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
Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries
Energies 2024, 17(10), 2273; https://doi.org/10.3390/en17102273 - 8 May 2024
Abstract
Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these
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Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.
Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System—2nd Edition)
Open AccessArticle
Performance Analysis Based on Fuel Valve Train Control Optimization of Ammonia-Fuel Ships
by
Lim Seungtaek, Lee Hosaeng and Seo Youngkyun
Energies 2024, 17(10), 2272; https://doi.org/10.3390/en17102272 - 8 May 2024
Abstract
In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this,
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In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this, eco-friendly fuel propulsion technology is being developed, and the development of an ammonia fuel supply system is in progress. Among them, fuel valve train (FVT) technology was researched for the final supply and cutoff of fuel and purging through nitrogen for ammonia engines. In this paper, we analyzed the change in ammonia supply due to FVT opening and the change in nitrogen supply due to closure. In addition, a plan to minimize risk factors was presented by applying a control method to remove residual fuel in FVT. According to the presented FVT model, the difference in the flow rate of supplied fuel was as much as 17.8 kg/s. Additionally, by opening the gas bleed valve at intervals during the closing process and purging about 0.28 kg of nitrogen, the internal fuel could be completely discharged. This is expected to have an impact on improving the marine environment through the application of eco-friendly fuels and the development of fuel supply system technology.
Full article
(This article belongs to the Special Issue Advances in Fuel Energy)
Open AccessArticle
Global Energy Transition and the Efficiency of the Largest Oil and Gas Companies
by
Sami Jarboui and Hind Alofaysan
Energies 2024, 17(10), 2271; https://doi.org/10.3390/en17102271 - 8 May 2024
Abstract
The challenges posed by climate change and global warming loom large, necessitating a critical initial step towards the long-term growth and the enhancement of both environmental and operational efficiency. Within the energy sector, renewable energy sources are gaining increasing prominence. Consequently, traditional oil
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The challenges posed by climate change and global warming loom large, necessitating a critical initial step towards the long-term growth and the enhancement of both environmental and operational efficiency. Within the energy sector, renewable energy sources are gaining increasing prominence. Consequently, traditional oil and gas companies (OGC) are undergoing a gradual transformation into comprehensive energy corporations, aligning themselves with energy transition policies. This paper examines two types of efficiency measures—operational and environmental—for the 20 largest OGC during the period of 2010–2019. Secondly, this research aims to explore the effect of the global energy transition on both environmental and operational efficiency. Based on three estimation methods, two estimation steps are used in this research. In the first step, the True Fixed Effect (TFE) model and the Battese and coelli (1995) SFA model are applied to evaluate, measure and compare the environmental and operational efficiency scores. In the second step, the TFE model and GMM approach for the dynamic panel data model are used to explore, evaluate and verify the effect of global energy transition on the environmental and operational efficiency of the largest 20 OGC in the world. The results reveal that the average operational efficiency of major OGC measured using the BC.95 model and TFE model is 66% and 85%, respectively, and the overall average level of environmental efficiency for OGC over a 10-year period is 31% (based to B.C.95 model) and 13% (based to TFE model). Our findings reveal that biofuels, solar and hydropower contribute to promote the operational and environmental efficiency of the largest 20 OGC. However, the analysis suggests that while the global energy transition significantly influences and bolsters environmental efficiency, its effect on operational efficiency among these major OGC remains less pronounced and insufficient.
Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System, Volume II)
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Open AccessArticle
Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO
by
Ahmad El Sayed and Gokturk Poyrazoglu
Energies 2024, 17(10), 2270; https://doi.org/10.3390/en17102270 - 8 May 2024
Abstract
The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address
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The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.
Full article
(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity)
Open AccessArticle
Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries
by
Francesco Conte, Marco Giallongo, Daniele Kaza, Gianluca Natrella, Ryohei Tachibana, Shinji Tsuji, Federico Silvestro and Giovanni Vichi
Energies 2024, 17(10), 2269; https://doi.org/10.3390/en17102269 - 8 May 2024
Abstract
Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the
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Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.
Full article
(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)
Open AccessArticle
Model-Predictive-Control-Based Centralized Disturbance Suppression Strategy for Distributed Drive Electric Vehicle
by
Aiping Tan, Lixiao Gao and Yanfeng Chen
Energies 2024, 17(10), 2268; https://doi.org/10.3390/en17102268 - 8 May 2024
Abstract
This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a
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This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities.
Full article
(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)
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Open AccessArticle
The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid
by
Soobok Yoon and Roger Dargaville
Energies 2024, 17(10), 2267; https://doi.org/10.3390/en17102267 - 8 May 2024
Abstract
The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency.
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The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus.
Full article
(This article belongs to the Special Issue Advanced Optimization Strategy of Electric Vehicle and Smart Grids)
Open AccessCorrection
Correction: Pourdaryaei et al. Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market. Energies 2021, 14, 6104
by
Alireza Pourdaryaei, Mohammad Mohammadi, Mazaher Karimi, Hazlie Mokhlis, Hazlee A. Illias, Seyed Hamidreza Aghay Kaboli and Shameem Ahmad
Energies 2024, 17(10), 2266; https://doi.org/10.3390/en17102266 - 8 May 2024
Abstract
There was an error in the original publication [...]
Full article
Open AccessArticle
LES and RANS Spray Combustion Analysis of OME3-5 and n-Dodecane
by
Frederik Wiesmann, Tuan M. Nguyen, Julien Manin, Lyle M. Pickett, Kevin Wan, Fabien Tagliante and Thomas Lauer
Energies 2024, 17(10), 2265; https://doi.org/10.3390/en17102265 - 8 May 2024
Abstract
Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of
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Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C H ), enabling soot-free combustion without the soot-NO trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH O. However, CH O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH O formation/consumption during the quasi-steady combustion period captured by the LES.
Full article
(This article belongs to the Section I1: Fuel)
Open AccessArticle
Assessment of the Efficiency of a Hybrid Photovoltaic and Photovoltaic Heating System (PV–Solar) in the Context of a Warehouse for a Housing Community in Poland
by
Andrzej Gawlik, Marcin Nowakowski, Marcin Rabe, Dariusz Rajchel, Yuriy Bilan, Agnieszka Łopatka, Jurgita Martinkiene and Serhiy Kozmenko
Energies 2024, 17(10), 2264; https://doi.org/10.3390/en17102264 - 8 May 2024
Abstract
In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO
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In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO2 emissions. This article aims to explore the feasibility of integrating photovoltaic systems (utilizing vacuum collectors) and combined utilities (system heat and electricity) in a hybrid setup, leveraging existing technical infrastructure with necessary modifications. A key aspect is to perform calculations on the amount of heat and electricity generated from these systems. The study analyzes the demand for heat and electricity among consumers compared to the estimated production from renewable sources. Calculations also include the potential energy savings and CO2 emission reductions achievable through the proposed solutions. The findings indicate that hybrid photovoltaic systems with heat storage could effectively address energy issues in urban housing estates, given adequate support and community involvement. The innovative methodology employed in this study encompasses both analytical and experimental research approaches. The analysis employs advanced statistical techniques and data integration to enhance understanding of the phenomena studied, while the experimental research provides robust results through controlled variable manipulation and precise measurement tools, thereby verifying the study’s objectives.
Full article
(This article belongs to the Special Issue Sustainable Energy & Society II)
Open AccessArticle
The Second-Order Features Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (2nd-FASAM-L): Mathematical Framework and Illustrative Application to an Energy System
by
Dan Gabriel Cacuci
Energies 2024, 17(10), 2263; https://doi.org/10.3390/en17102263 - 8 May 2024
Abstract
The Second-Order Features Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-FASAM-L”), presented in this work, enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to the
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The Second-Order Features Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-FASAM-L”), presented in this work, enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to the functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters. This characteristic enables the 2nd-FASAM-L methodology to maximize the efficiency and accuracy of any other method for computing exact expressions of first- and second-order response sensitivities with respect to the model’s features and/or primary uncertain parameters. The application of the 2nd-FASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.
Full article
(This article belongs to the Section B4: Nuclear Energy)
Open AccessReview
The Application of Crystallization Kinetics in Optimizing Morphology of Active Layer in Non-Fullerene Solar Cells
by
Longjing Wan, Wangbo Wu, Ming Jiang, Xipeng Yin, Zemin He and Jiangang Liu
Energies 2024, 17(10), 2262; https://doi.org/10.3390/en17102262 - 8 May 2024
Abstract
Organic photovoltaics (OPVs) have attracted widespread attention and became an important member of clean energy. Recently, their power conversion efficiency (PCE) has surpassed 19%. As is well known, the morphology of the active layer in OPVs crucially influences the PCE. In consideration of
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Organic photovoltaics (OPVs) have attracted widespread attention and became an important member of clean energy. Recently, their power conversion efficiency (PCE) has surpassed 19%. As is well known, the morphology of the active layer in OPVs crucially influences the PCE. In consideration of the intricate interactions between the donor molecules and acceptor molecules, the precise control of the morphology of the active layer is extremely challenging. Hence, it is urgent to develop effective methods to fabricate the hierarchical structure of the active layer. One significant driving force for the morphological evolution of the active layer is crystallization. Therefore, regulating the crystallization kinetics is an effective strategy for morphology control. In this review, we present the kinetic strategies recently developed to highlight their significance and effectiveness in morphology control. By applying these kinetic strategies, the hierarchical structure, including phase separation, domain size, crystallinity, and molecular orientation of the active layer can be optimized in different blend systems, leading to an improved PCE of OPVs. The outcomes set the stage for future advancements in device performance.
Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Open AccessArticle
Role of Foreign Phases, Synergistic Effects, and Morphology in the HER Performance of Trimetallic Pentlandites with Non-Equimolar Co:Fe:Ni Ratio
by
Maciej Kubowicz, Miłosz Kożusznik, Tomasz Kurek, Krzysztof Mars and Andrzej Mikuła
Energies 2024, 17(10), 2261; https://doi.org/10.3390/en17102261 - 8 May 2024
Abstract
Since pentlandites are among the most promising catalysts for hydrogen evolution reactions (HER), in this study, we investigated the influence of different cobalt, iron, and nickel substitutions in the cationic sublattice and the form of the material (powder, ingot, sintered pellet) on catalytic
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Since pentlandites are among the most promising catalysts for hydrogen evolution reactions (HER), in this study, we investigated the influence of different cobalt, iron, and nickel substitutions in the cationic sublattice and the form of the material (powder, ingot, sintered pellet) on catalytic performance. This complements previous results regarding a multi-component approach in these chalcogenides. It was shown that in the case of sulfur-rich pentlandites with a non-equimolar ratio of Co, Fe, and Ni, the impact of intrinsic material properties is smaller than the surface-related effects. Among powder forms, catalysts based on a combination of Fe and Co perform the best. However, in volumetric forms, extremely high contents of individual metals are favorable, albeit they are associated with active precipitations of foreign phases. The presence of these phases positively affects the recorded currents but slows down the reaction kinetics. These findings shed light on the nuanced interplay between material composition, form, and HER properties, offering insights for tailored catalyst design.
Full article
(This article belongs to the Special Issue Advances in Hydrogen Energy III)
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Open AccessArticle
Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques
by
Osman Akbulut, Muhammed Cavus, Mehmet Cengiz, Adib Allahham, Damian Giaouris and Matthew Forshaw
Energies 2024, 17(10), 2260; https://doi.org/10.3390/en17102260 - 8 May 2024
Abstract
Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems
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Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.
Full article
(This article belongs to the Special Issue The Development and Modeling of Energy Storage Systems for Renewable-Based Electric Systems)
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Open AccessArticle
Monte Carlo Simulation of the CO2 Flooding Efficiency at a Core Scale for Different Oil Compositions
by
Anna Andreeva and Andrey Afanasyev
Energies 2024, 17(10), 2259; https://doi.org/10.3390/en17102259 - 8 May 2024
Abstract
The evaluation of water-alternating-gas (WAG) efficiency and profitability is complicated by a large number of reservoir, operating, and economic parameters and constraints. This study aims at understanding the influence of the oil composition on different WAG injections. By employing compositional reservoir modeling and
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The evaluation of water-alternating-gas (WAG) efficiency and profitability is complicated by a large number of reservoir, operating, and economic parameters and constraints. This study aims at understanding the influence of the oil composition on different WAG injections. By employing compositional reservoir modeling and the Monte Carlo method to characterize the diversity of oils occurring in nature, we simulate the microscopic displacement efficiency of CO2 flooding when it is applied to both light- and heavy-oil reservoirs. We find that the economic performance of WAG in both miscible and immiscible scenarios is mainly characterized by the dimensionless injection rate and the oil density at surface conditions. Neither the bubble point pressure nor the minimum miscibility pressure can be used for the quantification of the optimal WAG parameters. We present our estimates of the best strategies for the miscible and immiscible injections and verify some of our previous results for randomly sampled oils. In particular, we demonstrate that CO2 flooding is better to apply at higher-dimensionless injection rates. We show that the injection of CO2 organized at a light-oil reservoir results in a higher profitability of WAG, although this comes at the cost of lower carbon storage efficiency.
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(This article belongs to the Section H1: Petroleum Engineering)
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Open AccessArticle
MnO/Mn2O3 Aerogels as Effective Materials for Supercapacitor Applications
by
Ramya Ramkumar, Sanjeevamuthu Suganthi, Ahamed Milton, Jungbin Park, Jae-Jin Shim, Tae Hwan Oh and Woo Kyoung Kim
Energies 2024, 17(10), 2258; https://doi.org/10.3390/en17102258 - 8 May 2024
Abstract
Mixed-oxide transition-metal aerogels (AGLs), particularly manganese-based AGLs, have attracted considerable interest over the past decade owing to their extraordinary properties, including high porosity, good surface area, and ultralow density. To develop easy and lightweight materials for the ever-increasing energy storage demands of the
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Mixed-oxide transition-metal aerogels (AGLs), particularly manganese-based AGLs, have attracted considerable interest over the past decade owing to their extraordinary properties, including high porosity, good surface area, and ultralow density. To develop easy and lightweight materials for the ever-increasing energy storage demands of the near future, we designed a novel Mn-based electrode material to meet these rising requirements. MnO/Mn2O3 AGLs were synthesized using a novel borohydride hydrolysis method and then annealed at 200, 400, and 550 °C. The as-synthesized AGLs yielded flower-like network structures, but their porosity increased with increasing temperatures, to a high temperature of 400 °C. This increased porosity and network structure facilitate a high capacitance. A supercapacitor (SC) constructed with the three-electrode material yielded 230 F/g for the MnAGL@400 sample, followed by yields from the MnAGL@200 and MnAGL@550 electrodes. Furthermore, the device constructed with MnAGL@400 exhibited an energy density of 9.8 Wh/kg and a power density of ~16,500 W/kg at a current density of 20 A/g. The real-time applicability of the AGL was demonstrated by engineering a two-electrode device employing MnAGL@400 as the positive electrode, which exhibited 97% capacity retention and 109% Coulombic efficiency over 20,000 cycles.
Full article
(This article belongs to the Topic Advances in Energy Storage Materials/Devices and Solid-State Batteries)
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Open AccessReview
A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed
by
Flavie Didier, Yong-Chao Liu, Salah Laghrouche and Daniel Depernet
Energies 2024, 17(10), 2257; https://doi.org/10.3390/en17102257 - 8 May 2024
Abstract
This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper
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This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.
Full article
(This article belongs to the Special Issue Numerical Analysis, Field Testing and Experimental Assessment of Offshore Wind Turbines 2024)
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Open AccessArticle
Exploring Corn Cob Gasification as a Low-Carbon Technology in the Corn Flour Industry in Mexico
by
Fabio Manzini, Jorge M. Islas-Samperio and Genice K. Grande-Acosta
Energies 2024, 17(10), 2256; https://doi.org/10.3390/en17102256 - 8 May 2024
Abstract
In 2021, Mexico produced approximately 24.2 million tons of white corn, generating 3.6 million tons of corn cob residue. The final disposal of corn cob poses an environmental challenge in certain regions. This study examines the technical–economic feasibility and the greenhouse gas (GHG)
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In 2021, Mexico produced approximately 24.2 million tons of white corn, generating 3.6 million tons of corn cob residue. The final disposal of corn cob poses an environmental challenge in certain regions. This study examines the technical–economic feasibility and the greenhouse gas (GHG) mitigation potential of integrating a small-scale cogenerating gasifier fueled by corn cob into a nixtamalized corn flour manufacturing small and medium-sized enterprise (SME). This integration enables the generation of heat and electricity from the produced synthesis gas. Moreover, the process yields residual carbon, which can be used as biochar for soil restoration and removing atmospheric CO2. This option holds significance for the corn flour agroindustry in Mexico, as, in 2021, it consumed approximately 601.9 GWh of electrical energy and 938,279 GJ of thermal energy from LP Gas in its manufacturing processes to produce 2.6 million tons of nixtamalized white corn flour. These processes contributed to a total emission of 410,232 tons of CO2 into the atmosphere. The findings of this study demonstrate a cumulative reduction of 51.7% in CO2 emissions, resulting in economic benefits of USD 85,401 in 2017 for a case study SME that annually produces 1039 tons of corn flour. This study reveals the integration of a gasifier–cogenerator system fueled by corn cob as an economically viable low-carbon technology in the corn flour manufacturing industry.
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(This article belongs to the Section A: Sustainable Energy)
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Open AccessArticle
Effects Analysis of FAME on the Engine Characteristics of Different Polymerized Biofuels in Compression Ignition Engine
by
Hongting Zhao, Zhiqing Zhang, Kai Lu, Yanshuai Ye and Sheng Gao
Energies 2024, 17(10), 2255; https://doi.org/10.3390/en17102255 - 8 May 2024
Abstract
Environmental pollution caused by marine engines fueled with fossil fuels is a matter of growing significance. The search for renewable and clean energy sources and improvements in the way fossil fuels are burnt aims to reduce the environmental impact of these engines. For
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Environmental pollution caused by marine engines fueled with fossil fuels is a matter of growing significance. The search for renewable and clean energy sources and improvements in the way fossil fuels are burnt aims to reduce the environmental impact of these engines. For this purpose, fatty acid methyl esters were produced from pure canola oil using KOH-assisted methanol-based transesterification with a maximum yield of 90.68 ± 1.6%. The marine engine’s model was created with CONVERGE software, followed by experimental verification. This paper examines the blended fuel characteristics of a diesel engine with biodiesel blends (0%, 5%, 10%, and 15%) at different loads of engines (50%, 75%, and 100%). It also explores the variation in these characteristics of B10 (10% biodiesel–diesel blends) at three different load conditions and four different EGR rates (0%, 5%, 10%, and 15%). The results indicate that the addition of biodiesel to diesel fuel reduces CO, HC, and soot emissions, while increasing NOx emissions. Additionally, the EGR rate decreases NOx emissions but results in higher levels of soot, CO, and HC emissions. Finally, response surface methodology was used to elicit the engine’s characteristics. It was determined that the optimum experimental operating conditions were 100% engine load, 6.9% biodiesel addition, and 7.7% EGR. The corresponding BTE, BSFC, NOx, and HC emissions were 38.15%, 282.62 g/(kW-h), 274.38 ppm, and 410.37 ppm, respectively.
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(This article belongs to the Special Issue Advanced Research on Internal Combustion Engines and Engine Fuels—2nd Edition)
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Open AccessArticle
Effects of an Owl Airfoil on the Aeroacoustics of a Small Wind Turbine
by
Dean Sesalim and Jamal Naser
Energies 2024, 17(10), 2254; https://doi.org/10.3390/en17102254 - 8 May 2024
Abstract
Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms
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Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms inspired by owl wings and their airfoil sections were found to be most effective. However, their effect/s on the tip vortex noise remain underexplored. Therefore, this paper investigates the effects of implementing an owl airfoil design on the tip vortex noise generated by the National Renewable Energy Laboratory (NREL) Phase VI wind turbine to gain an understanding of the relationship, if any, between airfoil design and the tip vortex noise mechanism. Numerical prediction of aeroacoustics is employed using the Ansys Fluent Broadband Noise Sources function for airfoil self-noise radiation. Detailed comparisons and evaluations of the generated acoustic power levels (APLs) for two distinguished inlet velocities were made with no loss in torque. Although the owl airfoil design increased the maximum generated APL by the baseline model from 105 dB to 110 dB at the lower inlet velocity, it significantly reduced the surface area generating the noise, and reduced the maximum APL generated by the baseline model by 4 dB as the inlet velocity increased. The ability of the owl airfoil to mitigate the velocity effects along the span of the blade was found to be its main noise reduction mechanism.
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(This article belongs to the Special Issue Computational Fluid Dynamics: Technologies and Applications for Renewable Energy Systems)
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