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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 2660

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 policies can be found here.

Published Papers (6 papers)

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Research

14 pages, 3036 KiB  
Article
Particle Swarm Optimization Support Vector Machine-Based Grounding Fault Detection Method in Distribution Network
by Zhongqin Xiong, Shichang Huang, Shen Ren, Yutong Lin, Zewen Li, Dongyu Li and Fangming Deng
Algorithms 2025, 18(5), 259; https://doi.org/10.3390/a18050259 - 29 Apr 2025
Abstract
With the present fault detection method for low-voltage distribution networks, it is difficult to detect single-phase grounding faults under complex working conditions. In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-based grounding fault detection method is proposed for distribution networks. [...] Read more.
With the present fault detection method for low-voltage distribution networks, it is difficult to detect single-phase grounding faults under complex working conditions. In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-based grounding fault detection method is proposed for distribution networks. By improving the inertia weight value and introducing a flight-time factor, the PSO algorithm can be improved. The parameters C and g of the SVM can be optimized based on the improved PSO algorithm. Based on the PSO-SVM-based method, a grounding fault detection method can be established. By testing the proposed model in the simulation and experiment, its effectiveness and detection accuracy is validated. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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20 pages, 8096 KiB  
Article
Simulating Intraday Electricity Consumption with ForGAN
by Ralf Korn and Laurena Ramadani
Algorithms 2025, 18(5), 256; https://doi.org/10.3390/a18050256 - 27 Apr 2025
Viewed by 72
Abstract
Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German [...] Read more.
Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German electricity consumption data. Electricity consumption time series have been selected due to their typical combination of (non-linear) seasonal behavior on different time scales and of local random effects. The primary objective is to demonstrate that ForGAN is able to capture such complicated seasonal figures and to generate data with the correct underlying conditional distribution without data preparation, such as de-seasonalization. In particular, ForGAN does so without assuming an underlying model for the evolution of the time series and is purely data-based. The training and validation procedures are described in great detail. Specifically, a long iteration process of the interplay between the generator and discriminator is required to obtain convergence of the parameters that determine the conditional distribution from which additional artificial data can be generated. Additionally, extensive quality assessments of the generated data are conducted by looking at histograms, auto-correlation structures, and further features comparing the real and the generated data. As a result, the generated data match the conditional distribution of the next consumption value of the training data well. Thus, the trained generator of ForGAN can be used to simulate additional time series of German electricity consumption. This can be seen as a kind of proof for the applicabilty of ForGAN. Through a detailed descriptions of the necessary steps of training and validation procedures, a detailed quality check before the actual use of the simulated data, and by providing the intuition and mathematical background behind ForGAN, this contribution aims to demystify the application of GANs to motivate both theorists and researchers in applied sciences to use them for data generation in similar applications. The proposed framework has laid out a plan for doing so. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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20 pages, 1983 KiB  
Article
A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization
by Haitao Li, Chenyu Wang, Shufu Yuan, Hui Zhu and Li Sun
Algorithms 2025, 18(4), 196; https://doi.org/10.3390/a18040196 - 1 Apr 2025
Viewed by 160
Abstract
Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm [...] Read more.
Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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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 - 2 Feb 2025
Cited by 1 | Viewed by 819
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 703
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 541
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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