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
Enhanced Torrefied Oil-Palm Biomass as an Alternative Bio-Circular Solid Fuel: Innovative Modeling of Optimal Conditions and Ecoefficiency Analysis
Energies 2024, 17(9), 2192; https://doi.org/10.3390/en17092192 - 02 May 2024
Abstract
Energy production from coal combustion is responsible for nearly 40% of global CO2 emissions including SOx and NOx. This study aims to produce solid biomass fuels from oil-palm residues by torrefaction, having a high heating value (HHV) equivalent to
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Energy production from coal combustion is responsible for nearly 40% of global CO2 emissions including SOx and NOx. This study aims to produce solid biomass fuels from oil-palm residues by torrefaction, having a high heating value (HHV) equivalent to fossil coals. The experiments were designed using Design Expert version 13 software to optimize the conditions affecting the fuel characteristics of the torrefied products. The statistical analysis suggested that the optimal conditions to achieve a high HHV and fixed carbon content while retaining the mass yield of biomass mainly depended on the temperature and torrefying time, while the size played a less important role in affecting the properties. The optimal conditions were observed to be at 283 °C (120 min) for EFBs, 301 °C (111 min) for PF, and 285 °C (120 min) for PKSs. The maximum HHV of 5229, 5969, and 5265 kcal/kg were achieved for the torrefied EFBs, PF, and PKSs, respectively. The energy efficiency of torrefied biomass was increased to 1.25–1.35. Ecoefficiency analysis suggested that torrefaction should be carried out at high temperatures with a short torrefying time. This low-cost bio-circular torrefied biomass showed promising fuel characteristics that could be potentially used as an alternative to coals.
Full article
(This article belongs to the Special Issue Bioenergy and Waste-to-Energy Technologies to Reach Climate Neutrality)
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Open AccessArticle
Application of Machine Learning for Shale Oil and Gas “Sweet Spots” Prediction
by
Hongjun Wang, Zekun Guo, Xiangwen Kong, Xinshun Zhang, Ping Wang and Yunpeng Shan
Energies 2024, 17(9), 2191; https://doi.org/10.3390/en17092191 - 02 May 2024
Abstract
With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots”
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With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots” prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment.
Full article
(This article belongs to the Section H1: Petroleum Engineering)
Open AccessArticle
Experimental Study on Thermal Environment and Thermal Comfort of Passenger Compartment in Winter with Personal Comfort System
by
Yuxin Hu, Lanping Zhao, Xin Xu, Guomin Wu and Zhigang Yang
Energies 2024, 17(9), 2190; https://doi.org/10.3390/en17092190 - 02 May 2024
Abstract
The combined heating method of seat heating and air conditioning (A/C) was applied in the passenger compartment under different experiment conditions, using thermocouples to continuously measure the wall surfaces and air temperatures in the passenger compartment and the passengers’ skin temperatures of 17
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The combined heating method of seat heating and air conditioning (A/C) was applied in the passenger compartment under different experiment conditions, using thermocouples to continuously measure the wall surfaces and air temperatures in the passenger compartment and the passengers’ skin temperatures of 17 segments. Meanwhile, a subjective evaluation questionnaire survey was conducted using a nine-point evaluation scale on the local and overall thermal sensation and thermal comfort of the passengers, and the data from the questionnaire were analyzed with the ANOVA method. The results showed that the use of the heating pad directly affected the changes in human skin temperature, which in turn affected the local and overall thermal sensation and thermal comfort. For the two thermally stimulated segments of the back and under the thighs, the skin temperature of the back was higher than that of the thighs. Using the heating pad resulted in a rapid increase in the mean skin temperature in the early period of the experiment. Thermal sensation of the back and under-thighs shifted rapidly towards the hot zone in the first 10 min, and then settled around +3, with even more significant differences between the groups. Thermal sensations in non-thermally stimulated segments changed in relation to their position on the heating pad, with slower changes in those at the “distal” end of the body, the head and the feet. Continued use of the heating pads at lower ambient temperatures maintained overall thermal comfort at a neutral level in the range of 0–1, whereas at higher ambient temperatures there was a gradual deterioration of local and overall thermal comfort.
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(This article belongs to the Section B: Energy and Environment)
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Open AccessArticle
A Comparative Study on Load Assessment Methods for Offshore Wind Turbines Using a Simplified Method and OpenFAST Simulations
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Satish Jawalageri, Subhamoy Bhattacharya, Soroosh Jalilvand and Abdollah Malekjafarian
Energies 2024, 17(9), 2189; https://doi.org/10.3390/en17092189 - 02 May 2024
Abstract
Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the
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Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively.
Full article
(This article belongs to the Special Issue Offshore Wind Support Structure Design)
Open AccessArticle
Research on Hybrid Rectifier for High Power Electrolytic Hydrogen Production Based on Modular Multilevel Converter
by
Cheng Huang, Yang Tan and Xin Meng
Energies 2024, 17(9), 2188; https://doi.org/10.3390/en17092188 - 02 May 2024
Abstract
Aiming at the problem that silicon-controlled rectifiers (SCR) and pulse width modulation (PWM) rectifiers cannot balance high power levels, high hydrogen production efficiency, and high grid connected quality in the current research on rectifier power supplies for electrolytic hydrogen production, a new hybrid
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Aiming at the problem that silicon-controlled rectifiers (SCR) and pulse width modulation (PWM) rectifiers cannot balance high power levels, high hydrogen production efficiency, and high grid connected quality in the current research on rectifier power supplies for electrolytic hydrogen production, a new hybrid rectifier topology based on a modular multilevel converter (MMC) is proposed. The hybrid topology integrates a silicon-controlled rectifier (SCR) with an auxiliary power converter, wherein the SCR is designated as the primary power source for electrolytic hydrogen production. The auxiliary converter employs a cascaded modular multilevel converter (MMC) and an input-series-output-parallel (ISOP) phase-shifted full-bridge (PSFB) arrangement. This configuration allows the auxiliary converter to effectively mitigate AC-side harmonics and minimize DC-side ripple, concurrently transmitting a small amount of power. The effectiveness of the hybrid rectifier in achieving low ripple and harmonic distortion outputs was substantiated through hardware-in-the-loop experiments. Notably, the hybrid topology is characterized by its enhanced electric-to-hydrogen conversion efficiency, elevated power density, cost efficiency, and improved grid compatibility.
Full article
(This article belongs to the Special Issue Power Electronics Applications in Microgrid and Renewable Energy Systems)
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The Role of Flexibility in the Integrated Operation of Low-Carbon Gas and Electricity Systems: A Review
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Mohammad Mehdi Amiri, Mohammad Taghi Ameli, Goran Strbac, Danny Pudjianto and Hossein Ameli
Energies 2024, 17(9), 2187; https://doi.org/10.3390/en17092187 - 02 May 2024
Abstract
The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This
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The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system’s ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems.
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(This article belongs to the Special Issue Whole-Energy System Modeling)
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Do Energy Consumption and CO2 Emissions Significantly Influence Green Tax Levels in European Countries?
by
Claudia Diana Sabău-Popa, Alexandra Maria Bele, Adrian Negrea, Dorin Cristian Coita and Adriana Giurgiu
Energies 2024, 17(9), 2186; https://doi.org/10.3390/en17092186 - 02 May 2024
Abstract
In this article, we analyze the correlation between GDP/capita variation, primary and renewable energy consumption and greenhouse gas emissions on the one hand, and green taxes on the other. Green taxes are the main instruments used to limit activities that have a negative
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In this article, we analyze the correlation between GDP/capita variation, primary and renewable energy consumption and greenhouse gas emissions on the one hand, and green taxes on the other. Green taxes are the main instruments used to limit activities that have a negative impact on the environment. These consist of taxes paid by producers and/or consumers for any activity that generates pollution. The results of dynamic regressions, validated by the applied robustness tests, indicate a significant and positive correlation between primary energy consumption and total environmental taxes, respectively energy taxes. At the same time, this shows that variation in GDP/capita significantly and positively influences transport taxes and pollution taxes. In contrast, net greenhouse gas emissions and the supply, transformation and consumption of renewable sources and waste do not significantly influence the total green taxes and their components. This finding is useful to both academic research and government policies for the realistic substantiation of the levels of green tax revenues and for establishing appropriate measures meant to reduce CO2 emissions.
Full article
(This article belongs to the Section C: Energy Economics and Policy)
Open AccessArticle
Research on Thermal Adaptability of Flexible Operation in Different Types of Coal-Fired Power Units
by
Haijiao Wei, Yuanwei Lu, Yanchun Yang, Yuting Wu, Kaifeng Zheng and Liang Li
Energies 2024, 17(9), 2185; https://doi.org/10.3390/en17092185 - 02 May 2024
Abstract
The flexible mode of operation of coal-fired units can accommodate large-scale renewable power integration into the grid, providing more grid capacity. The flexibility transformation of coal-fired units in thermal power plants can be achieved through main steam extraction and reheated steam extraction. A
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The flexible mode of operation of coal-fired units can accommodate large-scale renewable power integration into the grid, providing more grid capacity. The flexibility transformation of coal-fired units in thermal power plants can be achieved through main steam extraction and reheated steam extraction. A 300 MW subcritical unit, 600 MW subcritical unit and 660 MW ultra-supercritical unit with six flexible operation modes were chosen as the research model to investigate the thermal adaptability for flexible operation. The results show that from the perspective of the source of steam extraction, the main steam extraction scheme is suitable for the flexible adjustment of peak load capacity, and the reheated extraction scheme is suitable for the flexible operation of low load and high thermal efficiency. Moreover, from the perspective of thermal performance adaptability, the 600 MW unit has a wider load regulation capacity than the 300 MW and 660 MW units, and is suitable as the peak shaving unit. This work can provide theoretical guidance for different types of coal-fired units in choosing flexible operation schemes.
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(This article belongs to the Special Issue Advanced Applications of Solar and Thermal Storage Energy)
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Forecasting the Power Generation Mix in Italy Based on Grey Markov Models
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Guglielmo D’Amico, Alex Karagrigoriou and Veronica Vigna
Energies 2024, 17(9), 2184; https://doi.org/10.3390/en17092184 - 02 May 2024
Abstract
This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The
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This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.
Full article
(This article belongs to the Section F: Electrical Engineering)
Open AccessArticle
Enhancement of perovskite photodetector using MAPbI3 with formamidinium bromide
by
DongJae Shin and HyungWook Choi
Energies 2024, 17(9), 2183; https://doi.org/10.3390/en17092183 - 02 May 2024
Abstract
In this study, a perovskite-based mixed cation/anion ultraviolet photodetector with an added halide material is fabricated using perovskite combined with an ABX_3 structure. Mixed cation/anion perovskite thin films of MAPbI3/FABr are manufactured through a relatively simple solution process and employed as
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In this study, a perovskite-based mixed cation/anion ultraviolet photodetector with an added halide material is fabricated using perovskite combined with an ABX_3 structure. Mixed cation/anion perovskite thin films of MAPbI3/FABr are manufactured through a relatively simple solution process and employed as light-absorption layers. In the produced thin film, SnO2–sodium dodecylbenzenesulfonate acts as an electron transport layer and spiro-OMeTAD acts as a hole injection layer. Compared to a single cation/anion perovskite, the fabricated device exhibits phase stability and optoelectronic properties, and demonstrates a responsivity of 72.2 mA/W and a detectability of 4.67 × 1013 Jones. In addition, the films show an external quantum efficiency of 56%. This suggests that mixed cation/anion films can replace single cation/anion perovskite films. Thus, photodetectors based on lead halides that can be applied in various fields have recently been manufactured.
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Open AccessArticle
Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling
by
Pedro Moreno, Isabel Figuerola-Ferretti and Antonio Muñoz
Energies 2024, 17(9), 2182; https://doi.org/10.3390/en17092182 - 02 May 2024
Abstract
The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to
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The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.
Full article
(This article belongs to the Topic Energy Market and Energy Finance)
Open AccessArticle
Choosing the Most Suitable Working Fluid for a CTEC
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Aliet Achkienasi, Rodolfo Silva, Edgar Mendoza and Luis D. Luna
Energies 2024, 17(9), 2181; https://doi.org/10.3390/en17092181 - 02 May 2024
Abstract
This study aims to explore additional fluids beneficial for coastal thermal energy converter (CTEC) operation. Ammonia’s thermodynamic properties, characterized by higher condensation temperatures and pressures, demand significantly elevated operating pressures, resulting in a substantial energy load for efficient operation. Thus, exploring alternatives such
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This study aims to explore additional fluids beneficial for coastal thermal energy converter (CTEC) operation. Ammonia’s thermodynamic properties, characterized by higher condensation temperatures and pressures, demand significantly elevated operating pressures, resulting in a substantial energy load for efficient operation. Thus, exploring alternatives such as R134a becomes crucial, particularly considering its potential as a better working fluid for power generation in a Rankine cycle. The research methodology involves employing computational fluid dynamics (CFD) simulations alongside experimental investigations to examine the performance of an axial turbine concept under different working fluids. The results obtained indicate that R134a is the most appropriate working fluid for an axial turbine within a CTEC, outperforming ammonia, thereby implying significantly better operational efficiency.
Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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Open AccessArticle
Li4SiO4-Based Heat Carrier Derived from Different Silica Sources for Thermochemical Energy Storage
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Xicheng Wang, Wentao Xia, Wenlong Xu, Zengqiao Chen, Xiaohan Ren and Yuandong Yang
Energies 2024, 17(9), 2180; https://doi.org/10.3390/en17092180 - 02 May 2024
Abstract
Thermochemical energy storage (TCES) is one of the key technologies facilitating the integration of renewable energy sources and mitigating the climate crisis. Recently, Li4SiO4 has been reported to be a promising heat carrier material for TCES applications, owing to its
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Thermochemical energy storage (TCES) is one of the key technologies facilitating the integration of renewable energy sources and mitigating the climate crisis. Recently, Li4SiO4 has been reported to be a promising heat carrier material for TCES applications, owing to its moderate operation temperature and stability. During the synthetic processes, the properties of the Si source used directly influence the performance of derived Li4SiO4 materials; however, the internal relations and effects are not yet clear. Hence, in this work, six kinds of SiO2 sources with different phases, morphology, particle size, and surface area were selected to synthesize a Li4SiO4-based TCES heat carrier. The physicochemical properties of the SiO2 and the corresponding derived Li4SiO4 were characterized, and the comprehensive performance (e.g., heat storage/releasing capacity, rate, and cyclic stability) of the Li4SiO4 samples was systematically tested. It was found that the silica microspheres (SPs), which possess an amorphous phase, uniform micro-scale structure, and small particle size, could generate Li4SiO4 TCES materials with a highest initial capacity of 777.7 kJ/kg at 720 °C/900 °C under pure CO2. As a result, the SP-L showed an excellent cumulative heat storage amount of 5.84 MJ/kg within 10 heat-releasing/storage cycles, which was nearly 1.5 times greater than the value of Li4SiO4 derived from commonly used silicon dioxide. Furthermore, the effects of the utilized Si source on the performance of as-prepared Li4SiO4 and corresponding mechanisms were discussed, which offers guidance for the future selection of Si sources to produce high-performance Li4SiO4-based TCES heat carriers.
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(This article belongs to the Section D: Energy Storage and Application)
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Open AccessArticle
Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation
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Mariusz Izdebski, Marianna Jacyna and Jerzy Bogdański
Energies 2024, 17(9), 2179; https://doi.org/10.3390/en17092179 - 02 May 2024
Abstract
This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability
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This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.
Full article
(This article belongs to the Special Issue Electric Vehicles for Smart Cities: Trends, Challenges and Opportunities)
Open AccessArticle
Adaptive PMSM Control of Ship Electric Propulsion with Energy-Saving Features
by
Zenon Zwierzewicz, Dariusz Tarnapowicz and Arkadiusz Nerć
Energies 2024, 17(9), 2178; https://doi.org/10.3390/en17092178 - 02 May 2024
Abstract
Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors
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Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem.
Full article
(This article belongs to the Special Issue Trends and Applications in Permanent Magnet Synchronous Motor)
Open AccessArticle
Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network
by
Jessica Berry, Paul Romano and Andrew Osborne
Energies 2024, 17(9), 2177; https://doi.org/10.3390/en17092177 - 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 - 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.
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(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 - 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.
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(This article belongs to the Topic Advances in Wind Energy Technology)
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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 - 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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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.
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(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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