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Energies, Volume 15, Issue 23 (December-1 2022) – 484 articles

Cover Story (view full-size image): This paper provides an assessment of the technical and economic impacts of a microgrid at the building level, considering photovoltaic generation, battery storage and the use of electric vehicles in a vehicle-to-building system. Several tests were conducted using real on-site data to calculate the overall efficiencies of the different assets during their operation. An economic assessment was carried out to evaluate the potential benefits of coordinating battery storage with a vehicle-to-building system regarding the flexibility and cost-efficient operation of the microgrid. The results show that these two systems effectively increase the levels of self-consumption and available flexibility. Furthermore, economic benefits are highly dependent on the variability of tariffs and the costs of energy storage systems, as well as the efficiency of the equipment used in the conversion chain. View this paper
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17 pages, 11903 KiB  
Article
OrcaFlex Modelling of a Multi-Body Floating Solar Island Subjected to Waves
by Maria Ikhennicheu, Arthur Blanc, Benoat Danglade and Jean-Christophe Gilloteaux
Energies 2022, 15(23), 9260; https://doi.org/10.3390/en15239260 - 6 Dec 2022
Cited by 8 | Viewed by 3753
Abstract
Floating solar energy is an industry with great potential. As the industry matures, floating solar farms are considered in more challenging environments, where the presence of waves must be accounted for in mismatch studies and fatigue and mechanical considerations regarding electrical cables and [...] Read more.
Floating solar energy is an industry with great potential. As the industry matures, floating solar farms are considered in more challenging environments, where the presence of waves must be accounted for in mismatch studies and fatigue and mechanical considerations regarding electrical cables and mooring lines. Computational modelling of floating solar islands is now a critical step. The representation of such islands on industry-validated software is very complex, as it includes a large number of elements, each interacting with its neighbours. This study focuses on conditions with small waves (amplitude of <1 m) that are relevant to sheltered areas where generic float technologies can be utilized. A multi-body island composed of 3 × 3 floats is modelled in OrcaFlex. A solution to model the kinematic constraint chain between floats is presented. Three different modelling solutions are compared in terms of results and computation time. The most accurate model includes a multi-body computation of float responses in a potential flow solver (OrcaWave). However, solving the equations for a single float and applying the results to each float individually also gives accurate results and reduces the computation time by a factor of 3. These results represent a basis for further works in which larger and more realistic floating islands can be modelled. Full article
(This article belongs to the Special Issue Solar Photovoltaics and Solar Power Plants)
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13 pages, 3376 KiB  
Article
Fuel Consumption Dependence on a Share of Reduction Processes in Imperial Smelting Furnace
by Mikolaj Bernasowski, Ryszard Stachura and Arkadiusz Klimczyk
Energies 2022, 15(23), 9259; https://doi.org/10.3390/en15239259 - 6 Dec 2022
Cited by 2 | Viewed by 1673
Abstract
The paper shows the use of novel modelling techniques adapted from ironmaking in the pyrometallurgical process of zinc production. Firstly, regarding the purpose to determine the boundary conditions of reduction processes taking part in the working volume of an Imperial Smelting Furnace (ISF), [...] Read more.
The paper shows the use of novel modelling techniques adapted from ironmaking in the pyrometallurgical process of zinc production. Firstly, regarding the purpose to determine the boundary conditions of reduction processes taking part in the working volume of an Imperial Smelting Furnace (ISF), a deep thermochemical analysis was conducted. On this basis and using Ramm’s principles of direct and indirect reduction optimal share, the fuel rate minimization model was built. The model’s leading role is minimizing coke consumption in the ISF while maintaining the thermal state of the furnace at the correct level. In addition, the proposed presentation of the ISF thermal state shows in a unified way all the shortcomings in the correct process operation. Verification in real conditions on the ISF in Miasteczko Śląskie shows that model implementation can bring tangible benefits. Coke savings can reach over 30 kg per tonne of raw zinc. Full article
(This article belongs to the Topic Advanced Processes in Metallurgical Technologies)
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17 pages, 2705 KiB  
Article
Heat Integration for Phenols and Ammonia Recovery Process of Coal Gasification Wastewater Considering Optimization of Process Parameters
by Qiliang Ye, Jiang Zeng, Yuan Li, Peiqing Yuan and Fuchen Wang
Energies 2022, 15(23), 9258; https://doi.org/10.3390/en15239258 - 6 Dec 2022
Cited by 1 | Viewed by 1890
Abstract
A heat integration optimization method that considers the changes in process parameters is proposed to find the global optimal process scheme for a coal chemical company’s phenols and ammonia recovery process. The phenols and ammonia recovery process is simulated by Aspen Plus, and [...] Read more.
A heat integration optimization method that considers the changes in process parameters is proposed to find the global optimal process scheme for a coal chemical company’s phenols and ammonia recovery process. The phenols and ammonia recovery process is simulated by Aspen Plus, and a programming method for heat exchanger networks synthesis that can simultaneously optimize process parameters and heat integration is constructed by Matlab. Taking the total annual cost as the objective function, the following process parameters are optimized: the hot feed temperature and cold/hot feed ratio of sour water stripper, the temperature of three-step partial condensation system, the feed temperature and column pressure of both solvent distillation column and solvent stripper. The result shows that, compared with the heat integration process under original process parameters, the new heat integration process saves 14.3% energy consumption and reduces the total annual cost by about 15.1%. The new heat integration process provides guidance for the optimization of the phenols and ammonia recovery process. The proposed heat integration optimization method based on changing process parameters is an effective and practical tool that offers good application prospects. Full article
(This article belongs to the Special Issue Volume II: Heat Transfer and Heat Recovery Systems)
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18 pages, 3825 KiB  
Article
A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling
by Simon Heslop, Baran Yildiz, Mike Roberts, Dong Chen, Tim Lau, Shayan Naderi, Anna Bruce, Iain MacGill and Renate Egan
Energies 2022, 15(23), 9257; https://doi.org/10.3390/en15239257 - 6 Dec 2022
Cited by 1 | Viewed by 1615
Abstract
Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the [...] Read more.
Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes. Full article
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17 pages, 765 KiB  
Article
Federated System for Transport Mode Detection
by Iago C. Cavalcante, Rodolfo I. Meneguette, Renato H. Torres, Leandro Y. Mano, Vinícius P. Gonçalves, Jó Ueyama, Gustavo Pessin, Georges D. Amvame Nze and Geraldo P. Rocha Filho
Energies 2022, 15(23), 9256; https://doi.org/10.3390/en15239256 - 6 Dec 2022
Cited by 3 | Viewed by 1423
Abstract
Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, [...] Read more.
Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, such works have focused on developing centralized machine learning mechanisms. This centralized approach requires user data to be transferred to a central server and, therefore, does not satisfy a transport mode detection mechanism’s practical response time and privacy needs. This research presents the Federated System for Transport Mode Detection (FedTM). The main contribution of FedTM is exploring Federated Learning on transport mode detection using smartphone sensors. In FedTM, both the training and inference process is moved to the client side (smartphones), reducing response time and increasing privacy. The FedTM was designed using a Neural Network for the classification task and obtained an average accuracy of 80.6% in three transport classes (cars, buses and motorcycles). Other contributions of this work are: (i) The use of data collected only on the curves of the route. Such reduction in data collection is important, given that the system is decentralized and the training and inference phases take place on smartphones with less computational capacity. (ii) FedTM and centralized classifiers are compared with regard to execution time and detection performance. Such a comparison is important for measuring the pros and cons of using Federated Learning in the transport mode detection task. Full article
(This article belongs to the Special Issue Development of Intelligent Electric Vehicles and Smart Transportation)
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10 pages, 3903 KiB  
Article
A Study on Reduction of Cogging Torque and Magnet Usage through Intersect Magnet Consequent Pole Structure
by Si-Woo Song, Min-Ki Hong, Ju Lee and Won-Ho Kim
Energies 2022, 15(23), 9255; https://doi.org/10.3390/en15239255 - 6 Dec 2022
Cited by 1 | Viewed by 1284
Abstract
Owing to the shortage of rare-earth magnetic materials, various methods are being examined to reduce the use of magnets. One of these is a consequent pole. The consequent pole model can reduce the use of magnets by 50% using only one pole of [...] Read more.
Owing to the shortage of rare-earth magnetic materials, various methods are being examined to reduce the use of magnets. One of these is a consequent pole. The consequent pole model can reduce the use of magnets by 50% using only one pole of the magnet and replacing the other pole with iron. However, the consequent pole has the disadvantage of generating back EMF asymmetry and a high cogging torque. In this study, an intersect magnet consequent pole structure is proposed to overcome the disadvantages of the existing consequent pole. Two methods have been proposed to improve axial leakage magnetic flux caused by the intersect magnet consequent pole structure. Finally, we propose a method to reduce the cogging torque and minimize the use of magnets with the same performance standard. For motor design, two-dimensional and three-dimensional finite element analysis was used, and comparative analysis was performed via simulations for several models. The existing model and the final model were compared and verified. Full article
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18 pages, 3723 KiB  
Article
Enhanced Virtual Inertia Control for Microgrids with High-Penetration Renewables Based on Whale Optimization
by Asmaa Faragalla, Omar Abdel-Rahim, Mohamed Orabi and Esam H. Abdelhameed
Energies 2022, 15(23), 9254; https://doi.org/10.3390/en15239254 - 6 Dec 2022
Cited by 13 | Viewed by 1421
Abstract
High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance [...] Read more.
High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties. Full article
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28 pages, 10533 KiB  
Article
Numerical Modeling of Shell-and-Tube-like Elastocaloric Regenerator
by Žiga Ahčin, Parham Kabirifar, Luka Porenta, Miha Brojan and Jaka Tušek
Energies 2022, 15(23), 9253; https://doi.org/10.3390/en15239253 - 6 Dec 2022
Cited by 9 | Viewed by 2111
Abstract
Elastocaloric cooling is considered an environmentally friendly future alternative to vapor-compression technology. Recently, a shell-and-tube-like elastocaloric regenerator loaded in compression has demonstrated record-breaking heat-pumping performance and fatigue-resistant operation. The aim of this work is thus to present a new 1D numerical model to [...] Read more.
Elastocaloric cooling is considered an environmentally friendly future alternative to vapor-compression technology. Recently, a shell-and-tube-like elastocaloric regenerator loaded in compression has demonstrated record-breaking heat-pumping performance and fatigue-resistant operation. The aim of this work is thus to present a new 1D numerical model to simulate and optimize the operation of an elastocaloric regenerator with a shell-and-tube-like design. In the first part of this work, the superelastic and elastocaloric properties of a single NiTi tube, which serve as input data for the numerical model, were determined through experimental characterization and phenomenological modeling. In the second part, the results of the numerical model were compared with the experimentally obtained results. Relatively good agreement was found regarding the temperature span, cooling and heating power, and COP values, which indicates that the developed numerical model could be used for accurate optimization of shell-and-tube-like elastocaloric regenerators. Finally, the effects of operating conditions and hysteresis losses on the performance of the shell-and-tube-like elastocaloric regenerator are modeled and discussed. This work shows that the shell-and-tube-like elastocaloric regenerator with this configuration can achieve a maximum temperature span of more than 50 K at zero-thermal-load conditions and a maximum cooling/heating power of up to 4000 W·kg−1 and COP of about 4 (at zero temperature span). Full article
(This article belongs to the Topic Cooling Technologies and Applications)
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21 pages, 8515 KiB  
Review
A Review of Research on Cavity Growth in the Context of Underground Coal Gasification
by Huijun Fang, Yuewu Liu, Tengze Ge, Taiyi Zheng, Yueyu Yu, Danlu Liu, Jiuge Ding and Longlong Li
Energies 2022, 15(23), 9252; https://doi.org/10.3390/en15239252 - 6 Dec 2022
Cited by 3 | Viewed by 1594
Abstract
Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth [...] Read more.
Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth pattern is a prerequisite to ensure the efficient and economic development of UCG. At present, scholars have conducted numerous research works on cavity growth, but the simulation conditions limit the research results. Hence, it is necessary to summarize and sort out the research results of cavity growth patterns, which contribute to deepening the understanding of UCG and pointing out the direction for subsequent research. First of all, this paper summarizes the development history of UCG technology and describes the cavity growth mechanism from chemical reactions and thermo-mechanical failure. Then, the research methods of cavity growth are summarized from three aspects: a field test, laboratory experiment, and numerical simulation. The results show that the appearance of the cavity is teardrop-shaped, and its growth direction is obviously related to the gas injection method, including the injection direction and rate. Subsequently, the factors affecting the cavity growth process are expounded from the geological factors (permeability, moisture content, and coal rank) and operating factors (temperature, pressure, gasification agent’s composition, and gasification agent’s flow pattern). Finally, the existing problems and development trends in the cavity growth are discussed. The follow-up research direction should focus on clarifying the cavity growth mechanism of the controlled-retractable-injection-point (CRIP) method of UCG in the deep coal seam and ascertain the influence of the moisture content in the coal seam on cavity growth. Full article
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26 pages, 4677 KiB  
Article
Robust Approach to Battery Equivalent-Circuit-Model Parameter Extraction Using Electrochemical Impedance Spectroscopy
by Marzia Abaspour, Krishna R. Pattipati, Behnam Shahrrava and Balakumar Balasingam
Energies 2022, 15(23), 9251; https://doi.org/10.3390/en15239251 - 6 Dec 2022
Cited by 3 | Viewed by 3214
Abstract
Electrochemical impedance spectroscopy (EIS) is a well-established method of battery analysis, where the response of a battery to either a voltage or current excitation signal spanning a wide frequency spectrum is measured and analyzed. State-of-the-art EIS analysis is limited to high-precision measurement systems [...] Read more.
Electrochemical impedance spectroscopy (EIS) is a well-established method of battery analysis, where the response of a battery to either a voltage or current excitation signal spanning a wide frequency spectrum is measured and analyzed. State-of-the-art EIS analysis is limited to high-precision measurement systems within laboratory environments. In order to be relevant in practical applications, EIS analysis needs to be carried out with low-cost sensors, which suffer from high levels of measurement noise. This article presents an approach to estimate the equivalent circuit model (ECM) parameters of a Li-Ion battery pack based on EIS measurements in the presence of high levels of noise. The proposed algorithm consists of a fast Fourier transform, feature extraction, curve fitting, and least-squares estimation. The results of the proposed parameter-estimation algorithm are compared to that of recent work for objective performance comparison. The error analysis of the proposed approach, in comparison to the existing approach, demonstrated significant improvement in parameter estimation accuracy in low signal-to-noise ratio (SNR) regions. Results show that the proposed algorithm significantly outperforms the previous method under high-measurement-noise scenarios without requiring a significant increase in computational resources. Full article
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23 pages, 3635 KiB  
Article
A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources
by Umar Waleed, Abdul Haseeb, Muhammad Mansoor Ashraf, Faisal Siddiq, Muhammad Rafiq and Muhammad Shafique
Energies 2022, 15(23), 9250; https://doi.org/10.3390/en15239250 - 6 Dec 2022
Cited by 9 | Viewed by 1573
Abstract
This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two [...] Read more.
This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework. Full article
(This article belongs to the Section G1: Smart Cities and Urban Management)
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26 pages, 12569 KiB  
Article
Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs
by Aleksander Suti, Gianpietro Di Rito and Giuseppe Mattei
Energies 2022, 15(23), 9249; https://doi.org/10.3390/en15239249 - 6 Dec 2022
Cited by 5 | Viewed by 1669
Abstract
Lithium batteries employed in lightweight fixed-wing UAVs are required to operate with large temperature variations and, especially for the emerging applications in hybrid propulsion systems, with relevant transient loads. The detailed dynamic modelling of battery packs is thus of paramount importance to verify [...] Read more.
Lithium batteries employed in lightweight fixed-wing UAVs are required to operate with large temperature variations and, especially for the emerging applications in hybrid propulsion systems, with relevant transient loads. The detailed dynamic modelling of battery packs is thus of paramount importance to verify the feasibility of innovative hybrid systems, as well as to support the design of battery management systems for safety/reliability enhancement. This paper deals with the development of a generalised approach for the dynamic modelling of battery packs via Thevenin circuits with modular hysteretic elements (open circuit voltage, internal resistance, RC grids). The model takes into account the parameters’ dependency on the state of charge, temperature, and both the amplitude and sign of the current load. As a relevant case study, the modelling approach is here applied to the Li-Po battery pack (1850 mAh, 6 cells, 22.2 V) employed in the lightweight fixed-wing UAV Rapier X-25 developed by Sky Eye Systems (Cascina, Italy). The procedure for parameter identification with experimental measurements, obtained at different temperatures and current loads, is firstly presented, and then the battery model is verified by simulating an entire Hybrid Pulse Power Characterisation test campaign. Finally, the model is used to evaluate the battery performance within the altitude (i.e., temperature) envelope of the reference UAV. The experiments demonstrate the relevant hysteretic behaviour of the characteristic relaxation times, and this phenomenon is here modelled by inserting Bouc–Wen hysteresis models on RC grid capacitances. The maximum relative error in the terminal output voltage of the battery is smaller than 1% for any value of state of charge greater than 10%. Full article
(This article belongs to the Special Issue Lithium Batteries for Vehicular Applications)
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17 pages, 2477 KiB  
Article
Study on the Accuracy of Fracture Criteria in Predicting Fracture Characteristics of Granite with Different Occurrence Depths
by Chenbo Liu, Gan Feng, Hongqiang Xie, Jilan Wang, Zhipan Duan, Ye Tao, Gongda Lu, Huining Xu, Yaoqing Hu, Chun Li, Yuefei Hu, Qiuhong Wu and Lu Chen
Energies 2022, 15(23), 9248; https://doi.org/10.3390/en15239248 - 6 Dec 2022
Cited by 5 | Viewed by 1278
Abstract
The fracture network of a deep geothermal reservoir forms the place for heat exchange between injected fluid and rock mass with high temperature. The fracture resistance ability of reservoir rocks will affect the formation of fracture-network structure, heat exchange and transmission characteristics, and [...] Read more.
The fracture network of a deep geothermal reservoir forms the place for heat exchange between injected fluid and rock mass with high temperature. The fracture resistance ability of reservoir rocks will affect the formation of fracture-network structure, heat exchange and transmission characteristics, and reservoir mechanical stability. However, there are few reports on the fracture toughness and trajectory prediction of geothermal reservoirs with different depths. In this paper, the modified maximum tangential stress criterion (MMTS) is analyzed. The results show that the experimental data are significantly different from the theoretical estimate of MMTS under the influence of different occurrence depths. It is found that the fracture process zone (FPZ) seriously affects the accuracy of predicting fracture initiation angle and mixed-mode (I+II) fracture toughness by MMTS. The FPZ value, considering the influence of different occurrence depths, is modified, and the accuracy of MMTS in predicting the fracture mechanical characteristics of granite is improved. In addition, the mechanical test results show that the Brazilian splitting strength (σt) of granite fluctuates increase with the increase in temperature. With the increase in deviatoric stress, the Brazilian splitting strength and the Brazilian splitting modulus of rock show a trend of first increasing, then decreasing, and then increasing. Full article
(This article belongs to the Special Issue Advanced Coal, Petroleum and Nature Gas Exploration Technology)
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26 pages, 5721 KiB  
Review
Membrane Electrode Assembly Degradation Modeling of Proton Exchange Membrane Fuel Cells: A Review
by Ahmed Mohmed Dafalla, Lin Wei, Bereket Tsegai Habte, Jian Guo and Fangming Jiang
Energies 2022, 15(23), 9247; https://doi.org/10.3390/en15239247 - 6 Dec 2022
Cited by 13 | Viewed by 3050
Abstract
Proton exchange membrane fuel cells (PEMFCs) have been recognized as a promising power generation source for a wide range of automotive, stationary, and portable electronic applications. However, the durability of PEMFCs remains as one of the key barriers to their wide commercialization. The [...] Read more.
Proton exchange membrane fuel cells (PEMFCs) have been recognized as a promising power generation source for a wide range of automotive, stationary, and portable electronic applications. However, the durability of PEMFCs remains as one of the key barriers to their wide commercialization. The membrane electrode assembly (MEA) as a central part of a PEMFC, which consists of a proton exchange membrane with a catalyst layer (CL) and gas diffusion layer (GDL) on each side, is subject to failure and degradation in long-running and cycling load conditions. The real-time monitoring of the degradation evolution process through experimental techniques is challenging. Therefore, different numerical modeling approaches were proposed in the literature to assist the understanding of the degradation mechanisms in PEMFCs. To provide modeling progress in the addressed field, this paper briefly discusses the different degradation mechanisms occurring in the MEA. In particular, we present a detailed review of MEA degradation modeling research work, with special attention paid to the physical-based models (mechanistic models). Following the most recent relevant literature, the results showed that the combination of microstructure component models with macro-scale comprehensive PEMFC models provides a better understanding of degradation mechanisms when compared to single-scale degradation models. In this sense, it is concluded that in order to develop an accurate and efficient predictive degradation model, the different relevant scales ranging from nano- to macro-sized scales should be considered, and coupling techniques for multiscale modeling have to be advanced. Finally, the paper summarizes the degradation models for different MEA components. It is highlighted that the GDL chemical degradation models that describe damage accumulation are relatively limited. The paper provides a useful reference for the recent developments in the MEA degradation modeling of PEMFCs. Full article
(This article belongs to the Special Issue Advances in Electrochemical Energy System)
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21 pages, 989 KiB  
Review
A Review of Energy Industry Chain and Energy Supply Chain
by Lijing Zhang, Shuke Fu, Jiali Tian and Jiachao Peng
Energies 2022, 15(23), 9246; https://doi.org/10.3390/en15239246 - 6 Dec 2022
Cited by 5 | Viewed by 5452
Abstract
The reduction of carbon emissions from the energy industry chain and the coordinated development of the energy supply chain have attracted widespread attention. This paper conducts a systematic review of the existing literature on the energy industry chain and energy supply chain. Based [...] Read more.
The reduction of carbon emissions from the energy industry chain and the coordinated development of the energy supply chain have attracted widespread attention. This paper conducts a systematic review of the existing literature on the energy industry chain and energy supply chain. Based on the analytical results, this paper finds that research gaps exist in the studies of energy consumption structure and resource consumption in energy industry chain. In addition, the studies of coordinated operation mechanisms, risk control and the impact of government policies on the energy supply chain still have some shortcomings. Furthermore, this paper shows that the exploitation and utilization of renewable energy and the sustainable development of the energy industry chain and supply chain have become the major focus of scholars and governments in recent years. Accordingly, this article finally presents the future research prospects and provides managerial insights for policy makers and enterprise managers to accelerate the development of renewable energy resources and to achieve green, low-carbon, coordinated and sustainable development. Full article
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38 pages, 7266 KiB  
Article
Small-Scale Solar-Powered Desalination Plants: A Sustainable Alternative Water-Energy Nexus to Obtain Water for Chile’s Coastal Areas
by Lorena Cornejo-Ponce, Patricia Vilca-Salinas, María Janet Arenas-Herrera, Claudia Moraga-Contreras, Héctor Tapia-Caroca and Stavros Kukulis-Martínez
Energies 2022, 15(23), 9245; https://doi.org/10.3390/en15239245 - 6 Dec 2022
Cited by 3 | Viewed by 3889
Abstract
The natural potential of Chile—solar energy and 8 km of coastline—make the implementation of small-scale reverse osmosis desalination plants (RODPs) in coastal areas energetically supported with photovoltaic systems (PVs) feasible. This work considers a survey of the plants in Chile. As a demonstration [...] Read more.
The natural potential of Chile—solar energy and 8 km of coastline—make the implementation of small-scale reverse osmosis desalination plants (RODPs) in coastal areas energetically supported with photovoltaic systems (PVs) feasible. This work considers a survey of the plants in Chile. As a demonstration of a RODP, a technical/economic evaluation is carried out, analyzing four possible cases in which different energy configurations are proposed: electric grid, diesel generator, and photovoltaic systems, without or with batteries. Finally, the challenges and opportunities of these plants are presented. The results obtained indicate that there are 39 plants in operation, which produce an average permeate water flow of Qp 1715 m3d−1. Solar Explorer, and Homer Pro software are used for a plant that generates 8 m3day−1 of permeate water, resulting in the conclusion that Case 3 is the most economically viable, as it has a useful life of 20 years and will have an annual solar contribution of more than 65%. The levelized cost of water production is 0.56 USDm−3 (RODP/PV) and 0.02 USDkW−1h−1 was obtained for the LCOE. Finally, this case contributes to the mitigation of climate change. Full article
(This article belongs to the Topic Energy-Water Nexus)
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17 pages, 3482 KiB  
Article
Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control
by Abebe Tilahun Tadie, Zhizhong Guo and Ying Xu
Energies 2022, 15(23), 9244; https://doi.org/10.3390/en15239244 - 6 Dec 2022
Cited by 3 | Viewed by 1437
Abstract
This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion [...] Read more.
This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. Full article
(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)
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19 pages, 3530 KiB  
Article
A Study on the Development of a Novel ESS Simulation Model for Transmission-Level Power-System Analysis
by Dong-Hee Yoon
Energies 2022, 15(23), 9243; https://doi.org/10.3390/en15239243 - 6 Dec 2022
Viewed by 1412
Abstract
Thanks to technological advances, the number of large-scale ESS applications is increasing in power systems. Therefore, the need for an ESS model to analyze the effect through simulation has also increased. In this study, an ESS analysis model was developed for transmission-level power-system [...] Read more.
Thanks to technological advances, the number of large-scale ESS applications is increasing in power systems. Therefore, the need for an ESS model to analyze the effect through simulation has also increased. In this study, an ESS analysis model was developed for transmission-level power-system analysis. The developed model has a relatively uncomplicated configuration compared to the previous model. The ESS model was developed in a form that can be exploited in the PSS®E, commercial power-system analysis program. The programming language FORTRAN was used for model development. A detailed process and necessary data for the model development are described. The operation of the developed ESS model was verified through simulation using two test systems. The results of this study can be used for power-system analysis and as reference material for model development. Full article
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7 pages, 232 KiB  
Editorial
Recent Trends in Power Systems Modeling and Analysis
by Rodolfo Araneo, Salvatore Celozzi, Stefano Lauria, Erika Stracqualursi, Gianfranco Di Lorenzo and Marco Graziani
Energies 2022, 15(23), 9242; https://doi.org/10.3390/en15239242 - 6 Dec 2022
Cited by 1 | Viewed by 1729
Abstract
In recent years, the explosion of renewable energy sources, the increase in the demand for electrical energy, and several improvements in related technologies have fostered research in many relevant areas of interest [...] Full article
22 pages, 4011 KiB  
Article
Characteristics of Generic Dielectric Materials and Char as Bed Materials of a Dielectric Barrier Discharge Reactor under High Temperature and Wide Frequency Range
by Saravanakumar Arumugam, Philipp Schröder, Thomas Schoenemann and York Neubauer
Energies 2022, 15(23), 9241; https://doi.org/10.3390/en15239241 - 6 Dec 2022
Viewed by 1008
Abstract
This paper investigates the characteristics of generic dielectric materials and char, which are intended to be used as the fixed bed materials of a non-thermal-plasma (NTP)-based dielectric barrier discharge (DBD) reactor. Such data are very essential when upgrading the fixed bed to a [...] Read more.
This paper investigates the characteristics of generic dielectric materials and char, which are intended to be used as the fixed bed materials of a non-thermal-plasma (NTP)-based dielectric barrier discharge (DBD) reactor. Such data are very essential when upgrading the fixed bed to a fluidised bed, which may provide further improvement in the production and quality of the producer gas. This measure would eventually cause a better producer gas and effective biomass-based power generation. Pertinent data that are currently available focus on either improving the design requirements of the producer gas or studying the impact of individual dielectric-material-specific applications to produce useful gases by decomposing the polluting gases. Considering that there has only been a meagre attempt to gather this information, this study gains its importance. In this context, the collective electrical behaviour of bed materials viz. quartz-sand, olivine, and char under ambient and higher temperatures is recorded and their frequency dependencies are analysed. First, the electrical behaviour of the chosen materials is resolved over a wide frequency range. For this purpose, two test cells, i.e., one for the ambient conditions and the other for higher temperatures, are built. Subsequently, the surface and volumetric properties of the chosen bed materials under ambient and higher temperatures are studied. As these materials are not as conductive as metal, such an approach is necessary to understand the apparent behaviour of the materials and anticipate their direct or indirect effects in the presence of non-thermal plasma. In summary, the data from the test cell under ambient and higher temperatures and the influence of materials in the dielectric barrier discharge reactor qualitatively define the material usage and may provide an opportunity to optimise their performance. Full article
(This article belongs to the Special Issue Diagnostic Testing and Condition Monitoring Methods)
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24 pages, 6177 KiB  
Article
Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability
by Estefania Alexandra Tapia, Delia Graciela Colomé and José Luis Rueda Torres
Energies 2022, 15(23), 9240; https://doi.org/10.3390/en15239240 - 6 Dec 2022
Cited by 4 | Viewed by 1735
Abstract
Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of [...] Read more.
Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power plants). There is little research on the assessment of both types of stability together, despite the fact that they develop over the same short-term period, and that they can have a major influence on the overall transient performance driven by large electrical disturbances (e.g., short circuits). This work addresses this open research challenge by proposing a methodology for the joint assessment of TS and STVS. The methodology aims at estimating the resulting short-term stability state (STSS) in stable, or unstable conditions, following critical events, such as the synchronism loss of synchronous generators (SG) or the stalling of induction motors (IM). The estimations capture the mechanisms responsible for the degradations of TS and STVS, respectively. The paper overviews the off-line design of the data-driven STSS classification methodology, which supports the design and training of a hybrid deep neural network RCNN (recurrent convolutional neural network). The RCNN can automatically capture spatial and temporal features from the power system through a time series of selected physical variables, which results in a high estimation degree for STSS in real-time applications. The methodology is tested on the New England 39-bus system, where the results demonstrate the superiority of the proposed methodology over other traditional and deep learning-based methodologies. For reference purposes, the numerical tests also illustrate the classification performance in special situations, when the training is performed by exclusively using measurements from generation and motor load buses, which constitute locations where the investigated stability can be observed. Full article
(This article belongs to the Special Issue Power Converter Control Applications in Low-Inertia Power Systems)
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13 pages, 6279 KiB  
Article
Heat Transfer and Pressure Drops in a Helical Flow Channel Liquid/Solid Fluidized Bed
by Oscar García-Aranda, Christopher Heard, José Javier Valencia-López and Francisco Javier Solorio-Ordaz
Energies 2022, 15(23), 9239; https://doi.org/10.3390/en15239239 - 6 Dec 2022
Cited by 1 | Viewed by 1506
Abstract
Industrial liquid/solid fluidized bed heat exchangers are commonly used with particle recycling systems to allow an increased superficial velocity and higher heat transfer rates. Here, experimental results are reported on a novel helical flow channel geometry for liquid/solid fluidized beds which allow higher [...] Read more.
Industrial liquid/solid fluidized bed heat exchangers are commonly used with particle recycling systems to allow an increased superficial velocity and higher heat transfer rates. Here, experimental results are reported on a novel helical flow channel geometry for liquid/solid fluidized beds which allow higher heat transfer rates and reduced complexity by operating below the particle transport fluid velocity. This eliminates the complexity of particle recycle systems whilst still delivering a compact heat exchanger. The qualitative character of the fluidization was studied for a range of particle types and sizes under several inclinations of the helices and various hydraulic diameters. The best fluidization combinations were further studied to obtain heat transfer coefficients and pressure drops. Improvements over the heat exchange from a plain concentric tube in an annulus were obtained to the following degree: vertical fluidized bed, 27%; helical baffles, 34 to 54%; and fluidized bed with helical baffles, 69 to 89%. Full article
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19 pages, 1443 KiB  
Article
Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network
by Issah Babatunde Majeed and Nnamdi I. Nwulu
Energies 2022, 15(23), 9238; https://doi.org/10.3390/en15239238 - 6 Dec 2022
Cited by 8 | Viewed by 5524
Abstract
Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power [...] Read more.
Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power flow in a low-voltage network with high PV penetration. A simulation model of a real urban electricity company in Ghana is investigated against various PV penetration levels by load flows with ETAP software. The impact of reverse power flow on the radial network transformer loadings is examined for high PV penetrations. Using the least squares method, simulation results are modelled in Excel software. Transformer backflow limitations are determined by correlating operating loads with PV penetration. At high PV penetration, the models predict reverse power flow into the transformer. Interpolations from the correlation models show transformer backflow operating limits of 78.04 kVA and 24.77% at the threshold of reverse power flow. These limits correspond to a maximum PV penetration limit of 88.30%. In low-voltage networks with high PV penetration; therefore, planners should consider transformer overload limits caused by reverse power flow, which degrades transformer life. This helps select control schemes near substation transformers to limit reverse power flow. Full article
(This article belongs to the Special Issue Power System Dynamics and Renewable Energy Integration)
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13 pages, 2719 KiB  
Article
Pricing Decision Models of the Dual Channel Supply Chain with Service Level and Return
by Xuelong Zhang, Huili Xu, Chenhui Zhang, Shuang Xiao and Ying Zhang
Energies 2022, 15(23), 9237; https://doi.org/10.3390/en15239237 - 6 Dec 2022
Viewed by 1182
Abstract
The interests of upstream, midstream, downstream companies and consumers in the supply chain are jointly affected by service levels and returns. Improving service levels can increase market demand and improve market position, as well as reduce return rates. But the increase in service [...] Read more.
The interests of upstream, midstream, downstream companies and consumers in the supply chain are jointly affected by service levels and returns. Improving service levels can increase market demand and improve market position, as well as reduce return rates. But the increase in service level will bring an increase in service cost. How to balance the service cost and return cost through pricing decision, so that the profit of supply chain members can be improved, is the problem studied in this paper. In this paper, we consider the effect of service level of network channel on consumers’ return behavior in the context of manufacturer’s dual-channel supply chain when dual channels provide services at the same time, and discuss the effect of service level and return rate on pricing decision of dual-channel supply chain. It was found that return behavior can stimulate manufacturers to improve service levels and increase overall supply chain profits. The higher the return rate in the network channel, the greater the benefits from improved service levels by the manufacturer and the less detrimental to retailers’ returns. This study enriches the research on pricing decisions in dual-channel supply chains, increases the motivation of merchants to improve service levels, and has some guiding implications for supply chain members to develop price and service strategies. Full article
(This article belongs to the Special Issue Energy Saving Manufacturing System Optimization)
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24 pages, 5565 KiB  
Review
A Review on Up-to-Date Gearbox Technologies and Maintenance of Tidal Current Energy Converters
by Gang Li and Weidong Zhu
Energies 2022, 15(23), 9236; https://doi.org/10.3390/en15239236 - 6 Dec 2022
Cited by 2 | Viewed by 3242
Abstract
This paper presents a review-based comparative study of state-of-the-art technologies, technical challenges and research barriers, and development trends of gearboxes used in tidal current energy converters (TCECs). Currently, the development of commercial projects using TCECs is still in the demonstration phase. While many [...] Read more.
This paper presents a review-based comparative study of state-of-the-art technologies, technical challenges and research barriers, and development trends of gearboxes used in tidal current energy converters (TCECs). Currently, the development of commercial projects using TCECs is still in the demonstration phase. While many drivetrain designs and configurations of TCECs inherit from those of wind turbines, different operational constraints, e.g., high-torque and low-speed conditions, make TCECs potentially suffer from high failure rates in harsh deep-sea environments. Evidence of these potentially high failure rates highlights the need for adopting the most resilient drivetrain options with a high degree of maintainability. The gearbox option is a critical issue that needs to be addressed for the choice of the drivetrain configuration due to its longest downtime per failure among all drivetrain components of TCECs. The main purpose of this study is to review current gearbox technologies of TCECs with advantages and disadvantages as well as to identify future technical challenges and research barriers. Gearbox maintenance is also a focal point in this study. We present a discussion of the operation phase to highlight operational maintenance methods currently used in the tidal energy industry. This study will, therefore, address the critical issue by proposing a review-based gearbox option comparison and discussing potential solutions to reduce operation and maintenance costs of gearboxes of TCECs. Full article
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32 pages, 5484 KiB  
Review
Progress of Single-Crystal Nickel-Cobalt-Manganese Cathode Research
by Ruixia Chu, Yujian Zou, Peidong Zhu, Shiwei Tan, Fangyuan Qiu, Wenjun Fu, Fu Niu and Wanyou Huang
Energies 2022, 15(23), 9235; https://doi.org/10.3390/en15239235 - 6 Dec 2022
Cited by 5 | Viewed by 4963
Abstract
The booming electric vehicle industry continues to place higher requirements on power batteries related to economic-cost, power density and safety. The positive electrode materials play an important role in the energy storage performance of the battery. The nickel-rich NCM (LiNixCoy [...] Read more.
The booming electric vehicle industry continues to place higher requirements on power batteries related to economic-cost, power density and safety. The positive electrode materials play an important role in the energy storage performance of the battery. The nickel-rich NCM (LiNixCoyMnzO2 with x + y + z = 1) materials have received increasing attention due to their high energy density, which can satisfy the demand of commercial-grade power batteries. Prominently, single-crystal nickel-rich electrodes with s unique micron-scale single-crystal structure possess excellent electrochemical and mechanical performance, even when tested at high rates, high cut-off voltages and high temperatures. In this review, we outline in brief the characteristics, problems faced and countermeasures of nickel-rich NCM materials. Then the distinguishing features and main synthesis methods of single-crystal nickel-rich NCM materials are summarized. Some existing issues and modification methods are also discussed in detail, especially the optimization strategies under harsh conditions. Finally, an outlook on the future development of single-crystal nickel-rich materials is provided. This work is expected to provide some reference for research on single-crystal nickel-rich ternary materials with high energy density, high safety levels, long-life, and their contribution to sustainable development. Full article
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31 pages, 31881 KiB  
Article
A Retrofit Strategy for Real-Time Monitoring of Building Electrical Circuits Based on the SmartLVGrid Metamodel
by Rubens A. Fernandes, Raimundo C. S. Gomes, Ozenir Dias, Celso Carvalho, Israel G. Torné, Jozias P. Oliveira and Carlos T. C. Júnior
Energies 2022, 15(23), 9234; https://doi.org/10.3390/en15239234 - 6 Dec 2022
Cited by 1 | Viewed by 1840
Abstract
The Internet of things (IoT) paradigm promotes the emergence of solutions to enable energy-management strategies. However, these solutions may favor the disposal or replacement of outdated but still necessary systems. Thus, a proposal that advocates the retrofit of pre-existing systems would be an [...] Read more.
The Internet of things (IoT) paradigm promotes the emergence of solutions to enable energy-management strategies. However, these solutions may favor the disposal or replacement of outdated but still necessary systems. Thus, a proposal that advocates the retrofit of pre-existing systems would be an alternative to implement energy monitoring. In this sense, this work presents a strategy for monitoring electrical parameters in real time by using IoT solutions, cloud-resident applications, and retrofitting of legacy building electrical systems. In this implementation, we adapted the SmartLVGrid metamodel to systematize the insertion of remote monitoring resources in low-voltage circuits. For this, we developed embedded platforms for monitoring the circuits of a building electrical panel and application for visualization and data storage in the cloud. With this, remote monitoring of the consumer unit was carried out in relation to energy demand, power factor, and events of variations of electrical parameters in the circuits of the legacy distribution board. We also carried out a case study with the proposed system, identifying events of excess demand in the consumer unit, mitigating the individual contribution of the installation circuits in this process. Therefore, our proposal presents an alternative to enable energy management and maximum use of existing resources. Full article
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3 pages, 156 KiB  
Editorial
On CFD-Assisted Research and Design in Engineering
by Dmitry Eskin
Energies 2022, 15(23), 9233; https://doi.org/10.3390/en15239233 - 6 Dec 2022
Viewed by 996
Abstract
At present, computational fluid dynamics (CFD) is an inherent component of the development procedure of a majority of technological processes involving fluid flows and/or heat and mass transfers. Practicing engineers and investigators employ different commercial CFD software, open-source codes and even develop their [...] Read more.
At present, computational fluid dynamics (CFD) is an inherent component of the development procedure of a majority of technological processes involving fluid flows and/or heat and mass transfers. Practicing engineers and investigators employ different commercial CFD software, open-source codes and even develop their own computational codes (in house) for solving tasks, requiring accounting for nonstandard effects. Full article
(This article belongs to the Topic Computational Fluid Dynamics (CFD) and Its Applications)
13 pages, 3705 KiB  
Article
Improved Method for Determining Voltage Unbalance Factor Using Induction Motors
by Luis Guasch-Pesquer, Sara García-Ríos, Adolfo Andres Jaramillo-Matta and Enric Vidal-Idiarte
Energies 2022, 15(23), 9232; https://doi.org/10.3390/en15239232 - 6 Dec 2022
Cited by 1 | Viewed by 1612
Abstract
This work shows an alternative method to determine the Voltage Unbalance Factor in a power grid by using both the mean value of the line voltages and Current Unbalance Factor in induction motors. Twenty unbalanced voltage points on three induction motors were used [...] Read more.
This work shows an alternative method to determine the Voltage Unbalance Factor in a power grid by using both the mean value of the line voltages and Current Unbalance Factor in induction motors. Twenty unbalanced voltage points on three induction motors were used in order to compare the two methods. The influence of the measurement error of both the voltmeters and the ammeters on the resulting Voltage Unbalance Factor was studied, and the validation was made with laboratory data for one of the three motors analyzed, in addition to the simulations carried out. The proposed Voltage Unbalance Factor was compared with the most typical method in the standards to obtain this factor, showing that the proposed factor has a better approach than the standard factor to determine the value of the Voltage Unbalance Factor in an unbalanced power system. Full article
(This article belongs to the Special Issue Power Quality in the Modeling of Machines and Electrical Devices)
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22 pages, 3448 KiB  
Article
Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques
by Muhammad S. Aliero, Muhammad F. Pasha, David T. Smith, Imran Ghani, Muhammad Asif, Seung Ryul Jeong and Moveh Samuel
Energies 2022, 15(23), 9231; https://doi.org/10.3390/en15239231 - 6 Dec 2022
Cited by 7 | Viewed by 2054
Abstract
Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, [...] Read more.
Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest. Full article
(This article belongs to the Special Issue Climate Change and Sustainable Energy Transition)
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