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Processes
Special Issues
Smart Optimization Techniques for Microgrid Management
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Smart Optimization Techniques for Microgrid Management

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 1 September 2025 | Viewed by 893

Special Issue Editor

Dr. Zineb Cabrane

E-Mail Website
Guest Editor
Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan 93000, Morocco
Interests: smart-grid; energy management; energy storage; batteries; supercapacitors

Special Issue Information

Dear Colleagues,

In the rapidly evolving field of energy management, microgrids have emerged as a vital solution to enhance energy resilience, efficiency, and sustainability. The Special Issue "Smart Optimization Techniques for Microgrid Management" focuses on innovative approaches and methodologies that leverage advanced optimization techniques to improve the operation and control of microgrids.

This collection aims to explore a range of smart optimization strategies, including but not limited to the following:

  • Artificial Intelligence and Machine Learning: Utilizing AI-driven algorithms to predict energy demand, optimize resource allocation, and improve decision making processes in real-time.
  • Multi-objective Optimization: Developing frameworks that balance competing objectives such as cost reduction, emissions minimization, and reliability enhancement within microgrid operations.
  • Stochastic Optimization: Addressing uncertainties in renewable energy generation and load demand through probabilistic modeling and robust optimization techniques.
  • Game Theory Applications: Analyzing interactions among microgrid participants to facilitate cooperative strategies for energy trading and resource sharing.
  • Distributed Optimization: Exploring decentralized methods that allow local controllers to operate independently while achieving overarching system goals.

Contributions to this Special Issue should highlight novel algorithms, case studies, or simulations that demonstrate the efficacy of these optimization techniques in real-world microgrid scenarios. By bringing together cutting-edge research, we aim to foster collaboration and drive forward-thinking solutions that can support the transition towards cleaner and more efficient energy systems.

Researchers, practitioners, and industry experts are invited to submit their findings, providing insights into how smart optimization can revolutionize microgrid management and contribute to a sustainable energy future.

Dr. Zineb Cabrane
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. Processes 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 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

  • microgrid management
  • smart optimization techniques
  • artificial intelligence
  • multi-objective optimization
  • stochastic optimization
  • distributed optimization
  • energy resilience
  • renewable energy integration
  • resource allocation
  • multi agent systems

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

Published Papers (2 papers)

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Research

17 pages, 3130 KiB  
Article
New Method for Locating Traveling Wave Faults in Rural Distribution Networks of Power Grids
by Bohan Liu, Liming Ding, Lijun Huang, Chao Deng and Chuyu Hu
Processes 2025, 13(4), 1117; https://doi.org/10.3390/pr13041117 - 8 Apr 2025
Viewed by 249
Abstract
Rural distribution networks have complex structures and numerous branches, making it difficult to locate the fault point when a fault occurs. This article studies the precise positioning problem of single-phase grounding faults in complex rural distribution networks. A new method for locating multi-terminal [...] Read more.
Rural distribution networks have complex structures and numerous branches, making it difficult to locate the fault point when a fault occurs. This article studies the precise positioning problem of single-phase grounding faults in complex rural distribution networks. A new method for locating multi-terminal traveling wave faults based on the principle of time information matching is proposed. Firstly, according to the distribution network structure, a time database of the traveling wave arrival time of each detection device is established in advance. Then, after the fault occurs, the time of detection device is compared with the database, and the section of the fault point is screened. Finally, the double-terminal traveling wave positioning method is used to determine the precise location of the fault. The simulation results show that this method could be applied to all kinds of complex fault situations. It is easy to achieve, with high accuracy and fewer errors, and it is not affected by the type of short circuit, transition resistance, initial phase angle of the fault, or fault location. It effectively solves the problem of fault location in rural distribution networks of a power grid. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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21 pages, 4772 KiB  
Article
A New Precise Power Quality Disturbance Identification Framework Based on Two-Dimensional Characterization Feature Enhancement and Deep Learning
by Yichen Ge, Zonglin Li, Wenbin Zhou, Xinyu Guo, Zhi Peng and Fei Dong
Processes 2025, 13(3), 675; https://doi.org/10.3390/pr13030675 - 27 Feb 2025
Viewed by 406
Abstract
The increasing integration of renewable energy sources into electrical grids has exacerbated power quality issues, necessitating advanced methods for the rapid detection and precise classification of power quality disturbances (PQDs). This study presents a novel PQD identification approach that integrates two-dimensional feature enhancement [...] Read more.
The increasing integration of renewable energy sources into electrical grids has exacerbated power quality issues, necessitating advanced methods for the rapid detection and precise classification of power quality disturbances (PQDs). This study presents a novel PQD identification approach that integrates two-dimensional feature enhancement with a deep learning framework to address these challenges. The proposed method employs the relative position matrix (RPM) technique to transform PQD signals into visual representations, enhancing 2D feature extraction by capturing temporal dependencies and inter-point relationships through spatial arrangement. Building on this, Spatial Group-wise Enhance (SGE)-MobileViT, an advanced identification and classification technique that autonomously extracts image features, was introduced for accurate PQD detection. The SGE-MobileViT model incorporates an attention mechanism that adaptively adjusts the feature map significance, optimizing feature space scalability and enabling the effective capture of both local features and global contextual relationships. Experimental results demonstrated the model’s superior performance, achieving 99.17% classification accuracy in noiseless environments and maintaining high accuracy (95.13%, 97.00%, and 97.50%) at signal-to-noise ratios of 20 dB, 30 dB, and 50 dB, respectively. The robustness and practical applicability of SGE-MobileViT were further validated through comprehensive simulations and hardware platform implementations including an embedded system demonstration. This study offers a significant advancement in PQD identification, providing a reliable solution for power quality management in modern electrical grids with high renewable energy penetration. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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