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Algorithms
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Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources
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Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1349

Special Issue Editor

Dr. Tushar Kanti Roy

E-Mail Website
Guest Editor
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 addressing the challenges and opportunities in electrical and electronic engineering, with a special focus on renewable energy sources (RES). As the integration of RES 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 RES. 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:

  • 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 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. 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 1600 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 polices can be found here.

Published Papers (3 papers)

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Research

20 pages, 3878 KiB  
Article
Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm
by Haitao Li, Chenyu Wang, Shufu Yuan, Hui Zhu, Bo Li, Yuexin Liu and Li Sun
Algorithms 2025, 18(2), 80; https://doi.org/10.3390/a18020080 (registering DOI) - 2 Feb 2025
Viewed by 280
Abstract
Energy scheduling for hybrid unmanned aerial vehicles (UAVs) is of critical importance to their safe and stable operation. However, traditional approaches, predominantly rule-based, often lack the dynamic adaptability and stability necessary to address the complexities of changing operational environments. To overcome these limitations, [...] Read more.
Energy scheduling for hybrid unmanned aerial vehicles (UAVs) is of critical importance to their safe and stable operation. However, traditional approaches, predominantly rule-based, often lack the dynamic adaptability and stability necessary to address the complexities of changing operational environments. To overcome these limitations, this paper proposes a novel energy scheduling framework that integrates the Model Predictive Control (MPC) with a Deep Reinforcement Learning algorithm, specifically the Deep Deterministic Policy Gradient (DDPG). The proposed method is designed to optimize energy management in hydrogen-powered UAVs across diverse flight missions. The energy system comprises a proton exchange membrane fuel cell (PEMFC), a lithium-ion battery, and a hydrogen storage tank, enabling robust optimization through the synergistic application of MPC and DDPG. The simulation results demonstrate that the MPC effectively minimizes electric power consumption under various flight conditions, while the DDPG achieves convergence and facilitates efficient scheduling. By leveraging advanced mechanisms, including continuous action space representation, efficient policy learning, experience replay, and target networks, the proposed approach significantly enhances optimization performance and system stability in complex, continuous decision-making scenarios. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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21 pages, 1481 KiB  
Article
Design of a New Energy Microgrid Optimization Scheduling Algorithm Based on Improved Grey Relational Theory
by Dong Mo, Qiuwen Li, Yan Sun, Yixin Zhuo and Fangming Deng
Algorithms 2025, 18(1), 36; https://doi.org/10.3390/a18010036 - 9 Jan 2025
Viewed by 448
Abstract
In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the [...] Read more.
In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluctuation of the outputs of wind turbines and distributed photovoltaic power plants by changing their robustness indicators, generates economic operating cost data for microgrids in multiple scenarios, and uses an improved grey correlation theory algorithm to analyze the correlation between new energy and various scheduling costs. Subsequently, a weighted analysis is performed on each correlation degree to obtain the correlation degree between new energy and total scheduling operating costs. The experimental results show that the improved grey correlation theory optimization scheduling algorithm for new energy microgrids proposed obtains weighted correlation degrees of 0.730 and 0.798 for photovoltaic power stations and wind turbines, respectively, which are 3.1% and 4.6% higher than traditional grey correlation theory. In addition, the equipment maintenance costs of this method are 0.413 and 0.527, respectively, which are 25.1% and 5.4% lower compared to the traditional method, respectively, indicating that the method effectively improves the accuracy of quantitative analysis. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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18 pages, 3035 KiB  
Article
Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm
by Dong Mo, Qiuwen Li, Yan Sun, Yixin Zhuo and Fangming Deng
Algorithms 2025, 18(1), 13; https://doi.org/10.3390/a18010013 - 2 Jan 2025
Viewed by 363
Abstract
To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new [...] Read more.
To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new features were added to the traditional grey wolf optimization (GWO) algorithm to solve the multi-target optimization scheduling of grid-connected microgrids, aiming to improve solution quality and convergence speed. Furthermore, Gaussian walk and Lévy flight are introduced to enhance the search capability of the proposed OGGWO algorithm. This method expands the search range while sacrificing only a small amount of search speed, contributing to obtaining the global optimal solution. Finally, the gradient direction is considered in the feature search process, allowing for a comprehensive understanding of the search space, which facilitates achieving the global optimum. Experimental results indicate that, compared to traditional methods, the proposed improved OGGWO algorithm can achieve standard deviations of 4.88 and 4.46 in two different scenarios, demonstrating significant effectiveness in reducing costs and pollution. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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