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Algorithms for Electrical and Electronic Engineering with a Focus on...
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Algorithms for Electrical and Electronic Engineering with a Focus on Renewable Energy Sources (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 952

Special Issue Editor

Dr. Tushar Kanti Roy

E-Mail Website
Guest Editor
School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: dynamical modeling, stability, and control of power systems; robust adaptive control of modern power systems (with photovoltaic and wind generators); robust control of microgrids (AC, DC, and hybrid AC/DC)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest algorithmic advancements in addressing the challenges and opportunities within electrical and electronic engineering, with a special focus on renewable energy sources (RESs). As the integration of RESs grows, efficient algorithms are critical for enhancing system stability, optimizing energy management, ensuring grid reliability, and minimizing operational costs. We invite research on novel control algorithms, optimization techniques, AI-driven approaches, and real-time management strategies tailored for power systems involving RESs. Contributions that explore hybrid systems, energy storage management, microgrid optimization, and smart grid applications are especially welcome, fostering sustainable solutions for the energy transition.

Potential topics include, but are not limited to, the following:

  • Control and optimization algorithms for RES integration;
  • AI and machine learning for energy forecasting and RES management;
  • Power electronics algorithms for efficient RES conversion;
  • Metaheuristic approaches for microgrid energy optimization;
  • Real-time algorithms for hybrid AC/DC microgrid control;
  • Fault detection and resilience algorithms in RES-based systems;
  • Energy storage management algorithms (batteries, hydrogen, etc.);
  • Algorithms for smart grid stability and decentralized energy control;
  • Demand response algorithms for RES-based systems;
  • Predictive maintenance and fault diagnosis for renewable systems.

Dr. Tushar Kanti Roy
Guest Editor

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. Algorithms 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 1800 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

  • renewable energy sources
  • optimization algorithms
  • microgrid energy management
  • power electronics control
  • AI and machine learning for RES
  • smart grids
  • hybrid AC/DC systems
  • energy storage algorithms
  • sustainable power systems

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

Related Special Issue

Published Papers (2 papers)

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Research

37 pages, 4259 KB  
Article
Image-Based Segmentation of Hydrogen Bubbles in Alkaline Electrolysis: A Comparison Between Ilastik and U-Net
by José Pereira, Reinaldo Souza, Arthur Normand and Ana Moita
Algorithms 2026, 19(1), 77; https://doi.org/10.3390/a19010077 - 16 Jan 2026
Viewed by 363
Abstract
This study aims to enhance the efficiency of hydrogen production through alkaline water electrolysis by analyzing hydrogen bubble dynamics using high-speed image processing and machine learning algorithms. The experiments were conducted to evaluate the effects of electrical current and ultrasound oscillations on the [...] Read more.
This study aims to enhance the efficiency of hydrogen production through alkaline water electrolysis by analyzing hydrogen bubble dynamics using high-speed image processing and machine learning algorithms. The experiments were conducted to evaluate the effects of electrical current and ultrasound oscillations on the system performance. The bubble formation and detachment process were recorded and analyzed using two segmentation models: Ilastik, a GUI-based tool, and U-Net, a deep learning convolutional network implemented in PyTorch. v. 2.9.0. Both models were trained on a dataset of 24 images under varying experimental conditions. The evaluation metrics included Intersection over Union (IoU), Root Mean Square Error (RMSE), and bubble diameter distribution. Ilastik achieved better accuracy and lower RMSE, while U-Net. U-Net offered higher scalability and integration flexibility within Python environments. Both models faced challenges when detecting small bubbles and under complex lighting conditions. Improvements such as expanding the training dataset, increasing image resolution, and adopting patch-based processing were proposed. Overall, the result demonstrates the automated image segmentation can provide reliable bubble characterization, contributing to the optimization of electrolysis-based hydrogen production. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with a Focus on Renewable Energy Sources (2nd Edition))
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15 pages, 3298 KB  
Article
Automatic Algorithm Based on Simpson Seventh-Order Integration of Current Minus Short-Circuit Current: Extracting Photovoltaic Device Parameters Within One-Diode Model
by Victor-Tapio Rangel-Kuoppa
Algorithms 2026, 19(1), 17; https://doi.org/10.3390/a19010017 - 24 Dec 2025
Viewed by 279
Abstract
Simpson’s seventh-order integration has been implemented in an automatically executable program to integrate the current minus the short-circuit current. Then, a regression of this integral to a second-degree polynomial in two variables, namely the voltage and the short-circuit current, is performed, obtaining six [...] Read more.
Simpson’s seventh-order integration has been implemented in an automatically executable program to integrate the current minus the short-circuit current. Then, a regression of this integral to a second-degree polynomial in two variables, namely the voltage and the short-circuit current, is performed, obtaining six regression constants. The series (Rs) and shunt resistance (Rsh), the ideality factor (n), and the saturation (Isat) and light current (Ilig) are extracted from these regression constants. The standard errors of these five photovoltaic device parameters are also calculated. Rs, Rsh, n, Ilig, and Isat can be extracted with less than 1% error when the percentage noise is pn< 0.05%, with just N≥26 (N≥101 for Isat), in contrast with a value of N≥751, in the case of the trapezoidal integration method being used. The program calculates the photovoltaic device parameters in less than a second for 1001 data points, four seconds for 10,001 data points, and nineteen seconds for 20,001 data points, which is in striking contrast with the tenths of minutes when using the trapezoidal integration provided by the software Origin, as it has to be performed manually. It is worth mentioning that for the case of pn≥ 0.1%, both trapezoidal and Simpson seventh-order integration practically yield the same accuracy; nevertheless, the program outstands the trapezoidal integration, as it achieves the extraction in nineteen seconds or less. The results reported in this article are valid for the one-diode solar cell model, and might not be valid for other models. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with a Focus on Renewable Energy Sources (2nd Edition))
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