>) During combined stress of saline contamination and acidic cold fog; (<b>b</b>) during combined stress of saline contamination and normal cold fog.</p> Full article ">Figure 4 Cont.
<p>E-field distribution along creepage distance: (<b>a</b>) During combined stress of saline contamination and acidic cold fog; (<b>b</b>) during combined stress of saline contamination and normal cold fog.</p> Full article ">Figure 5
<p>Pieces of aged samples for physico-chemical analysis.</p> Full article ">Figure 6
<p>Contact angles of virgin and aged samples.</p> Full article ">Figure 7
<p>SEM picture of insulator surface: (<b>A</b>) Surface aged in acidic cold fog for 150 h, (<b>a</b>) Virgin; (<b>b</b>) SiR; (<b>c</b>) EPDM; (<b>d</b>) HDPE; (<b>B</b>) surface aged in clean cold fog for 150 h, (<b>a</b>) SiR; (<b>b</b>) EPDM; (<b>c</b>) HDPE.</p> Full article ">Figure 8
<p>Fourier Transform Infrared (FTIR) spectrum: (<b>a</b>) Aged sample of SiR, EPDM and HDPE in acidic cold fog; (<b>b</b>) aged samples of SiR, EPDM and HDPE in normal cold fog.</p> Full article ">
23206 KiB  
Article
Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan
by Chih-Chiang Wei
Energies 2017, 10(10), 1660; https://doi.org/10.3390/en10101660 - 20 Oct 2017
Cited by 32 | Viewed by 8653
Abstract
In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, [...] Read more.
In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m2 per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m2 per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°. Full article
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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<p>Location of the study site.</p> Full article ">Figure 2
<p>Flowchart of the proposed methodology.</p> Full article ">Figure 3
<p>Flowchart of estimating observed and predicted solar irradiance with tilted solar panels.</p> Full article ">Figure 4
<p>Calibration of model parameters: (<b>a</b>) learning rate for MLP; (<b>b</b>) momentum correction for MLP; (<b>c</b>) size of each bag for RF; and (<b>d</b>) number of neighbors for <span class="html-italic">k</span>NN.</p> Full article ">Figure 5
<p>Performance of dataset combinations for solar irradiance at <span class="html-italic">t</span> + 1: (<b>a</b>) MAE, (<b>b</b>) RMSE, and (<b>c</b>) r.</p> Full article ">Figure 6
<p>Improvement rates for MLP, RF, <span class="html-italic">k</span>NN, and LR with all dataset combinations: (<b>a</b>) MAE, (<b>b</b>) RMSE, and (<b>c</b>) r.</p> Full article ">Figure 7
<p>Prediction errors by all models over a 12-h forecast horizon in the year 2016: (<b>a</b>) MAE, (<b>b</b>) RMSE, and (<b>c</b>) r.</p> Full article ">Figure 8
<p>Observed and predicted changes in solar irradiance for the 4 consecutive days within 1 h (<span class="html-italic">t</span> + 1), 3 h (<span class="html-italic">t</span> + 3), 6 h (<span class="html-italic">t</span> + 6), and 12 h (<span class="html-italic">t</span> + 12) starting from: (<b>a</b>–<b>d</b>) the vernal equinox on 20 March 2016, and (<b>e</b>–<b>h</b>) the summer solstice on 21 June 2016.</p> Full article ">Figure 9
<p>Observed and predicted changes in solar irradiance for the 4 consecutive days within 1 h (<span class="html-italic">t</span> + 1), 3 h (<span class="html-italic">t</span> + 3), 6 h (<span class="html-italic">t</span> + 6), and 12 h (<span class="html-italic">t</span> + 12) starting from: (<b>a</b>–<b>d</b>) the autumnal equinox on 22 September 2016, and (<b>e</b>–<b>h</b>) the winter solstice on 21 December 2016.</p> Full article ">Figure 10
<p>Performance of all models over a 12-h forecast horizon: (<b>a</b>–<b>c</b>) in summer, and (<b>d</b>–<b>f</b>) in winter.</p> Full article ">Figure 11
<p>Observed versus predicted changes in the direct irradiance within <span class="html-italic">t</span> + 1, <span class="html-italic">t</span> + 3, <span class="html-italic">t</span> + 6, and <span class="html-italic">t</span> + 12 for 4 consecutive days starting from: (<b>a</b>–<b>d</b>) the summer solstice, and (<b>e</b>–<b>h</b>) the winter solstice.</p> Full article ">Figure 12
<p>Observed values versus predicted changes in the diffuse horizontal irradiance within <span class="html-italic">t</span> + 1, <span class="html-italic">t</span> + 3, <span class="html-italic">t</span> + 6, and <span class="html-italic">t</span> + 12 for four consecutive days starting from: (<b>a</b>–<b>d</b>) the summer solstice, and (<b>e</b>–<b>h</b>) the winter solstice.</p> Full article ">Figure 13
<p>Hourly changes in the observed and predicted global irradiance with a <span class="html-italic">β</span>′ of 23° for the four consecutive days starting from: (<b>a</b>–<b>d</b>) the summer solstice, and (<b>e</b>–<b>h</b>) the winter solstice.</p> Full article ">Figure 14
<p>Hourly changes in the observed and predicted global irradiance with a <span class="html-italic">β</span>′ of 33° for the four consecutive days starting from: (<b>a</b>–<b>d</b>) the summer solstice, and (<b>e</b>–<b>h</b>) the winter solstice.</p> Full article ">Figure 15
<p>Results with a <span class="html-italic">β</span>′ of 0–50°: (<b>a</b>) amount of total annual global irradiance, and (<b>b</b>) increase rate of the total annual global irradiance.</p> Full article ">Figure 16
<p>Observed and predicted values of the total annual global irradiance with a <span class="html-italic">β</span>′ of 0–41° within (<b>a</b>) <span class="html-italic">t</span> + 1, (<b>b</b>) <span class="html-italic">t</span> + 3, (<b>c</b>) <span class="html-italic">t</span> + 6, and (<b>d</b>) <span class="html-italic">t</span> + 12.</p> Full article ">Figure 17
<p>Relative error of the predicted total annual global irradiance within (<b>a</b>) <span class="html-italic">t</span> + 1, (<b>b</b>) <span class="html-italic">t</span> + 3, (<b>c</b>) <span class="html-italic">t</span> + 6, and (<b>d</b>) <span class="html-italic">t</span> + 12.</p> Full article ">
1117 KiB  
Article
Newton Power Flow Methods for Unbalanced Three-Phase Distribution Networks
by Baljinnyam Sereeter, Kees Vuik and Cees Witteveen
Energies 2017, 10(10), 1658; https://doi.org/10.3390/en10101658 - 20 Oct 2017
Cited by 60 | Viewed by 8692
Abstract
Two mismatch functions (power or current) and three coordinates (polar, Cartesian and complex form) result in six versions of the Newton–Raphson method for the solution of power flow problems. In this paper, five new versions of the Newton power flow method developed for [...] Read more.
Two mismatch functions (power or current) and three coordinates (polar, Cartesian and complex form) result in six versions of the Newton–Raphson method for the solution of power flow problems. In this paper, five new versions of the Newton power flow method developed for single-phase problems in our previous paper are extended to three-phase power flow problems. Mathematical models of the load, load connection, transformer, and distributed generation (DG) are presented. A three-phase power flow formulation is described for both power and current mismatch functions. Extended versions of the Newton power flow method are compared with the backward-forward sweep-based algorithm. Furthermore, the convergence behavior for different loading conditions, R / X ratios, and load models, is investigated by numerical experiments on balanced and unbalanced distribution networks. On the basis of these experiments, we conclude that two versions using the current mismatch function in polar and Cartesian coordinates perform the best for both balanced and unbalanced distribution networks. Full article
(This article belongs to the Special Issue Selected Papers from International Workshop of Energy-Open)
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<p>Wye and delta connections for three-phase loads [<a href="#B68-energies-10-01658" class="html-bibr">68</a>].</p> Full article ">Figure 2
<p>Combination of power converters and energy sources [<a href="#B69-energies-10-01658" class="html-bibr">69</a>].</p> Full article ">Figure 3
<p>Flow chart of the polar current mismatch version.</p> Full article ">Figure 4
<p>Computed voltage magnitude of DCase69. (<b>a</b>) Computed voltage magnitude <math display="inline"> <semantics> <mrow> <mo stretchy="false">|</mo> <mi>V</mi> <mo stretchy="false">|</mo> </mrow> </semantics> </math>; (<b>b</b>) Difference between proposed methods and existing method [<a href="#B48-energies-10-01658" class="html-bibr">48</a>] for the computed voltage magnitude.</p> Full article ">Figure 5
<p>Convergence results for different loading conditions (<math display="inline"> <semantics> <mrow> <mi>S</mi> <mo>=</mo> <mi>k</mi> <mo>∗</mo> <mi>S</mi> </mrow> </semantics> </math>) in DCase69.</p> Full article ">Figure 6
<p>Convergence results for different <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>/</mo> <mi>X</mi> </mrow> </semantics> </math> ratios (<math display="inline"> <semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mi>k</mi> <mo>∗</mo> <mi>R</mi> <mo>+</mo> <mi>ı</mi> <mi>X</mi> </mrow> </semantics> </math>) in DCase69.</p> Full article ">Figure 7
<p>Convergence result for different load models (constant power (PQ) and polynomial (Po)) in DCase69.</p> Full article ">Figure 8
<p>Convergence results for different loading conditions (<math display="inline"> <semantics> <mrow> <mi>S</mi> <mo>=</mo> <mi>k</mi> <mo>∗</mo> <mi>S</mi> </mrow> </semantics> </math>) in DCase13.</p> Full article ">Figure 9
<p>Convergence results for different <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>/</mo> <mi>X</mi> </mrow> </semantics> </math> ratios (<math display="inline"> <semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mi>k</mi> <mo>∗</mo> <mi>R</mi> <mo>+</mo> <mi>ı</mi> <mi>X</mi> </mrow> </semantics> </math>) in DCase13.</p> Full article ">Figure 10
<p>Convergence result for different load models (constant power (PQ) and polynomial (Po)) in DCase13.</p> Full article ">
637 KiB  
Article
A Survey on PEV Charging Infrastructure: Impact Assessment and Planning
by Ahmed Abdalrahman and Weihua Zhuang
Energies 2017, 10(10), 1650; https://doi.org/10.3390/en10101650 - 20 Oct 2017
Cited by 25 | Viewed by 5409
Abstract
Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to [...] Read more.
Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified. Full article
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<p>Evaluation of the global electrical car stock, 2010–2015 [<a href="#B1-energies-10-01650" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Comparison between different energy storage technologies [<a href="#B6-energies-10-01650" class="html-bibr">6</a>].</p> Full article ">
4453 KiB  
Article
Regenerative Intelligent Brake Control for Electric Motorcycles
by Juan Jesús Castillo Aguilar, Javier Pérez Fernández, Juan María Velasco García and Juan Antonio Cabrera Carrillo
Energies 2017, 10(10), 1648; https://doi.org/10.3390/en10101648 - 20 Oct 2017
Cited by 8 | Viewed by 6604
Abstract
Vehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of [...] Read more.
Vehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of polluting gases and noise and the effectiveness of electric motors compared to combustion engines. Some of the disadvantages that electric vehicle manufacturers still have to solve are their low autonomy due to inefficient energy storage systems, vehicle cost, which is still too high, and reducing the recharging time. Current regenerative systems in motorcycles are designed with a low fixed maximum regeneration rate in order not to cause the rear wheel to slip when braking with the regenerative brake no matter what the road condition is. These types of systems do not make use of all the available regeneration power, since more importance is placed on safety when braking. An optimized regenerative braking strategy for two-wheeled vehicles is described is this work. This system is designed to recover the maximum energy in braking processes while maintaining the vehicle’s stability. In order to develop the previously described regenerative control, tyre forces, vehicle speed and road adhesion are obtained by means of an estimation algorithm. A based-on-fuzzy-logic algorithm is programmed to carry out an optimized control with this information. This system recuperates maximum braking power without compromising the rear wheel slip and safety. Simulations show that the system optimizes energy regeneration on every surface compared to a constant regeneration strategy. Full article
(This article belongs to the Special Issue Methods to Improve Energy Use in Road Vehicles)
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<p>Torque curves of the electric motor.</p> Full article ">Figure 2
<p>Second order response model of the electric motor.</p> Full article ">Figure 3
<p>Real and simulated response to step input.</p> Full article ">Figure 4
<p>Simplified motorcycle model.</p> Full article ">Figure 5
<p>Motorcycle regenerative control scheme.</p> Full article ">Figure 6
<p>Estimation of speed, tractor effort and vertical forces.</p> Full article ">Figure 7
<p>Estimation of speed and brake torque.</p> Full article ">Figure 8
<p>Control of road type and estimation of optimum slip.</p> Full article ">Figure 9
<p>Membership functions for input and output variables of the road type detection block.</p> Full article ">Figure 10
<p>Membership functions for the input and output variables of the regeneration rate block.</p> Full article ">Figure 11
<p>Estimation of road type and optimum slip (<b>a</b>) and regeneration rate (<b>b</b>).</p> Full article ">Figure 12
<p>Optimum slip, real slip and speed.</p> Full article ">Figure 13
<p>Estimation of road type and optimum slip (<b>a</b>) and regeneration rate (<b>b</b>).</p> Full article ">Figure 14
<p>Optimum slip, real slip and speed.</p> Full article ">Figure 15
<p>Estimation of road type and optimum slip (<b>a</b>) and regeneration rate (<b>b</b>).</p> Full article ">Figure 16
<p>Optimum slip, real slip and speed.</p> Full article ">Figure 17
<p>Slip and vehicle speed. High adhesion surface (<b>a</b>); Low adhesion surface (<b>b</b>).</p> Full article ">
1529 KiB  
Article
Aggregation Potentials for Buildings—Business Models of Demand Response and Virtual Power Plants
by Zheng Ma, Joy Dalmacio Billanes and Bo Nørregaard Jørgensen
Energies 2017, 10(10), 1646; https://doi.org/10.3390/en10101646 - 20 Oct 2017
Cited by 52 | Viewed by 7338
Abstract
Buildings as prosumers have an important role in the energy aggregation market due to their potential flexible energy consumption and distributed energy resources. However, energy flexibility provided by buildings can be very complex and depend on many factors. The immaturity of the current [...] Read more.
Buildings as prosumers have an important role in the energy aggregation market due to their potential flexible energy consumption and distributed energy resources. However, energy flexibility provided by buildings can be very complex and depend on many factors. The immaturity of the current aggregation market with unclear incentives is still a challenge for buildings to participate in the aggregation market. However, few studies have investigated business models for building participation in the aggregation market. Therefore, this paper develops four business models for buildings to participate in the energy aggregation market: (1) buildings participate in the implicit Demand Response (DR) program via retailers; (2) buildings with small energy consumption participate in the explicit DR via aggregators; (3) buildings directly access the explicit DR program; (4) buildings access energy market via Virtual Power Plant (VPP) aggregators by providing Distributed Energy Resources (DER)s. This paper also determines that it is essential to understand building owners’ needs, comforts, and behaviours to develop feasible market access strategies for different types of buildings. Meanwhile, the incentive programs, national regulations and energy market structures strongly influence buildings’ participation in the aggregation market. Under the current Nordic market regulation, business model one is the most feasible one, and business model two faces more challenges due to regulation barriers and limited monetary incentives. Full article
(This article belongs to the Special Issue Distributed Energy Resources Management)
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<p>Danish energy consumption by sector in 2012 [<a href="#B30-energies-10-01646" class="html-bibr">30</a>].</p> Full article ">Figure 2
<p>Residential primary energy end-use in the USA 2005 [<a href="#B34-energies-10-01646" class="html-bibr">34</a>].</p> Full article ">Figure 3
<p>Commercial primary energy end-use in the USA 2005 [<a href="#B36-energies-10-01646" class="html-bibr">36</a>].</p> Full article ">Figure 4
<p>Business model by Afuah.</p> Full article ">Figure 5
<p>Business model canvas by Osterwalder and Pigneur.</p> Full article ">
3584 KiB  
Article
Grid-Connected Control Strategy of Five-level Inverter Based on Passive E-L Model
by Tao Li, Qiming Cheng, Weisha Sun and Lu Chen
Energies 2017, 10(10), 1657; https://doi.org/10.3390/en10101657 - 19 Oct 2017
Cited by 13 | Viewed by 4781
Abstract
At present, the research on five-level inverters mainly involves the modulation algorithm and topology, and few articles study the five-level inverter from the control strategy. In this paper, the nonlinear passivity-based control (PBC) method is proposed for single-phase uninterruptible power supply inverters. The [...] Read more.
At present, the research on five-level inverters mainly involves the modulation algorithm and topology, and few articles study the five-level inverter from the control strategy. In this paper, the nonlinear passivity-based control (PBC) method is proposed for single-phase uninterruptible power supply inverters. The proposed PBC method is based on an energy shaping and damping injection idea, which is performed to regulate the energy flow of an inverter to a desired level and to assure global asymptotic stability, respectively. Furthermore, this paper presents a control algorithm based on the theory of passivity that gives an inverter in a photovoltaic system additional functions: power factor correction, harmonic currents compensation, and the ability to stabilize the system under varying injection damping. Finally, the effectiveness of the PBC method in terms of both stability and harmonic distortion is verified by the simulation and experiments under resistive and inductive loads. Full article
(This article belongs to the Special Issue Emerging Power Electronics Technologies for Power Systems and Machine Drives)
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<p>The topology of the diode-clamped five-level inverter.</p> Full article ">Figure 2
<p>Comparison of modulation wave and carrier wave.</p> Full article ">Figure 3
<p>The block diagram of the system.</p> Full article ">Figure 4
<p>Simulation of passivity-based control. (<b>a</b>) Phase voltage of the inverter; (<b>b</b>) Line voltage of the inverter; (<b>c</b>) The output function <span class="html-italic">u</span><sub>d</sub>; and (<b>d</b>) The output function <span class="html-italic">u</span><sub>q</sub>.</p> Full article ">Figure 5
<p>Comparison of static and dynamic stability under two strategies. (<b>a</b>) The output active power of the inverter under a passivity-based control (PBC) strategy; (<b>b</b>) The output active power of the inverter under the traditional PI control strategy; (<b>c</b>) The <span class="html-italic">d</span>-axis current under a PBC strategy; and (<b>d</b>) The <span class="html-italic">d</span>-axis current under the traditional PI control strategy.</p> Full article ">Figure 6
<p>Comparison of phase and harmonics in two strategies; (<b>a</b>) A-phase grid voltage and current under a passivity-based control strategy; (<b>b</b>) A-phase grid voltage and current under the traditional PI control strategy; (<b>c</b>) A-phase current harmonics of the inverter under a passivity-based control strategy; and (<b>d</b>) A-phase current harmonics of the inverter under the traditional PI control strategy.</p> Full article ">Figure 7
<p>Photo of the hardware experimental platform.</p> Full article ">Figure 8
<p>Comparison of hardware experiments under two strategies; (<b>a</b>) A-phase grid-connected voltage and current under a PBC strategy; (<b>b</b>) A-phase grid-connected voltage and current under the traditional PI control strategy; (<b>c</b>) The <span class="html-italic">d</span>-axis current of different injection dampings under the two control strategies; and (<b>d</b>) The polar film capacitor voltage under a PBC strategy.</p> Full article ">Figure 8 Cont.
<p>Comparison of hardware experiments under two strategies; (<b>a</b>) A-phase grid-connected voltage and current under a PBC strategy; (<b>b</b>) A-phase grid-connected voltage and current under the traditional PI control strategy; (<b>c</b>) The <span class="html-italic">d</span>-axis current of different injection dampings under the two control strategies; and (<b>d</b>) The polar film capacitor voltage under a PBC strategy.</p> Full article ">Figure 9
<p>Phase voltage and line voltage of the inverter; (<b>a</b>) Phase voltage of the inverter; and (<b>b</b>) Line voltage of the inverter.</p> Full article ">
6564 KiB  
Review
Constant Power Loads (CPL) with Microgrids: Problem Definition, Stability Analysis and Compensation Techniques
by Mohammed Kh. AL-Nussairi, Ramazan Bayindir, Sanjeevikumar Padmanaban, Lucian Mihet-Popa and Pierluigi Siano
Energies 2017, 10(10), 1656; https://doi.org/10.3390/en10101656 - 19 Oct 2017
Cited by 119 | Viewed by 15525
Abstract
This paper provides a comprehensive review of the major concepts associated with the μgrid, such as constant power load (CPL), incremental negative resistance or impedance (INR/I) and its dynamic behaviours on the μgrid, and power system distribution (PSD). In general, a μgrid is [...] Read more.
This paper provides a comprehensive review of the major concepts associated with the μgrid, such as constant power load (CPL), incremental negative resistance or impedance (INR/I) and its dynamic behaviours on the μgrid, and power system distribution (PSD). In general, a μgrid is defined as a cluster of different types of electrical loads and renewable energy sources (distributed generations) under a unified controller within a certain local area. It is considered a perfect solution to integrate renewable energy sources with loads as well as with a traditional grid. In addition, it can operate with a conventional grid, for example, by energy sourcing or a controllable load, or it can operate alone as an islanding mode to feed required electric energy to a grid. Hence, one of the important issues regarding the μgrid is the constant power load that results from the tightly designed control when it is applied to power electronic converters. The effect of CPL is incremental negative resistance that impacts the power quality of a power system and makes it at negative damping. Also, in this paper, a comprehensive study on major control and compensation techniques for μgrid has been included to face the instability effects of constant power loads. Finally, the merits and limitations of the compensation techniques are discussed. Full article
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
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<p>Typical hybrid μgrid configurations.</p> Full article ">Figure 2
<p>DC-DC converter with resistive load behaves as a constant power load (CPL).</p> Full article ">Figure 3
<p>V–I curve of load converter.</p> Full article ">Figure 4
<p>DC-AC inverter that presents a constant power load characteristic.</p> Full article ">Figure 5
<p>Negative impedance behaviour of constant power loads.</p> Full article ">Figure 6
<p>A constant power load parallel with a capacitor.</p> Full article ">Figure 7
<p>Passive damping techniques: (<b>a</b>) <span class="html-italic">RC</span> parallel damping; (<b>b</b>) <span class="html-italic">RL</span> parallel damping; (<b>c</b>) <span class="html-italic">RL</span> series damping.</p> Full article ">Figure 8
<p>The proposed active-damping method based on adding virtual resistance: (<b>a</b>) [<a href="#B45-energies-10-01656" class="html-bibr">45</a>]; (<b>b</b>) [<a href="#B64-energies-10-01656" class="html-bibr">64</a>].</p> Full article ">Figure 9
<p>Negative input-resistance compensator of motor drive.</p> Full article ">Figure 10
<p>Pole placement control.</p> Full article ">Figure 11
<p>Block diagram of the pulse adjustment control technique.</p> Full article ">Figure 12
<p>DC-DC Buck converter feeding CPL.</p> Full article ">Figure 13
<p>Sliding mode control with CPL (<b>a</b>) ref. [<a href="#B77-energies-10-01656" class="html-bibr">77</a>]; (<b>b</b>) ref. [<a href="#B57-energies-10-01656" class="html-bibr">57</a>].</p> Full article ">Figure 14
<p>Model predictive control with power buffer.</p> Full article ">Figure 15
<p>DC-DC buck converter feeding CPL and constant voltage load (CVL).</p> Full article ">Figure 16
<p>Feedback linearization of buck converter in [<a href="#B52-energies-10-01656" class="html-bibr">52</a>].</p> Full article ">
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Article
Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition
by Yaping Huang, Haibin Di, Reza Malekian, Xuemei Qi and Zhixiong Li
Energies 2017, 10(10), 1655; https://doi.org/10.3390/en10101655 - 19 Oct 2017
Cited by 8 | Viewed by 5458
Abstract
Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic [...] Read more.
Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone. Full article
(This article belongs to the Section L: Energy Sources)
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Figure 1

Figure 1
<p>Analysis results of a simple artificial signal using empirical mode decomposition (EMD): (<b>a</b>) the signal; (<b>b</b>) IMF1 of the signal; (<b>c</b>) IMF2 of the signal; and (<b>d</b>) IMF3 of the signal.</p> Full article ">Figure 2
<p>Analysis results of the simple artificial signal using complementary ensemble empirical mode decomposition (CEEMD): (<b>a</b>) the signal; (<b>b</b>) IMF1 of the signal; (<b>c</b>) IMF2 of the signal; and (<b>d</b>) IMF3 of the signal.</p> Full article ">Figure 3
<p>Analysis results of the simple artificial signal with 15% noise using CEEMD: (<b>a</b>) the signal; (<b>b</b>) IMF1 of the signal; (<b>c</b>) IMF2 of the signal; and (<b>d</b>) IMF3 of the signal.</p> Full article ">Figure 4
<p>(<b>a</b>) Reflection coefficient; (<b>b</b>) Ricker wavelet; and (<b>c</b>) synthetic seismic record.</p> Full article ">Figure 5
<p>Analysis results of the synthetic seismic record using EMD: (<b>a</b>) the synthetic seismic record with 15% noise; (<b>b</b>) IMF1 of the record; (<b>c</b>) IMF2 of the record; and (<b>d</b>) IMF3 of the record.</p> Full article ">Figure 6
<p>Analysis results of the synthetic seismic record using CEEMD: (<b>a</b>) the synthetic seismic record with 15% noise; (<b>b</b>) IMF1 of the record; (<b>c</b>) IMF2 of the record; and (<b>d</b>) IMF3 of the record.</p> Full article ">Figure 7
<p>White Gaussian noise and IMF1: (<b>a</b>) white Gaussian noise; (<b>b</b>) IMF1 of EMD; (<b>c</b>) IMF1 of CEEMD.</p> Full article ">Figure 8
<p>Synthetic record and seismic record.</p> Full article ">Figure 9
<p>The original seismic data.</p> Full article ">Figure 10
<p>Results of EMD: (<b>a</b>) IMF1 of original seismic data; (<b>b</b>) IMF2 of original seismic data; (<b>c</b>) IMF3 of original seismic data.</p> Full article ">Figure 11
<p>Results of CEEMD: (<b>a</b>) IMF1of original seismic data; (<b>b</b>) IMF2 of original seismic data; (<b>c</b>) IMF3 of original seismic data.</p> Full article ">Figure 11 Cont.
<p>Results of CEEMD: (<b>a</b>) IMF1of original seismic data; (<b>b</b>) IMF2 of original seismic data; (<b>c</b>) IMF3 of original seismic data.</p> Full article ">Figure 12
<p>The instantaneous attributes of IMF2. (<b>a</b>) the instantaneous amplitude of IMF2; (<b>b</b>) the instantaneous frequency of IMF2; (<b>c</b>) the instantaneous phase of IMF2.</p> Full article ">Figure 12 Cont.
<p>The instantaneous attributes of IMF2. (<b>a</b>) the instantaneous amplitude of IMF2; (<b>b</b>) the instantaneous frequency of IMF2; (<b>c</b>) the instantaneous phase of IMF2.</p> Full article ">
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