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Energies, Volume 11, Issue 7 (July 2018)

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Cover Story (view full-size image) Power to Gas has been discussed for its conversion of renewable electricity, which often fluctuates [...] Read more.
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Open AccessArticle Small-Scale Compressed Air Energy Storage Application for Renewable Energy Integration in a Listed Building
Energies 2018, 11(7), 1921; https://doi.org/10.3390/en11071921
Received: 20 June 2018 / Revised: 17 July 2018 / Accepted: 18 July 2018 / Published: 23 July 2018
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Abstract
In the European Union (EU), where architectural heritage is significant, enhancing the energy performance of historical buildings is of great interest. Constraints such as the lack of space, especially within the historical centers and architectural peculiarities, make the application of technologies for renewable
[...] Read more.
In the European Union (EU), where architectural heritage is significant, enhancing the energy performance of historical buildings is of great interest. Constraints such as the lack of space, especially within the historical centers and architectural peculiarities, make the application of technologies for renewable energy production and storage a challenging issue. This study presents a prototype system consisting of using the renewable energy from a photovoltaic (PV) array to compress air for a later expansion to produce electricity when needed. The PV-integrated small-scale compressed air energy storage system is designed to address the architectural constraints. It is located in the unoccupied basement of the building. An energy analysis was carried out for assessing the performance of the proposed system. The novelty of this study is to introduce experimental data of a CAES (compressed air energy storage) prototype that is suitable for dwelling applications as well as integration accounting for architectural constraints. The simulation, which was carried out for an average summer day, shows that the compression phase absorbs 32% of the PV energy excess in a vessel of 1.7 m3, and the expansion phase covers 21.9% of the dwelling energy demand. The electrical efficiency of a daily cycle is equal to 11.6%. If air is compressed at 225 bar instead of 30 bar, 96.0% of PV energy excess is stored in a volume of 0.25 m3, with a production of 1.273 kWh, which is 26.0% of the demand. Full article
(This article belongs to the Section Energy Storage and Application)
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Open AccessArticle Assessment of the Impact of Geomagnetic Disturbances on Korean Electric Power Systems
Energies 2018, 11(7), 1920; https://doi.org/10.3390/en11071920
Received: 11 June 2018 / Revised: 8 July 2018 / Accepted: 17 July 2018 / Published: 23 July 2018
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Abstract
Geomagnetic disturbances have the potential to impact the operation of electric power systems, and thus the assessment of their impacts is required as the first step for secure power system operations. While the effects of the disturbances have been observed primarily at higher
[...] Read more.
Geomagnetic disturbances have the potential to impact the operation of electric power systems, and thus the assessment of their impacts is required as the first step for secure power system operations. While the effects of the disturbances have been observed primarily at higher latitudes, geomagnetic problems are also observed at mid and low latitude locations, in particular including neighboring countries to Korea such as China and Japan. This paper deals with the assessment of impact of geomagnetic disturbances on Korean electric power systems. For the assessment, the geoelectric fields induced by the geomagnetic disturbances are calculated using geomagnetic data measured over the past 20 years in order to quantify the strength of geomagnetic events in Korea. Then, the geomagnetic currents on the grid driven by the geoelectric fields are computed. Finally, the increased reactive power absorption in high voltage transformers is analyzed and accordingly the change of system voltage magnitudes is identified to evaluate whether the system maintains the voltage stability. The systematic study concludes that during a strong geomagnetic disturbance, the Korean electric power system satisfies the associated standards in the U.S. and maintains system stability. Full article
(This article belongs to the Special Issue Electric Power Systems Research 2018)
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Open AccessArticle A Torque Impulse Balance Control for Multi-Tooth Fault Tolerant Switched-Flux Machines under Open-Circuit Fault
Energies 2018, 11(7), 1919; https://doi.org/10.3390/en11071919
Received: 18 June 2018 / Revised: 11 July 2018 / Accepted: 16 July 2018 / Published: 23 July 2018
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Abstract
The multi-tooth fault tolerant switched-flux machines (MTFTSFM) providing both excellent fault tolerant capability and relatively high torque density are good choices for high reliability applications. A rapid control of the electromagnetic torque under open-circuit fault can always be achieved by the direct torque
[...] Read more.
The multi-tooth fault tolerant switched-flux machines (MTFTSFM) providing both excellent fault tolerant capability and relatively high torque density are good choices for high reliability applications. A rapid control of the electromagnetic torque under open-circuit fault can always be achieved by the direct torque control with voltage vector reconstruction (RDTC); however, with respect to the rotor speed, its dynamic performance is still impacted by the proportion-integration (PI) parameters. Therefore, a torque impulse balance control (TIBC) is investigated in this paper for the MTFTSFM under open-circuit fault to obtain excellent dynamic performance of the rotor speed. During the dynamic state, the electromagnetic torque and the speed can converge at the same time after only one adjustment of the speed by using the optimized voltage vector sequence based on torque impulse balance, thus, achieving the best possible dynamic process for the speed. The TIBC method is carried out on an MTFTSPM machine system, and the correctness and effectiveness are verified. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessArticle Complementarity Roses Evaluating Spatial Complementarity in Time between Energy Resources
Energies 2018, 11(7), 1918; https://doi.org/10.3390/en11071918
Received: 26 May 2018 / Revised: 10 July 2018 / Accepted: 10 July 2018 / Published: 23 July 2018
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Abstract
Hybrid energy systems have higher initial costs than systems that are based on only one renewable resource, but allow for the fulfillment of the demands of consumer loads with lower values for the cost of energy. The possible complementarity between the resources used
[...] Read more.
Hybrid energy systems have higher initial costs than systems that are based on only one renewable resource, but allow for the fulfillment of the demands of consumer loads with lower values for the cost of energy. The possible complementarity between the resources used can contribute to a better use of the available energy. On a large scale, complementarity between power plants can serve as a tool for the management of energy resources. A complete evaluation of complementarity needs to consider three components: time complementarity, energy complementarity, and complementarity between amplitudes of variation. Complementarity can also be assessed between energy resources in one place (which may be termed temporal complementarity) and between resources at different sites (termed spatial complementarity). This paper proposes a method for quantifying spatial complementarity over time and for its expression through maps. The method suggests the establishment of a hexagonal network of cells and the determination of complementary roses for each cell that contains power plants. This article also applies the method proposed to some hydroelectric plants and wind farms in the State of Rio Grande do Sul, in southern Brazil, and present the map of spatial complementarity in time obtained. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessArticle Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks
Energies 2018, 11(7), 1917; https://doi.org/10.3390/en11071917
Received: 28 June 2018 / Revised: 17 July 2018 / Accepted: 19 July 2018 / Published: 23 July 2018
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Abstract
Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal
[...] Read more.
Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal placement with minimal numbers of μPMUs in the distribution system. An optimal μPMU placement (OPP) based on the information entropy evaluation and node selection strategy (IENS) using greedy algorithm is presented in this paper. The uncertainties of distributed generations (DGs) and pseudo measurements are taken into consideration, and the two-point estimation method (2PEM) is utilized for solving stochastic state estimation problems. The set of buses selected by improved IENS, which can minimize the uncertainties of network and obtain system observability is considered as the optimal deployment of μPMUs. The proposed method utilizes the measurements of smart meters and pseudo measurements of load powers in the distribution systems to reduce the number of μPMUs and enhance the observability of the network. The results of the simulations prove the effectiveness of the proposed algorithm with the comparison of traditional topological methods for the OPP problem. The improved IENS method can obtain the optimal complete and incomplete μPMU placement in the distribution systems. Full article
(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)
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Open AccessArticle An Experimental Study on Flame Puffing of a Swirl Partially Premixed Combustion under Varying Mass Flow Rate of Primary Air
Energies 2018, 11(7), 1916; https://doi.org/10.3390/en11071916
Received: 10 July 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 23 July 2018
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Abstract
It is of practical significance to understand the flame puffing behavior under varying mass flow rate of primary air pri. An experiment was conducted to study the impact of pri on flame puffing in a swirl partially premixed combustor, the
[...] Read more.
It is of practical significance to understand the flame puffing behavior under varying mass flow rate of primary air pri. An experiment was conducted to study the impact of pri on flame puffing in a swirl partially premixed combustor, the puffing behavior of six significant flame properties was examined. The results showed that almost every spectrum had two fundamental frequencies, which is different from the single-peak spectrum of non-swirl flame. The flame heat-release rate, flame area, and flame equivalent width had identical dominant frequency and sub-dominant frequency, both decreased with the increasing of pri. It was attributed to the decreased overall flame temperature caused by the improved mixing of fuel and primary air. All measured frequencies were in the range of 3–14 Hz, but the predicted frequencies from the theoretical models based on non-swirl flame were larger than the measured. This indicates the puffing frequency of swirl flame was much more sensitive to the variation in pri than the frequency of non-swirl flame. Moreover, the amplitude of flame length was the smallest in all properties, with the most weakened oscillating intensity. While the amplitude of the flame area and flame equivalent width were the largest, with the strongest oscillation level. Consequently, the flame puffing is mainly attributed to the oscillation in width direction. Full article
(This article belongs to the Section Energy Fundamentals and Conversion)
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Open AccessArticle Energy Evaluation of Multiple Stage Commercial Refrigeration Architectures Adapted to F-Gas Regulation
Energies 2018, 11(7), 1915; https://doi.org/10.3390/en11071915
Received: 28 June 2018 / Revised: 16 July 2018 / Accepted: 18 July 2018 / Published: 23 July 2018
Cited by 2 | Viewed by 575 | PDF Full-text (11068 KB) | HTML Full-text | XML Full-text
Abstract
This work analyses different refrigeration architectures for commercial refrigeration providing service to medium and low temperature simultaneously: HFC/R744 cascade, R744 transcritical booster, R744 transcritical booster with parallel compression, R744 transcritical booster with gas ejectors, R513A cascade/R744 subcritical booster, and R513A cascade/R744 subcritical booster
[...] Read more.
This work analyses different refrigeration architectures for commercial refrigeration providing service to medium and low temperature simultaneously: HFC/R744 cascade, R744 transcritical booster, R744 transcritical booster with parallel compression, R744 transcritical booster with gas ejectors, R513A cascade/R744 subcritical booster, and R513A cascade/R744 subcritical booster with parallel compression. The models were developed using compressor manufacturers’ data and real restrictions of each system component. Limitations and operating range of each component and architecture were analysed for environment temperatures from 0 to 40 °C considering thermal loads and environment temperature profiles for warm climates. For booster systems, cascade with subcritical booster with parallel compression provide highest coefficient of performance (COP) for temperatures below 12 °C and above 30 °C with COP increases compared basic booster up to 60.6%, whereas for transcritical boosters, architecture with gas ejectors obtains the highest COP with COP increases compared to the basic booster up to 29.5%. In annual energy terms, differences among improved booster systems are below 8% in the locations analysed. In Total Equivalent Warming Impact (TEWI) terms, booster architectures get the lowest values with small differences between improved boosters. Full article
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Open AccessArticle An Accurate Online Dynamic Security Assessment Scheme Based on Random Forest
Energies 2018, 11(7), 1914; https://doi.org/10.3390/en11071914
Received: 29 May 2018 / Revised: 10 July 2018 / Accepted: 17 July 2018 / Published: 23 July 2018
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Abstract
With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for
[...] Read more.
With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessFeature PaperArticle Numerical Simulations of a Gas–Solid Two-Phase Impinging Stream Reactor with Dynamic Inlet Flow
Energies 2018, 11(7), 1913; https://doi.org/10.3390/en11071913
Received: 5 June 2018 / Revised: 16 July 2018 / Accepted: 19 July 2018 / Published: 23 July 2018
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Abstract
Fluid flow characteristics and particle motion behavior of an impinging stream reactor with dynamic inlet flow (both inlet velocity patterns exhibit step variation) are investigated and discussed with the computational fluid dynamics–discrete element method (CFD–DEM). The effect of T (variation period of the
[...] Read more.
Fluid flow characteristics and particle motion behavior of an impinging stream reactor with dynamic inlet flow (both inlet velocity patterns exhibit step variation) are investigated and discussed with the computational fluid dynamics–discrete element method (CFD–DEM). The effect of T (variation period of the dynamic inlet flow) and ∆u (inlet velocity difference) on the motion characteristics of single and multiple particles, as well as the mean particle residence time, are studied and discussed. The research results indicate that, compared with the traditional impinging stream reactor (both inlet velocities are equal and constant) with equal mean inlet velocity (um) within one period, the impinging surface is instantaneously moving and the flow regime is varied with time in the impinging stream reactor with dynamic inlet flow. The impinging stream reactor with dynamic inlet flow provides higher cost performance over the traditional impinging stream reactor, under equal um, in terms of single-particle residence time. Moreover, three new particle motion modes exist in multi-particle motions of the impinging stream reactor with dynamic inlet flow; particles are accelerated by the original or reverse fluid and perform oscillatory motion at least once after an interparticle collision. Whether it is a single particle or multi-particles, the mean particle residence time reaches a maximum value when T/2 is approximately equal to the first particle acceleration time, since the maximum axial kinetic energy increases in every oscillatory motion compared with traditional impinging stream, and the number of oscillatory motions is increasing. The mean residence time of a particle in the impinging stream reactor with a dynamic inlet flow increases with increasing ∆u, since the dynamic inlet conditions and increasing ∆u can continuously supply more energy to particles and thus cause more particles to enter one of the three new modes of particle motion. Full article
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Open AccessArticle Coupling-Independent Capacitive Wireless Power Transfer Using Frequency Bifurcation
Energies 2018, 11(7), 1912; https://doi.org/10.3390/en11071912
Received: 28 June 2018 / Revised: 17 July 2018 / Accepted: 20 July 2018 / Published: 22 July 2018
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Abstract
Capacitive wireless power transfer can be realized by mutually coupled capacitors operating at a common resonant frequency. An optimal load exists that maximizes either the efficiency or the power transfer to the load. In this work, we utilize the frequency bifurcation effect to
[...] Read more.
Capacitive wireless power transfer can be realized by mutually coupled capacitors operating at a common resonant frequency. An optimal load exists that maximizes either the efficiency or the power transfer to the load. In this work, we utilize the frequency bifurcation effect to propose a frequency agile mode that allows for a nearly coupling-independent regime. We analytically determine the operating conditions of the coupling-independent mode based on the different system gains. In this way, we obtain a solution that achieves nearly constant efficiency and power transfer, even at varying coupling. We compare our results to inductive wireless power transfer where a perfect coupling-independent mode is achievable. Full article
(This article belongs to the Special Issue Wireless Power Transfer 2018)
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Open AccessArticle Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process
Energies 2018, 11(7), 1911; https://doi.org/10.3390/en11071911
Received: 24 June 2018 / Revised: 7 July 2018 / Accepted: 9 July 2018 / Published: 22 July 2018
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Abstract
The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions,
[...] Read more.
The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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Open AccessArticle Effect of Modified Natural Filler O-Methylene Phosphonic κ-Carrageenan on Chitosan-Based Polymer Electrolytes
Energies 2018, 11(7), 1910; https://doi.org/10.3390/en11071910
Received: 18 June 2018 / Revised: 6 July 2018 / Accepted: 6 July 2018 / Published: 22 July 2018
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The potential for using O-methylene phosphonic κ-carrageenan (OMPk) as a filler in the chitosan-based polymer electrolyte N-methylene phosphonic chitosan (NMPC) was investigated. OMPk, a derivative of κ-carrageenan, was synthesized via phosphorylation and characterized using infrared spectroscopy (IR) and nuclear magnetic resonance
[...] Read more.
The potential for using O-methylene phosphonic κ-carrageenan (OMPk) as a filler in the chitosan-based polymer electrolyte N-methylene phosphonic chitosan (NMPC) was investigated. OMPk, a derivative of κ-carrageenan, was synthesized via phosphorylation and characterized using infrared spectroscopy (IR) and nuclear magnetic resonance (NMR). Both the IR and NMR results confirmed the phosphorylation of the parent carrageenan. The solid polymer electrolyte (SPE)-based NMPC was prepared by solution casting with different weight percentages of OMPk ranging from 2 to 8 wt %. The tensile strength of the polymer membrane increased from 18.02 to 38.95 MPa as the amount of OMPk increased to 6 wt %. However, the increase in the ionic conductivity did not match the increase in the tensile strength. The highest ionic conductivity was achieved with 4 wt % OMPk, which resulted in 1.43 × 10−5 Scm−1. The κ-carrageenan-based OMPk filler strengthened the SPE while maintaining an acceptable level of ionic conductivity. Full article
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Open AccessArticle Development of Window-Mounted Air Cap Roller Module
Energies 2018, 11(7), 1909; https://doi.org/10.3390/en11071909
Received: 6 July 2018 / Revised: 17 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
Cited by 1 | Viewed by 466 | PDF Full-text (9793 KB) | HTML Full-text | XML Full-text
Abstract
While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to
[...] Read more.
While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to detach and attach the air-caps. In this study, a window-mounted air-cap roller module using Velcro tape that may be easily attached, detached, and rolled up or down was developed and performance tested in a full-scale test bed. It was found that as the area of the air-caps attached on a window increased, the required indoor lighting energy increased. However, the window insulation improved, thus reducing the cooling and heating energy needed. Attaching the air-caps to the entire window surface effectively reduced the building’s energy consumption, but views through the window may be disturbed. Thus, the developed window-mounted air-caps enable an occupant to reduce the building energy consumption and maintain their view according to their need. The findings of this study may contribute to a reduction in building energy consumption without sacrificing a pleasant indoor environment. Further studies may be needed to verify their efficacy under varying indoor and outdoor conditions. Full article
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Open AccessReview Voltage Correction Factors for Air-Insulated Transmission Lines Operating in High-Altitude Regions to Limit Corona Activity: A Review
Energies 2018, 11(7), 1908; https://doi.org/10.3390/en11071908
Received: 30 June 2018 / Revised: 17 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Nowadays there are several transmission lines projected to be operating in high-altitude regions. It is well known that the installation altitude has an impact on the dielectric behavior of air-insulated systems. As a result, atmospheric and voltage correction factors must be applied in
[...] Read more.
Nowadays there are several transmission lines projected to be operating in high-altitude regions. It is well known that the installation altitude has an impact on the dielectric behavior of air-insulated systems. As a result, atmospheric and voltage correction factors must be applied in air-insulated transmission systems operating in high-altitude conditions. This paper performs an exhaustive literature review, including state-of-the-art research papers and International Standards of the available correction factors to limit corona activity and ensure proper performance when planning air-insulated transmission lines intended for high-altitude areas. It has been found that there are substantial differences among the various correction methods, differences that are more evident at higher altitudes. Most high-voltage standards were not conceived to test samples to be installed in high-altitude regions and, therefore, most high-voltage laboratories are not ready to face this issue, since more detailed information is required. It is proposed to conduct more research on this topic so that the atmospheric corrections and altitude correction factors found in the current International Standards can be updated and/or modified so that high-voltage components to be installed in high-altitude regions can be tested with more accuracy, taking into account their insulation structure. Full article
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
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Open AccessArticle Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition
Energies 2018, 11(7), 1907; https://doi.org/10.3390/en11071907
Received: 23 June 2018 / Revised: 7 July 2018 / Accepted: 13 July 2018 / Published: 21 July 2018
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Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon
[...] Read more.
Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon price sequence. First, the ensemble empirical mode decomposition (EEMD) is applied to decompose a carbon price sequence, SZA2013, into several intrinsic mode functions (IMFs) and one residual. Second, the IMFs and the residual are restructured via a fine-to-coarse reconstruction algorithm to generate three stationary and regular frequency components that high frequency component, low frequency component, and trend component. The fluctuation of each component can effectively reveal the factors that influence market operation. Third, extreme learning machine (ELM) is applied to forecast the trend component, support vector machine (SVM) is applied to forecast the low frequency component and the high frequency component is predicted via PSO-ELM, which means extreme learning machine whose input weights and bias threshold were optimized by particle swarm optimization. Then, the predicted values are combined to form a final predicted value. Finally, using the relevant error-type and trend-type performance indexes, the proposed multiscale ensemble forecasting model is shown to be more robust and accurate than the single format models. Three additional emission allowances from the Shenzhen Emissions Exchange are used to validate the model. The empirical results indicate that the established model is effective, efficient, and practical in terms of its statistical measures and prediction performance. Full article
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Open AccessArticle Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices
Energies 2018, 11(7), 1906; https://doi.org/10.3390/en11071906
Received: 28 June 2018 / Revised: 17 July 2018 / Accepted: 20 July 2018 / Published: 21 July 2018
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Abstract
The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price
[...] Read more.
The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively. Full article
(This article belongs to the collection Smart Grid)
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Open AccessFeature PaperReview Expander Technologies for Automotive Engine Organic Rankine Cycle Applications
Energies 2018, 11(7), 1905; https://doi.org/10.3390/en11071905
Received: 13 June 2018 / Revised: 11 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
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Abstract
The strive towards ever increasing automotive engine efficiencies for both diesel and gasoline engines has in recent years been forced by ever-stringent emissions regulations, as well as the introduction of fuel consumption regulations. The untapped availability of waste heat in the internal combustion
[...] Read more.
The strive towards ever increasing automotive engine efficiencies for both diesel and gasoline engines has in recent years been forced by ever-stringent emissions regulations, as well as the introduction of fuel consumption regulations. The untapped availability of waste heat in the internal combustion engine (ICE) exhaust and coolant systems has become a very attractive focus of research attention by industry and academia alike. Even state of the art diesel engines operating at their optimum lose approximately 50% of their fuel energy in the form of heat. As a result, waste heat recovery (WHR) systems have gained popularity as they can deliver a reduction in fuel consumption and associated CO2 emissions. Of these, the Organic Rankine Cycle (ORC) is a well matured waste heat recovery technology that can be applied in vehicle powertrains, mainly due to the low additional exhaust backpressure on the engine and the potential opportunity to utilize various engine waste heat sources. ORCs have attracted high interest again recently but without commercial exploitation as of today due to the significant on-cost they represent to the engine and vehicle. In ORCs, expansion machines are the interface where useable power production takes place; therefore, selection of the expander technology is directly related to the thermal efficiency of the system. Moreover, the cost of the expander-generator units accounts for the largest proportion of the total cost. Therefore, selection of the most appropriate expander is of great importance at the early stage of any automotive powertrain project. This study aims to review the relevant research studies for expansion machines in ORC-ICE applications, analyzing the effects of specific speed on expander selection, exploring the operational characteristics of each expander to further assist in the selection of the most appropriate expander, and comparing the costs of various expanders based on publically available data and correlations. Full article
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Open AccessReview Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies
Energies 2018, 11(7), 1904; https://doi.org/10.3390/en11071904
Received: 14 May 2018 / Revised: 16 June 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
Cited by 1 | Viewed by 663 | PDF Full-text (1084 KB) | HTML Full-text | XML Full-text
Abstract
Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the
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Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the most used to provide electrical energy. However, their intermittency and variability limit the power supply reliability. To solve this drawback, storage systems and Diesel generators are often used. Otherwise, among all marine renewable energies, tidal and wave energies are reaching an interesting technical level of maturity. The better predictability of these sources makes them more reliable than other alternatives. Thus, combining different renewable energy sources would reduce the intermittency and variability of the total production and so diminish the storage and genset requirements. To foster marine energy integration and new multisource system development, an up-to-date review of projects already carried out in this field is proposed. This article first presents the main characteristics of the different sources which can provide electrical energy in remote maritime areas: solar, wind, tidal, and wave energies. Then, a review of multi-source systems based on marine energies is presented, concerning not only industrial projects but also concepts and research work. Finally, the main advantages and limits are discussed. Full article
(This article belongs to the Special Issue Offshore Renewable Energy: Ocean Waves, Tides and Offshore Wind)
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Open AccessArticle CO2 Price Volatility Effects on Optimal Power System Portfolios
Energies 2018, 11(7), 1903; https://doi.org/10.3390/en11071903
Received: 4 July 2018 / Revised: 16 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
This paper investigates the effects of CO2 price volatility on optimal power system portfolios and on CO2 emissions assessment. In a stochastic setting in which three sources of uncertainty are considered, namely fossil fuels (gas and coal) and CO2 prices,
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This paper investigates the effects of CO2 price volatility on optimal power system portfolios and on CO2 emissions assessment. In a stochastic setting in which three sources of uncertainty are considered, namely fossil fuels (gas and coal) and CO2 prices, we discuss a unifying scheme for quantifying the impact of integrated environmental and renewable energy policies on the power system. We will show that the effects produced by a given environmental policy scheme strongly depend on the configuration of the power system, i.e., on the composition of the generating sources in the power system portfolio. In the empirical analysis performed on U.S. technical and cost data, we found that a non-volatile carbon tax scheme can produce significant effects on the power system portfolio selection problem in the presence of a carbon-free dispatchable source, like nuclear power, but it may have a negligible impact if the (non-renewable) dispatchable part of the power system portfolio is fully composed by fossil fuel, gas and coal, sources. On the other side, generating CO2 price volatility market-oriented mechanisms can produce relevant effects on both power system configurations. Although the empirical analysis is performed on U.S. data, the proposed methodology is general and can be used as a quantitative support by policy makers in their attempts to reconcile environmental and economic issues. Full article
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Open AccessArticle The Eddy Dissipation Concept—Analysis of Different Fine Structure Treatments for Classical Combustion
Energies 2018, 11(7), 1902; https://doi.org/10.3390/en11071902
Received: 12 June 2018 / Revised: 9 July 2018 / Accepted: 19 July 2018 / Published: 20 July 2018
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Abstract
The Eddy Dissipation Concept (EDC) is common in modeling turbulent combustion. Several model improvements have been proposed in literature; recent modifications aim to extend its validity to Moderate or Intense Low oxygen Dilution (MILD) conditions. In general, the EDC divides a fluid into
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The Eddy Dissipation Concept (EDC) is common in modeling turbulent combustion. Several model improvements have been proposed in literature; recent modifications aim to extend its validity to Moderate or Intense Low oxygen Dilution (MILD) conditions. In general, the EDC divides a fluid into a reacting and a non-reacting part. The reacting part is modeled as perfectly stirred reactor (PSR) or plug flow reactor (PFR). EDC theory suggests PSR treatment, while PFR treatment provides numerical advantages. Literature lacks a thorough evaluation of the consequences of employing the PFR fine structure treatment. Therefore, these consequences were evaluated by employing tests to isolate the effects of the EDC variations and fine structure treatment and by conducting a Sandia Flame D modeling study. Species concentration as well as EDC species consumption/production rates were evaluated. The isolated tests revealed an influence of the EDC improvements on the EDC rates, which is prominent at low shares of the reacting fluid. In contrast, PSR and PFR differences increase at large fine fraction shares. The modeling study revealed significant differences in the EDC rates of intermediate species. Summarizing, the PFR fine structure treatment might be chosen for schematic investigations, but for detailed investigations a careful evaluation is necessary. Full article
(This article belongs to the Section Energy Fundamentals and Conversion)
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Open AccessArticle A Parametric Study of Blast Damage on Hard Rock Pillar Strength
Energies 2018, 11(7), 1901; https://doi.org/10.3390/en11071901
Received: 6 July 2018 / Revised: 10 July 2018 / Accepted: 19 July 2018 / Published: 20 July 2018
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Abstract
Pillar stability is an important factor for safe working and from an economic standpoint in underground mines. This paper discusses the effect of blast damage on the strength of hard rock pillars using numerical models through a parametric study. The results indicate that
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Pillar stability is an important factor for safe working and from an economic standpoint in underground mines. This paper discusses the effect of blast damage on the strength of hard rock pillars using numerical models through a parametric study. The results indicate that blast damage has a significant impact on the strength of pillars with larger width-to-height (W/H) ratios. The blast damage causes softening of the rock at the pillar boundaries leading to the yielding of the pillars in brittle fashion beyond the blast damage zones. The models show that the decrease in pillar strength as a consequence of blasting is inversely correlated with increasing pillar height at a constant W/H ratio. Inclined pillars are less susceptible to blast damage, and the damage on the inclined sides has a greater impact on pillar strength than on the normal sides. A methodology to analyze the blast damage on hard rock pillars using FLAC3D is presented herein. Full article
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Open AccessArticle Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method
Energies 2018, 11(7), 1900; https://doi.org/10.3390/en11071900
Received: 29 June 2018 / Revised: 14 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
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Abstract
Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach.
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Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h–1) at the prediction moment. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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Open AccessArticle Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours
Energies 2018, 11(7), 1899; https://doi.org/10.3390/en11071899
Received: 25 June 2018 / Revised: 10 July 2018 / Accepted: 12 July 2018 / Published: 20 July 2018
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Abstract
To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to
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To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting. Full article
(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)
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Open AccessArticle Unit Commitment of a Power System Including Battery Swap Stations Under a Low-Carbon Economy
Energies 2018, 11(7), 1898; https://doi.org/10.3390/en11071898
Received: 5 June 2018 / Revised: 15 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
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Abstract
Battery swap stations (BSSs) are of great importance as an energy supplement for electric vehicles (EVs). The batteries in these stations not only charge instantaneous energy from the grid (G2B) but also discharge stored energy to the grid (B2G). This bidirectional energy consuming
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Battery swap stations (BSSs) are of great importance as an energy supplement for electric vehicles (EVs). The batteries in these stations not only charge instantaneous energy from the grid (G2B) but also discharge stored energy to the grid (B2G). This bidirectional energy consuming scheme provides more flexibility to the grid operation options, and henceforth, may bring in new challenges to the systems as well. In the meanwhile, the carbon trading mechanisms for the low-carbon economy can also have impacts on the generation scheduling of the power system. Therefore, a unit commitment (UC) model of the power system with BSSs in the low-carbon economic background is proposed to coordinate the G2B/B2G process of BSSs. Our model weighted the carbon dioxide emission in the cost function and tightened the constraint set with BSSs limits and the carbon trading mechanism. Case studies on a 10-unit test system demonstrate the effectiveness of the proposed model. Full article
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Open AccessArticle Variable Reactivity Control in Small Modular High Temperature Reactors Using Moderation Manipulation Techniques
Energies 2018, 11(7), 1897; https://doi.org/10.3390/en11071897
Received: 11 June 2018 / Revised: 4 July 2018 / Accepted: 6 July 2018 / Published: 20 July 2018
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Abstract
With extensive research being undertaken into small modular reactor design concepts, this has brought new challenges to the industry. One key challenge is to be able to compete with large scale nuclear power plants economically. In this article, a novel approach is applied
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With extensive research being undertaken into small modular reactor design concepts, this has brought new challenges to the industry. One key challenge is to be able to compete with large scale nuclear power plants economically. In this article, a novel approach is applied to reduce the overall dependence on fixed burnable poisons during high reactivity periods within a high temperature graphite moderated reactor. To reduce the excess activity, we aim to harden the flux spectrum across the core by removing part of the central moderation column, thus breeding more plutonium, in a later period the flux spectrum is softened again to utilise this plutonium again. This provides a neutron storage effect within the 238U and the resulting breeding of Plutonium. Due to the small size and the annular design of the high temperature reactor, the central reflector is key to the thermalization process. By removing a large proportion of the central reflector, the fuel within the proximity of the central reflector are less likely to receive neutrons within the thermal energy range. In addition to this, the fuel at the extremities of the core have a higher chance of fission due to the higher number of neutrons reaching them. This works as a method of balancing the power distribution between the central and outside fuel pins. During points of low reactivity, such as the end of the fuel cycle, the central reflector can be reinserted and the additionally bred plutonium and U235 at the centre of the core will encounter a higher probability of fission due to more thermal neutrons within this region. By removing the central reflector, this provided a 320 pcm reactivity drop for the duration of the fuel cycle. The plutonium buildup provided additional fissile material up until the central reflector was reinserted. The described method created a two-fold benefit. The overall full power days within the core was increased by ~31 days due to the additional fissile material within the core and secondly the highest loaded power pins saw a 30% power reduction during the removal of the central reflector column. Full article
(This article belongs to the Section Energy Sources)
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Open AccessArticle Prediction of Layered Thermal Conductivity Using Artificial Neural Network in Order to Have Better Design of Ground Source Heat Pump System
Energies 2018, 11(7), 1896; https://doi.org/10.3390/en11071896
Received: 9 May 2018 / Revised: 11 June 2018 / Accepted: 30 June 2018 / Published: 20 July 2018
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Abstract
Ground source heat pumps (GSHPs) have been widely applied worldwide in recent years because of their high efficiency and environmental friendliness. An accurate estimation of the thermal conductivity of rock and soil layers is important in the design of GSHP systems. The distributed
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Ground source heat pumps (GSHPs) have been widely applied worldwide in recent years because of their high efficiency and environmental friendliness. An accurate estimation of the thermal conductivity of rock and soil layers is important in the design of GSHP systems. The distributed thermal response test (DTRT) method incorporates the standard test with a pair of fiber optic-distributed temperature sensors in the U-tube to accurately calculate the layered thermal conductivity of the rock/soil. In this work, in situ layered thermal conductivity was initially obtained by DTRT for four boreholes in the study region. A series of laboratory tests was also conducted on the rock samples obtained from drilling. Then, an artificial neural network (ANN) model was developed to predict the layered thermal conductivity on the basis of the DTRT results. The primary modeling factors were water content, density, and porosity. The results showed that the ANN models can predict the layered thermal conductivity with an absolute error of less than 0.1 W/(m·K). Finally, the trained ANN models were used to predict the layered thermal conductivity for another study region, in which only the effective thermal conductivity was measured with the thermal response test (TRT). To verify the accuracy of the prediction, the product of pipe depth and layered thermal conductivity was suggested to represent heat transfer capacity. The results showed that the discrepancies between the TRT and ANN models were 5.43% and 6.37% for two boreholes, respectively. The results prove that the proposed method can be used to determine layered thermal conductivity. Full article
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Open AccessArticle Load Estimation of Offshore Wind Turbines
Energies 2018, 11(7), 1895; https://doi.org/10.3390/en11071895
Received: 6 May 2018 / Revised: 16 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
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Abstract
The influence of 3 MW Hywind-II wind turbine wakes from an upstream offshore floating wind turbine on a downstream turbine with a separation distance of seven rotor diameters was studied for a site in the Gulf of Maine. The turbines and the platforms
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The influence of 3 MW Hywind-II wind turbine wakes from an upstream offshore floating wind turbine on a downstream turbine with a separation distance of seven rotor diameters was studied for a site in the Gulf of Maine. The turbines and the platforms were subjected to atmospheric boundary layer flows. Various sensitivity studies on fatigue loads with respect to the positions of the downstream turbine were performed and validated with a large-eddy simulation tool. In particular, the effect of various lateral positions of the downstream turbine relative to the upstream turbine were considered using time-series turbine wake data generated from the large-eddy simulation tool which served as an input to an aero-elastic wind turbine model to assess the loads. The load response from the rotor, tower, and the floating platform for the downstream turbine were sensitive to the lateral offset positions where turbines that were partially exposed to upstream turbine wakes yielded significant increases in the cyclic load range. For the given set of lateral positions for the downstream turbine, the largest damage equivalent load occurred when the turbine was one rotor diameter to the left of the centerline, when looking upstream, which is the position of the turbine fully exposed to upstream turbine wake. On the other hand, the fatigue load on the downstream turbine placed on the right side of the position fully exposed to the upstream turbine wake, yielded lower stress due to the non-symmetric shape of the turbine wake. The configuration associated with the largest damage equivalent loads was further investigated in a large-eddy simulation, modeling both the upstream and downstream turbines. It was found that the energy spectra at the blade rotational frequency were a magnitude order higher for the downstream turbine, especially for surge, heave, pitch, and yaw motion of the platform. The increase of the damage equivalent load for the flapwise blade root moment was 45% compared to the upstream turbine, which can potentially reduce the turbine service life time. Full article
(This article belongs to the collection Wind Turbines)
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Open AccessReview The Electric Bicycle: Worldwide Research Trends
Energies 2018, 11(7), 1894; https://doi.org/10.3390/en11071894
Received: 26 May 2018 / Revised: 16 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
The bicycle has gone from being an old-fashioned recreational product to a less polluting means of transport and a compact, ultra-light personal mobility tool. This is how electrical bicycles will be used as the pillar that could support individual public transport in large
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The bicycle has gone from being an old-fashioned recreational product to a less polluting means of transport and a compact, ultra-light personal mobility tool. This is how electrical bicycles will be used as the pillar that could support individual public transport in large cities worldwide. The objective of this manuscript is to detect how worldwide research on the electric bicycle is being developed, and, especially, around which scientific domains is it clustered, to finally identify the main trends in the field. This study has been carried out based on the Scopus database, where all the publications related to the topic of the electric bicycle have been analyzed up to the year 2017. ¨Therefore, research on the global research trends of this topic was conducted. Its evolution over time shows that since 2008 the growth of publications is much higher than in the previous period. The main countries are China and the USA, and it can be inferred that there are two major trend countries with high environmental awareness, which also have a large population and that the electric bicycle is a suitable and sustainable form of transport. Among the main scientific fields, engineering leads in research. The keyword analysis shows that the central theme is electric, then battery and motor stand out. A community detection was applied to detect the six main clusters of this research, largely dedicated to the following topics: Transportation–Environment, Electrical Engineering, Safety, Batteries, Sporting Goods–Urban Planning, and Mechanical Engineering. This manuscript shows that global research trends about the electric bicycle are increasing, and that it should be considered a means of sustainable urban transport and will therefore contribute to energy saving and sustainable energy. Full article
(This article belongs to the Section Sustainable Energy)
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Open AccessArticle Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting
Energies 2018, 11(7), 1893; https://doi.org/10.3390/en11071893
Received: 29 June 2018 / Revised: 12 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
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Abstract
Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the
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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available. Full article
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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Open AccessArticle Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by Dragonfly Algorithm with Grid
Energies 2018, 11(7), 1892; https://doi.org/10.3390/en11071892
Received: 5 June 2018 / Revised: 3 July 2018 / Accepted: 4 July 2018 / Published: 20 July 2018
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Abstract
A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a
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A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one. Full article
(This article belongs to the Section Electrical Power and Energy System)
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