Energies

1024 KiB

Open AccessEditor’s ChoiceArticle

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

by Sheraz Aslam, Zafar Iqbal, Nadeem Javaid, Zahoor Ali Khan, Khursheed Aurangzeb and Syed Irtaza Haider

Energies 2017, 10(12), 2065; https://doi.org/10.3390/en10122065 - 5 Dec 2017

Cited by 113 | Viewed by 8242

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 [...] Read more.

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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628 KiB

Open AccessEditor’s ChoiceArticle

Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach

by Pedro G. Lind, Luis Vera-Tudela, Matthias Wächter, Martin Kühn and Joachim Peinke

Energies 2017, 10(12), 1944; https://doi.org/10.3390/en10121944 - 23 Nov 2017

Cited by 41 | Viewed by 6718

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 [...] Read more.

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 F: Electrical Engineering)

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5348 KiB

Open AccessEditor’s ChoiceArticle

Factors Influencing the Thermal Efficiency of Horizontal Ground Heat Exchangers

by Eloisa Di Sipio and David Bertermann

Energies 2017, 10(11), 1897; https://doi.org/10.3390/en10111897 - 18 Nov 2017

Cited by 36 | Viewed by 6439

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 [...] Read more.

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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15742 KiB

Open AccessEditor’s ChoiceArticle

Modeling of Supersonic Combustion Systems for Sustained Hypersonic Flight

by Stephen M. Neill and Apostolos Pesyridis

Energies 2017, 10(11), 1900; https://doi.org/10.3390/en10111900 - 18 Nov 2017

Cited by 15 | Viewed by 8111

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 [...] Read more.

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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5933 KiB

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

by Yiyi Zhang, Jiefeng Liu, Hanbo Zheng, Hua Wei and Ruijin Liao

Energies 2017, 10(11), 1842; https://doi.org/10.3390/en10111842 - 11 Nov 2017

Cited by 28 | Viewed by 5247

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 [...] Read more.

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 F: Electrical Engineering)

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5828 KiB

Open AccessEditor’s ChoiceArticle

Experimental Study of Hydrogen Addition Effects on a Swirl-Stabilized Methane-Air Flame

by Mao Li, Yiheng Tong, Jens Klingmann and Marcus Thern

Energies 2017, 10(11), 1769; https://doi.org/10.3390/en10111769 - 3 Nov 2017

Cited by 8 | Viewed by 4767

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 [...] Read more.

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 I: Energy Fundamentals and Conversion)

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5416 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids

by Christos-Spyridon Karavas, Konstantinos Arvanitis and George Papadakis

Energies 2017, 10(11), 1756; https://doi.org/10.3390/en10111756 - 1 Nov 2017

Cited by 87 | Viewed by 9749

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 [...] Read more.

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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9604 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Energy Production by Means of Pumps As Turbines in Water Distribution Networks

by Mauro Venturini, Stefano Alvisi, Silvio Simani and Lucrezia Manservigi

Energies 2017, 10(10), 1666; https://doi.org/10.3390/en10101666 - 20 Oct 2017

Cited by 29 | Viewed by 4414

Abstract

This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different [...] Read more.

This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different characteristics are investigated to estimate the producible yearly electric energy. The performance curves of Pumps As Turbines (PATs), which relate head, power, and efficiency to the volume flow rate over the entire PAT operation range, were derived by using published experimental data. The four considered water distribution networks, for which experimental data taken during one year were available, are characterized by significantly different hydraulic features (average flow rate in the range 10–116 L/s; average pressure reduction in the range 12–53 m). Therefore, energy production accounts for actual flow rate and head variability over the year. The conversion efficiency is also estimated, for both the whole water distribution network and the PAT alone. Full article

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23206 KiB

Open AccessEditor’s ChoiceArticle

Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

by Chih-Chiang Wei

Energies 2017, 10(10), 1660; https://doi.org/10.3390/en10101660 - 20 Oct 2017

Cited by 32 | Viewed by 8561

Abstract

In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, [...] Read more.

In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m² per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m² per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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1918 KiB

Open AccessEditor’s ChoiceArticle

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

by Chengdong Li, Zixiang Ding, Dongbin Zhao, Jianqiang Yi and Guiqing Zhang

Energies 2017, 10(10), 1525; https://doi.org/10.3390/en10101525 - 7 Oct 2017

Cited by 235 | Viewed by 16894

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 [...] Read more.

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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6119 KiB

Open AccessEditor’s ChoiceArticle

Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

by Zuchang Gao, Cheng Siong Chin, Joel Hay King Chiew, Junbo Jia and Caizhi Zhang

Energies 2017, 10(10), 1503; https://doi.org/10.3390/en10101503 - 27 Sep 2017

Cited by 44 | Viewed by 9564

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 [...] Read more.

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 D: Energy Storage and Application)

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465 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Control Strategies for Improving Energy Efficiency and Reliability in Autonomous Microgrids with Communication Constraints

by Francisco Martins Portelinha Júnior, Antonio Carlos Zambroni de Souza, Miguel Castilla, Denisson Queiroz Oliveira and Paulo Fernando Ribeiro

Energies 2017, 10(9), 1443; https://doi.org/10.3390/en10091443 - 19 Sep 2017

Cited by 14 | Viewed by 5013

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 [...] Read more.

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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1818 KiB

Open AccessEditor’s ChoiceArticle

Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks

by Vasiliki Vita

Energies 2017, 10(9), 1433; https://doi.org/10.3390/en10091433 - 18 Sep 2017

Cited by 142 | Viewed by 9060

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 [...] Read more.

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 F: Electrical Engineering)

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8217 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Flame Front Propagation in an Optical GDI Engine under Stoichiometric and Lean Burn Conditions

by Santiago Martinez, Adrian Irimescu, Simona Silvia Merola, Pedro Lacava and Pedro Curto-Riso

Energies 2017, 10(9), 1337; https://doi.org/10.3390/en10091337 - 5 Sep 2017

Cited by 32 | Viewed by 9056

Abstract

Lean fueling of spark ignited (SI) engines is a valid method for increasing efficiency and reducing nitric oxide (NO_x) emissions. Gasoline direct injection (GDI) allows better fuel economy with respect to the port-fuel injection configuration, through greater flexibility to load changes, [...] Read more.

Lean fueling of spark ignited (SI) engines is a valid method for increasing efficiency and reducing nitric oxide (NO_x) 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 I: Energy Fundamentals and Conversion)

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8968 KiB

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

by Gyujin Kim, Sunyoung Moon, Seungha Lee and Kyoungdoug Min

Energies 2017, 10(9), 1292; https://doi.org/10.3390/en10091292 - 29 Aug 2017

Cited by 13 | Viewed by 6132

Abstract

The multiple injection strategy has been widely used in diesel engines to reduce engine noise, NO_x 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 [...] Read more.

The multiple injection strategy has been widely used in diesel engines to reduce engine noise, NO_x 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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7508 KiB

Open AccessEditor’s ChoiceReview

Models for Flow Rate Simulation in Gear Pumps: A Review

by Massimo Rundo

Energies 2017, 10(9), 1261; https://doi.org/10.3390/en10091261 - 24 Aug 2017

Cited by 98 | Viewed by 16401

Abstract

Gear pumps represent the majority of the fixed displacement machines used for flow generation in fluid power systems. In this context, the paper presents a review of the different methodologies used in the last years for the simulation of the flow rates generated [...] Read more.

Gear pumps represent the majority of the fixed displacement machines used for flow generation in fluid power systems. In this context, the paper presents a review of the different methodologies used in the last years for the simulation of the flow rates generated by gerotor, external gear and crescent pumps. As far as the lumped parameter models are concerned, different ways of selecting the control volumes into which the pump is split are analyzed and the main governing equations are presented. The principles and the applications of distributed models from 1D to 3D are reported. A specific section is dedicated to the methods for the evaluation of the necessary geometric quantities: analytic, numerical and Computer-Aided Design (CAD)-based. The more recent studies taking into account the influence on leakages of the interactions between the fluid and the mechanical parts are explained. Finally the models for the simulation of the fluid aeration are described. The review brings to evidence the increasing effort for improving the simulation models used for the design and the optimization of the gear machines. Full article

(This article belongs to the Section L: Energy Sources)

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4027 KiB

Open AccessEditor’s ChoiceArticle

A Metric-Based Validation Process to Assess the Realism of Synthetic Power Grids

by Adam B. Birchfield, Eran Schweitzer, Mir Hadi Athari, Ti Xu, Thomas J. Overbye, Anna Scaglione and Zhifang Wang

Energies 2017, 10(8), 1233; https://doi.org/10.3390/en10081233 - 19 Aug 2017

Cited by 57 | Viewed by 7180

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 [...] Read more.

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 F: Electrical Engineering)

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28972 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

by Fuad Un-Noor, Sanjeevikumar Padmanaban, Lucian Mihet-Popa, Mohammad Nurunnabi Mollah and Eklas Hossain

Energies 2017, 10(8), 1217; https://doi.org/10.3390/en10081217 - 17 Aug 2017

Cited by 562 | Viewed by 74840

Abstract

Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is [...] Read more.

Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector. Full article

(This article belongs to the Special Issue Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid)

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5252 KiB

Open AccessEditor’s ChoiceArticle

Nusselt Number Correlation for Vertical Tubes with Inverted Triangular Fins under Natural Convection

by Byeong Dong Kang, Hyun Jung Kim and Dong-Kwon Kim

Energies 2017, 10(8), 1183; https://doi.org/10.3390/en10081183 - 10 Aug 2017

Cited by 3 | Viewed by 5920

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 [...] Read more.

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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1829 KiB

Open AccessEditor’s ChoiceReview

The Concept of Segmented Wind Turbine Blades: A Review

by Mathijs Peeters, Gilberto Santo, Joris Degroote and Wim Van Paepegem

Energies 2017, 10(8), 1112; https://doi.org/10.3390/en10081112 - 31 Jul 2017

Cited by 40 | Viewed by 14274

Abstract

There is a trend to increase the length of wind turbine blades in an effort to reduce the cost of energy (COE). This causes manufacturing and transportation issues, which have given rise to the concept of segmented wind turbine blades. In this concept, [...] Read more.

There is a trend to increase the length of wind turbine blades in an effort to reduce the cost of energy (COE). This causes manufacturing and transportation issues, which have given rise to the concept of segmented wind turbine blades. In this concept, multiple segments can be transported separately. While this idea is not new, it has recently gained renewed interest. In this review paper, the concept of wind turbine blade segmentation and related literature is discussed. The motivation for dividing blades into segments is explained, and the cost of energy is considered to obtain requirements for such blades. An overview of possible implementations is provided, considering the split location and orientation, as well as the type of joint to be used. Many implementations draw from experience with similar joints such as the joint at the blade root, hub and root extenders and joints used in rotor tips and glider wings. Adhesive bonds are expected to provide structural and economic efficiency, but in-field assembly poses a big issue. Prototype segmented blades using T-bolt joints, studs and spar bridge concepts have proven successful, as well as aerodynamically-shaped root and hub extenders. Full article

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7156 KiB

Open AccessEditor’s ChoiceArticle

Investigation on the Development of a Sliding Mode Controller for Constant Power Loads in Microgrids

by Eklas Hossain, Ron Perez, Sanjeevikumar Padmanaban and Pierluigi Siano

Energies 2017, 10(8), 1086; https://doi.org/10.3390/en10081086 - 26 Jul 2017

Cited by 37 | Viewed by 6368

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 [...] Read more.

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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1461 KiB

Open AccessEditor’s ChoiceReview

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

by Yuri Merizalde, Luis Hernández-Callejo and Oscar Duque-Perez

Energies 2017, 10(7), 1056; https://doi.org/10.3390/en10071056 - 21 Jul 2017

Cited by 75 | Viewed by 7943

Abstract

Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a [...] Read more.

Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies. Full article

(This article belongs to the Special Issue Electric Machines and Drives for Renewable Energy Harvesting 2017)

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1442 KiB

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

by Madeleine McPherson, Theofilos Sotiropoulos-Michalakakos, LD Danny Harvey and Bryan Karney

Energies 2017, 10(7), 1007; https://doi.org/10.3390/en10071007 - 16 Jul 2017

Cited by 20 | Viewed by 6459

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 [...] Read more.

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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4524 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Energy-Based Design of Powertrain for a Re-Engineered Post-Transmission Hybrid Electric Vehicle

by Laura Tribioli

Energies 2017, 10(7), 918; https://doi.org/10.3390/en10070918 - 3 Jul 2017

Cited by 22 | Viewed by 6393

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 [...] Read more.

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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1564 KiB

Open AccessEditor’s ChoiceArticle

Site-Dependent Environmental Impacts of Industrial Hydrogen Production by Alkaline Water Electrolysis

by Jan Christian Koj, Christina Wulf, Andrea Schreiber and Petra Zapp

Energies 2017, 10(7), 860; https://doi.org/10.3390/en10070860 - 28 Jun 2017

Cited by 95 | Viewed by 16336

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 [...] Read more.

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 Technologies)

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1536 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Economic Optimization of Component Sizing for Residential Battery Storage Systems

by Holger C. Hesse, Rodrigo Martins, Petr Musilek, Maik Naumann, Cong Nam Truong and Andreas Jossen

Energies 2017, 10(7), 835; https://doi.org/10.3390/en10070835 - 22 Jun 2017

Cited by 163 | Viewed by 13689

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, [...] Read more.

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 D: Energy Storage and Application)

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7925 KiB

Open AccessEditor’s ChoiceArticle

A Novel Decentralized Economic Operation in Islanded AC Microgrids

by Hua Han, Lang Li, Lina Wang, Mei Su, Yue Zhao and Josep M. Guerrero

Energies 2017, 10(6), 804; https://doi.org/10.3390/en10060804 - 13 Jun 2017

Cited by 30 | Viewed by 4908

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 [...] Read more.

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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3866 KiB

Open AccessEditor’s ChoiceArticle

Biomass Chars: The Effects of Pyrolysis Conditions on Their Morphology, Structure, Chemical Properties and Reactivity

by Chamseddine Guizani, Mejdi Jeguirim, Sylvie Valin, Lionel Limousy and Sylvain Salvador

Energies 2017, 10(6), 796; https://doi.org/10.3390/en10060796 - 11 Jun 2017

Cited by 135 | Viewed by 9651

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 [...] Read more.

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

(This article belongs to the Special Issue Biomass Chars: Elaboration, Characterization and Applications)

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8722 KiB

Open AccessEditor’s ChoiceArticle

Effect of Gas Velocity Distribution on Heat Recovery Process in Packed Bed of Plate-Shaped Slag

by Nobuyuki Shigaki, Sumito Ozawa and Ikuhiro Sumi

Energies 2017, 10(6), 755; https://doi.org/10.3390/en10060755 - 28 May 2017

Cited by 7 | Viewed by 5721

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 [...] Read more.

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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3646 KiB

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

by Giorgia Mondino, Carlos A. Grande and Richard Blom

Energies 2017, 10(6), 745; https://doi.org/10.3390/en10060745 - 25 May 2017

Cited by 20 | Viewed by 6169

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 [...] Read more.

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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5933 KiB

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

by Takashi Mitani, Muhammad Aziz, Takuya Oda, Atsuki Uetsuji, Yoko Watanabe and Takao Kashiwagi

Energies 2017, 10(6), 738; https://doi.org/10.3390/en10060738 - 23 May 2017

Cited by 28 | Viewed by 4575

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. [...] Read more.

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 F: Electrical Engineering)

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993 KiB

Open AccessEditor’s ChoiceArticle

Economic Assessment of Network-Constrained Transactive Energy for Managing Flexible Demand in Distribution Systems

by Junjie Hu, Guangya Yang and Yusheng Xue

Energies 2017, 10(5), 711; https://doi.org/10.3390/en10050711 - 18 May 2017

Cited by 10 | Viewed by 4676

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 [...] Read more.

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 F: Electrical Engineering)

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950 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review

by Josh Davidson and John V. Ringwood

Energies 2017, 10(5), 666; https://doi.org/10.3390/en10050666 - 11 May 2017

Cited by 140 | Viewed by 13970

Abstract

Mathematical analysis is an essential tool for the successful development and operation of wave energy converters (WECs). Mathematical models of moorings systems are therefore a requisite in the overall techno-economic design and operation of floating WECs. Mooring models (MMs) can be applied to [...] Read more.

Mathematical analysis is an essential tool for the successful development and operation of wave energy converters (WECs). Mathematical models of moorings systems are therefore a requisite in the overall techno-economic design and operation of floating WECs. Mooring models (MMs) can be applied to a range of areas, such as WEC simulation, performance evaluation and optimisation, control design and implementation, extreme load calculation, mooring line fatigue life evaluation, mooring design, and array layout optimisation. The mathematical modelling of mooring systems is a venture from physics to numerics, and as such, there are a broad range of details to consider when applying MMs to WEC analysis. A large body of work exists on MMs, developed within other related ocean engineering fields, due to the common requirement of mooring floating bodies, such as vessels and offshore oil and gas platforms. This paper reviews the mathematical modelling of the mooring systems for WECs, detailing the relevant material developed in other offshore industries and presenting the published usage of MMs for WEC analysis. Full article

(This article belongs to the Special Issue Marine Energy)

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8079 KiB

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

by Taeho Kim and Monika Ivantysynova

Energies 2017, 10(5), 645; https://doi.org/10.3390/en10050645 - 6 May 2017

Cited by 12 | Viewed by 7155

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 [...] Read more.

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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2465 KiB

Open AccessEditor’s ChoiceArticle

Global Energy-Optimal Redundancy Resolution of Hydraulic Manipulators: Experimental Results for a Forestry Manipulator

by Jarmo Nurmi and Jouni Mattila

Energies 2017, 10(5), 647; https://doi.org/10.3390/en10050647 - 6 May 2017

Cited by 27 | Viewed by 7766

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 [...] Read more.

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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5085 KiB

Open AccessEditor’s ChoiceArticle

A Cost Optimized Fully Sustainable Power System for Southeast Asia and the Pacific Rim

by Ashish Gulagi, Dmitrii Bogdanov and Christian Breyer

Energies 2017, 10(5), 583; https://doi.org/10.3390/en10050583 - 25 Apr 2017

Cited by 48 | Viewed by 9177

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 [...] Read more.

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

by Nan Zhou, Jixiong Zhang, Hao Yan and Meng Li

Energies 2017, 10(4), 557; https://doi.org/10.3390/en10040557 - 18 Apr 2017

Cited by 44 | Viewed by 5387

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, [...] Read more.

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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3364 KiB

Open AccessEditor’s ChoiceArticle

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

by Adnan Ahmad, Asif Khan, Nadeem Javaid, Hafiz Majid Hussain, Wadood Abdul, Ahmad Almogren, Atif Alamri and Iftikhar Azim Niaz

Energies 2017, 10(4), 549; https://doi.org/10.3390/en10040549 - 17 Apr 2017

Cited by 222 | Viewed by 16073

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 [...] Read more.

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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3149 KiB

Open AccessEditor’s ChoiceArticle

Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)

by Ting-Chia Ou, Kai-Hung Lu and Chiou-Jye Huang

Energies 2017, 10(4), 488; https://doi.org/10.3390/en10040488 - 5 Apr 2017

Cited by 112 | Viewed by 7598

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 [...] Read more.

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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3319 KiB

Open AccessFeature PaperEditor’s ChoiceReview

A Review of Smart Cities Based on the Internet of Things Concept

by Saber Talari, Miadreza Shafie-khah, Pierluigi Siano, Vincenzo Loia, Aurelio Tommasetti and João P. S. Catalão

Energies 2017, 10(4), 421; https://doi.org/10.3390/en10040421 - 23 Mar 2017

Cited by 447 | Viewed by 35921

Abstract

With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of [...] Read more.

With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of smart cities more accessible and applicable. The goal of the current paper is to provide an inclusive review on the concept of the smart city besides their different applications, benefits, and advantages. In addition, most of the possible IoT technologies are introduced, and their capabilities to merge into and apply to the different parts of smart cities are discussed. The potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper. Meanwhile, some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed. Full article

(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)

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5202 KiB

Open AccessEditor’s ChoiceArticle

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

by Syed Zulqadar Hassan, Hui Li, Tariq Kamal, Uğur Arifoğlu, Sidra Mumtaz and Laiq Khan

Energies 2017, 10(3), 394; https://doi.org/10.3390/en10030394 - 20 Mar 2017

Cited by 49 | Viewed by 7112

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 [...] Read more.

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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8277 KiB

Open AccessEditor’s ChoiceArticle

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

by Bo Jiang, Haifeng Dai, Xuezhe Wei, Letao Zhu and Zechang Sun

Energies 2017, 10(3), 390; https://doi.org/10.3390/en10030390 - 19 Mar 2017

Cited by 23 | Viewed by 7356

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 [...] Read more.

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

(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))

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4361 KiB

Open AccessEditor’s ChoiceArticle

A Top-Down Spatially Resolved Electrical Load Model

by Martin Robinius, Felix ter Stein, Adrien Schwane and Detlef Stolten

Energies 2017, 10(3), 361; https://doi.org/10.3390/en10030361 - 14 Mar 2017

Cited by 20 | Viewed by 6262

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 [...] Read more.

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 F: Electrical Engineering)

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13699 KiB

Open AccessEditor’s ChoiceArticle

An Autonomous Coil Alignment System for the Dynamic Wireless Charging of Electric Vehicles to Minimize Lateral Misalignment

by Karam Hwang, Jaeyong Cho, Dongwook Kim, Jaehyoung Park, Jong Hwa Kwon, Sang Il Kwak, Hyun Ho Park and Seungyoung Ahn

Energies 2017, 10(3), 315; https://doi.org/10.3390/en10030315 - 7 Mar 2017

Cited by 63 | Viewed by 10517

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 [...] Read more.

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

(This article belongs to the Special Issue Advanced Power Electronics and Control for Wireless Power Transfer Systems)

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463 KiB

Open AccessEditor’s ChoiceArticle

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

by Nadeem Javaid, Sakeena Javaid, Wadood Abdul, Imran Ahmed, Ahmad Almogren, Atif Alamri and Iftikhar Azim Niaz

Energies 2017, 10(3), 319; https://doi.org/10.3390/en10030319 - 7 Mar 2017

Cited by 153 | Viewed by 9843

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 [...] Read more.

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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2977 KiB

Open AccessEditor’s ChoiceArticle

Environmental Assessment of Possible Future Waste Management Scenarios

by Yevgeniya Arushanyan, Anna Björklund, Ola Eriksson, Göran Finnveden, Maria Ljunggren Söderman, Jan-Olov Sundqvist and Åsa Stenmarck

Energies 2017, 10(2), 247; https://doi.org/10.3390/en10020247 - 19 Feb 2017

Cited by 35 | Viewed by 8758

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 [...] Read more.

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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8689 KiB

Open AccessEditor’s ChoiceArticle

Geospatial Analysis of Photovoltaic Mini-Grid System Performance

by Thomas Huld, Magda Moner-Girona and Akos Kriston

Energies 2017, 10(2), 218; https://doi.org/10.3390/en10020218 - 15 Feb 2017

Cited by 39 | Viewed by 7380

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 [...] Read more.

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

(This article belongs to the Special Issue Solar Photovoltaics Trilemma: Efficiency, Stability and Cost Reduction 2017)

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2771 KiB

Open AccessEditor’s ChoiceArticle

Hydrothermal Carbonization of Waste Biomass: Process Design, Modeling, Energy Efficiency and Cost Analysis

by Michela Lucian and Luca Fiori

Energies 2017, 10(2), 211; https://doi.org/10.3390/en10020211 - 13 Feb 2017

Cited by 227 | Viewed by 20724

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 [...] Read more.

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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1735 KiB

Open AccessEditor’s ChoiceArticle

From Theory to Econometrics to Energy Policy: Cautionary Tales for Policymaking Using Aggregate Production Functions

by Matthew K. Heun, João Santos, Paul E. Brockway, Randall Pruim, Tiago Domingos and Marco Sakai

Energies 2017, 10(2), 203; https://doi.org/10.3390/en10020203 - 10 Feb 2017

Cited by 20 | Viewed by 7057

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 [...] Read more.

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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6620 KiB

Open AccessEditor’s ChoiceArticle

Application of Liquid Hydrogen with SMES for Efficient Use of Renewable Energy in the Energy Internet

by Xin Wang, Jun Yang, Lei Chen and Jifeng He

Energies 2017, 10(2), 185; https://doi.org/10.3390/en10020185 - 8 Feb 2017

Cited by 17 | Viewed by 6614

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 [...] Read more.

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

(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))

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