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Eng
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Emerging Trends in Numerical Methods for Renewable Energy Technologies
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Emerging Trends in Numerical Methods for Renewable Energy Technologies

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1937

Special Issue Editors

Dr. Mateus Das Neves Gomes

E-Mail Website
Guest Editor
Instituto Federal do Paraná (IFPR)–Campus Paranaguá, Paranaguá, Brazil
Interests: computational fluid dynamics; renewable energy; construction design; computational models; mathematical models
Prof. Dr. Liércio André Isoldi

E-Mail Website
Guest Editor
Escola de Engenharia, Universidade Federal do Rio Grande—FURG, Rio Grande, Brazil
Interests: computational fluid dynamics; computational solid mechanics; renewable energy; construction design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The importance of renewable energy sources has grown significantly in recent decades, as society increasingly acknowledges both ecological concerns and the urgent need to transition toward cleaner, more sustainable, and more efficient energy alternatives.

Examples of renewable energy sources include solar, wind, ocean, geothermal, biomass, and hydropower, each offering promising opportunities for innovation and large-scale application.

This Special Issue invites submissions that present advances in numerical methods and computational modeling applied to Renewable Energy Technologies. Contributions may cover theoretical developments, numerical simulations, optimization strategies, and practical applications.

Topics of this Special Issue mainly include, but are not restricted to, the following:

  • Ocean energy; 
  • Solar energy;
  • Wind energy;
  • Geothermal energy;
  • Biomass and bioenergy systems;
  • Hydropower and small hydro turbines.

Dr. Mateus Das Neves Gomes
Prof. Dr. Liércio André Isoldi
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 250 words) can be sent to the Editorial Office for assessment.

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. Eng is an international peer-reviewed open access monthly 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 1400 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

  • ocean energy
  • solar energy
  • wind energy
  • geothermal energy
  • biomass and bioenergy systems
  • hydropower and small hydro turbines
  • numerical methods
  • computational models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

18 pages, 1650 KB  
Article
Renewable Microgrid Frequency Regulation Using Active Disturbance Rejection Control and Elephant Herding Optimization
by Ehab H. E. Bayoumi, Hisham M. Soliman and Mostafa Soliman
Eng 2026, 7(3), 103; https://doi.org/10.3390/eng7030103 - 27 Feb 2026
Viewed by 369
Abstract
This paper introduces an enhanced load frequency regulation strategy for isolated renewable microgrids, leveraging an Active Disturbance Rejection Control (ADRC) framework optimized through Elephant Herding Optimization (EHO). A detailed microgrid model, encompassing a variety of energy generation and storage units, is implemented in [...] Read more.
This paper introduces an enhanced load frequency regulation strategy for isolated renewable microgrids, leveraging an Active Disturbance Rejection Control (ADRC) framework optimized through Elephant Herding Optimization (EHO). A detailed microgrid model, encompassing a variety of energy generation and storage units, is implemented in a simulation environment. The effectiveness of the proposed ADRC-EHO method was assessed through comparative analysis with established control techniques: Particle Swarm Optimization (PSO)-tuned ADRC and H∞ control under diverse operational scenarios. These scenarios included deterministic and stochastic load disturbances, as well as variations in microgrid parameters. The findings demonstrate that the ADRC-EHO approach consistently yields superior performance, with improved robustness and a more rapid response to frequency fluctuations. The optimization of ADRC parameters using EHO effectively countered the challenges of intermittent renewable energy integration. Full article
(This article belongs to the Special Issue Emerging Trends in Numerical Methods for Renewable Energy Technologies)
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25 pages, 13738 KB  
Article
Real-Time Temperature Prediction of Partially Shaded PV Modules
by Yu Shen, Xinyi Chen, Chaoliu Tong, Shixiong Fang, Kanjian Zhang and Haikun Wei
Eng 2026, 7(2), 92; https://doi.org/10.3390/eng7020092 - 16 Feb 2026
Viewed by 425
Abstract
Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be [...] Read more.
Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module. Full article
(This article belongs to the Special Issue Emerging Trends in Numerical Methods for Renewable Energy Technologies)
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23 pages, 4301 KB  
Article
Accurate Solar Radiation Forecasting Using Spectral Feature Engineering and Bayesian Optimization
by Farrukh Hafeez, Zeeshan Ahmad Arfeen, Muhammad I. Masud, Mehreen Kausar Azam, Saud Al-Shammari, Mohammed Aman, Muhammad Hamid and Muhammad Inam ul Haq
Eng 2026, 7(2), 77; https://doi.org/10.3390/eng7020077 - 10 Feb 2026
Viewed by 410
Abstract
For efficient grid operation and energy management, accurate forecasting of solar radiation is essential. The unpredictable nature of weather makes this task challenging to accomplish. Existing forecasting models fail to deliver accurate results under these conditions, which results in decreased operational efficiency for [...] Read more.
For efficient grid operation and energy management, accurate forecasting of solar radiation is essential. The unpredictable nature of weather makes this task challenging to accomplish. Existing forecasting models fail to deliver accurate results under these conditions, which results in decreased operational efficiency for renewable energy systems. We are proposing a novel methodology that combines feature engineering, machine learning, and Bayesian Optimization (BO) to obtain optimal performance. First, time frequency characteristics are extracted using a Fast Fourier Transform (FFT)-based feature engineering approach to capture dominant patterns from meteorological data. The FFT features reveal essential periodic patterns, which describe solar irradiance and its associated variables, enabling models to perform better over different time periods. The model hyperparameter tuning process, which uses Bayesian Optimization, improves prediction results. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2. The results show clear improvements across Random Forest (RF), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) models, with the MLP model achieving the strongest overall performance. Specifically, the MLP achieved an R2 value of 0.92, with MAE and RMSE values of 1.78 and 2.75, respectively. The proposed method also demonstrates robustness under varying weather conditions and time-series cross-validation (TSCV). Overall, the combined effects of frequency-domain feature engineering and Bayesian Optimization enable robust and adaptive forecasting of solar radiation resources. Full article
(This article belongs to the Special Issue Emerging Trends in Numerical Methods for Renewable Energy Technologies)
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28 pages, 7884 KB  
Article
Numerical Analysis of Deformation Behavior in the Double-Layer Flexible Photovoltaic Support Structure
by Xin Ye, Ming Luo, Hang Zou, Zhu Zhu, Ronglin Hong, Yehui Cui and Jiachen Zhao
Eng 2026, 7(1), 27; https://doi.org/10.3390/eng7010027 - 5 Jan 2026
Viewed by 403
Abstract
Flexible photovoltaic (PV) support systems, referring to cable-supported structural systems that carry conventional rigid PV modules rather than flexible thin-film modules, have attracted increasing attention as a promising solution for photovoltaic construction in complex terrains due to their advantages of broad-span design and [...] Read more.
Flexible photovoltaic (PV) support systems, referring to cable-supported structural systems that carry conventional rigid PV modules rather than flexible thin-film modules, have attracted increasing attention as a promising solution for photovoltaic construction in complex terrains due to their advantages of broad-span design and simplified installation. However, the deformation behavior of flexible PV supports remains insufficiently understood, which restricts its application and engineering optimization. To address this issue, a three-dimensional finite element model of a flexible PV support system was developed using an in-house Python code to investigate its deformation characteristics. The model discretizes the structure into beam and cable elements according to their mechanical properties, and the coupling relationship between their degrees of freedom is established by means of a multi-point constraint. The validation of the proposed model is confirmed by comparison with theoretical solutions. Simulation results reveal that the deformation of flexible PV supports is more sensitive to horizontal loads, indicating that their overall deformation performance is primarily governed by lateral rather than vertical loading. Furthermore, dynamic analyses show that higher loading frequencies induce noticeable torsional de-formation of the structure, which may compromise the stability of the PV panels. These findings provide valuable theoretical guidance for the design and optimization of flexible PV support systems deployed in complex terrains. Full article
(This article belongs to the Special Issue Emerging Trends in Numerical Methods for Renewable Energy Technologies)
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