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Open AccessEditor’s ChoiceArticle Flow Adjustment Inside and Around Large Finite-Size Wind Farms
Energies 2017, 10(12), 2164; https://doi.org/10.3390/en10122164
Received: 1 December 2017 / Revised: 13 December 2017 / Accepted: 14 December 2017 / Published: 18 December 2017
Cited by 5 | PDF Full-text (23119 KB) | HTML Full-text | XML Full-text
Abstract
In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and
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In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and farm wake regions. The wind farms extend 20 km in the streamwise direction and comprise 36 wind turbine rows arranged in aligned and staggered configurations. Results show that, under weak free-atmosphere stratification ( Γ = 1 K/km), the flow inside and above both wind farms, and thus the turbine power, do not reach the fully-developed regime even though the farm length is two orders of magnitude larger than the boundary layer height. In that case, the wind farm induction region, affected by flow blockage, extends upwind about 0.8 km and leads to a power reduction of 1.3% and 3% at the first row of turbines for the aligned and staggered layouts, respectively. The wind farm wake leads to velocity deficits at hub height of around 3.5% at a downwind distance of 10 km for both farm layouts. Under stronger stratification ( Γ = 5 K/km), the vertical deflection of the subcritical flow induced by the wind farm at its entrance and exit regions triggers standing gravity waves whose effects propagate upwind. They, in turn, induce a large decelerating induction region upwind of the farm leading edge, and an accelerating exit region upwind of the trailing edge, both extending about 7 km. As a result, the turbine power output in the entrance region decreases more than 35% with respect to the weakly stratified case. It increases downwind as the flow adjusts, reaching the fully-developed regime only for the staggered layout at a distance of about 8.5 km from the farm edge. The flow acceleration in the exit region leads to an increase of the turbine power with downwind distance in that region, and a relatively fast (compared with the weakly stratified case) recovery of the farm wake, which attains its inflow hub height speed at a downwind distance of 5 km. Full article
(This article belongs to the collection Wind Turbines)
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Open AccessEditor’s ChoiceArticle Spray Combustion Characteristics and Soot Emission Reduction of Hydrous Ethanol Diesel Emulsion Fuel Using Color-Ratio Pyrometry
Energies 2017, 10(12), 2062; https://doi.org/10.3390/en10122062
Received: 30 October 2017 / Revised: 24 November 2017 / Accepted: 28 November 2017 / Published: 5 December 2017
Cited by 4 | PDF Full-text (2995 KB) | HTML Full-text | XML Full-text
Abstract
To elucidate the relationship between physicochemical properties, spray characteristics, and combustion performance, a series of experiments have been conducted in a constant volume vessel with injection of hydrous ethanol diesel emulsion and regular diesel. HE30 (emulsion with 30% volume fraction of 20% water-containing
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To elucidate the relationship between physicochemical properties, spray characteristics, and combustion performance, a series of experiments have been conducted in a constant volume vessel with injection of hydrous ethanol diesel emulsion and regular diesel. HE30 (emulsion with 30% volume fraction of 20% water-containing ethanol and 70% volume fraction of 0# diesel) is developed using Shah’s technique and regular diesel is also employed for comparison. Firstly, the physicochemical properties of two kinds of fuels are investigated. Then, the non-evaporating and evaporating spray characteristics are examined through the high-speed shadowgraphs. Finally, spray combustion experiments under different ambient oxygen concentrations are carried out, and color-ratio pyrometry (CRP) is applied to measure the flame temperature and soot concentration (KL) distributions. The results indicate that the physicochemical properties, such as density, surface tension, kinematic viscosity, cetane number, and oxygen content, have significant impact on the spray mixture formation and combustion performance. HE30 exhibits lower soot emissions than that of regular diesel. Further analysis supports the standpoint that the hydrous ethanol diesel emulsion can suppress the soot and NOx simultaneously. Therefore, the hydrous ethanol diesel emulsion has great potential to be an alternative clean energy resource. Full article
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Open AccessEditor’s ChoiceArticle Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes
Energies 2017, 10(12), 2065; https://doi.org/10.3390/en10122065
Received: 2 November 2017 / Revised: 24 November 2017 / Accepted: 28 November 2017 / Published: 5 December 2017
Cited by 14 | PDF Full-text (1024 KB) | HTML Full-text | XML Full-text
Abstract
The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the
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The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting. Full article
(This article belongs to the Special Issue Intelligent Management and Control of Energy Storage Systems)
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Open AccessEditor’s ChoiceArticle Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach
Energies 2017, 10(12), 1944; https://doi.org/10.3390/en10121944
Received: 29 October 2017 / Revised: 16 November 2017 / Accepted: 17 November 2017 / Published: 23 November 2017
Cited by 9 | PDF Full-text (628 KB) | HTML Full-text | XML Full-text
Abstract
To monitor wind turbine vibrations, normal behaviour models are built to predict tower top accelerations and drive-train vibrations. Signal deviations from model prediction are labelled as anomalies and are further investigated. In this paper we assess a stochastic approach to reconstruct the 1
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To monitor wind turbine vibrations, normal behaviour models are built to predict tower top accelerations and drive-train vibrations. Signal deviations from model prediction are labelled as anomalies and are further investigated. In this paper we assess a stochastic approach to reconstruct the 1 Hz tower top acceleration signal, which was measured in a wind turbine located at the wind farm Alpha Ventus in the German North Sea. We compare the resulting data reconstruction with that of a model based on a neural network, which has been previously reported as a data-mining algorithm suitable for reconstructing this signal. Our results present evidence that the stochastic approach outperforms the neural network in the high frequency domain (1 Hz). Although neural network retrieves accurate step-forward predictions, with low mean square errors, the stochastic approach predictions better preserve the statistics and the frequency components of the original signal, retaining high accuracy levels. The implementation of our stochastic approach is available as open source code and can easily be adapted for other situations involving stochastic data reconstruction. Based on our findings we argue that such an approach could be implemented in signal reconstruction for monitoring purposes or for abnormal behaviour detection. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle Factors Influencing the Thermal Efficiency of Horizontal Ground Heat Exchangers
Energies 2017, 10(11), 1897; https://doi.org/10.3390/en10111897
Received: 13 October 2017 / Revised: 12 November 2017 / Accepted: 14 November 2017 / Published: 18 November 2017
Cited by 6 | PDF Full-text (5348 KB) | HTML Full-text | XML Full-text
Abstract
The performance of very shallow geothermal systems (VSGs), interesting the first 2 m of depth from ground level, is strongly correlated to the kind of sediment locally available. These systems are attractive due to their low installation costs, less legal constraints, easy maintenance
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The performance of very shallow geothermal systems (VSGs), interesting the first 2 m of depth from ground level, is strongly correlated to the kind of sediment locally available. These systems are attractive due to their low installation costs, less legal constraints, easy maintenance and possibility for technical improvements. The Improving Thermal Efficiency of horizontal ground heat exchangers Project (ITER) aims to understand how to enhance the heat transfer of the sediments surrounding the pipes and to depict the VSGs behavior in extreme thermal situations. In this regard, five helices were installed horizontally surrounded by five different backfilling materials under the same climatic conditions and tested under different operation modes. The field test monitoring concerned: (a) monthly measurement of thermal conductivity and moisture content on surface; (b) continuous recording of air and ground temperature (inside and outside each helix); (c) continuous climatological and ground volumetric water content (VWC) data acquisition. The interactions between soils, VSGs, environment and climate are presented here, focusing on the differences and similarities between the behavior of the helix and surrounding material, especially when the heat pump is running in heating mode for a very long time, forcing the ground temperature to drop below 0 °C. Full article
(This article belongs to the Special Issue Low Enthalpy Geothermal Energy)
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Open AccessEditor’s ChoiceArticle Modeling of Supersonic Combustion Systems for Sustained Hypersonic Flight
Energies 2017, 10(11), 1900; https://doi.org/10.3390/en10111900
Received: 16 October 2017 / Revised: 8 November 2017 / Accepted: 9 November 2017 / Published: 18 November 2017
Cited by 4 | PDF Full-text (15742 KB) | HTML Full-text | XML Full-text
Abstract
Through Computational Fluid Dynamics and validation, an optimal scramjet combustor has been designed based on twin-strut Hydrogen injection to sustain flight at a desired speed of Mach 8. An investigation undertaken into the efficacy of supersonic combustion through various means of injection saw
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Through Computational Fluid Dynamics and validation, an optimal scramjet combustor has been designed based on twin-strut Hydrogen injection to sustain flight at a desired speed of Mach 8. An investigation undertaken into the efficacy of supersonic combustion through various means of injection saw promising results for Hydrogen-based systems, whereby strut-style injectors were selected over transverse injectors based on their pressure recovery performance and combustive efficiency. The final configuration of twin-strut injectors provided robust combustion and a stable region of net thrust (1873 kN) in the nozzle. Using fixed combustor inlet parameters and injection equivalence ratio, the finalized injection method advanced to the early stages of two-dimensional (2-D) and three-dimensional (3-D) scramjet engine integration. The overall investigation provided a feasible supersonic combustion system, such that Mach 8 sustained cruise could be achieved by the aircraft concept in a computational design domain. Full article
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Open AccessEditor’s ChoiceArticle Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model
Energies 2017, 10(11), 1842; https://doi.org/10.3390/en10111842
Received: 19 October 2017 / Revised: 6 November 2017 / Accepted: 7 November 2017 / Published: 11 November 2017
Cited by 11 | PDF Full-text (5933 KB) | HTML Full-text | XML Full-text
Abstract
Polarization-depolarization current (PDC) measurements are now being used as a diagnosis tool to predict the ageing condition of transformer oil-paper insulation. Unfortunately, it is somewhat difficult to obtain the ageing condition of transformer cellulose insulation using the PDC technique due to the variation
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Polarization-depolarization current (PDC) measurements are now being used as a diagnosis tool to predict the ageing condition of transformer oil-paper insulation. Unfortunately, it is somewhat difficult to obtain the ageing condition of transformer cellulose insulation using the PDC technique due to the variation in transformer insulation geometry. In this literature, to quantify the ageing condition of transformer cellulose insulation using the PDC technique, we firstly designed a series of experiments under controlled laboratory conditions, and then obtained the branch parameters of an extended Debye model using the technique of curve fitting the PDC data. Finally, the ageing effect and water effect on the parameters of large time constant branches were systematically investigated. In the present paper, it is observed that there is a good exponential correlation between large time constants and degree of polymerization (DP). Therefore, the authors believe that the large time constants may be regard as a sensitive ageing indicator and the nice correlations might be utilized for the quantitative assessment of ageing condition in transformer cellulose insulation in the future due to the geometry independence of large time constants. In addition, it is found that the water in cellulose pressboards has a predominant effect on large time constants. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle Experimental Study of Hydrogen Addition Effects on a Swirl-Stabilized Methane-Air Flame
Energies 2017, 10(11), 1769; https://doi.org/10.3390/en10111769
Received: 2 October 2017 / Revised: 27 October 2017 / Accepted: 1 November 2017 / Published: 3 November 2017
Cited by 3 | PDF Full-text (5828 KB) | HTML Full-text | XML Full-text
Abstract
The effects of H2 addition on a premixed methane-air flame was studied experimentally with a swirl-stabilized gas turbine model combustor. Experiments with 0%, 25%, and 50% H2 molar fraction in the fuel mixture were conducted under atmospheric pressure. The primary objectives
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The effects of H2 addition on a premixed methane-air flame was studied experimentally with a swirl-stabilized gas turbine model combustor. Experiments with 0%, 25%, and 50% H2 molar fraction in the fuel mixture were conducted under atmospheric pressure. The primary objectives are to study the impacts of H2 addition on flame lean blowout (LBO) limits, flame shapes and anchored locations, flow field characteristics, precessing vortex core (PVC) instability, as well as the CO emission performance. The flame LBO limits were identified by gradually reducing the equivalence ratio until the condition where the flame physically disappeared. The time-averaged CH chemiluminescence was used to reveal the characteristics of flame stabilization, e.g., flame structure and stabilized locations. In addition, the inverse Abel transform was applied to the time-averaged CH results so that the distribution of CH signal on the symmetric plane of the flame was obtained. The particle image velocimetry (PIV) was used to detect the characteristics of the flow field with a frequency of 2 kHz. The snapshot method of POD (proper orthogonal decomposition) and fast Fourier transform (FFT) were adopted to capture the most prominent coherent structures in the turbulent flow field. CO emission was monitored with an exhaust probe that was installed close to the combustor exit. The experimental results indicated that the H2 addition extended the flame LBO limits and the operation range of low CO emission. The influence of H2 addition on the flame shape, location, and flow field was observed. With the assistance of POD and FFT, the combustion suppression impacts on PVC was found. Full article
(This article belongs to the Section Energy Fundamentals and Conversion)
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Open AccessFeature PaperEditor’s ChoiceArticle A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids
Energies 2017, 10(11), 1756; https://doi.org/10.3390/en10111756
Received: 21 September 2017 / Revised: 22 October 2017 / Accepted: 26 October 2017 / Published: 1 November 2017
Cited by 10 | PDF Full-text (5416 KB) | HTML Full-text | XML Full-text
Abstract
Energy management systems are essential and indispensable for the secure and optimal operation of autonomous polygeneration microgrids which include distributed energy technologies and multiple electrical loads. In this paper, a multi-agent decentralized energy management system was designed. In particular, the devices of the
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Energy management systems are essential and indispensable for the secure and optimal operation of autonomous polygeneration microgrids which include distributed energy technologies and multiple electrical loads. In this paper, a multi-agent decentralized energy management system was designed. In particular, the devices of the microgrid under study were controlled as interactive agents. The energy management problem was formulated here through the application of game theory, in order to model the set of strategies between two players/agents, as a non-cooperative power control game or a cooperative one, according to the level of the energy produced by the renewable energy sources and the energy stored in the battery bank, for the purpose of accomplishing optimal energy management and control of the microgrid operation. The Nash equilibrium was used to compromise the possible diverging goals of the agents by maximizing their preferences. The proposed energy management system was then compared with a multi-agent decentralized energy management system where all the agents were assumed to be cooperative and employed agent coordination through Fuzzy Cognitive Maps. The results obtained from this comparison, demonstrate that the application of game theory based control, in autonomous polygeneration microgrids, can ensure operational and financial benefits over known energy management approaches incorporating distributed intelligence. Full article
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Open AccessFeature PaperEditor’s ChoiceArticle Energy Production by Means of Pumps As Turbines in Water Distribution Networks
Energies 2017, 10(10), 1666; https://doi.org/10.3390/en10101666
Received: 28 September 2017 / Revised: 13 October 2017 / Accepted: 18 October 2017 / Published: 20 October 2017
Cited by 2 | PDF Full-text (9604 KB) | HTML Full-text | XML Full-text
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
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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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Open AccessEditor’s ChoiceArticle Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan
Energies 2017, 10(10), 1660; https://doi.org/10.3390/en10101660
Received: 27 September 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 20 October 2017
Cited by 6 | PDF Full-text (23206 KB) | HTML Full-text | XML Full-text
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,
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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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Open AccessEditor’s ChoiceArticle Techno-Economic Comparison of Onshore and Offshore Underground Coal Gasification End-Product Competitiveness
Energies 2017, 10(10), 1643; https://doi.org/10.3390/en10101643
Received: 29 September 2017 / Revised: 11 October 2017 / Accepted: 13 October 2017 / Published: 18 October 2017
Cited by 3 | PDF Full-text (3340 KB) | HTML Full-text | XML Full-text
Abstract
Underground Coal Gasification (UCG) enables the utilisation of coal reserves that are currently not economically exploitable due to complex geological boundary conditions. Hereby, UCG produces a high-calorific synthesis gas that can be used for generation of electricity, fuels and chemical feedstock. The present
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Underground Coal Gasification (UCG) enables the utilisation of coal reserves that are currently not economically exploitable due to complex geological boundary conditions. Hereby, UCG produces a high-calorific synthesis gas that can be used for generation of electricity, fuels and chemical feedstock. The present study aims to identify economically competitive, site-specific end-use options for onshore and offshore produced UCG synthesis gas, taking into account the capture and storage (CCS) and/or utilisation (CCU) of resulting CO 2 . Modelling results show that boundary conditions that favour electricity, methanol and ammonia production expose low costs for air separation, high synthesis gas calorific values and H 2 /N 2 shares as well as low CO 2 portions of max. 10%. Hereby, a gasification agent ratio of more than 30% oxygen by volume is not favourable from economic and environmental viewpoints. Compared to the costs of an offshore platform with its technical equipment, offshore drilling costs are negligible. Thus, uncertainties related to parameters influenced by drilling costs are also negligible. In summary, techno-economic process modelling results reveal that scenarios with high CO 2 emissions are the most cost-intensive ones, offshore UCG-CCS/CCU costs are twice as high as the onshore ones, and yet all investigated scenarios except from offshore ammonia production are competitive on the European market. Full article
(This article belongs to the Section Energy Sources)
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Open AccessEditor’s ChoiceArticle Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Energies 2017, 10(10), 1525; https://doi.org/10.3390/en10101525
Received: 23 August 2017 / Revised: 25 September 2017 / Accepted: 25 September 2017 / Published: 7 October 2017
Cited by 23 | PDF Full-text (1918 KB) | HTML Full-text | XML Full-text
Abstract
Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the
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Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption. Full article
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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Open AccessEditor’s ChoiceArticle Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles
Energies 2017, 10(10), 1503; https://doi.org/10.3390/en10101503
Received: 13 September 2017 / Revised: 23 September 2017 / Accepted: 26 September 2017 / Published: 27 September 2017
Cited by 12 | PDF Full-text (6119 KB) | HTML Full-text | XML Full-text
Abstract
Lithium-ion battery (LIB) power systems have been commonly used for energy storage in electric vehicles. However, it is quite challenging to implement a robust real-time fault diagnosis and protection scheme to ensure battery safety and performance. This paper presents a resilient framework for
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Lithium-ion battery (LIB) power systems have been commonly used for energy storage in electric vehicles. However, it is quite challenging to implement a robust real-time fault diagnosis and protection scheme to ensure battery safety and performance. This paper presents a resilient framework for real-time fault diagnosis and protection in a battery-power system. Based on the proposed system structure, the self-initialization scheme for state-of-charge (SOC) estimation and the fault-diagnosis scheme were tested and implemented in an actual 12-cell series battery-pack prototype. The experimental results validated that the proposed system can estimate the SOC, diagnose the fault and provide necessary protection and self-recovery actions under the load profile for an electric vehicle. Full article
(This article belongs to the Section Energy Storage and Application)
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Open AccessFeature PaperEditor’s ChoiceArticle Control Strategies for Improving Energy Efficiency and Reliability in Autonomous Microgrids with Communication Constraints
Energies 2017, 10(9), 1443; https://doi.org/10.3390/en10091443
Received: 4 September 2017 / Revised: 15 September 2017 / Accepted: 15 September 2017 / Published: 19 September 2017
Cited by 5 | PDF Full-text (465 KB) | HTML Full-text | XML Full-text
Abstract
Microgrids are a feasible path to deploy smart grids, an intelligent and highly automated power system. Their operation demands a dedicated communication infrastructure to manage, control and monitor the intermittent sources of energy and loads. Therefore, smart devices will be connected to support
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Microgrids are a feasible path to deploy smart grids, an intelligent and highly automated power system. Their operation demands a dedicated communication infrastructure to manage, control and monitor the intermittent sources of energy and loads. Therefore, smart devices will be connected to support the growth of grid smartness increasing the dependency on communication networks, which consumes a high amount of power. In an energy-limited scenario, one of the main issues is to enhance the power supply time. Therefore, this paper proposes a hybrid methodology for microgrid energy management, integrated with a communication infrastructure to improve and to optimize islanded microgrid operation at maximum energy efficiency. The hybrid methodology applies some control management rules, such as intentional load shedding, priority load management, and communication energy saving. These energy saving rules establish a trade-off between increasing microgrid energy availability and communication system reliability. To achieve a compromised solution, a continuous time Markov chain model describes the impact of energy saving policies into system reliability. The proposed methodology is simulated and tested with the help of the modified IEEE 34 node test-system. Full article
(This article belongs to the Special Issue Control and Communication in Distributed Generation Systems)
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Open AccessEditor’s ChoiceArticle Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks
Energies 2017, 10(9), 1433; https://doi.org/10.3390/en10091433
Received: 23 July 2017 / Revised: 1 September 2017 / Accepted: 14 September 2017 / Published: 18 September 2017
Cited by 15 | PDF Full-text (1818 KB) | HTML Full-text | XML Full-text
Abstract
The paper presents a decision-making algorithm that has been developed for the optimum size and placement of distributed generation (DG) units in distribution networks. The algorithm that is very flexible to changes and modifications can define the optimal location for a DG unit
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The paper presents a decision-making algorithm that has been developed for the optimum size and placement of distributed generation (DG) units in distribution networks. The algorithm that is very flexible to changes and modifications can define the optimal location for a DG unit (of any type) and can estimate the optimum DG size to be installed, based on the improvement of voltage profiles and the reduction of the network’s total real and reactive power losses. The proposed algorithm has been tested on the IEEE 33-bus radial distribution system. The obtained results are compared with those of earlier studies, proving that the decision-making algorithm is working well with an acceptable accuracy. The algorithm can assist engineers, electric utilities, and distribution network operators with more efficient integration of new DG units in the current distribution networks. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessFeature PaperEditor’s ChoiceArticle Flame Front Propagation in an Optical GDI Engine under Stoichiometric and Lean Burn Conditions
Energies 2017, 10(9), 1337; https://doi.org/10.3390/en10091337
Received: 13 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 5 September 2017
Cited by 6 | PDF Full-text (8217 KB) | HTML Full-text | XML Full-text
Abstract
Lean fueling of spark ignited (SI) engines is a valid method for increasing efficiency and reducing nitric oxide (NOx) emissions. Gasoline direct injection (GDI) allows better fuel economy with respect to the port-fuel injection configuration, through greater flexibility to load changes,
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Lean fueling of spark ignited (SI) engines is a valid method for increasing efficiency and reducing nitric oxide (NOx) emissions. Gasoline direct injection (GDI) allows better fuel economy with respect to the port-fuel injection configuration, through greater flexibility to load changes, reduced tendency to abnormal combustion, and reduction of pumping and heat losses. During homogenous charge operation with lean mixtures, flame development is prolonged and incomplete combustion can even occur, causing a decrease in stability and engine efficiency. On the other hand, charge stratification results in fuel impingement on the combustion chamber walls and high particle emissions. Therefore, lean operation requires a fundamentally new understanding of in-cylinder processes for developing the next generation of direct-injection (DI) SI engines. In this paper, combustion was investigated in an optically accessible DISI single cylinder research engine fueled with gasoline. Stoichiometric and lean operations were studied in detail through a combined thermodynamic and optical approach. The engine was operated at a fixed rotational speed (1000 rpm), with a wide open throttle, and at the start of the injection during the intake stroke. The excess air ratio was raised from 1 to values close to the flammability limit, and spark timing was adopted according to the maximum brake torque setting for each case. Cycle resolved digital imaging and spectroscopy were applied; the optical data were correlated to in-cylinder pressure traces and exhaust gas emission measurements. Flame front propagation speed, flame morphology parameters, and centroid motion were evaluated through image processing. Chemical kinetics were characterized based on spectroscopy data. Lean burn operation demonstrated increased flame distortion and center movement from the location of the spark plug compared to the stoichiometric case; engine stability decreased as the lean flammability limit was approached. Full article
(This article belongs to the Section Energy Fundamentals and Conversion)
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Open AccessEditor’s ChoiceArticle Numerical Analysis of the Combustion and Emission Characteristics of Diesel Engines with Multiple Injection Strategies Using a Modified 2-D Flamelet Model
Energies 2017, 10(9), 1292; https://doi.org/10.3390/en10091292
Received: 2 August 2017 / Revised: 18 August 2017 / Accepted: 27 August 2017 / Published: 29 August 2017
Cited by 5 | PDF Full-text (8968 KB) | HTML Full-text | XML Full-text
Abstract
The multiple injection strategy has been widely used in diesel engines to reduce engine noise, NOx and soot formation. Fuel injection developments such as the common-rail and piezo-actuator system provide more precise control of the injection quantity and time under higher injection
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The multiple injection strategy has been widely used in diesel engines to reduce engine noise, NOx and soot formation. Fuel injection developments such as the common-rail and piezo-actuator system provide more precise control of the injection quantity and time under higher injection pressures. As various injection strategies become accessible, it is important to understand the interaction of each fuel stream and following combustion process under the multiple injection strategy. To investigate these complex processes quantitatively, numerical analysis using CFD is a good alternative to overcome the limitation of experiments. A modified 2-D flamelet model is further developed from previous work to model multi-fuel streams with higher accuracy. The model was validated under various engine operating conditions and captures the combustion and emissions characteristics as well as several parametric variations. The model is expected to be used to suggest advanced injection strategies in engine development processes. Full article
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Open AccessEditor’s ChoiceArticle A Metric-Based Validation Process to Assess the Realism of Synthetic Power Grids
Energies 2017, 10(8), 1233; https://doi.org/10.3390/en10081233
Received: 27 July 2017 / Revised: 16 August 2017 / Accepted: 16 August 2017 / Published: 19 August 2017
Cited by 3 | PDF Full-text (4027 KB) | HTML Full-text | XML Full-text
Abstract
Public power system test cases that are of high quality benefit the power systems research community with expanded resources for testing, demonstrating, and cross-validating new innovations. Building synthetic grid models for this purpose is a relatively new problem, for which a challenge is
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Public power system test cases that are of high quality benefit the power systems research community with expanded resources for testing, demonstrating, and cross-validating new innovations. Building synthetic grid models for this purpose is a relatively new problem, for which a challenge is to show that created cases are sufficiently realistic. This paper puts forth a validation process based on a set of metrics observed from actual power system cases. These metrics follow the structure, proportions, and parameters of key power system elements, which can be used in assessing and validating the quality of synthetic power grids. Though wide diversity exists in the characteristics of power systems, the paper focuses on an initial set of common quantitative metrics to capture the distribution of typical values from real power systems. The process is applied to two new public test cases, which are shown to meet the criteria specified in the metrics of this paper. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle Nusselt Number Correlation for Vertical Tubes with Inverted Triangular Fins under Natural Convection
Energies 2017, 10(8), 1183; https://doi.org/10.3390/en10081183
Received: 12 July 2017 / Revised: 31 July 2017 / Accepted: 1 August 2017 / Published: 10 August 2017
Cited by 1 | PDF Full-text (5252 KB) | HTML Full-text | XML Full-text
Abstract
Vertical tubes with inverted triangular fins under natural convection are investigated experimentally. The thermal resistances of tubes with inverted triangular fins are measured for various fin numbers, fin heights, and heat inputs. A Nusselt number correlation that best predicts the measured thermal resistances
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Vertical tubes with inverted triangular fins under natural convection are investigated experimentally. The thermal resistances of tubes with inverted triangular fins are measured for various fin numbers, fin heights, and heat inputs. A Nusselt number correlation that best predicts the measured thermal resistances is proposed. The proposed correlation is applicable to the following conditions: Rayleigh numbers of 1000–125,000, fin height to fin length ratios of 0.2–0.6, and fin numbers of 9–72. Finally, a contour map of the thermal resistances calculated from the proposed correlation for various fin thicknesses and fin numbers is presented. The contour map shows that there exist optimal values of the fin thickness and fin number at which the thermal resistance of the inverted-triangular-finned tube is minimized. Therefore, the proposed correlation enables a search for the optimal dimensions and has potential to be used in the designing of inverted-triangular-finned tubes of various cooling devices. Full article
(This article belongs to the Special Issue Thermal Energy Storage and Thermal Management (TESM2017))
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Open AccessEditor’s ChoiceArticle Investigation on the Development of a Sliding Mode Controller for Constant Power Loads in Microgrids
Energies 2017, 10(8), 1086; https://doi.org/10.3390/en10081086
Received: 17 May 2017 / Revised: 22 June 2017 / Accepted: 18 July 2017 / Published: 26 July 2017
Cited by 12 | PDF Full-text (7156 KB) | HTML Full-text | XML Full-text
Abstract
To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over conventional utility grid systems, however, they face severe instability issues
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To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over conventional utility grid systems, however, they face severe instability issues due to the continually increasing constant power loads. To improve the stability of the entire system, the load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for a microgrid system in the presence of constant power loads to assure a certain control objective of keeping the output voltage constant at 480 V. After that, a robustness analysis of the sliding mode controller against parametric uncertainties was performed and the sliding mode controller’s robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are presented. Later, the performance of the proportional integral derivative (PID) and sliding mode controller are compared in the case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of the sliding mode controller over the PID controller. All the necessary calculations are reckoned mathematically and results are verified in a virtual platform such as MATLAB/Simulink with a positive outcome. Full article
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
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Open AccessEditor’s ChoiceArticle An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset
Energies 2017, 10(7), 1007; https://doi.org/10.3390/en10071007
Received: 27 June 2017 / Revised: 6 July 2017 / Accepted: 13 July 2017 / Published: 16 July 2017
Cited by 5 | PDF Full-text (1442 KB) | HTML Full-text | XML Full-text
Abstract
Wind and solar energy resources are an increasingly large fraction of generation in global electricity systems. However, the variability of these resources necessitates new datasets and tools for understanding their economics and integration in electricity systems. To enable such analyses and more, we
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Wind and solar energy resources are an increasingly large fraction of generation in global electricity systems. However, the variability of these resources necessitates new datasets and tools for understanding their economics and integration in electricity systems. To enable such analyses and more, we have developed a free web-based tool (Global Renewable Energy Atlas & Time-series, or GRETA) that produces hourly wind and solar photovoltaic (PV) generation time series for any location on the globe. To do so, this tool applies the Boland–Ridley–Laurent and Perez models to NASA’s (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) solar irradiance reanalysis dataset, and the Archer and Jacobson model to the MERRA wind reanalysis dataset to produce resource and power data, for a given technology’s power curve. This paper reviews solar and wind resource datasets and models, describes the employed algorithms, and introduces the web-based tool. Full article
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Open AccessFeature PaperEditor’s ChoiceArticle Energy-Based Design of Powertrain for a Re-Engineered Post-Transmission Hybrid Electric Vehicle
Energies 2017, 10(7), 918; https://doi.org/10.3390/en10070918
Received: 15 May 2017 / Revised: 22 June 2017 / Accepted: 24 June 2017 / Published: 3 July 2017
Cited by 5 | PDF Full-text (4524 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a systematic approach for the design of post-transmission hybrid electric vehicle powertrains, as an instrument aiding the designer in making the right decision. In particular, a post-transmission series/parallel hybrid electric powertrain is considered, and all of the possible energy paths
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This paper presents a systematic approach for the design of post-transmission hybrid electric vehicle powertrains, as an instrument aiding the designer in making the right decision. In particular, a post-transmission series/parallel hybrid electric powertrain is considered, and all of the possible energy paths are taken into account, in order to automatically select the configuration that gives the lowest fuel consumption, thus better fitting to the considered mission. The optimization problem is solved with the Dijkstra algorithm, which is more computationally efficient than other optimization algorithms in the case of massive design spaces. In this way, it is possible to design a vehicle in terms of architecture and component sizes, without making any a priori choices, which are usually based on common sense, likely compromising the overall system efficiency. In order to demonstrate the effectiveness of the methodology, different driving cycles have been simulated, and some results are presented. The methodology is particularly applied to re-engineered vehicles, aimed at maximizing the benefits of the vehicle hybridization process. Results show how the introduction, in the optimization algorithm, of the engine load factor and sharing factor, for the engine torque split between the generator and the wheels, is crucial. For example, a 10% reduction of the original engine size, suggested by a low load factor, is able to allow for a 24% reduction in the fuel consumption. On the other hand, the sharing factor is of particular importance in suggesting if the vehicle architecture should be series, parallel or rather combined. Full article
(This article belongs to the Special Issue Advances in Electric Vehicles and Plug-in Hybrid Vehicles 2017)
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Open AccessEditor’s ChoiceArticle Site-Dependent Environmental Impacts of Industrial Hydrogen Production by Alkaline Water Electrolysis
Energies 2017, 10(7), 860; https://doi.org/10.3390/en10070860
Received: 30 May 2017 / Revised: 20 June 2017 / Accepted: 24 June 2017 / Published: 28 June 2017
Cited by 6 | PDF Full-text (1564 KB) | HTML Full-text | XML Full-text
Abstract
Industrial hydrogen production via alkaline water electrolysis (AEL) is a mature hydrogen production method. One argument in favor of AEL when supplied with renewable energy is its environmental superiority against conventional fossil-based hydrogen production. However, today electricity from the national grid is widely
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Industrial hydrogen production via alkaline water electrolysis (AEL) is a mature hydrogen production method. One argument in favor of AEL when supplied with renewable energy is its environmental superiority against conventional fossil-based hydrogen production. However, today electricity from the national grid is widely utilized for industrial applications of AEL. Also, the ban on asbestos membranes led to a change in performance patterns, making a detailed assessment necessary. This study presents a comparative Life Cycle Assessment (LCA) using the GaBi software (version 6.115, thinkstep, Leinfelden-Echterdingen, Germany), revealing inventory data and environmental impacts for industrial hydrogen production by latest AELs (6 MW, Zirfon membranes) in three different countries (Austria, Germany and Spain) with corresponding grid mixes. The results confirm the dependence of most environmental effects from the operation phase and specifically the site-dependent electricity mix. Construction of system components and the replacement of cell stacks make a minor contribution. At present, considering the three countries, AEL can be operated in the most environmentally friendly fashion in Austria. Concerning the construction of AEL plants the materials nickel and polytetrafluoroethylene in particular, used for cell manufacturing, revealed significant contributions to the environmental burden. Full article
(This article belongs to the Special Issue Environmental Impact Assessment of Energy Technolgies)
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Open AccessFeature PaperEditor’s ChoiceArticle Economic Optimization of Component Sizing for Residential Battery Storage Systems
Energies 2017, 10(7), 835; https://doi.org/10.3390/en10070835
Received: 7 May 2017 / Revised: 6 June 2017 / Accepted: 12 June 2017 / Published: 22 June 2017
Cited by 17 | PDF Full-text (1536 KB) | HTML Full-text | XML Full-text
Abstract
Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies,
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Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging. Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation. Full article
(This article belongs to the Section Energy Storage and Application)
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Open AccessEditor’s ChoiceArticle A Novel Decentralized Economic Operation in Islanded AC Microgrids
Energies 2017, 10(6), 804; https://doi.org/10.3390/en10060804
Received: 16 April 2017 / Revised: 27 May 2017 / Accepted: 9 June 2017 / Published: 13 June 2017
Cited by 13 | PDF Full-text (7925 KB) | HTML Full-text | XML Full-text
Abstract
Droop schemes are usually applied to the control of distributed generators (DGs) in microgrids (MGs) to realize proportional power sharing. The objective might, however, not suit MGs well for economic reasons. Addressing that issue, this paper proposes an alternative droop scheme for reducing
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Droop schemes are usually applied to the control of distributed generators (DGs) in microgrids (MGs) to realize proportional power sharing. The objective might, however, not suit MGs well for economic reasons. Addressing that issue, this paper proposes an alternative droop scheme for reducing the total active generation costs (TAGC). Optimal economic operation, DGs’ capacity limitations and system stability are fully considered basing on DGs’ generation costs. The proposed scheme utilizes the frequency as a carrier to realize the decentralized economic operation of MGs without communication links. Moreover, a fitting method is applied to balance DGs’ synchronous operation and economy. The effectiveness and performance of the proposed scheme are verified through simulations and experiments. Full article
(This article belongs to the Special Issue Advanced Operation and Control of Smart Microgrids)
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Open AccessEditor’s ChoiceArticle Biomass Chars: The Effects of Pyrolysis Conditions on Their Morphology, Structure, Chemical Properties and Reactivity
Energies 2017, 10(6), 796; https://doi.org/10.3390/en10060796
Received: 12 April 2017 / Revised: 30 May 2017 / Accepted: 6 June 2017 / Published: 11 June 2017
Cited by 24 | PDF Full-text (3866 KB) | HTML Full-text | XML Full-text
Abstract
Solid char is a product of biomass pyrolysis. It contains a high proportion of carbon, and lower contents of H, O and minerals. This char can have different valorization pathways such as combustion for heat and power, gasification for Syngas production, activation for
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Solid char is a product of biomass pyrolysis. It contains a high proportion of carbon, and lower contents of H, O and minerals. This char can have different valorization pathways such as combustion for heat and power, gasification for Syngas production, activation for adsorption applications, or use as a soil amendment. The optimal recovery pathway of the char depends highly on its physical and chemical characteristics. In this study, different chars were prepared from beech wood particles under various pyrolysis operating conditions in an entrained flow reactor (500–1400 °C). Their structural, morphological, surface chemistry properties, as well as their chemical compositions, were determined using different analytical techniques, including elementary analysis, Scanning Electronic Microscopy (SEM) coupled with an energy dispersive X-ray spectrometer (EDX), Fourier Transform Infra-Red spectroscopy (FTIR), and Raman Spectroscopy. The biomass char reactivity was evaluated in air using thermogravimetric analysis (TGA). The yield, chemical composition, surface chemistry, structure, morphology and reactivity of the chars were highly affected by the pyrolysis temperature. In addition, some of these properties related to the char structure and chemical composition were found to be correlated to the char reactivity. Full article
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Open AccessEditor’s ChoiceArticle Effect of Gas Velocity Distribution on Heat Recovery Process in Packed Bed of Plate-Shaped Slag
Energies 2017, 10(6), 755; https://doi.org/10.3390/en10060755
Received: 31 March 2017 / Revised: 22 May 2017 / Accepted: 23 May 2017 / Published: 28 May 2017
Cited by 2 | PDF Full-text (8722 KB) | HTML Full-text | XML Full-text
Abstract
A new twin-roll continuous slag solidification process and heat recovery process from a slag packed bed was developed for utilization of the waste heat of steelmaking slag. Plate-shaped slag with the thickness about 7 mm was successfully produced in a pilot plant, and
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A new twin-roll continuous slag solidification process and heat recovery process from a slag packed bed was developed for utilization of the waste heat of steelmaking slag. Plate-shaped slag with the thickness about 7 mm was successfully produced in a pilot plant, and the sensible heat of the slag was recovered by blowing air into the slag chamber. However, the gas distribution inside the slag packed bed was unclear because of the unique shape of the slag plates, and this remained a concern for further scale-up designing of the slag chamber. Therefore, in order to estimate the gas distribution in the packed bed, a simple computational fluid dynamics (CFD) model which considers the wall effect around the inner wall of the chamber was developed, and this model was fitted to the results of laboratory-scale velocity distribution measurements. The results showed that the gas velocity distribution was properly estimated, and the intensity of the wall effect was similar in both cases. As the next step, the gas velocity distribution and its effect on the slag heat recovery process in a pilot-scale slag chamber were evaluated with the assistance of the CFD simulation model. The simulation results were compared with the measured data obtained in a pilot-scale test, and as the result, a similar wall effect was also observed in the pilot-scale chamber. However, the intensity of the wall effect was limited enough to prevent serious deterioration of the uniformity of the gas distribution. Full article
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Open AccessEditor’s ChoiceArticle Effect of Gas Recycling on the Performance of a Moving Bed Temperature-Swing (MBTSA) Process for CO2 Capture in a Coal Fired Power Plant Context
Energies 2017, 10(6), 745; https://doi.org/10.3390/en10060745
Received: 2 April 2017 / Revised: 18 May 2017 / Accepted: 20 May 2017 / Published: 25 May 2017
Cited by 1 | PDF Full-text (3646 KB) | HTML Full-text | XML Full-text
Abstract
A mathematical model of a continuous moving-bed temperature-swing adsorption (MBTSA) process for post-combustion CO2 capture in a coal-fired power plant context has been developed. Process simulations have been done using single component isotherms and measured gas diffusion parameters of an activated carbon
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A mathematical model of a continuous moving-bed temperature-swing adsorption (MBTSA) process for post-combustion CO2 capture in a coal-fired power plant context has been developed. Process simulations have been done using single component isotherms and measured gas diffusion parameters of an activated carbon adsorbent. While a simple process configuration with no gas re-circulation gives quite low capture rate and CO2 purity, 86% and 65%, respectively, more advanced process configurations where some of the captured gas is recirculated to the incoming flue gas drastically increase both the capture rate and CO2 purity, the best configuration reaching capture rate of 86% and CO2 purity of 98%. Further improvements can be achieved by using adsorbents with higher CO2/N2 selectivity and/or higher temperature of the regeneration section. Full article
(This article belongs to the Special Issue CO2 Capture)
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Open AccessEditor’s ChoiceArticle Annual Assessment of Large-Scale Introduction of Renewable Energy: Modeling of Unit Commitment Schedule for Thermal Power Generators and Pumped Storages
Energies 2017, 10(6), 738; https://doi.org/10.3390/en10060738
Received: 31 March 2017 / Revised: 10 May 2017 / Accepted: 19 May 2017 / Published: 23 May 2017
Cited by 2 | PDF Full-text (5933 KB) | HTML Full-text | XML Full-text
Abstract
The fast-increasing introduction of renewable energy sources (RESes) leads to some problems in electrical power network due to fluctuating generated power. A power system must be operated with provision of various reserve powers like governor free capacity, load frequency control and spinning reserve.
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The fast-increasing introduction of renewable energy sources (RESes) leads to some problems in electrical power network due to fluctuating generated power. A power system must be operated with provision of various reserve powers like governor free capacity, load frequency control and spinning reserve. Therefore, the generator’s schedule (unit commitment schedule) should include the consideration of the various power reserves. In addition, it is necessary to calculate the annual operational costs of electric power systems by solving the unit commitment per week of thermal power generators and pumped storages in order to compare and examine the variance of the operational costs and the operating ratio of the generators throughout the year. This study proposes a novel annual analysis for the thermal power generator and pumped storages under a massive introduction of RESes. A weekly unit commitment schedule (start/stop planning) for thermal power generator and pumped storages has been modeled and calculated for one year evaluation. To solve the generator start/stop planning problem, Tabu search and interior point methods are adopted to solve the operation planning for thermal power generators and the output decision for pumped storages, respectively. It is demonstrated that the proposed method can analyze a one-year evaluation within practical time. In addition, by assuming load frequency control (LFC) constraints to cope with photovoltaic (PV) output fluctuations, the impact of the intensity of LFC constraints on the operational cost of the thermal power generator has been elucidated. The increment of the operational cost of the power supply with increasing PV introduction amount has been shown in concrete terms. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle Economic Assessment of Network-Constrained Transactive Energy for Managing Flexible Demand in Distribution Systems
Energies 2017, 10(5), 711; https://doi.org/10.3390/en10050711
Received: 8 April 2017 / Revised: 10 May 2017 / Accepted: 15 May 2017 / Published: 18 May 2017
Cited by 3 | PDF Full-text (993 KB) | HTML Full-text | XML Full-text
Abstract
The increasing number of distributed energy resources such as electric vehicles and heat pumps connected to power systems raises operational challenges to the network operator, for example, introducing grid congestion and voltage deviations in the distribution network level if their operations are not
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The increasing number of distributed energy resources such as electric vehicles and heat pumps connected to power systems raises operational challenges to the network operator, for example, introducing grid congestion and voltage deviations in the distribution network level if their operations are not properly coordinated. Coordination and control of a large number of distributed energy resources requires innovative approaches. In this paper, we follow up on a recently proposed network-constrained transactive energy (NCTE) method for scheduling of electric vehicles and heat pumps within a retailer’s aggregation at distribution system level. We extend this method with: (1) a new modeling technique that allows the resulting congestion price to be directly interpreted as a locational marginal pricing in the system; (2) an explicit analysis of the benefits and costs of different actors when using the NCTE method in the system, given the high penetration of distributed energy resources. This paper firstly describes the NCTE-based distribution system that introduces a new interacting scheme for actors at the distribution system level. Then, technical modeling and economic interpretation of the NCTE-based distribution system are described. Finally, we show the benefits and costs of different actors within the NCTE-based distribution system. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle Active Vibration Control of Swash Plate-Type Axial Piston Machines with Two-Weight Notch Least Mean Square/Filtered-x Least Mean Square (LMS/FxLMS) Filters
Energies 2017, 10(5), 645; https://doi.org/10.3390/en10050645
Received: 30 March 2017 / Revised: 21 April 2017 / Accepted: 2 May 2017 / Published: 6 May 2017
Cited by 4 | PDF Full-text (8079 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, swash plate active vibration control techniques were investigated utilizing the weight-limited multi-frequency two-weight notch Least Mean Square (LMS) filter with unit delay compensation and multi-frequency two-weight notch Filtered-x Least Mean Sqaure (FxLMS) filter with offline modeling to achieve adjustable swash
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In this paper, swash plate active vibration control techniques were investigated utilizing the weight-limited multi-frequency two-weight notch Least Mean Square (LMS) filter with unit delay compensation and multi-frequency two-weight notch Filtered-x Least Mean Sqaure (FxLMS) filter with offline modeling to achieve adjustable swash plate vibration reduction at the desired frequency. Simulation studies of the high fidelity pump control system model including realistic swash plate moments are presented to demonstrate the feasibility of the swash plate active vibration control. A 75-cm3/rev swash plate type axial piston pump was modified to implement a high bandwidth pump control system which is required for canceling the swash plate vibration. High speed real-time controllers were proposed and realized using an National Instrument LabVIEW Field Programmable Gate Array (FPGA). Vibration measurements using a tri-axial swash plate acceleration sensor were conducted to show the influence and effectiveness of the proposed swash plate active vibration control system and algorithms. Full article
(This article belongs to the Special Issue Energy Efficiency and Controllability of Fluid Power Systems)
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Open AccessEditor’s ChoiceArticle Global Energy-Optimal Redundancy Resolution of Hydraulic Manipulators: Experimental Results for a Forestry Manipulator
Energies 2017, 10(5), 647; https://doi.org/10.3390/en10050647
Received: 15 March 2017 / Revised: 13 April 2017 / Accepted: 3 May 2017 / Published: 6 May 2017
Cited by 3 | PDF Full-text (2465 KB) | HTML Full-text | XML Full-text
Abstract
This paper addresses the energy-inefficiency problem of four-degrees-of-freedom (4-DOF) hydraulic manipulators through redundancy resolution in robotic closed-loop controlled applications. Because conventional methods typically are local and have poor performance for resolving redundancy with respect to minimum hydraulic energy consumption, global energy-optimal redundancy resolution
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This paper addresses the energy-inefficiency problem of four-degrees-of-freedom (4-DOF) hydraulic manipulators through redundancy resolution in robotic closed-loop controlled applications. Because conventional methods typically are local and have poor performance for resolving redundancy with respect to minimum hydraulic energy consumption, global energy-optimal redundancy resolution is proposed at the valve-controlled actuator and hydraulic power system interaction level. The energy consumption of the widely popular valve-controlled load-sensing (LS) and constant-pressure (CP) systems is effectively minimised through cost functions formulated in a discrete-time dynamic programming (DP) approach with minimum state representation. A prescribed end-effector path and important actuator constraints at the position, velocity and acceleration levels are also satisfied in the solution. Extensive field experiments performed on a forestry hydraulic manipulator demonstrate the performance of the proposed solution. Approximately 15–30% greater hydraulic energy consumption was observed with the conventional methods in the LS and CP systems. These results encourage energy-optimal redundancy resolution in future robotic applications of hydraulic manipulators. Full article
(This article belongs to the Special Issue Energy Efficiency and Controllability of Fluid Power Systems)
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Open AccessEditor’s ChoiceArticle A Cost Optimized Fully Sustainable Power System for Southeast Asia and the Pacific Rim
Energies 2017, 10(5), 583; https://doi.org/10.3390/en10050583
Received: 2 March 2017 / Revised: 14 April 2017 / Accepted: 19 April 2017 / Published: 25 April 2017
Cited by 10 | PDF Full-text (5085 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, a cost optimal 100% renewable energy based system is obtained for Southeast Asia and the Pacific Rim region for the year 2030 on an hourly resolution for the whole year. For the optimization, the region was divided into 15 sub-regions
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In this paper, a cost optimal 100% renewable energy based system is obtained for Southeast Asia and the Pacific Rim region for the year 2030 on an hourly resolution for the whole year. For the optimization, the region was divided into 15 sub-regions and three different scenarios were set up based on the level of high voltage direct current grid connections. The results obtained for a total system levelized cost of electricity showed a decrease from 66.7 €/MWh in a decentralized scenario to 63.5 €/MWh for a centralized grid connected scenario. An integrated scenario was simulated to show the benefit of integrating additional demand of industrial gas and desalinated water which provided the system the required flexibility and increased the efficiency of the usage of storage technologies. This was reflected in the decrease of system cost by 9.5% and the total electricity generation by 5.1%. According to the results, grid integration on a larger scale decreases the total system cost and levelized cost of electricity by reducing the need for storage technologies due to seasonal variations in weather and demand profiles. The intermittency of renewable technologies can be effectively stabilized to satisfy hourly demand at a low cost level. A 100% renewable energy based system could be a reality economically and technically in Southeast Asia and the Pacific Rim with the cost assumptions used in this research and it may be more cost competitive than the nuclear and fossil carbon capture and storage (CCS) alternatives. Full article
(This article belongs to the Special Issue Sustainable Energy Technologies)
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Open AccessEditor’s ChoiceArticle Deformation Behavior of Hard Roofs in Solid Backfill Coal Mining Using Physical Models
Energies 2017, 10(4), 557; https://doi.org/10.3390/en10040557
Received: 13 March 2017 / Revised: 12 April 2017 / Accepted: 12 April 2017 / Published: 18 April 2017
Cited by 10 | PDF Full-text (15679 KB) | HTML Full-text | XML Full-text
Abstract
Solid backfill coal mining technology has been widely applied in coal seams that are at risk of hard roof. Using actual measured strain–stress curves of the backfill body and the similarity theory, this study designed and employed four experimental models for physical simulation,
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Solid backfill coal mining technology has been widely applied in coal seams that are at risk of hard roof. Using actual measured strain–stress curves of the backfill body and the similarity theory, this study designed and employed four experimental models for physical simulation, corresponding to roof-controlled backfilling ratios of 0%, 40%, 82.5% and 97% using the geological conditions of Face No. 6304 in the Jining No. 3 coal mine—a solid backfill coal mining face under a hard roof. A non-contact strain measurement system and pressure sensors were used to monitor the deformation of the overlying strata and changes in abutment stress ahead of the face during mining of the models for varying roof-controlled backfilling ratios. The results indicated that the solid backfill body was able to support the roof. As the roof-controlled backfilling ratio was increased, the maximum subsidence of the roof and the maximum height of the cracks decreased. When the roof-controlled backfilling ratio was 82.5% or higher, the working face did not display any obvious initial fractures or periodic fractures, and both the value and the impact range of the abutment stress ahead of the face decreased. Full article
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Open AccessEditor’s ChoiceArticle An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources
Energies 2017, 10(4), 549; https://doi.org/10.3390/en10040549
Received: 14 February 2017 / Revised: 15 March 2017 / Accepted: 11 April 2017 / Published: 17 April 2017
Cited by 22 | PDF Full-text (3364 KB) | HTML Full-text | XML Full-text
Abstract
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response
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Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%. Full article
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Open AccessEditor’s ChoiceArticle Effect of Nanoparticles on Spontaneous Imbibition of Water into Ultraconfined Reservoir Capillary by Molecular Dynamics Simulation
Energies 2017, 10(4), 506; https://doi.org/10.3390/en10040506
Received: 19 January 2017 / Revised: 17 March 2017 / Accepted: 6 April 2017 / Published: 8 April 2017
Cited by 5 | PDF Full-text (4844 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Imbibition is one of the key phenomena underlying processes such as oil recovery and others. In this paper, the influence of nanoparticles on spontaneous water imbibition into ultraconfined channels is investigated by molecular dynamics simulation. By combining the dynamic process of imbibition, the
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Imbibition is one of the key phenomena underlying processes such as oil recovery and others. In this paper, the influence of nanoparticles on spontaneous water imbibition into ultraconfined channels is investigated by molecular dynamics simulation. By combining the dynamic process of imbibition, the water contact angle in the capillary and the relationship of displacement (l) and time (t), a competitive mechanism of nanoparticle effects on spontaneous imbibition is proposed. The results indicate that the addition of nanoparticles decreases the displacement of fluids into the capillary dramatically, and the relationship between displacement and time can be described by l(t) ~ t1/2. Based on the analysis of the dynamic contact angle and motion behavior of nanoparticles, for water containing hydrophobic nanoparticles, the displacement decreases with the decrease of hydrophobicity, and the properties of fluids, such as viscosity and surface tension, play a major role. While for hydrophilic nanoparticles, the displacement of fluids increases slightly with the increase of hydrophilicity in the water-wet capillary and simulation time, which can be ascribed to disjoining pressure induced by “sticking nanoparticles”. This study provides new insights into the complex interactions between nanoparticles and other components in nanofluids in the spontaneous imbibition, which is crucially important to enhanced oil recovery. Full article
(This article belongs to the Special Issue Nanotechnology for Oil and Gas Applications)
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Open AccessEditor’s ChoiceArticle Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)
Energies 2017, 10(4), 488; https://doi.org/10.3390/en10040488
Received: 8 November 2016 / Revised: 30 March 2017 / Accepted: 31 March 2017 / Published: 5 April 2017
Cited by 55 | PDF Full-text (3149 KB) | HTML Full-text | XML Full-text
Abstract
This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm
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This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions. Full article
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Open AccessEditor’s ChoiceArticle Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems
Energies 2017, 10(3), 394; https://doi.org/10.3390/en10030394
Received: 2 February 2017 / Revised: 6 March 2017 / Accepted: 10 March 2017 / Published: 20 March 2017
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Abstract
An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the
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An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency. Full article
(This article belongs to the Special Issue Electric Power Systems Research 2017)
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Open AccessEditor’s ChoiceArticle Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs
Energies 2017, 10(3), 390; https://doi.org/10.3390/en10030390
Received: 22 January 2017 / Revised: 27 February 2017 / Accepted: 7 March 2017 / Published: 19 March 2017
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Abstract
The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet
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The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference. Full article
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Open AccessEditor’s ChoiceArticle A Top-Down Spatially Resolved Electrical Load Model
Energies 2017, 10(3), 361; https://doi.org/10.3390/en10030361
Received: 24 January 2017 / Revised: 17 February 2017 / Accepted: 8 March 2017 / Published: 14 March 2017
Cited by 7 | PDF Full-text (4361 KB) | HTML Full-text | XML Full-text
Abstract
The increasing deployment of variable renewable energy sources (VRES) is changing the source regime in the electrical energy sector. However, VRES feed-in from wind turbines and photovoltaic systems is dependent on the weather and only partially predictable. As a result, existing energy sector
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The increasing deployment of variable renewable energy sources (VRES) is changing the source regime in the electrical energy sector. However, VRES feed-in from wind turbines and photovoltaic systems is dependent on the weather and only partially predictable. As a result, existing energy sector models must be re-evaluated and adjusted as necessary. In long-term forecast models, the expansion of VRES must be taken into account so that future local overloads can be identified and measures taken. This paper focuses on one input factor for electrical energy models: the electrical load. We compare two different types to describe this, namely vertical grid load and total load. For the total load, an approach for a spatially-resolved electrical load model is developed and applied at the municipal level in Germany. This model provides detailed information about the load at a quarterly-hour resolution across 11,268 German municipalities. In municipalities with concentrations of energy-intensive industry, high loads are expected, which our simulation reproduces with a good degree of accuracy. Our results also show that municipalities with energy-intensive industry have a higher simulated electric load than neighboring municipalities that do not host energy-intensive industries. The underlying data was extracted from publically accessible sources and therefore the methodology introduced is also applicable to other countries. Full article
(This article belongs to the Section Electrical Power and Energy System)
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Open AccessEditor’s ChoiceArticle An Autonomous Coil Alignment System for the Dynamic Wireless Charging of Electric Vehicles to Minimize Lateral Misalignment
Energies 2017, 10(3), 315; https://doi.org/10.3390/en10030315
Received: 2 January 2017 / Revised: 27 February 2017 / Accepted: 2 March 2017 / Published: 7 March 2017
Cited by 12 | PDF Full-text (13699 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper proposes an autonomous coil alignment system (ACAS) for electric vehicles (EVs) with dynamic wireless charging (DWC) to mitigate the reduction in received power caused by lateral misalignment between the source and load coils. The key component of the ACAS is a
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This paper proposes an autonomous coil alignment system (ACAS) for electric vehicles (EVs) with dynamic wireless charging (DWC) to mitigate the reduction in received power caused by lateral misalignment between the source and load coils. The key component of the ACAS is a novel sensor coil design, which can detect the load coil’s left or right position relative to the source coil by observing the change in voltage phase. This allows the lateral misalignment to be estimated through the wireless power transfer (WPT) system alone, which is a novel tracking method for vehicular applications. Once misalignment is detected, the vehicle’s lateral position is self-adjusted by an autonomous steering function. The feasibility of the overall operation of the ACAS was verified through simulation and experiments. In addition, an analysis based on experimental results was conducted, demonstrating that 26% more energy can be transferred during DWC with the ACAS, just by keeping the vehicle’s load coil aligned with the source coil. Full article
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Open AccessEditor’s ChoiceArticle A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid
Energies 2017, 10(3), 319; https://doi.org/10.3390/en10030319
Received: 8 November 2016 / Revised: 5 February 2017 / Accepted: 24 February 2017 / Published: 7 March 2017
Cited by 32 | PDF Full-text (463 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is
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In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics. Full article
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Open AccessEditor’s ChoiceArticle Environmental Assessment of Possible Future Waste Management Scenarios
Energies 2017, 10(2), 247; https://doi.org/10.3390/en10020247
Received: 14 December 2016 / Revised: 5 February 2017 / Accepted: 7 February 2017 / Published: 19 February 2017
Cited by 8 | PDF Full-text (2977 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Waste management has developed in many countries and will continue to do so. Changes towards increased recovery of resources in order to meet climate targets and for society to transition to a circular economy are important driving forces. Scenarios are important tools for
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Waste management has developed in many countries and will continue to do so. Changes towards increased recovery of resources in order to meet climate targets and for society to transition to a circular economy are important driving forces. Scenarios are important tools for planning and assessing possible future developments and policies. This paper presents a comprehensive life cycle assessment (LCA) model for environmental assessments of scenarios and waste management policy instruments. It is unique by including almost all waste flows in a country and also allow for including waste prevention. The results show that the environmental impacts from future waste management scenarios in Sweden can differ a lot. Waste management will continue to contribute with environmental benefits, but less so in the more sustainable future scenarios, since the surrounding energy and transportation systems will be less polluting and also because less waste will be produced. Valuation results indicate that climate change, human toxicity and resource depletion are the most important environmental impact categories for the Swedish waste management system. Emissions of fossil CO2 from waste incineration will continue to be a major source of environmental impacts in these scenarios. The model is used for analyzing environmental impacts of several policy instruments including weight based collection fee, incineration tax, a resource tax and inclusion of waste in a green electricity certification system. The effect of the studied policy instruments in isolation are in most cases limited, suggesting that stronger policy instruments as well as combinations are necessary to reach policy goals as set out in for example the EU action plan on circular economy. Full article
(This article belongs to the Special Issue Energy and Waste Management)
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Open AccessEditor’s ChoiceArticle Geospatial Analysis of Photovoltaic Mini-Grid System Performance
Energies 2017, 10(2), 218; https://doi.org/10.3390/en10020218
Received: 2 December 2016 / Revised: 20 January 2017 / Accepted: 2 February 2017 / Published: 15 February 2017
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Abstract
We present a geographic information system (GIS)-based tool for estimating the performance of photovoltaic (PV) mini-grid system over large geographical areas. The methodology consists of geospatial analysis and mapping of the energy output and reliability of PV mini-grid system. The algorithm uses a
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We present a geographic information system (GIS)-based tool for estimating the performance of photovoltaic (PV) mini-grid system over large geographical areas. The methodology consists of geospatial analysis and mapping of the energy output and reliability of PV mini-grid system. The algorithm uses a combination of hourly solar radiation data from satellites combined with measured data on PV module and battery performance and estimated electricity consumption data. The methods also make it possible to optimize the PV array and battery storage size for a given location. Results are presented for an area covering Africa and most of Southern and Central Asia. We also investigate the effects of using Li-ion batteries instead of the traditional lead-acid batteries. The use of our spatial analysis as decision support tool could help governments, local authorities and non-governmental organizations to investigate the suitability of PV mini-grids for electrification of regions where access to electricity is lacking. In this way it is possible to identify areas where PV mini-grids are most suitable. Full article
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Open AccessEditor’s ChoiceArticle Hydrothermal Carbonization of Waste Biomass: Process Design, Modeling, Energy Efficiency and Cost Analysis
Energies 2017, 10(2), 211; https://doi.org/10.3390/en10020211
Received: 23 December 2016 / Revised: 27 January 2017 / Accepted: 4 February 2017 / Published: 13 February 2017
Cited by 24 | PDF Full-text (2771 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, a hydrothermal carbonization (HTC) process is designed and modeled on the basis of experimental data previously obtained for two representative organic waste materials: off-specification compost and grape marc. The process accounts for all the steps and equipment necessary to convert
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In this paper, a hydrothermal carbonization (HTC) process is designed and modeled on the basis of experimental data previously obtained for two representative organic waste materials: off-specification compost and grape marc. The process accounts for all the steps and equipment necessary to convert raw moist biomass into dry and pelletized hydrochar. By means of mass and thermal balances and based on common equations specific to the various equipment, thermal energy and power consumption were calculated at variable process conditions: HTC reactor temperature T: 180, 220, 250 °C; reaction time θ: 1, 3, 8 h. When operating the HTC plant with grape marc (65% moisture content) at optimized process conditions (T = 220 °C; θ = 1 h; dry biomass to water ratio = 0.19), thermal energy and power consumption were equal to 1170 kWh and 160 kWh per ton of hydrochar produced, respectively. Correspondingly, plant efficiency was 78%. In addition, the techno-economical aspects of the HTC process were analyzed in detail, considering both investment and production costs. The production cost of pelletized hydrochar and its break-even point were determined to be 157 €/ton and 200 €/ton, respectively. Such values make the use of hydrochar as a CO2 neutral biofuel attractive. Full article
(This article belongs to the Special Issue Thermo-Chemical Conversion of Waste Biomass)
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Open AccessEditor’s ChoiceArticle From Theory to Econometrics to Energy Policy: Cautionary Tales for Policymaking Using Aggregate Production Functions
Energies 2017, 10(2), 203; https://doi.org/10.3390/en10020203
Received: 18 November 2016 / Revised: 16 January 2017 / Accepted: 24 January 2017 / Published: 10 February 2017
Cited by 7 | PDF Full-text (1735 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical
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Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical economic and energy data; (4) comparing and evaluating modeling approaches; (5) interpreting the economy; and (6) formulating energy and economic policy. We find that choices made in Steps 1–4 can lead to very different interpretations of the economy (Step 5) and policies (Step 6). To investigate these effects, we use empirical data (Portugal and UK) and the Constant Elasticity of Substitution (CES) APF to evaluate four modeling choices: (a) rejecting (or not) the cost-share principle; (b) including (or not) energy; (c) quality-adjusting (or not) factors of production; and (d) CES nesting structure. Thereafter, we discuss two revealing examples for which different upstream modeling choices lead to very different policies. In the first example, the (kl)e nesting structure implies significant investment in energy, while other nesting structures suggest otherwise. In the second example, unadjusted factors of production suggest balanced investment in labor and energy, while quality-adjusting suggests significant investment in labor over energy. Divergent outcomes provide cautionary tales for policymakers: greater understanding of upstream modeling choices and their downstream implications is needed. Full article
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Open AccessEditor’s ChoiceArticle Application of Liquid Hydrogen with SMES for Efficient Use of Renewable Energy in the Energy Internet
Energies 2017, 10(2), 185; https://doi.org/10.3390/en10020185
Received: 6 January 2017 / Revised: 1 February 2017 / Accepted: 2 February 2017 / Published: 8 February 2017
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Abstract
Considering that generally frequency instability problems occur due to abrupt variations in load demand growth and power variations generated by different renewable energy sources (RESs), the application of superconducting magnetic energy storage (SMES) may become crucial due to its rapid response features. In
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Considering that generally frequency instability problems occur due to abrupt variations in load demand growth and power variations generated by different renewable energy sources (RESs), the application of superconducting magnetic energy storage (SMES) may become crucial due to its rapid response features. In this paper, liquid hydrogen with SMES (LIQHYSMES) is proposed to play a role in the future energy internet in terms of its combination of the SMES and the liquid hydrogen storage unit, which can help to overcome the capacity limit and high investment cost disadvantages of SMES. The generalized predictive control (GPC) algorithm is presented to be appreciatively used to eliminate the frequency deviations of the isolated micro energy grid including the LIQHYSMES and RESs. A benchmark micro energy grid with distributed generators (DGs), electrical vehicle (EV) stations, smart loads and a LIQHYSMES unit is modeled in the Matlab/Simulink environment. The simulation results show that the proposed GPC strategy can reschedule the active power output of each component to maintain the stability of the grid. In addition, in order to improve the performance of the SMES, a detailed optimization design of the superconducting coil is conducted, and the optimized SMES unit can offer better technical advantages in damping the frequency fluctuations. Full article
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Open AccessEditor’s ChoiceArticle An Improvement in Biodiesel Production from Waste Cooking Oil by Applying Thought Multi-Response Surface Methodology Using Desirability Functions
Energies 2017, 10(1), 130; https://doi.org/10.3390/en10010130
Received: 22 November 2016 / Revised: 11 January 2017 / Accepted: 13 January 2017 / Published: 21 January 2017
Cited by 9 | PDF Full-text (978 KB) | HTML Full-text | XML Full-text
Abstract
The exhaustion of natural resources has increased petroleum prices and the environmental impact of oil has stimulated the search for an alternative source of energy such as biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel.
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The exhaustion of natural resources has increased petroleum prices and the environmental impact of oil has stimulated the search for an alternative source of energy such as biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Biodiesel is synthesized by direct transesterification of vegetable oils, which is controlled by several inputs or process variables, including the dosage of catalyst, process temperature, mixing speed, mixing time, humidity and impurities of waste cooking oil that was studied in this case. Yield, turbidity, density, viscosity and higher heating value are considered as outputs. This paper used multi-response surface methodology (MRS) with desirability functions to find the best combination of input variables used in the transesterification reactions to improve the production of biodiesel. In this case, several biodiesel optimization scenarios have been proposed. They are based on a desire to improve the biodiesel yield and the higher heating value, while decreasing the viscosity, density and turbidity. The results demonstrated that, although waste cooking oil was collected from various sources, the dosage of catalyst is one of the most important variables in the yield of biodiesel production, whereas the viscosity obtained was similar in all samples of the biodiesel that was studied. Full article
(This article belongs to the collection Bioenergy and Biofuel)
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Open AccessEditor’s ChoiceArticle Exploring the Feasibility of Low-Carbon Scenarios Using Historical Energy Transitions Analysis
Energies 2017, 10(1), 116; https://doi.org/10.3390/en10010116
Received: 31 October 2016 / Revised: 13 December 2016 / Accepted: 5 January 2017 / Published: 18 January 2017
Cited by 6 | PDF Full-text (5493 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The scenarios generated by energy systems models provide a picture of the range of possible pathways to a low-carbon future. However, in order to be truly useful, these scenarios should not only be possible but also plausible. In this paper, we have used
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The scenarios generated by energy systems models provide a picture of the range of possible pathways to a low-carbon future. However, in order to be truly useful, these scenarios should not only be possible but also plausible. In this paper, we have used lessons from historical energy transitions to create a set of diagnostic tests to assess the feasibility of an example 2 °C scenario (generated using the least cost optimization model, TIAM-Grantham). The key assessment criteria included the rate of deployment of low carbon technologies and the rate of transition between primary energy resources. The rates of deployment of key low-carbon technologies were found to exceed the maximum historically observed rate of deployment of 20% per annum. When constraints were added to limit the scenario to within historically observed rates of change, the model no longer solved for 2 °C. Under these constraints, the lowest median 2100 temperature change for which a solution was found was about 2.1 °C and at more than double the cumulative cost of the unconstrained scenario. The analysis in this paper highlights the considerable challenge of meeting 2 °C, requiring rates of energy supply technology deployment and rates of declines in fossil fuels which are unprecedented. Full article
(This article belongs to the Special Issue Low Carbon Economy)
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Open AccessEditor’s ChoiceArticle Wireless DC Motor Drives with Selectability and Controllability
Energies 2017, 10(1), 49; https://doi.org/10.3390/en10010049
Received: 7 November 2016 / Revised: 20 December 2016 / Accepted: 26 December 2016 / Published: 4 January 2017
Cited by 6 | PDF Full-text (12397 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes and implements the concept of wireless DC motor drives, which can achieve the abilities of selective driving and controllable speed. Due to different resonant frequencies of the multiple energy receivers of the associated DC motor drives, the transmitter can be
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This paper proposes and implements the concept of wireless DC motor drives, which can achieve the abilities of selective driving and controllable speed. Due to different resonant frequencies of the multiple energy receivers of the associated DC motor drives, the transmitter can be purposely tuned to the specified resonant frequency which matches with the specified receiver, hence driving the specified motor selectively. In the meantime, the burst fire control is used to regulate the operating speed of the motor working at the resonant frequency, hence retaining the maximum power transmission efficiency. Both finite element analysis and experimentation are given to verify the validity of the proposed wireless DC motor drive system. For exemplification, three different resonant frequencies, namely 60 kHz, 100 kHz and 140 kHz, are selected to energize three DC motors. Under the burst fire control method, the speed of each motor can be regulated separately and the wireless power transfer (WPT) system can achieve the measured power transmission efficiency of about 60%. Full article
(This article belongs to the Special Issue Wireless Power Transfer 2016)
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Open AccessEditor’s ChoiceArticle Generic Combined Heat and Power (CHP) Model for the Concept Phase of Energy Planning Process
Energies 2017, 10(1), 11; https://doi.org/10.3390/en10010011
Received: 7 November 2016 / Revised: 7 December 2016 / Accepted: 9 December 2016 / Published: 23 December 2016
Cited by 7 | PDF Full-text (904 KB) | HTML Full-text | XML Full-text
Abstract
Micro gas turbines (MGTs) are regarded as combined heat and power (CHP) units which offer high fuel utilization and low emissions. They are applied in decentralized energy generation. To facilitate the planning process of energy systems, namely in the context of the increasing
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Micro gas turbines (MGTs) are regarded as combined heat and power (CHP) units which offer high fuel utilization and low emissions. They are applied in decentralized energy generation. To facilitate the planning process of energy systems, namely in the context of the increasing application of optimization techniques, there is a need for easy-to-parametrize component models with sufficient accuracy which allow a fast computation. In this paper, a model is proposed where the non-linear part load characteristics of the MGT are linearized by means of physical insight of the working principles of turbomachinery. Further, it is shown that the model can be parametrized by the data usually available in spec sheets. With this model a uniform description of MGTs from several manufacturers covering an electrical power range from 30 k W to 333 k W can be obtained. The MGT model was implemented by means of Modelica/Dymola. The resulting MGT system model, comprising further heat exchangers and hydraulic components, was validated using the experimental data of a 65 k W MGT from a trigeneration energy system. Full article
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Open AccessEditor’s ChoiceArticle Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications
Energies 2017, 10(1), 5; https://doi.org/10.3390/en10010005
Received: 3 October 2016 / Revised: 7 December 2016 / Accepted: 13 December 2016 / Published: 22 December 2016
Cited by 10 | PDF Full-text (7886 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account,
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This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method. Full article
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