Energies

21 pages, 6463 KiB

Open AccessArticle

A Fast State-of-Charge (SOC) Balancing and Current Sharing Control Strategy for Distributed Energy Storage Units in a DC Microgrid

by Qin Luo, Jiamei Wang, Xuan Huang and Shunliang Li

Energies 2024, 17(16), 3885; https://doi.org/10.3390/en17163885 (registering DOI) - 6 Aug 2024

Abstract

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge [...] Read more.

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios. Full article

(This article belongs to the Topic Application of Innovative Power Electronic Technologies, 2nd Volume)

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16 pages, 3047 KiB

Open AccessArticle

Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries

by Neha Bhushan, Saad Mekhilef, Kok Soon Tey, Mohamed Shaaban, Mehdi Seyedmahmoudian and Alex Stojcevski

Energies 2024, 17(16), 3884; https://doi.org/10.3390/en17163884 (registering DOI) - 6 Aug 2024

Abstract

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed [...] Read more.

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications. Full article

(This article belongs to the Special Issue Energy, Electrical and Power Engineering 2024)

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13 pages, 2141 KiB

Open AccessArticle

Study on the Influencing Factors of Electrical Characteristics of Articulated Split-Zone Insulator Arc

by Like Pan, Biaohuan Xie, Liming Chen, Tong Xing, Haibo Zhang, Yanming Lu and Yang Song

Energies 2024, 17(16), 3883; https://doi.org/10.3390/en17163883 (registering DOI) - 6 Aug 2024

Abstract

The frequent occurrence of the pantograph–catenary (PC) arc seriously threatens the safe and stable operation of electric multiple units (EMUs). In order to investigate the primary factors affecting the electrical characteristics of the PC arc, this paper first reveals the mechanism of arc [...] Read more.

The frequent occurrence of the pantograph–catenary (PC) arc seriously threatens the safe and stable operation of electric multiple units (EMUs). In order to investigate the primary factors affecting the electrical characteristics of the PC arc, this paper first reveals the mechanism of arc generation when EMUs leave the split-zone insulator according to the actual mechanical structure of the PC articulated split-zone insulator. Then, based on the Habedank black box arc model, considering the dynamic changes in arc length during the actual operation of EMUs, a numerical simulation model suitable for describing the PC articulated split-zone insulator arc is established. Furthermore, by comparing with existing research results, the effectiveness of the proposed model is verified. Next, a series of simulation experiments were conducted to analyze the influence of different operating parameters of the EMUs on the electrical characteristics of the PC arc. More importantly, the paper proposes using the entropy weight method to calculate the impact strength of different operating parameters of the EMUs on the arc duration time of the contact wire split-zone region. The research results of this paper can provide some technical references for reducing the erosion of the PC contact wire and slide caused by arcs when the EMUs pass through the split-zone insulator. Full article

(This article belongs to the Section E: Electric Vehicles)

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30 pages, 2658 KiB

Open AccessArticle

SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

by Davide Cerotti, Daniele Codetta Raiteri, Giovanna Dondossola, Lavinia Egidi, Giuliana Franceschinis, Luigi Portinale, Davide Savarro and Roberta Terruggia

Energies 2024, 17(16), 3882; https://doi.org/10.3390/en17163882 (registering DOI) - 6 Aug 2024

Abstract

SecuriDN v.

0.1

is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the [...] Read more.

SecuriDN v.

0.1

is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study. Full article

(This article belongs to the Special Issue Model Predictive Control-Based Approach for Microgrids)

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14 pages, 2748 KiB

Open AccessArticle

Novel Design and Synthesis of Ni-Mo-Co Ternary Hydroxides Nanoflakes for Advanced Energy Storage Device Applications

by Zhao Wang, Peifeng Li, Zhuolun Tang and Ka Yuen Simon Ng

Energies 2024, 17(16), 3881; https://doi.org/10.3390/en17163881 (registering DOI) - 6 Aug 2024

Abstract

Three-dimensional interconnected mesoporous nanoflakes of amorphous Ni-Mo-Co trimetallic hydroxides were successfully deposited on a Ni foam (NF) using a facile, environmentally friendly, and scalable electrochemical deposition method. The elemental composition of the nanoflakes, including Ni²⁺, Mo⁶⁺, and Co²⁺ [...] Read more.

Three-dimensional interconnected mesoporous nanoflakes of amorphous Ni-Mo-Co trimetallic hydroxides were successfully deposited on a Ni foam (NF) using a facile, environmentally friendly, and scalable electrochemical deposition method. The elemental composition of the nanoflakes, including Ni²⁺, Mo⁶⁺, and Co²⁺, was characterized by X-ray photoelectron spectroscopy (XPS), while the morphology and particle size of the synthesized nanomaterials were examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The Ni-Mo-Co trimetallic hydroxides on NF were employed as binder-free electrodes for supercapacitors. The Ni-Mo-Co trimetallic hydroxides with a Ni/Mo/Co ratio of 1/1/0.4 exhibited outstanding long-term cyclability over 5000 cycles, with a high reversible specific capacitance of 2700 F g⁻¹ and a high capacitance retention of 96.63% at 10 A g⁻¹. Furthermore, they demonstrated excellent rate performance, maintaining a capacitance of 2429 F g⁻¹ at a current of 50 A g⁻¹, which corresponds to approximately 80% capacitance retention compared to the capacitance at 2 A g⁻¹. The superior performance of these Ni-Mo-Co trimetallic hydroxides can be attributed to their mesoporous hierarchical architecture, which provides large open spaces between the interconnected nanoflakes, numerous electroactive surface sites, facile electron transmission paths, and the synergistic effects of the trimetallic components. These findings demonstrate that Ni-Mo-Co trimetallic hydroxides are promising electrode materials, offering both high capacitance and long-term cyclability for supercapacitors. Full article

(This article belongs to the Section F3: Power Electronics)

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24 pages, 5869 KiB

Open AccessArticle

Thermal Analysis of Parabolic and Fresnel Linear Solar Collectors Using Compressed Gases as Heat Transfer Fluid in CSP Plants

by Roberto Grena, Michela Lanchi, Marco Frangella, Vittorio Ferraro, Valerio Marinelli and Marco D’Auria

Energies 2024, 17(16), 3880; https://doi.org/10.3390/en17163880 (registering DOI) - 6 Aug 2024

Abstract

This study introduces the use of compressed air as a heat transfer fluid in small-scale, concentrated linear solar collector technology, evaluating its possible advantages over traditional fluids. This work assumes the adoption of readily available components for both linear parabolic trough and Fresnel [...] Read more.

This study introduces the use of compressed air as a heat transfer fluid in small-scale, concentrated linear solar collector technology, evaluating its possible advantages over traditional fluids. This work assumes the adoption of readily available components for both linear parabolic trough and Fresnel collectors and the coupling of the solar field with Brayton cycles for power generation. The aim is to provide a theoretical analysis of the applicability of this novel solar plant configuration for small-scale electricity generation. Firstly, a lumped thermal model was developed in a MatLab^® (v. 2023a) environment to assess the thermal performance of a PT collector with an evacuated receiver tube. This model was then modified to describe the performance of a Fresnel collector. The resulting optical–thermal model was validated through literature data and appears to provide realistic estimates of temperature distribution along the entire collector length, including both the receiver tube surface and the Fresnel collector’s secondary concentrator. The analysis shows a high thermal efficiency for both Fresnel and parabolic collectors, with average values above 0.9 (in different wind conditions). Th5s study also shows that the glass covering of the Fresnel evacuated receiver, under the conditions considered (solar field outlet temperature: 550 °C), reaches significant temperatures (above 300 °C). Furthermore, due to the presence of the secondary reflector, the temperature difference between the upper and the lower part of the glass envelope can be very high, well above 100 °C in the final part of the collector string. Differently, in the case of PTs, this temperature difference is quite limited (below 30 °C). Full article

(This article belongs to the Special Issue Advances in Solar Systems and Energy Efficiency: 2nd Edition)

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24 pages, 2188 KiB

Open AccessArticle

Investigation into PV Inverter Topologies from the Standards Compliance Viewpoint

by Muhammad Asif Hasan, Naresh Kumar Vemula, Ramesh Devarapalli and Łukasz Knypiński

Energies 2024, 17(16), 3879; https://doi.org/10.3390/en17163879 (registering DOI) - 6 Aug 2024

Abstract

Numerous reviews are available in the literature on PV inverter topologies. These reviews have intensively investigated the available PV inverter topologies from their modulation techniques, control strategies, cost, and performance aspects. However, their compliance with industrial standards has not been investigated in detail [...] Read more.

Numerous reviews are available in the literature on PV inverter topologies. These reviews have intensively investigated the available PV inverter topologies from their modulation techniques, control strategies, cost, and performance aspects. However, their compliance with industrial standards has not been investigated in detail so far in the literature. There are various standards such as North American standards (UL1741, IEEE1547, and CSA 22.2) and Australian and European safety standards and grid codes, which include IEC 62109 and VDE. These standards provide detailed guidelines and expectations to be fulfilled by a PV inverter topology. Adherence to these standards is essential and crucial for the successful operation of PV inverters, be it a standalone or grid-tied mode of operation. This paper investigates different PV inverter topologies from the aspect of their adherence to different standards. Both standalone and grid-tied mode of operation-linked conditions have been checked for different topologies. This investigation will help power engineers in selecting suitable PV inverter topology for their specific applications. Full article

(This article belongs to the Special Issue Experimental and Numerical Analysis of Photovoltaic Inverters)

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30 pages, 10130 KiB

Open AccessArticle

Justifying and Implementing Concept of Object-Oriented Observers of Thermal State of Rolling Mill Motors

by Stanislav S. Voronin, Andrey A. Radionov, Alexander S. Karandaev, Ivan N. Erdakov, Boris M. Loginov and Vadim R. Khramshin

Energies 2024, 17(16), 3878; https://doi.org/10.3390/en17163878 (registering DOI) - 6 Aug 2024

Abstract

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles [...] Read more.

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper’s contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. Full article

(This article belongs to the Section J: Thermal Management)

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13 pages, 670 KiB

Open AccessArticle

Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

by Honglin Xue, Junwei Ma, Jianliang Zhang, Penghui Jin, Jian Wu and Feng Du

Energies 2024, 17(16), 3877; https://doi.org/10.3390/en17163877 (registering DOI) - 6 Aug 2024

Abstract

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture [...] Read more.

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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20 pages, 3397 KiB

Open AccessArticle

The Impact of Additives on Gaseous Pollutants from the Combustion of Various Solid Fuels

by Ewa Szatyłowicz and Anna Siemieniuk

Energies 2024, 17(16), 3876; https://doi.org/10.3390/en17163876 (registering DOI) - 6 Aug 2024

Abstract

This article compares the emission of gaseous pollutants such as CO₂, CO, NO, SO₂, and HCl emissions from the combustion of the selected most popular solid fuels in a low-power boiler. The process was carried out under controlled conditions [...] Read more.

This article compares the emission of gaseous pollutants such as CO₂, CO, NO, SO₂, and HCl emissions from the combustion of the selected most popular solid fuels in a low-power boiler. The process was carried out under controlled conditions on a laboratory stand equipped with a Moderator Unica Vento Eko 25 kW boiler. Solid fuels were selected for comparison, such as hard coal with granulation above 60 mm, hard coal with a granulation of 25–80 mm, hard coal with a granulation of 8–25 mm, wood pellets, and mixed firewood. The experiment was carried out in two stages. In stage 1, previously selected solid fuels were combusted under controlled repeatable conditions, while simultaneously measuring gaseous components in the exhaust gases in real time. On the other hand, the second stage involves the combustion of the same fuels under the same conditions with combustion additives that modify the combustion process in terms of reducing the emission of pollutants. At the same time, in the second stage, gaseous components in the exhaust gas were also measured in real time. The experiments carried out have shown that, in addition to the additive, a testing system should be used to assess the profitability and improve the efficiency of energy production and distribution after using a given additive for fuel combustion. Implementation of the use of solid fuel activators on a common scale should also entail research on the emission of dioxins and furans, which may be emitted in increased amounts under the influence of some components contained in combustion modifiers. Full article

(This article belongs to the Collection Board Members’ Collection Series: Clean Coal Extraction and Using)

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35 pages, 4606 KiB

Open AccessReview

Review of Fourth-Order Maximum Entropy Based Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark: II. Best-Estimate Predicted Values and Uncertainties for Model Responses and Parameters

by Dan Gabriel Cacuci and Ruixian Fang

Energies 2024, 17(16), 3875; https://doi.org/10.3390/en17163875 - 6 Aug 2024

Abstract

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) [...] Read more.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “typical-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

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19 pages, 2790 KiB

Open AccessReview

Review of Fourth-Order Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark. I. Typical High-Order Sensitivity and Uncertainty Analysis

by Dan Gabriel Cacuci and Ruixian Fang

Energies 2024, 17(16), 3874; https://doi.org/10.3390/en17163874 - 6 Aug 2024

Abstract

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the [...] Read more.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of “large-scale computations”. The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

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36 pages, 5097 KiB

Open AccessReview

A Review of Thermal Management and Heat Transfer of Lithium-Ion Batteries

by Liang Xu, Shanyi Wang, Lei Xi, Yunlong Li and Jianmin Gao

Energies 2024, 17(16), 3873; https://doi.org/10.3390/en17163873 - 6 Aug 2024

Abstract

With the increasing demand for renewable energy worldwide, lithium-ion batteries are a major candidate for the energy shift due to their superior capabilities. However, the heat generated by these batteries during their operation can lead to serious safety issues and even fires and [...] Read more.

With the increasing demand for renewable energy worldwide, lithium-ion batteries are a major candidate for the energy shift due to their superior capabilities. However, the heat generated by these batteries during their operation can lead to serious safety issues and even fires and explosions if not managed effectively. Lithium-ion batteries also suffer from significant performance degradation at low temperatures, including reduced power output, a shorter cycle life, and reduced usable capacity. Deploying an effective battery thermal management system (BTMS) is crucial to address these obstacles and maintain stable battery operation within a safe temperature range. In this study, we review recent developments in the thermal management and heat transfer of Li-ion batteries to offer more effective, secure, and cost-effective solutions. We evaluate different technologies in BTMSs, such as air cooling, liquid cooling, phase change materials, heat pipes, external preheating, and internal preheating, discussing their advantages and disadvantages. Through comparative analyses of high-temperature cooling and low-temperature preheating, we highlight the research trends to inspire future researchers. According to the review of the literature, submerged liquid BTMS configurations show the greatest potential as a research focus to enhance thermal regulation in Li-ion batteries. In addition, there is considerable research potential in the innovation of air-based BTMSs, the optimization of liquid-based BTMSs, the coupling of heat pipes with PCMs, the integration of PCMs and liquid-cooled hybrid BTMSs, and the application of machine learning and topology optimization in BTMS design. The application of 3D printing in lithium-ion battery thermal management promises to enhance heat transfer efficiency and system adaptability through the design of innovative materials and structures, thereby improving the battery’s performance and safety. Full article

(This article belongs to the Special Issue Advanced Thermal Management Technologies and Heat Transfer)

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12 pages, 1076 KiB

Open AccessArticle

An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption

by Zhenhua Li and Jinghua Lu

Energies 2024, 17(16), 3872; https://doi.org/10.3390/en17163872 - 6 Aug 2024

Abstract

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the [...] Read more.

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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12 pages, 5278 KiB

Open AccessArticle

In 50 Shades of Orange: Germany’s Photovoltaic Power Generation Landscape

by Reinhold Lehneis and Daniela Thrän

Energies 2024, 17(16), 3871; https://doi.org/10.3390/en17163871 - 6 Aug 2024

Abstract

Spatiotemporally resolved data on photovoltaic (PV) power generation are very helpful to analyze the multiple impacts of this variable renewable energy on regional and local scales. In the absence of such disaggregated data for Germany, numerical simulations are needed to obtain the electricity [...] Read more.

Spatiotemporally resolved data on photovoltaic (PV) power generation are very helpful to analyze the multiple impacts of this variable renewable energy on regional and local scales. In the absence of such disaggregated data for Germany, numerical simulations are needed to obtain the electricity production from PV systems for a time period and region under study. This manuscript presents how a physical simulation model, which uses open access weather and plant data as input vectors, can be created. The developed PV model is then applied to an ensemble of approximately 1.95 million PV systems, consisting of ground-mounted and rooftop installations, in order to compute their electricity production in Germany for the year 2020. The resulting spatially aggregated time series closely matches the measured PV feed-in pattern of Germany throughout the simulated year. Such disaggregated data can be applied to investigate the German PV power generation landscape at various spatiotemporal levels, as each PV system is taken into account with its technical data and the weather conditions at its geo-location. Furthermore, the German PV power generation landscape is presented as detailed maps based on these simulation results, which can also be useful for many other scientific fields such as energy system modeling. Full article

(This article belongs to the Special Issue Advances in Solar Systems and Energy Efficiency: 2nd Edition)

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19 pages, 4624 KiB

Open AccessArticle

Investigation of the Pore Characteristics and Capillary Forces in Shale before and after Reaction with Supercritical CO₂ and Slickwater

by Chi Zhang, Qian Li, Yanlin Liu, Jiren Tang, Yunzhong Jia and Tianyi Gong

Energies 2024, 17(16), 3870; https://doi.org/10.3390/en17163870 - 6 Aug 2024

Abstract

CO₂–slickwater hybrid fracturing technology is an essential part of shale gas recovery and CO₂ geo-storage. However, the exposure to supercritical CO₂ (ScCO₂) and slickwater can result in potential changes of the pore structures and surface wetting behavior, [...] Read more.

CO₂–slickwater hybrid fracturing technology is an essential part of shale gas recovery and CO₂ geo-storage. However, the exposure to supercritical CO₂ (ScCO₂) and slickwater can result in potential changes of the pore structures and surface wetting behavior, which affect the gas transportation and CO₂ sequestration security in shale reservoirs. Therefore, in this paper, X-ray diffraction (XRD), low-pressure nitrogen gas adsorption (N₂GA), mercury intrusion porosimetry (MIP), and fractal analysis were used to describe the pore characteristics of shale before and after ScCO₂–slickwater coupling treatments. Shale’s surface wettability was confirmed by contact angle measurements. After the ScCO₂–slickwater treatments, the number of micropores (<3.5 nm) and mesopores (3.5–50 nm) increased, while that of macropores (>50 nm) declined based on the N₂GA and MIP experiments. Combined with fractal analysis, we argue that the pore connectivity diminished and the pore structure became more complicated. By analyzing the results of XRD, shale pore changes occurring after the ScCO₂–slickwater treatment can be explained by the adsorption of polyacrylamide (PAM). Contact angle measurement results showed that the shale’s surface treated by ScCO₂ and slickwater was more hydrophilic than that treated by ScCO₂ and water, and indirectly prove our argument above. Hence, the coupling using effect of ScCO₂ and slickwater can impair the negative effect of CO₂ on the shale capillary force to improve shale gas productivity, but it can negatively affect the security of CO₂ sequestration in shale reservoirs. Full article

(This article belongs to the Special Issue Application and Optimization of CCUS Technology in Shale Gas Production and Storage)

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16 pages, 6986 KiB

Open AccessArticle

Enhancing Solar Absorption with Double-Layered Nickel Coatings and WS₂ Nanoparticles on Copper Substrates

by Susana Devesa, Zohra Benzarti, Gabriel Santos, Diogo Cavaleiro, António Cunha, João Santos and Sandra Carvalho

Energies 2024, 17(16), 3869; https://doi.org/10.3390/en17163869 - 6 Aug 2024

Abstract

This study focused on the development and characterization of multi-layered nickel coatings doped with WS₂ nanoparticles and electrodeposited on copper substrates. To enhance the solar collector’s performance by improving the solar radiation conversion into heat, two distinct undercoatings were evaluated, along with [...] Read more.

This study focused on the development and characterization of multi-layered nickel coatings doped with WS₂ nanoparticles and electrodeposited on copper substrates. To enhance the solar collector’s performance by improving the solar radiation conversion into heat, two distinct undercoatings were evaluated, along with the incorporation of WS₂ nanoparticles in the black nickel layer. X-ray diffraction (XRD) analysis revealed that the bright and dull nickel undercoatings consisted of metallic nickel, whereas the black coatings comprised amorphous nickel oxide, inferred to be Ni₂O₃ based on energy-dispersive X-ray spectroscopy (EDS) analysis. Scanning electron microscopy (SEM) analysis of the undercoatings and black nickel morphology showed a compact surface with a relatively homogenous microstructure composed of polyhedric grains, which was free of visible cracks or pinholes. The undercoating influenced the brightness, the reflectivity and the reflectance of the black nickel films, with the dull undercoated sample showing the most promising results, with a total absorbance of 0.94. The incorporation of WS₂ nanoparticles induced the formation of cracks and increased the porosity of the black nickel film. With an appropriate content of WS₂ nanoparticles and the use of a dull undercoat, these drawbacks can be avoided. Concerning the integration of WS₂ nanoparticles, a minor decrease in the brightness of the black films and a subsequent increase in the total absorbance ultimately led to an enhancement of the conversion of solar energy into thermal energy. Full article

(This article belongs to the Special Issue Advanced Functional Materials for Sustainable Energy Harvesting and Efficient Storage Solutions)

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22 pages, 4857 KiB

Open AccessArticle

Numerical Calculation and Application for Crushing Rate and Fracture Conductivity of Combined Proppants

by Zixi Guo, Dong Chen and Yiyu Chen

Energies 2024, 17(16), 3868; https://doi.org/10.3390/en17163868 - 6 Aug 2024

Abstract

Proppant is one of the key materials for hydraulic fracturing. For special situations, such as middle-deep reservoirs and closure pressures ranging from 40 MPa to 60 MPa, using a single proppant cannot solve the contradiction between performance, which means crushing rate and fracture [...] Read more.

Proppant is one of the key materials for hydraulic fracturing. For special situations, such as middle-deep reservoirs and closure pressures ranging from 40 MPa to 60 MPa, using a single proppant cannot solve the contradiction between performance, which means crushing rate and fracture conductivity, and cost. However, using combined proppants is an economically effective method for hydraulic fracturing of such special reservoirs. Firstly, for different types, particle sizes, and proportions of combined proppants, various contact relationships between proppant particles are considered. The random phenomenon of proppant particle arrangement is described using the Monte Carlo method, and the deterministic phenomenon of proppant particles is processed using an optimization model, achieving computer simulation of the microscopic arrangement of proppant particles. Secondly, a mathematical model for the force analysis of combined proppant particles is established, and an improved singular value decomposition method is used for numerical solution. A computational model for the crushing rate and fracture conductivity of combined proppants is proposed. Thirdly, the numerical calculation results are compared and discussed with the test values, verifying the accuracy of the computational model. Finally, the application of combined proppants is discussed, and a model for optimizing the proportion of combined proppants is proposed. The onsite construction technology is introduced, and the cost and economic benefits of combined proppants are compared with those of all ceramic particles and excessive all-quartz sand. It is proved that combined proppants can balance performance and price, and are an economically effective method for hydraulic fracturing of special reservoirs. The research results can select the optimal proppant material and optimize the combination of different proppant types, which can help achieve cost reduction and efficiency increase in oil and gas development. Full article

(This article belongs to the Special Issue Oil and Gas Reservoirs: Phase Behavior, Seepage Mechanism, Productivity Prediction, and Novel Modelling Methods)

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16 pages, 9527 KiB

Open AccessArticle

Effect of Heat Exchange Area Margins on Thermal Characteristics of the Heat Exchange System in the Pressurized Water Test Loop during Fuel Assembly Irradiation

by Junping Si, Guang Zhao, Yun Wang, Sheng Sun, Mingyan Tong, Wei Zhu, Jin Lei, Jinkang Cheng, Yueyan Song and Mengkang Lu

Energies 2024, 17(16), 3867; https://doi.org/10.3390/en17163867 - 6 Aug 2024

Abstract

The performance of heat exchangers in the pressurized water test loop is a critical factor in ensuring the achievement of irradiation parameters for fuel assemblies and the safety of experimental operations. The effect of the heat exchange area margin on the heat exchangers [...] Read more.

The performance of heat exchangers in the pressurized water test loop is a critical factor in ensuring the achievement of irradiation parameters for fuel assemblies and the safety of experimental operations. The effect of the heat exchange area margin on the heat exchangers in the pressurized water test loop for the fuel assembly during the steady-state irradiation is analyzed. Additionally, optimization methods for determining the margin of heat exchange area and corresponding design strategies are further investigated. It shows that the effect of the heat exchange area margin on the heat exchange power is less affected by the inlet temperature of the primary water and is primarily influenced by the flow rate of the primary water. A decrease in the flow rate of the primary water reduces the compensatory effect of the cooling section on power and enhances the weakening effect of the regeneration section on power. Meanwhile, the correspondence between the margin of the regeneration section and the cooling section, established based on design conditions, can be applicable when there are changes in the inlet temperature of the primary water, but it is not suitable when there are changes in the flow rate of the primary water. When the flow rate of the primary water decreases, the cooling section margin required to compensate for the decrease in power caused by the regeneration section margin will increase significantly. In addition, short-circuiting the heat exchange tubes in the regeneration section can effectively enhance the heat transfer capability. Furthermore, setting the heat exchange area margins of the regeneration and cooling sections to zero can serve as a termination condition for iterative calculations in the verification of regenerative heat exchangers under off-design conditions. Full article

(This article belongs to the Section B4: Nuclear Energy)

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