Open AccessArticle
The Effect of Price-Dependent Demand on the Sustainable Electrical Energy Supply Chain
Energies 2018, 11(7), 1645; https://doi.org/10.3390/en11071645 (registering DOI) -
Abstract
In order to identify the optimal structure of an electricity power network under the main assumption of a price dependent demand of electrical energy, we presented an optimization model that aims at analyzing the effect of price-dependent demand on the sustainable electrical supply
[...] Read more.
In order to identify the optimal structure of an electricity power network under the main assumption of a price dependent demand of electrical energy, we presented an optimization model that aims at analyzing the effect of price-dependent demand on the sustainable electrical supply chain system (SESCS). The system included a power generation system, transmission and distribution substations, and many customers. The electrical energy was generated and transmitted through multiple substations to our customers, and the demand for electricity by the customers is dependent on the price of electricity. In the study, we considered the transmission and the distribution costs which depend on the capacities of power generation, transmission rates and distances between stations. We utilized the inventory theory to develop our model and proposed a procedure to derive an optimal solution for this problem. Finally, numerical examples and sensitivity analysis are provided to illustrate our study and consolidate managerial insights. Full article
Figures

Figure 1

Open AccessArticle
The Influence of Imports and Exports on the Evolution of Greenhouse Gas Emissions: The Case for the European Union
Energies 2018, 11(7), 1644; https://doi.org/10.3390/en11071644 (registering DOI) -
Abstract
Part of a country’s emissions are caused by producing goods for export to other countries, while a country’s own needs also generate emissions in other parts of the world that are associated with the products they import. Our interest was to evaluate the
[...] Read more.
Part of a country’s emissions are caused by producing goods for export to other countries, while a country’s own needs also generate emissions in other parts of the world that are associated with the products they import. Our interest was to evaluate the influence of imports and exports of goods and services on greenhouse gas (GHG) emissions in a data panel composed of 30 countries over 21 years. We included as control variables the gross domestic product per capita, employment, an indicator of the economic crisis and a non-linear trend and inferences were performed using a Bayesian framework. The results showed that it was the exports and imports of goods, rather than services, that were related to CO2-equivalent levels. Exports and imports of goods were very inelastic, albeit less so in the case of the index. In summary, the more a country imports, the higher their GHG emission levels are. However, it is important to point out that when employment rates are higher more energy is consumed and GHG emissions are greater. In richer countries, GDP per capita is the factor that best explains why their emissions are so high. Full article
Figures

Figure 1

Open AccessArticle
Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season
Energies 2018, 11(7), 1643; https://doi.org/10.3390/en11071643 (registering DOI) -
Abstract
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next
[...] Read more.
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems. Full article
Figures

Figure 1

Open AccessArticle
Analysis of the Operation of an Aerothermal Heat Pump in a Residential Building Using Building Information Modelling
Energies 2018, 11(7), 1642; https://doi.org/10.3390/en11071642 (registering DOI) -
Abstract
Heating, cooling and domestic hot water (DHW) are responsible for the largest share of energy use in residential buildings in Spain and play an important role in the implementation of nearly zero-energy buildings (NZEB). Building Information Modelling (BIM) is expected to promote more
[...] Read more.
Heating, cooling and domestic hot water (DHW) are responsible for the largest share of energy use in residential buildings in Spain and play an important role in the implementation of nearly zero-energy buildings (NZEB). Building Information Modelling (BIM) is expected to promote more efficient buildings through evaluation of different design options. BIM can be used as a platform from which to gather information that can be conveyed to energy efficiency simulation tools. The objective of this paper was to implement the model of a reversible air-to-water heat pump in EnergyPlus 8.9. This model was employed to analyze the performance of an aerothermal heat pump system (B) in a residential building under different Spanish climates compared to a conventional Heating, Ventilating and Air Conditioning (HVAC) system (A). Significant primary energy savings were achieved with system B compared to system A. These energy savings were higher in climates with a significant heating demand such as Madrid (27.4%) and Burgos (33.6%), and in cities with a mild climate such as Barcelona (37%). The residential building studied in this work was classified as Class A according to the CO2 emissions scale when using the aerothermal heat pump system, and as Class B when using the conventional HVAC system. Full article
Figures

Figure 1

Open AccessArticle
Valorization of Waste Wood as a Solid Fuel by Torrefaction
Energies 2018, 11(7), 1641; https://doi.org/10.3390/en11071641 (registering DOI) -
Abstract
The aim of this study was to investigate the optimal temperature range for waste wood and the effect torrefaction residence time had on torrefied biomass feedstock. Temperature range of 200–400 °C and residence time of 0–50 min were considered. In order to investigate
[...] Read more.
The aim of this study was to investigate the optimal temperature range for waste wood and the effect torrefaction residence time had on torrefied biomass feedstock. Temperature range of 200–400 °C and residence time of 0–50 min were considered. In order to investigate the effect of temperature and residence time, torrefaction parameters, such as mass yield, energy yield, volatile matter, ash content and calorific value were calculated. The Van Krevelen diagram was also used for clarification, along with the CHO index based on molecular C, H, and O data. Torrefaction parameters, such as net/gross calorific value and CHO increased with an increase in torrefaction temperature, while a reduction in energy yield, mass yield, and volatile content were observed. Likewise, elevated ash content was observed with higher torrefaction temperature. From the Van Krevelen diagram, it was observed that at 300 °C the torrefied feedstock came in the range of lignite. With better gross calorific value and CHO index, less ash content and nominal mass loss, 300 °C was found to be the optimal torrefaction temperature for waste wood. Full article
Figures

Figure 1

Open AccessArticle
Cycling Impact Assessment of Renewable Energy Generation in the Costs of Conventional Generators
Energies 2018, 11(7), 1640; https://doi.org/10.3390/en11071640 (registering DOI) -
Abstract
This paper proposes a set of indicators to quantify the impact of conventional thermal generating unit cycling on its non-fuel variable costs (NFVC) due to generation mix changes in the system. A novel iterative cost adjustment framework is developed to evaluate
[...] Read more.
This paper proposes a set of indicators to quantify the impact of conventional thermal generating unit cycling on its non-fuel variable costs (NFVC) due to generation mix changes in the system. A novel iterative cost adjustment framework is developed to evaluate the proposed indicators in order to assess the impacts of increasing installation of renewable resources on operation costs of the thermal units. The proposed framework allows private investors to estimate NFVC using a minimum level of information without a full knowledge of the system parameters. Additionally, the proposed framework is kept generic, which supports the NFVC adjustment for the conventional thermal units in a changing market environment. The impact of accelerated solar photovoltaic penetration on cycling and operational costs of existing thermal power plants in the Chilean power system is assessed using the indicators and methodology developed. The results suggest that natural gas driven peaking power plants are more susceptible to experiencing increased NFVC from solar photovoltaic growth than coal fired base load power plants. Full article
Figures

Figure 1

Open AccessArticle
Transient Stability Enhancement Using a Wide-Area Controlled SVC: An HIL Validation Approach
Energies 2018, 11(7), 1639; https://doi.org/10.3390/en11071639 (registering DOI) -
Abstract
This paper presents a control scheme of a wide-area controlled static VAr compensator (WAC-SVC) and its real-time implementation in a hardware-in-the-loop (HIL) simulation scheme with three control objectives: (1) to increase the critical clearing time, (2) to damp the power oscillations, and (3)
[...] Read more.
This paper presents a control scheme of a wide-area controlled static VAr compensator (WAC-SVC) and its real-time implementation in a hardware-in-the-loop (HIL) simulation scheme with three control objectives: (1) to increase the critical clearing time, (2) to damp the power oscillations, and (3) to minimize the maximum line current. The proposed control scheme considers a correction strategy to compensate the delays up to 200 ms. In addition to this, a generator tripping scheme based on synchrophasor measurements to determine the proximity to the loss of synchronism is proposed. A delay compensation algorithm based on polynomial approximations is also developed. The proposed WAC-SVC is experimentally validated using a Real-Time Digital Simulator platform (RTDS), industrial communication protocols, a commercial device for PMU-based control implementations, and digital relays with PMU capability. The real-time simulation results confirm its effectiveness and feasibility in real industrial applications. Furthermore, practical guidelines to implement this kind of control schemes are provided. Full article
Open AccessArticle
Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection
Energies 2018, 11(7), 1638; https://doi.org/10.3390/en11071638 (registering DOI) -
Abstract
Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration
[...] Read more.
Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments. Full article
Figures

Figure 1

Open AccessArticle
A Coordinated DC Power Support Strategy for Multi-Infeed HVDC Systems
Energies 2018, 11(7), 1637; https://doi.org/10.3390/en11071637 (registering DOI) -
Abstract
A DC power support strategy utilizes the flexibility of a High-voltage direct-current (HVDC) system in power modulation to optimize the operating point or compensate the power imbalance caused by a disturbance. The major impediment to the strategy is the difficulty in maintaining DC
[...] Read more.
A DC power support strategy utilizes the flexibility of a High-voltage direct-current (HVDC) system in power modulation to optimize the operating point or compensate the power imbalance caused by a disturbance. The major impediment to the strategy is the difficulty in maintaining DC voltage values at converter stations during the process of DC power support. To overcome the difficulty, a coordinated DC power support strategy for multi-infeed HVDC systems is proposed in this paper. Synchronous condensers are employed to provide dynamic reactive power compensation in sustaining DC voltage values at converter stations. Models are built for the optimal leading phase operation and adjusting excitation voltage reference value of synchronous condensers. Multiple HVDC links are coordinated to participate by using the DC power support factor to rank and select the links. Optimal DC power support values of the participating HVDC links are obtained with a comprehensive stability margin index that accounts for transient stability of the sending-end systems and frequency security of the receiving-end systems. An optimal load shedding model is used to ensure the frequency security of receiving-end systems. Case study results of a provincial power system in China demonstrate the effectiveness and performance of the proposed DC power support strategy. Full article
Figures

Figure 1

Open AccessArticle
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
Energies 2018, 11(7), 1636; https://doi.org/10.3390/en11071636 (registering DOI) -
Abstract
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as
[...] Read more.
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan’s electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area. Full article
Figures

Figure 1

Open AccessArticle
Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids
Energies 2018, 11(7), 1635; https://doi.org/10.3390/en11071635 (registering DOI) -
Abstract
Ever growing penetration of the behind-the-meter technologies is changing the electricity consumption profiles of end-users. Intelligent coordination of these emerging technologies through a robust communication infrastructure enables their seamless integration with electric utilities’ operation. In this context, an efficient and reliable communication infrastructure
[...] Read more.
Ever growing penetration of the behind-the-meter technologies is changing the electricity consumption profiles of end-users. Intelligent coordination of these emerging technologies through a robust communication infrastructure enables their seamless integration with electric utilities’ operation. In this context, an efficient and reliable communication infrastructure plays a pivotal role in enabling optimal integration of emerging resources. In this paper, we propose a game-theory based method to enhance efficiency of the underlying communication network. Specifically, we focus on Femtocell communication technology which is one the promising options for improving poor indoor communication coverage. The major drawback for using femtocell communication technology is cross-layer interference of femto users (FUs) and macro users (MUs) which adversely impact network performance. In this paper, we propose a novel approach for sharing spectrum in a cognitive radio system with FUs and MUs as primary and secondary users, respectively. The underlying problem is formulated as Stackelberg game that is joined with a convex optimization problem. In this study, MUs and FUs are assumed to be selfish, rational and motivated to achieve maximum utility function, while MUs are competing to obtain maximum bandwidth. Finally, we present a closed form solution for the proposed approach which obtains a unique Nash Equilibrium and prioritizes the access of MUs to femto-base stations. Simulation results provide proof of concept and verify the effectiveness of our mathematical modeling. Full article
Figures

Figure 1

Open AccessArticle
Design and Implementation of the Permanent- Magnet Synchronous Generator Drive in Wind Generation Systems
Energies 2018, 11(7), 1634; https://doi.org/10.3390/en11071634 (registering DOI) -
Abstract
The design and implementation of the permanent-magnet synchronous generator drive in wind generation systems is presented in this paper. The permanent-magnet synchronous generator (PMSG) can converse the alternating current (AC) power of the wind turbine to direct current (DC) power. In this paper,
[...] Read more.
The design and implementation of the permanent-magnet synchronous generator drive in wind generation systems is presented in this paper. The permanent-magnet synchronous generator (PMSG) can converse the alternating current (AC) power of the wind turbine to direct current (DC) power. In this paper, the dynamic model of a PMSG is first introduced. The current controller is designed based on T-S fuzzy models of the PMSG. The stability of the proposed PMSG drive system is analyzed and proved. The proposed T-S fuzzy current control possesses a disturbance suppression ability. Compared with the traditional fuzzy logic system, its stability can be proved and verified. Finally, the control performance of the PMSG drive is verified by experimental results. Full article
Figures

Figure 1

Open AccessArticle
A New Single-Phase Transformerless Current Source Inverter for Leakage Current Reduction
Energies 2018, 11(7), 1633; https://doi.org/10.3390/en11071633 (registering DOI) -
Abstract
A new single-phase transformerless current source inverter is proposed in this paper. The proposed inverter can achieve leakage current reduction, which is crucial for the conventional current source inverter. The basic concept of the proposed solution is to develop the new inverter by
[...] Read more.
A new single-phase transformerless current source inverter is proposed in this paper. The proposed inverter can achieve leakage current reduction, which is crucial for the conventional current source inverter. The basic concept of the proposed solution is to develop the new inverter by the duality principle from the voltage source inverter. The theoretical analysis is carried out to determine the switching states of the proposed inverter for the leakage current reduction. Also, a new modulation strategy is presented to achieve the optimized switching states. Finally, the experimental results are presented. Comparing with conventional single-phase current source inverter, the leakage current can be significantly reduced by the proposed inverter, which verifies the effectiveness of the proposed solution. Full article
Figures

Figure 1

Open AccessArticle
Interleaved High Step-Up DC-DC Converter Based on Voltage Multiplier Cell and Voltage-Stacking Techniques for Renewable Energy Applications
Energies 2018, 11(7), 1632; https://doi.org/10.3390/en11071632 (registering DOI) -
Abstract
A novel interleaved high step-up DC-DC converter based on voltage multiplier cell and voltage-stacking techniques is proposed for the power conversion in renewable energy power systems. The circuit configuration incorporates an input-parallel output-series boost converter with coupled inductors, clamp circuits and a voltage
[...] Read more.
A novel interleaved high step-up DC-DC converter based on voltage multiplier cell and voltage-stacking techniques is proposed for the power conversion in renewable energy power systems. The circuit configuration incorporates an input-parallel output-series boost converter with coupled inductors, clamp circuits and a voltage multiplier cell stacking on the output side to extend the voltage gain. The converter achieves high voltage conversion ratio without working at extreme large duty ratio. The voltage stresses on the power switches are significantly lower than the output voltage. As a result, the low-voltage-rated metal-oxide-semiconductor field-effect transistors (MOSFETs) can be employed to reduce the conduction losses and higher conversion efficiency can be expected. The interleaved operation reduces the input current ripple. The leakage inductances of the coupled inductors act on mitigating the diode reverse recovery problem. The operating principle, steady-state analysis and design guidelines of the proposed converter are presented in detail. Finally, a 1-kW prototype with 28-V input and 380-V output voltages was implemented and tested. The experimental results are presented to validate the performance of the proposed converter. Full article
Figures

Figure 1

Open AccessArticle
Gaussian Process Operational Curves for Wind Turbine Condition Monitoring
Energies 2018, 11(7), 1631; https://doi.org/10.3390/en11071631 (registering DOI) -
Abstract
Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profitable. Unexpected failures
[...] Read more.
Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation and maintenance costs to make the wind industry more profitable. Unexpected failures of turbine components make operation and maintenance (O&M) expensive, and because of transport and availability issues, the O&M cost is much higher in offshore wind farms (typically 30% of the levelized cost). To overcome this, supervisory control and data acquisition (SCADA) based predictive condition monitoring can be applied to remotely identify early failures and limit downtime, boost production and decrease the cost of energy (COE). A Gaussian Process is a nonlinear, nonparametric machine learning approach which is widely used in modelling complex nonlinear systems. In this paper, a Gaussian Process algorithm is proposed to estimate operational curves based on key turbine critical variables which can be used as a reference model in order to identify critical wind turbine failures and improve power performance. Three operational curves, namely, the power curve, rotor speed curve and blade pitch angle curve, are constructed using the Gaussian Process approach for continuous monitoring of the performance of a wind turbine. These developed GP operational curves can be useful for recognizing failures that force the turbines to underperform and result in downtime. Historical 10-min SCADA data are used for the model training and validation. Full article
Figures

Figure 1

Open AccessArticle
Numerical Study on the Dynamic Behavior of a Francis Turbine Runner Model with a Crack
Energies 2018, 11(7), 1630; https://doi.org/10.3390/en11071630 (registering DOI) -
Abstract
Crack appearance in the blade is the most common type of fatigue damage in Francis turbines. However, it is sometimes difficult to detect cracks in time using the current monitoring system, even when they are very large. To better monitor cracks, it is
[...] Read more.
Crack appearance in the blade is the most common type of fatigue damage in Francis turbines. However, it is sometimes difficult to detect cracks in time using the current monitoring system, even when they are very large. To better monitor cracks, it is imperative to research the effect of a crack on the dynamic behavior of a Francis turbine. In this paper, the dynamic behavior of a Francis turbine runner model with a crack has been researched numerically. The intact numerical model was first validated by the experimental data available. Then, a crack was created at the intersection line between one blade and the crown. The change in dynamic behavior with increasing crack length has been investigated. Crack-induced vibration localization theory has been used to explain the dynamic behavior changes due to the crack. Modal analysis showed that the adopted theory could basically explain the modal behavior change due to the crack. The FFT results of the modal shapes and the localization factors (LF) has been used to explain the forced response changes due to the crack. Based on the above analysis, the challenge of crack monitoring has been analyzed. This research provides some references for more advanced monitoring technologies. Full article
Figures

Figure 1

Open AccessArticle
Multiple Phase Change Material (PCM) Configuration for PCM-Based Heat Sinks—An Experimental Study
Energies 2018, 11(7), 1629; https://doi.org/10.3390/en11071629 (registering DOI) -
Abstract
A small-scale phase change material (PCM)-based heat sink can regulate the temperature of electronics due to high latent-heat capacity. Three different heat sinks are examined to study the effects of PCM combination, arrangement of PCMs in multiple-PCM heat sink, PCM thickness, melting temperature
[...] Read more.
A small-scale phase change material (PCM)-based heat sink can regulate the temperature of electronics due to high latent-heat capacity. Three different heat sinks are examined to study the effects of PCM combination, arrangement of PCMs in multiple-PCM heat sink, PCM thickness, melting temperature and intensity of heat source on the thermal behavior of heat sink. Results are obtained for the temperature distribution across the heat sink and the PCM melting profile. It is concluded that (i) PCM combination RT50–RT55 increases the thermal regulation period and also reduces the heat sink temperature at the end of the operation, (ii) the RT58–RT47 arrangement slightly reduces the maximum temperature as compared to RT47–RT58, (iii) As PCM thickness increases from 30 mm to 60 mm, the thermal-regulation-period increases by 50 min, (iv) As the PCM melting temperature increases, the thermal-regulation-period and the heat sink temperature increase and (v) The thermal-regulation-period decreases as the power rating increases from 1 to 2 W. Full article
Figures

Figure 1

Open AccessArticle
The Value of Day-Ahead Coordination of Power and Natural Gas Network Operations
Energies 2018, 11(7), 1628; https://doi.org/10.3390/en11071628 (registering DOI) -
Abstract
The operations of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination
[...] Read more.
The operations of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations. Full article
Figures

Figure 1

Open AccessArticle
Theoretical and Empirical Differences between Diagonal and Full BEKK for Risk Management
Energies 2018, 11(7), 1627; https://doi.org/10.3390/en11071627 (registering DOI) -
Abstract
The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer et al., 2017 show that
[...] Read more.
The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer et al., 2017 show that univariate GARCH is not a special case of multivariate GARCH, specifically, the Full BEKK model, and demonstrate that Full BEKK, which, in practice, is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties. Diagonal BEKK (DBEKK) does not suffer from these limitations, and hence provides a suitable benchmark. We use simulated financial returns series to contrast estimates of the conditional variances and covariances from DBEKK and BEKK. The results of non-parametric tests suggest evidence of considerable bias in the Full BEKK estimates. The results of quantile regression analysis show there is a systematic relationship between the two sets of estimates as we move across the quantiles. Estimates of conditional variances from Full BEKK, relative to those from DBEKK are relatively lower in the left tail and higher in the right tail. The BEKK model is a commonly applied multivariate volatility model frequently used in modelling and forecasting volatilities in financial applications. Our results suggest that it is subject to considerable bias and this should be considered by potential users. Full article
Figures

Figure 1

Open AccessArticle
An Experimental Study of the Solar Collection Performance of Liquid-Type Solar Collectors under Various Weather Conditions
Energies 2018, 11(7), 1626; https://doi.org/10.3390/en11071626 -
Abstract
To design and use a solar heating system properly, it is very important to evaluate the performance of its solar collector. Because the solar collection efficiency of a solar collector depends on the amount of solar radiation, the conditions of the heating medium
[...] Read more.
To design and use a solar heating system properly, it is very important to evaluate the performance of its solar collector. Because the solar collection efficiency of a solar collector depends on the amount of solar radiation, the conditions of the heating medium (e.g., flow rate and inlet temperature), and the outside air temperature, it is necessary to consider the performance of the solar collector in actual weather conditions that are likely to prevail when using the system for heating and hot water. In the present study, test equipment was manufactured to measure the efficiency of solar collectors. Using this equipment, the heating characteristics of seven types of solar collectors were measured. In addition, the amount of solar heat collected per unit area was calculated for seven regions in Japan to compare the solar collection performance for different weather conditions, such as the outside temperature and the amount of solar radiation. In addition, the amount of solar heat collected per unit area was calculated for seven regions in Japan to compare the solar collection performance for different weather conditions, such as outside temperature and the amount of solar radiation. The results show that the solar collection performance is climate dependent and that it is necessary to select a suitable collector for each region through a preliminary examination of the solar collection in the initial design stage. Full article
Figures

Figure 1