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Energies
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Future Multi-Energy Smart-Grids: Advances in Operation, Control, and Monitoring
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Future Multi-Energy Smart-Grids: Advances in Operation, Control, and Monitoring

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 5 December 2025 | Viewed by 1170

Special Issue Editors

Dr. Gibran David Agundis Tinajero

E-Mail Website
Guest Editor
Microgrid and Renewable Energy Research Center, Huanjiang Laboratory, Zhuji 311800, China
Interests: AC/DC microgrids; hierarchical controls; microgrid clusters; fast-time domain methods; harmonic analysis; stability analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Cesar Diaz-Londono

E-Mail Website
Guest Editor
Microgrid and Renewable Energy Research Center, Huanjiang Laboratory, Zhejiang University, Zhuji 311800, China
Interests: electric vehicle charging strategies; demand response for smart grids; flexible load modeling; optimization techniques; electrical grid analysis in real-time simulators
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan C. Vasquez

E-Mail Website
Guest Editor
Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: microgrid clusters; energy management; hierarchical control; distributed generation; digital twin; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, power systems worldwide are undergoing a key transition towards green energies, bringing about the integration of multiple distributed renewable energy sources into the grid. To cope with these advances, microgrid-based smart-grids have emerged as a potential electrification solution due to their operating characteristics, incorporating high levels of resilience and reliability. In this way, research efforts have been placed on these modern multi-energy systems for enhancing controllability, grid balancing, and stability when operating in isolated mode or interconnected with the main grid in both single smart grid systems and smart grid cluster systems.

Regarding the above, this Special Issue of Energies focuses on the future challenges associated with the operation, control, and monitoring of multi-energy smart grids. In detail, this Special Issue includes, but it is not limited to, the following:

  • Optimal energy management systems and AI.
  • Time- and frequency-domain analysis methods.
  • Electric vehicle integration.
  • Vehicle-to-grid strategies.
  • Balancing service provider methodologies.
  • Energy storage systems’ integration and control.
  • Black-box modeling and machine learning.
  • Stability and power quality enhancement.
  • Renewable energies’ planning and sizing.
  • Black-start and self-restoration.
  • P2X and carbon capture.
  • Hierarchical control schemes for cluster operation.
  • Grid-fault ride-through.
  • Smart grid protection schemes.

Dr. Gibran David Agundis Tinajero
Dr. Cesar Diaz-Londono
Prof. Dr. Juan C. Vasquez
Guest Editors

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • smart grids
  • renewable energy integration
  • time- and frequenc- domain analysis
  • stability analysis
  • electric vehicle
  • hierarchical control strategies
  • AI and machine learning
  • energy storage systems
  • power quality

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Published Papers (2 papers)

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Research

20 pages, 3465 KiB  
Article
Phase-Controlled Closing Strategy for UHV Circuit Breakers with Arc-Chamber Insulation Deterioration Consideration
by Hao Li, Qi Long, Xu Yang, Xiang Ju, Haitao Li, Zhongming Liu, Dehua Xiong, Xiongying Duan and Minfu Liao
Energies 2025, 18(13), 3558; https://doi.org/10.3390/en18133558 - 5 Jul 2025
Viewed by 365
Abstract
To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF6 circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for [...] Read more.
To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF6 circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for the breakdown voltage of mixed gases is derived based on the synergistic effect. Considering the influence of contact gap on electric field distortion, an adaptive switching strategy is designed to quantify the dynamic relationship among operation times, insulation strength degradation, and electric field distortion. Then, multi-round switching-on and switching-off tests are carried out under the condition of fixed single-arc ablation amount, and the laws of voltage–current, gas decomposition products, and pre-breakdown time are obtained. The test data are processed by the least squares method, adaptive switching algorithm, and machine learning method. The results show that the coincidence degree of the pre-breakdown time obtained by the adaptive switching algorithm and the test value reaches 90%. Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. It can also explain the physical mechanism of the decay of insulation degradation rate with the number of operations. Compared with the machine learning method, this algorithm has stronger generalization ability, effectively overcoming the defects of difficult interpretation of physical causes and the poor engineering adaptability of the black box model. Full article
(This article belongs to the Special Issue Future Multi-Energy Smart-Grids: Advances in Operation, Control, and Monitoring)
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19 pages, 1561 KiB  
Article
Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning
by Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo and Giambattista Gruosso
Energies 2025, 18(10), 2513; https://doi.org/10.3390/en18102513 - 13 May 2025
Cited by 1 | Viewed by 459
Abstract
The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for [...] Read more.
The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids. Full article
(This article belongs to the Special Issue Future Multi-Energy Smart-Grids: Advances in Operation, Control, and Monitoring)
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