Journal Description
Energies
Energies
is a peer-reviewed, open access journal of related scientific research, technology development, engineering policy, and management studies related to the general field of energy, from technologies of energy supply, conversion, dispatch, and final use to the physical and chemical processes behind such technologies. Energies is published semimonthly online by MDPI. The European Biomass Industry Association (EUBIA), Association of European Renewable Energy Research Centres (EUREC), Institute of Energy and Fuel Processing Technology (ITPE), International Society for Porous Media (InterPore), CYTED and others are affiliated with Energies and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, RePEc, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: CiteScore - Q1 (Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 41 topical sections.
- Testimonials: See what our editors and authors say about Energies.
- Companion journals for Energies include: Fuels, Gases, Nanoenergy Advances and Solar.
Impact Factor:
3.2 (2022);
5-Year Impact Factor:
3.3 (2022)
Latest Articles
Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network
Energies 2024, 17(9), 2177; https://doi.org/10.3390/en17092177 (registering DOI) - 02 May 2024
Abstract
The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving
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The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.
Full article
(This article belongs to the Section B4: Nuclear Energy)
Open AccessArticle
Assessment of Biomass Energy Potential for Biogas Technology Adoption and Its Determinant Factors in Rural District of Limmu Kossa, Jimma, Ethiopia
by
Ashenafi Getaneh, Kasahun Eba and Gudina Terefe Tucho
Energies 2024, 17(9), 2176; https://doi.org/10.3390/en17092176 (registering DOI) - 02 May 2024
Abstract
Increasing clean energy access for the rural population of developing countries is a priority to meet the United Nation’s Sustainable Development Goals-Zero hunger and affordable modern/clean energy for all. Similarly, to meet this goal, Ethiopia moved towards the development of renewable energy. However,
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Increasing clean energy access for the rural population of developing countries is a priority to meet the United Nation’s Sustainable Development Goals-Zero hunger and affordable modern/clean energy for all. Similarly, to meet this goal, Ethiopia moved towards the development of renewable energy. However, there is a limited knowledge on the biomass energy potential for biogas technology adoption at the local/district level. Thus, this study aimed at assessing the biomass energy potential for biogas technology adoption and its determinant factors among rural households in Limmu Kossa district, Ethiopia. Data was collected from 411 households from 13–24 June 2021. The quantitative data was analyzed using Statistical software Package for Social Science (SPSS) version 23 and Microsoft Word-Excel. The qualitative data was analyzed using content analysis. The study showed that over 96% of households rely on the traditional use of biomass energy for cooking. Nevertheless, on average, about 1 m3 of biogas energy can be potentially available from livestock dung and human excreta per household per day. However, the huge potential of biomass energy did not contribute to improved energy technologies such as biogas. The adoption of biogas is hampered by the non-functionality of the installed biogas, a lack of awareness, the availability of firewood, and the socio-economic characteristics of the households. Thus, improving the awareness of the community, arranging financial access, and training biogas technicians, especially from the local community, would increase the adoption of the technology. However, meeting the digester water demand with the water collected from the walking distances of 15–20 min can be challenging. Community-based biogas digesters or biogas involving income generation with a water supply around the digester would be a better and more sustainable option for biogas energy adoption and use.
Full article
(This article belongs to the Section A4: Bio-Energy)
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Open AccessArticle
Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network
by
Yujie Zhang, Nasser Kehtarnavaz, Mario Rotea and Teja Dasari
Energies 2024, 17(9), 2175; https://doi.org/10.3390/en17092175 (registering DOI) - 02 May 2024
Abstract
Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the
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Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of for future times of up to 48 h.
Full article
(This article belongs to the Topic Advances in Wind Energy Technology)
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Open AccessArticle
A Study on the Hydro-Liquefaction Kinetics of Shengli Lignite during the Heating-Up and Isothermal Stages under Mild Conditions
by
Liang Li, Quan Zhang, Shunjin Huang, Yanyan Yan, Yingyue Qin, Xiaochen Huang, Muxin Liu, Shiyong Wu and Jinsheng Gao
Energies 2024, 17(9), 2174; https://doi.org/10.3390/en17092174 (registering DOI) - 02 May 2024
Abstract
Studying the hydro-liquefaction kinetics of lignite contributes to optimizing the mild liquefaction process for lignite. In this paper, the direct liquefaction performance of Shengli lignite (SL) was investigated using a H2/THN system with 4 MPa of initial pressure, and reaction kinetic
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Studying the hydro-liquefaction kinetics of lignite contributes to optimizing the mild liquefaction process for lignite. In this paper, the direct liquefaction performance of Shengli lignite (SL) was investigated using a H2/THN system with 4 MPa of initial pressure, and reaction kinetic models were established for the heating-up stage and the isothermal stage. The result showed that the liquefaction performance of the SL was excellent, with a conversion of 62.18% and an oil and gas (O + G) yield of 29.88% at 698.15 K. After one hour of reaction, the conversion and O + G yield were 94.61% and 76.78%, respectively. During the heating-up stage, the easily reactive part of the SL was 50.07%, and it was converted directly into oil, gas, asphaltene (AS), and preasphaltene (PA) simultaneously. There was no significant secondary hydrogenation conversion of the AS and PA products. During the isothermal stage, the hard-to-react part was predominantly converted into AS and PA, while the remaining easily reactive part continue to react completely. The conversion of AS and PA into oil and gas was a rate-controlling step during this stage. The amount of unreacted coal estimated using the model calculated in the isothermal stage was 2.98%, which was significantly consistent with the experimental value of 2.81%.
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(This article belongs to the Section I1: Fuel)
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Open AccessArticle
Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level
by
Sajid Ali, Hongbae Park, Adnan Aslam Noon, Aamer Sharif and Daeyong Lee
Energies 2024, 17(9), 2173; https://doi.org/10.3390/en17092173 - 01 May 2024
Abstract
The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull
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The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.
Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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Open AccessArticle
Experimental Evaluation of the Methane Number Measurement Procedure for Gaseous Fuel Rating
by
Dawson P. Baucke, Daniel M. Wise, Robin J. Bremmer and Daniel B. Olsen
Energies 2024, 17(9), 2172; https://doi.org/10.3390/en17092172 - 01 May 2024
Abstract
Methane Number (MN) is a fuel rating technique for gaseous fuels analogous to Octane Number. This study establishes and shares a repeatable and reproducible method for MN determination of a gaseous fuel using a modified Cooperative Fuel Research Engine (CFR). Adaptations required to
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Methane Number (MN) is a fuel rating technique for gaseous fuels analogous to Octane Number. This study establishes and shares a repeatable and reproducible method for MN determination of a gaseous fuel using a modified Cooperative Fuel Research Engine (CFR). Adaptations required to convert a CFR engine for use in the MN test procedure are identified. The investigation includes allowable environmental parameters and operating variation limits. An essential aspect of the MN method involves identifying and quantifying Knock Intensity (KI) during engine operation. CFR engines, originally designed for gasoline testing, come equipped with their own knock measurement systems utilizing a capacitive detonation sensor. The original system is compared with a Fast Fourier Transform (FFT) approach that uses a piezoelectric pressure transducer. Quantification of methane number requires an accurate assessment of the reference fuel blend (CH4 + H2). A comparison is carried out between dynamic blending using mass flow meters and bracketing using certified gas bottles containing various CH4/H2 blends from a gas supplier.
Full article
(This article belongs to the Section B: Energy and Environment)
Open AccessArticle
Liquid Fuel Generation from Onion Shell: An Experimental Approach of Pyrolysis Process
by
Md. Alamgir Hossain, Fazlur Rashid, Md. Shamim Akhter, Muhammad Aziz and Md. Emdadul Hoque
Energies 2024, 17(9), 2171; https://doi.org/10.3390/en17092171 - 01 May 2024
Abstract
Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a
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Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a fixed bed for generating alternative liquid fuel. This paper also compares alternative fuel characteristics, including higher heating value, viscosity, density, pour point, and flash point, with conventional petroleum fuels at optimal process conditions. The work adopted pyrolysis to produce liquid fuel at a temperature range of 400–550 °C and utilized LPG to provide a heat source. The liquid product (fuel oil) was collected, and non-condensable gas was flared. The liquid product was tested for various properties, and the results of the analyses show that alternative fuel has a higher heating value of 12.227 MJ/kg, density of 800 kg/m3, viscosity of 4.3 cP at 30 °C, pour point below −6.2 °C, and flash point around 137 °C, with a variation due to the volatile matters. To obtain favorable conditions for pyrolysis, some parameters, including bed temperature, sample quantity, average particle size, and operating time, were varied and analyzed. The physio-chemical properties made the alternative fuels isolated from conventional petroleum fuels due to the variation in distillation temperature. This work shows that the fuel oil generated from the pyrolysis of onion shells could be considered an alternative source of fuel.
Full article
(This article belongs to the Special Issue Biomass and Municipal Solid Waste Thermal Conversion Technologies II)
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Open AccessArticle
A New Feed-Forward Control for Dynamics Improvement in a Dual-Input DC–DC Converter for Hybrid Vehicle Applications
by
Alessandro Benevieri, Lorenzo Carbone, Simone Cosso, Mario Marchesoni, Massimiliano Passalacqua, Stefano Savio and Luis Vaccaro
Energies 2024, 17(9), 2170; https://doi.org/10.3390/en17092170 - 01 May 2024
Abstract
In this study, a double-input single-output bidirectional DC–DC converter is considered. This particular architecture allows less switches to be used than a conventional solution. A new feed-forward current control for this DC–DC converter with three switches is presented in this paper. The modulation
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In this study, a double-input single-output bidirectional DC–DC converter is considered. This particular architecture allows less switches to be used than a conventional solution. A new feed-forward current control for this DC–DC converter with three switches is presented in this paper. The modulation technique proposed in the literature for the aforementioned converter leads to a consistent loss reduction at low load, exploiting the DCM. As a drawback, when using this control strategy, the dynamic response worsens significantly. To speed up the control, a feed-forward approach is designed and implemented using a simplified converter electrical model. The proposed strategy is compared with the conventional PI controller, and it is validated and verified through simulation results in the MATLAB/Simulink/PLECS environment and through experimental tests using a converter prototype.
Full article
(This article belongs to the Section F3: Power Electronics)
Open AccessArticle
Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines
by
Luís Brito Palma
Energies 2024, 17(9), 2169; https://doi.org/10.3390/en17092169 - 01 May 2024
Abstract
In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based
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In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.
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(This article belongs to the Section F: Electrical Engineering)
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Open AccessArticle
Sulfur Encapsulation into Carbon Nanospheres as an Effective Technique to Limit Sulfide Dissolution and Extend the Cycle Life of Lithium–Sulfur Batteries
by
Wissam Fawaz, Zhao Wang and Ka Yuen Simon Ng
Energies 2024, 17(9), 2168; https://doi.org/10.3390/en17092168 - 01 May 2024
Abstract
Lithium–sulfur batteries suffer from a reduced cycle life and diminished coulombic efficiency, which is attributed to the polysulfide shuttle effect. We herein present a process for the fabrication of lithium–sulfur battery cathode material via the recrystallization of dissolved sulfur inside self-assembled carbon nanospheres
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Lithium–sulfur batteries suffer from a reduced cycle life and diminished coulombic efficiency, which is attributed to the polysulfide shuttle effect. We herein present a process for the fabrication of lithium–sulfur battery cathode material via the recrystallization of dissolved sulfur inside self-assembled carbon nanospheres synthesized through the carbonization of d-glucose. Trapping sulfur in the carbonaceous matrix lessens the rapid dissolution of polysulfides and minimizes the loss of active sulfur, thus extending the cycling stability of these batteries. The carbon–sulfur composite material was characterized via X-ray diffraction (XRD), field emission scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). Electrochemical analysis of the material and its functionality as an electrode for lithium–sulfur battery systems was evaluated in a coin cell format using impedance spectroscopy and a life cycle study. The as-prepared cathode has shown remarkable electrochemical performance with a specific capacity of 781 mA/g at 0.1 C after 500 charge/discharge cycles and 83.4% capacity retention.
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(This article belongs to the Collection Renewable Energy and Energy Storage Systems)
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Open AccessReview
Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies
by
Gabriel Pesántez, Wilian Guamán, José Córdova, Miguel Torres and Pablo Benalcazar
Energies 2024, 17(9), 2167; https://doi.org/10.3390/en17092167 - 01 May 2024
Abstract
The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has
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The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.
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(This article belongs to the Section B3: Carbon Emission and Utilization)
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Open AccessArticle
Design and Implementation of Robust H∞ Control for Improving Disturbance Rejection of Grid-Connected Three-Phase PWM Rectifiers
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Naima Ait Ramdane, Adel Rahoui, Boussad Boukais, Mohamed Fouad Benkhoris, Mourad Ait-Ahmed and Ali Djerioui
Energies 2024, 17(9), 2166; https://doi.org/10.3390/en17092166 - 01 May 2024
Abstract
In response to the high performance requirements of pulse width modulation (PWM) converters in grid-connected power systems, H-Infinity (H∞) control has attracted significant research interest due to its robustness against parameter variations and external disturbances. In this work, an advanced robust
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In response to the high performance requirements of pulse width modulation (PWM) converters in grid-connected power systems, H-Infinity (H∞) control has attracted significant research interest due to its robustness against parameter variations and external disturbances. In this work, an advanced robust H∞ control is proposed for a grid-connected three-phase PWM rectifier. A two-level control strategy is adopted, where cascaded H∞ controllers are designed to simultaneously regulate the DC bus voltage and input currents even under load disturbances and non-ideal grid conditions. As a result, unit power factor, stable DC bus voltage, and sinusoidal input currents with lower harmonics can be accurately achieved. The design methodology and stability of the proposed controller are verified through a comprehensive analysis. Simulation tests and experimental implementation on a dSPACE 1103 board demonstrate that the proposed control scheme can effectively enhance disturbance rejection performance under various operating conditions.
Full article
(This article belongs to the Section F3: Power Electronics)
Open AccessArticle
Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers
by
Ali Zabihi, Farhood Aghdasi, Chadi Ellouzi, Nand Kishore Singh, Ratneshwar Jha and Chen Shen
Energies 2024, 17(9), 2165; https://doi.org/10.3390/en17092165 - 01 May 2024
Abstract
In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite
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In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way.
Full article
(This article belongs to the Special Issue Latest Developments in Offshore Wind Technologies)
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Open AccessArticle
Experimental Study on the Thermal Reduction of CO2 by Activated Solid Carbon-Based Fuels
by
Siyuan Zhang, Chen Liang, Zhiping Zhu and Ruifang Cui
Energies 2024, 17(9), 2164; https://doi.org/10.3390/en17092164 - 01 May 2024
Abstract
For achieving CO2 thermal reduction, a technology combining solid carbon activation and high-temperature CO2 reduction was proposed, named as activated-reduction technology. In this study, this technology is realized by using a circulating fluidized bed and downdraft reactor. Reduced agent parameters (O
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For achieving CO2 thermal reduction, a technology combining solid carbon activation and high-temperature CO2 reduction was proposed, named as activated-reduction technology. In this study, this technology is realized by using a circulating fluidized bed and downdraft reactor. Reduced agent parameters (O2/C and CO2 concentration) greatly affect the reduction effect of CO2. In addition, the effect of the activation process on different carbon-based materials can help to broaden the range of carbon-based materials used for CO2 reduction, which is also an important issue. The following three points have been studied through experiments: (1) the influence of the characteristics of the reduced agent (CO2 concentration and O2/C) on CO2 reduction; (2) the performance of different chars in CO2 reduction; and (3) the activation effect of solid carbon. The activation process can develop the pore structure of coal gasification char and transform it into activated char with higher reactivity. The CO concentration in the tail gas is a crucial factor limiting the effectiveness of CO2 reduction, with an experimentally determined upper limit of around 55% at 1200 °C. If CO concentration is far from the upper limit, temperature becomes the significant influencing factor. When the reduced agent O2/C is 0.18, the highest net CO2 reduction of 0.021 Nm3/kg is achieved at 60% CO2 concentration. When the reduced agent CO2 concentration is 50%, the highest net CO2 reduction of 0.065 Nm3/kg is achieved at 0.22 O2/C. Compared with CPGC, YHGC has higher reactivity and is more suitable for CO2 reduction. The activation process helps to reduce the differences between raw materials.
Full article
(This article belongs to the Special Issue Advances in Efficient Thermal Conversion of Carbon-Based Fuels)
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Open AccessArticle
Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles
by
Joseph Omakor, Mohamad Alzayed and Hicham Chaoui
Energies 2024, 17(9), 2163; https://doi.org/10.3390/en17092163 - 30 Apr 2024
Abstract
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management
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A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 C, which is the highest operating temperature attained under the proposed strategy.
Full article
(This article belongs to the Special Issue Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II)
Open AccessArticle
Analyzing Geospatial Cost Variability of Hybrid Solar–Gravity Storage System in High-Curtailment Suburban Areas
by
Soumya Basu, Tetsuhito Hoshino and Hideyuki Okumura
Energies 2024, 17(9), 2162; https://doi.org/10.3390/en17092162 - 30 Apr 2024
Abstract
The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However,
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The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city’s economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain.
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(This article belongs to the Topic Multiple Roads to Achieve Net-Zero Emissions by 2050)
Open AccessArticle
An Off-Site Power Purchase Agreement (PPA) as a Tool to Protect against Electricity Price Spikes: Developing a Framework for Risk Assessment and Mitigation
by
Karolina Kapral, Kobe Soetaert and Rui Castro
Energies 2024, 17(9), 2161; https://doi.org/10.3390/en17092161 - 30 Apr 2024
Abstract
Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food
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Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food insecurity, business bankruptcies and recession. A well-known tool to protect energy consumers from energy price spikes, while at the same time contributing to the development of sustainable technologies, is Power Purchase Agreements. PPAs are long-term bilateral contracts for the purchase and sale of a certain amount of electricity, usually generated from renewable sources. The primary goal of this paper is to assess how the risk associated with PPAs has evolved between 2020 and 2023. It aims to examine whether, after the events in 2022, PPAs remain a robust solution that protects the off-taker from energy price spikes, ensures greater energy budget stability and enables savings. To achieve this, the probability of PPA prices being higher than market prices is evaluated, considering the changing market landscape. Furthermore, this paper intends to gain a thorough understanding of each risk related to PPAs and the best strategies for mitigating it, to maximize the protection of the off-taker.
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(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)
Open AccessReview
Review of Fuel-Cell Electric Vehicles
by
Tingke Fang, Coleman Vairin, Annette von Jouanne, Emmanuel Agamloh and Alex Yokochi
Energies 2024, 17(9), 2160; https://doi.org/10.3390/en17092160 - 30 Apr 2024
Abstract
This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology
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This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future.
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(This article belongs to the Section E: Electric Vehicles)
Open AccessArticle
A New Method for the Techno-Economic Analysis and the Identification of Expansion Strategies of Neutral-Temperature District Heating and Cooling Systems
by
Selva Calixto, Marco Cozzini, Roberto Fedrizzi and Giampaolo Manzolini
Energies 2024, 17(9), 2159; https://doi.org/10.3390/en17092159 - 30 Apr 2024
Abstract
Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach
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Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach for the evaluation and planning of NT-DHC systems. The methodology involves the use of a knapsack optimization algorithm to perform a comprehensive analysis of the conditions that make the NT-DHC solution competitive against individual heating and cooling technologies. The algorithm determines the optimal combination of potential extensions that maximizes overall economic value. The results of a case study, which was conducted in Italy, show that NT-DHC is more suitable in dense urban areas, while air-to-water heat pumps are better suited for low heat density zones. This methodology aims to reduce the risks associated with energy demand and provide more certainty about which areas a network can expand into to be competitive. It is targeted at energy planners, utilities experts, energy engineers, and district heating experts who require assistance and guidance in the planning and early stages of designing a NT-DHC system. This method might enable pre-feasibility studies and preliminary design to determine the opportunities and limitations of a system of this kind from an economic and technological perspective.
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(This article belongs to the Topic District Heating and Cooling Systems)
Open AccessArticle
Decision Process for Identifying Appropriate Devices for Power Transfer between Voltage Levels in Distribution Grids
by
Nassipkul Dyussembekova, Reiner Schütt, Ingmar Leiße and Bente Ralfs
Energies 2024, 17(9), 2158; https://doi.org/10.3390/en17092158 (registering DOI) - 30 Apr 2024
Abstract
During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids
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During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered.
Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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