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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 8556

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Special Issue Editors

Prof. Dr. Demis Pandelidis

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Guest Editor

Department of Mechanical and Power Engineering, Wroclaw University of Technology, 50-370 Wroclaw, Poland
Interests: renewable energy; new energy technologies; heat and mass transfer; numerical modeling; fluid mechanics; engineering thermodynamics; heating ventilation and air conditioning systems
Special Issues, Collections and Topics in MDPI journals

Dr. Katrzyna Wartalska

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Guest Editor

Department of Environmental Engineering, Wroclaw University of Technology, 50-370 Wroclaw, Poland
Interests: renewable energy; new energy technologies; rainfall precipitation; rainfall runoff modelling; watershed management; urban hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Martyna Grzegorzek

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Guest Editor

Department of Environmental Engineering, Wroclaw University of Technology, 50-370 Wroclaw, Poland
Interests: renewable energy; new energy technologies; heat and mass transfer; numerical modeling; fluid mechanics; engineering thermodynamics

Special Issue Information

Dear Colleagues,

Energy science and technology is critical for the modern world. The world needs a radical change in terms of the efficiency of energy technologies that can effectively offset the global energy demand while improving the quality of life and health of users. The growing rate of energy consumption has become a global concern. Due to this, new and innovative technologies need to be developed in order to face this challenge. This Special Issue is dedicated to showcasing feature papers in energy science and technology

This Special Issue is open to all contributors in the field of energy science and technology. We invite novel and original submissions that extend and advance our scientific/technical understanding of energy science and technology in areas that include, but are not limited to, the following:

Energy;
Energy technologies;
Solar energy;
Wind energy;
Renewable energy;
Energy savings;
Heating;
Ventilation;
Air conditioning;
Energy efficiency;
Smart systems;
Cooling;
Energy efficiency;
Thermal storage;
Evaporative cooling;
Desiccant systems;
Adsorption;
Advanced buildings;
New power sources;
Energy recovery.

Prof. Dr. Demis Pandelidis
Dr. Katrzyna Wartalska
Dr. Martyna Grzegorzek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

energy
energy technologies
solar energy
wind energy
renewable energy
energy savings
heating
ventilation
air conditioning
energy efficiency
smart systems
cooling
energy efficiency
thermal storage
evaporative cooling
desiccant systems
adsorption
advanced buildings
new power sources
energy recovery

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Published Papers (10 papers)

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Research

26 pages, 6220 KiB

Open AccessArticle

Green Hydrogen Production—Fidelity in Simulation Models for Technical–Economic Analysis

by Adrián Criollo, Luis I. Minchala-Avila, Dario Benavides, Danny Ochoa-Correa, Marcos Tostado-Véliz, Wisam Kareem Meteab and Francisco Jurado

Appl. Sci. 2024, 14(22), 10720; https://doi.org/10.3390/app142210720 - 19 Nov 2024

Viewed by 539

Abstract

Green hydrogen production is a sustainable energy solution with great potential, offering advantages such as adaptability, storage capacity and ease of transport. However, there are challenges such as high energy consumption, production costs, demand and regulation, which hinder its large-scale adoption. This study explores the role of simulation models in optimizing the technical and economic aspects of green hydrogen production. The proposed system, which integrates photovoltaic and energy storage technologies, significantly reduces the grid dependency of the electrolyzer, achieving an energy self-consumption of 64 kWh per kilogram of hydrogen produced. By replacing the high-fidelity model of the electrolyzer with a reduced-order model, it is possible to minimize the computational effort and simulation times for different step configurations. These findings offer relevant information to improve the economic viability and energy efficiency in green hydrogen production. This facilitates decision-making at a local level by implementing strategies to achieve a sustainable energy transition. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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23 pages, 7277 KiB

Open AccessArticle

Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers

by Alejandra Marmol, Elyas Zamiri, Marziye Purraji, Duberney Murillo, Jairo Tuñón Díaz, Aitor Vazquez and Angel de Castro

Appl. Sci. 2024, 14(22), 10318; https://doi.org/10.3390/app142210318 - 9 Nov 2024

Viewed by 837

Abstract

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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29 pages, 6976 KiB

Open AccessArticle

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

by Christos Emexidis and Panagiotis Gkonis

Appl. Sci. 2024, 14(22), 10276; https://doi.org/10.3390/app142210276 - 8 Nov 2024

Viewed by 658

Abstract

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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25 pages, 20721 KiB

Open AccessArticle

Experimental Verification of a Compressor Drive Simulation Model to Minimize Dangerous Vibrations

by Marek Moravič, Daniela Marasová, Peter Kaššay, Maksymilian Ozdoba, František Lopot and Piotr Bortnowski

Appl. Sci. 2024, 14(22), 10164; https://doi.org/10.3390/app142210164 - 6 Nov 2024

Viewed by 390

Abstract

The article highlights the importance of analytical computational models of torsionally oscillating systems and their simulation for estimating the lowest resonance frequencies. It also identifies the pitfalls of the application of these models in terms of the accuracy of their outputs. The aim of the paper is to control the dangerous vibration of a mechanical system actuator using a pneumatic elastic coupling using different approaches such as analytical calculations, experimental measurement results, and simulation models. Based on the known mechanical properties of the laboratory system, its dynamic model in the form of a twelve-mass chain torsionally oscillating mechanical system is developed. Subsequently, the model is reduced to a two-mass system using the method of partial frequencies according to Rivin. The total load torque of the piston compressor under fault-free and fault conditions is simulated to obtain the amplitudes and phases of the harmonic components of the dynamic torque. After calculating the natural frequency and the natural shape of the oscillation, the Campbell diagram is processed to determine the critical revolutions. There is a pneumatic flexible coupling between the rotating masses, which changes the dynamic torsional stiffness. The dynamic torque curves transmitted by the coupling are compared with different dynamic torsional stiffnesses during steady-state operation and one cylinder failure. The monitored values are the position of the critical revolutions, the natural frequency, the natural shape of the oscillation, and the RMS of the dynamic load torque. The experimental model is verified by the simulation model. The accuracy of the developed simulation model with the experimental data are apparently very good (even more than 99% of the critical revolutions value obtained by calculation); however, it depends on the dynamic stiffness of the coupling. In this study, a detailed, comprehensive approach combining analytical procedures with simulation models is presented. Experimental data are verified with simulation results, which show a good agreement in the case of 700 kPa coupling pressure. The inaccuracy of some of the experiments (at 300 and 500 kPa pressures) is due to the interaction of the coupling’s apparent stiffness and the level of the damped vibration energy in the coupling, which is manifested by its different heating. Based on further experiments, a solution to these problems will be proposed by introducing this phenomenon effectively into the simulation model. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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20 pages, 4537 KiB

Open AccessArticle

Full Road Transport Sector Transition Towards 100% Autonomous Renewable Energy Supply in Isolated Systems: Tenerife Island Test Case

by Itziar Santana-Méndez, Óscar García-Afonso and Benjamín González-Díaz

Appl. Sci. 2024, 14(21), 9734; https://doi.org/10.3390/app14219734 - 24 Oct 2024

Viewed by 628

Abstract

The transition towards sustainable energy systems is a key challenge faced by society. Among the different sectors, road transport becomes one of the most difficult due to the large energy consumption and infrastructure requirements. In this context, although zero-tailpipe-emission vehicle adoption is seen as a promising route, the energy provision through renewable sources is still uncertain, especially with hydrogen. This paper explores a 100% renewable energy supply scenario for both power-generation and road transport sectors in the isolated system of Tenerife. With this aim, the island’s energy system has been modelled in the software EnergyPLAN. Taking as reference the current renewable technology roadmap in the island, the impact of a full deployment of zero-tailpipe-emission vehicles on the energy system has been evaluated, providing the power and energy storage capacity requirements. The obtained results indicate the need for 6 GW of renewable power (nearly 20 times the current figures) and 12 GWh of a yet non-existent storage capacity. This deployment must be accompanied with approximately 1 GW of dispatchable sources and 1.3 GW of electrolysis capacity to carry out a complete decarbonisation of the transport sector in the island. Finally, a series of recommendations to policy makers are suggested to support the definition of future roadmaps. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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17 pages, 2091 KiB

Open AccessArticle

The Assessment of the Influence of Low-Frequency Electromagnetic Fields Originated from the Power Infrastructure on Humans’ Health

by Leszek Sławomir Litzbarski, Marek Olesz, Grzegorz Redlarski, Piotr Mateusz Tojza, Arkadiusz Żak, Emanuel Gifuni, Zuzanna Cieślikowska and Mieszko Czapliński

Appl. Sci. 2024, 14(21), 9668; https://doi.org/10.3390/app14219668 - 23 Oct 2024

Viewed by 652

Abstract

The objective of this study is to assess the impact of low-frequency electromagnetic fields (LF EMFs) generated by power infrastructure on the nearby environment. Measurements of electric (E) and magnetic (H) field intensities were conducted around high-voltage power lines, transformer stations and facilities related to them. Numerical simulations were also performed to model the distribution of the field values around real buildings in close proximity to power delivery systems. Given the ongoing scientific debate regarding the effects of EMFs on living organisms, the current analysis was based on the existing standards—particularly ICNIRP 2010 guidelines, which set the maximum allowable E and magnetic induction (B) values at 5 kV/m and 200 μT, respectively. Stricter national regulations were also examined, such as Poland’s 1 kV/m E limit in residential areas and Belgium’s 10 μT limit for B. The results showed that while most cases complied with ICNIRP 2010 standards, certain stricter local regulations were exceeded. Specifically, 9 of 14 cases exceeded Poland’s E limits, and 8 failed to meet Belgium’s B requirements. Only in one place—a warehouse near 110 kV power lines (in a critical case)—the ICNIRP limit B was exceeded. These findings underscore the variability in regulatory standards and highlight the need for localized assessments of EMF exposure. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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13 pages, 3472 KiB

Open AccessArticle

Eularian–Eularian Model for Agglomeration Behavior of Combusted Iron Particles

by Warnakulasooriya Dinoja Sammani Fernando and Jamal Naser

Appl. Sci. 2024, 14(17), 7829; https://doi.org/10.3390/app14177829 - 4 Sep 2024

Viewed by 754

Abstract

Direct reduction of iron (DRI) technology in fluidized beds has been identified as a promising approach due to its environmental benefits over other methods. Nevertheless, the process of iron particle sintering in the DRI approach poses a significant obstacle to its advancement. The present work investigated the phenomenon of agglomeration in fine iron particles across various temperatures and with multiple sintering force models of different intensities of solid bridge force. The study utilized a simple but comprehensive and cost-effective CFD model developed using the Eularian–Eularian two-fluid model. The model was explicitly incorporated with user-defined subroutines for the solid phase, while the gas phase was modeled with AVL Fire advance simulation software. The solid bridge force between solid particles was modeled as the inter-particle cohesive force. The model was validated with the experimental results and results from another CFD-DEM model for the same experiment. High temperatures with increased sintering forces were observed to have the most impact on the iron particle agglomeration, while the gas’s superficial velocity had a minimal effect on it. The predictions of this model closely align with the CFD-DEM model results, providing sufficient reliability to implement this model on a large scale. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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22 pages, 7411 KiB

Open AccessArticle

On-Site Sensor Sensitivity Adjustment Technique for a Maintenance-Free Heat Flow Monitoring in Building Systems

by Jan Kočí, Jiří Maděra and Robert Černý

Appl. Sci. 2024, 14(16), 7323; https://doi.org/10.3390/app14167323 - 20 Aug 2024

Viewed by 657

Abstract

In this paper, an advanced solution for measuring heat flow through opaque building elements is presented. The solution is based on the implementation of a computer-aided technique for continuous monitoring of heat flow sensor performance and silent checking of their accuracy. In principle, the technique provides an ex-post compensation of potential deviations and inaccuracies detected during the measurement, which can be done without interfering with the ongoing experiment. As a consequence, traditional ‘non-smart’ sensors can be turned into advanced sensors with self-sensing or self-adjustment features at nearly zero additional costs. The high efficiency of the proposed approach was validated against experimental data obtained from an independent set of advanced high-sensitive sensors. Considering the validation results, the proposed technique brings an entirely new potential for maintenance-free applications for thermal performance monitoring in the building sector, typically for long-term experiments or measurements under dynamic environments. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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18 pages, 7842 KiB

Open AccessArticle

Voltage Problems on Farms with Agricultural Biogas Plants—A Case Study

by Zbigniew Skibko, Andrzej Borusiewicz, Wacław Romaniuk, Marta Pietruszynska, Anna Milewska and Andrzej Marczuk

Appl. Sci. 2024, 14(16), 7003; https://doi.org/10.3390/app14167003 - 9 Aug 2024

Viewed by 909

Abstract

Constructing agricultural microbial gasification plants near livestock farms is essential for technical, economic, and environmental reasons. Utilising substrates from these farms allows for producing electricity, heat, and environmentally friendly manure. However, biogas plants often face technical challenges. This study evaluates the power quality of an agricultural biogas plant on a dairy farm. It was found that the plant was connected via a cable with an insufficient conductor cross-section, leading to significant voltage overshoots exceeding 14.6%, which prevented the activation of the second generator. Both generators could operate after replacing the feed-in cable, but considerable fluctuations in the feed-in voltage persisted. Further measurements indicated the need for changes in the digester design. Specifically, replacing the current two mixers with more lower-powered mixers operating alternately was proposed. Sharing these solutions more broadly can help prevent similar issues in future microbial gas plant constructions and optimise electricity production. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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23 pages, 5349 KiB

Open AccessArticle

Enhancing Weather Forecasting Integrating LSTM and GA

by Rita Teixeira, Adelaide Cerveira, Eduardo J. Solteiro Pires and José Baptista

Appl. Sci. 2024, 14(13), 5769; https://doi.org/10.3390/app14135769 - 1 Jul 2024

Viewed by 1530

Abstract

Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa Bárbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Energy Science and Technology’)

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