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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 3806

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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Published Papers (9 papers)

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Research

17 pages, 3726 KiB  
Article
LEAD-Net: Semantic-Enhanced Anomaly Feature Learning for Substation Equipment Defect Detection
by Linghao Zhang, Junwei Kuang, Yufei Teng, Siyu Xiang, Lin Li and Yingjie Zhou
Processes 2025, 13(8), 2341; https://doi.org/10.3390/pr13082341 - 23 Jul 2025
Viewed by 162
Abstract
Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation [...] Read more.
Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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16 pages, 1842 KiB  
Article
A Fault Recovery Scheme for Active Distribution Networks Based on the Chaotic Binary Sparrow Search Algorithm Considering Operational Risks
by Weijie Huang, Gang Chen, Xiaoming Jiang, Xiong Xiao, Yiyi Chen and Chong Liu
Processes 2025, 13(7), 2128; https://doi.org/10.3390/pr13072128 - 4 Jul 2025
Viewed by 289
Abstract
In order to improve the reliability of power systems with high penetration of distributed generation (DG), this paper proposes a fault recovery scheme for active distribution networks based on the chaotic binary sparrow search algorithm, taking into account the operational risks. First, the [...] Read more.
In order to improve the reliability of power systems with high penetration of distributed generation (DG), this paper proposes a fault recovery scheme for active distribution networks based on the chaotic binary sparrow search algorithm, taking into account the operational risks. First, the connection line is equivalent to the virtual DG, which simplifies the comprehensive power supply recovery problem to a generalized DG-based islanding problem. Secondly, to adequately quantify the risk of islanding during the fault period, the islanding operation risk index is defined from the perspective of power balance and voltage stability. Next, a generalized dynamic islanding strategy for distribution networks considering operational risks is proposed. This strategy can dynamically adjust the island range according to the risk factors, such as DG output, the change of load, and node voltage levels. Then, the multi-objective function is established by comprehensively considering the factors of restoring important loads, the number of switch actions, and the network loss. The binary sparrow search algorithm is used to solve the problem and outputs the optimal fault recovery strategy for the active distribution network. Finally, the simulation experiments and analysis are carried out based on an IEEE40 node distribution network. The simulation experiments and analysis show that the solution speed of the proposed algorithm reaches the second level, which is 10 s to 70 s faster than that of the heuristic and genetic algorithms, and the load recovery rate of the fault recovery strategy is also higher. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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28 pages, 6299 KiB  
Article
DC Microgrid Enhancement via Chaos Game Optimization Algorithm
by Abdelrahman S. Heikal, Ibrahim Mohamed Diaaeldin, Niveen M. Badra, Mahmoud A. Attia, Ahmed O. Badr, Othman A. M. Omar, Ahmed H. EL-Ebiary and Hyun-Soo Kang
Processes 2025, 13(7), 2042; https://doi.org/10.3390/pr13072042 - 27 Jun 2025
Viewed by 360
Abstract
Microgrids are increasingly being adopted as alternatives to traditional power transmission networks, necessitating improved performance strategies. Various mathematical optimization techniques are used to determine optimal controller parameters for these systems. These optimization methods can generally be categorized into natural, biological, and engineering-based approaches. [...] Read more.
Microgrids are increasingly being adopted as alternatives to traditional power transmission networks, necessitating improved performance strategies. Various mathematical optimization techniques are used to determine optimal controller parameters for these systems. These optimization methods can generally be categorized into natural, biological, and engineering-based approaches. In this research, the authors evaluated and compared several optimization techniques to enhance the secondary controller of DC microgrids, focusing on reducing operating time and minimizing error rates. Optimization tools were utilized to identify the optimal gain control parameters, aiming to achieve the best possible system performance. The enhanced controller response enables quicker recovery to steady-state conditions during sudden disturbances. The root-mean-square error (RMSE) served as a performance metric, with the proposed approach achieving a 15% reduction in RMSE compared to previous models. This improvement contributes to faster response times and lower energy consumption in microgrid operation. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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24 pages, 2101 KiB  
Article
Analysis on the Influence of the Active Power Recovery Rate on the Transient Stability Margin of a New Power System
by Yanxin Gu and Yibo Zhou
Processes 2025, 13(7), 2020; https://doi.org/10.3390/pr13072020 - 26 Jun 2025
Viewed by 273
Abstract
With the large-scale integration of wind power, transient stability issues in power systems have become increasingly prominent, among which the impact of the active power recovery rate of wind turbines on system stability cannot be ignored. This paper establishes a sensitivity analytical model [...] Read more.
With the large-scale integration of wind power, transient stability issues in power systems have become increasingly prominent, among which the impact of the active power recovery rate of wind turbines on system stability cannot be ignored. This paper establishes a sensitivity analytical model between the transient stability index of the system and the active power recovery rate of doubly fed induction generators (DFIGs), revealing the influence of active power recovery rate on system stability. First, the trajectory analysis method is adopted as the transient stability assessment approach, proposing a stability index incorporating accelerating power and transient potential energy. Analytical sensitivity models for synchronous generator accelerating power and transient potential energy to the active power recovery rate of wind turbines are derived in a simplified system. Second, a sensitivity model of the stability margin index to the active power recovery rate is constructed to analyze the influence patterns of the active power recovery rate, initial active power output of wind turbines, and fault duration time on system stability. This research demonstrates that: accelerating the active power recovery rate can restore power balance more quickly but it reduces the rate of transient potential energy variation and delays the peak response of potential energy, thereby decreasing the stability margin; higher initial active power output of wind turbines suppresses the oscillation amplitude of synchronous generators but increases the risk of power imbalance; and prolonged fault duration exacerbates transient energy accumulation and significantly degrades system stability. Additionally, for each 0.1 p.u./s increase in the active power recovery rate of the wind turbine, the absolute value of the stability index of the synchronous machine in the single-machine system decreases by approximately 0.5–1.0, while the sensitivity decreases by approximately 0.01–0.02 s−1. In the multi-machine system, the absolute value of the stability index of the critical machine decreases by approximately 5–10, and the sensitivity decreases by approximately 0.5–1.0 s−1. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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14 pages, 1922 KiB  
Article
Secondary System Status Assessment of Smart Substation Based on Multi-Model Fusion Ensemble Learning in Power System
by Shidan Liu, Ye Peng, Wei Liu, Yiquan Li, Jiafu Cheng, Liang Guo and Guangshi Shao
Processes 2025, 13(7), 1986; https://doi.org/10.3390/pr13071986 - 24 Jun 2025
Viewed by 328
Abstract
In order to accurately evaluate the operating status of secondary equipment in smart substations, this paper establishes a secondary equipment status evaluation index system and proposes a secondary equipment status evaluation method based on multi-model fusion ensemble learning according to the differences of [...] Read more.
In order to accurately evaluate the operating status of secondary equipment in smart substations, this paper establishes a secondary equipment status evaluation index system and proposes a secondary equipment status evaluation method based on multi-model fusion ensemble learning according to the differences of multiple machine learning algorithms as learners. The method consists of a two-layer structure. First, the original data is divided, and the divided data is used to perform k-fold verification on several base learners in the first layer. Then, the fully connected cascade (FCC) neural network in the second layer is used to fuse multiple base learners, and the Levenberg–Marquardt (LM) algorithm is used to train the FCC neural network so that the model converges quickly and stably. Simulation experimental analysis shows that the accuracy of secondary equipment status assessment of the proposed method is 98.71%, which can effectively evaluate the operating status of secondary equipment and provide guidance for the maintenance of smart substation systems and secondary equipment. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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19 pages, 2938 KiB  
Article
Research on Self-Healing Distribution Network Operation Optimization Method Considering Carbon Emission Reduction
by Weijie Huang, Gang Chen, Xiaoming Jiang, Xiong Xiao, Yiyi Chen and Chong Liu
Processes 2025, 13(6), 1850; https://doi.org/10.3390/pr13061850 - 11 Jun 2025
Viewed by 424
Abstract
To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used [...] Read more.
To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used in upper-level optimization to reduce the network loss and solar curtailment cost of the system and to realize the optimal economic operation of the distribution network. Secondly, based on carbon emission flow theory in lower-level optimization, a low-carbon demand response model with a dynamic carbon emission factor as the guiding signal is established to promote carbon emission reduction on the user side. Then, the second-order cone planning and improved dung beetle optimization algorithm are used to solve the model. Finally, it is verified on the test system that the method can effectively reduce the risk of voltage overruns and enhance the low-carbonization and economy of distribution network operation. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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12 pages, 2176 KiB  
Article
Edge-Side Cross-Area State Synchronization Method Adapted to Multiple Delay-Sensitive Services
by Yi Zhou, Li Li, Chang Wang and Lin Yang
Processes 2025, 13(5), 1616; https://doi.org/10.3390/pr13051616 - 21 May 2025
Viewed by 369
Abstract
With the integration of high proportions of new energy and high proportions of power electronic devices, the spatial–temporal correlation scale of emerging distribution business has experienced a sudden increase, and the demand for inter-distribution area collaboration is increasing steadily. Currently, distribution systems heavily [...] Read more.
With the integration of high proportions of new energy and high proportions of power electronic devices, the spatial–temporal correlation scale of emerging distribution business has experienced a sudden increase, and the demand for inter-distribution area collaboration is increasing steadily. Currently, distribution systems heavily rely on cloud master stations to facilitate the state synchronization process across feeders for such business. However, this approach struggles to adapt to the diverse delay-sensitive characteristics anticipated in future large-scale integrations. Therefore, a novel edge-side state synchronization method for cross-feeder operations in distribution systems tailored to diverse delay-sensitive services is proposed. Firstly, the traditional integrated hierarchical vertical network structure is evolved to construct “edge–cloud–edge” and “edge–edge” dual data transmission channels for inter-distribution area nodes. Secondly, considering the unique characteristics of each channel, their expected synchronization delays for differentiated services are calculated. An optimization problem is then formulated with the objective of maximizing the minimum expected synchronization delay redundancy rate. Finally, an iterative variable weighting method is designed to solve this optimization problem. Simulation analysis shows that the proposed algorithm can better adapt to the high-concurrency differentiated inter-distribution area status synchronization demands of diverse time-sensitive businesses, efficiently supporting the flexible, intelligent, and digital transformation of distribution networks. Full article
(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)
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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 416
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 701
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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