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Symmetry
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Symmetry with Power Systems: Control and Optimization
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Symmetry with Power Systems: Control and Optimization

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1882

Special Issue Editors

Prof. Dr. Hui Gao

E-Mail Website
Guest Editor
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: electric vehicle charging process safety state monitoring and interaction with the power grid process state analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Xu

E-Mail Website
Guest Editor
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: power quality; renewable hosting capacity; smart grid; photovoltaic systems; photovoltaic inverters; energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid integration of flexible resources—such as distributed solar photovoltaics and electric vehicle charging stations—into distribution power grids, conventional centralized control architectures are increasingly challenged by issues including high communication and computational demands, limited flexibility, and constrained scalability. These limitations further complicate the coordinated power dispatch of heterogeneous flexible resources. Given the spatiotemporal asymmetry in the deployment of distributed flexible resources within the grid, decentralized and distributed control strategies are gaining attention due to their advantages in edge computing, plug-and-play operation, and seamless expandability. In such frameworks, resources with strong similarities are managed by dedicated aggregators or agents equipped with tailored bargaining strategies and information. To enable coordination among multiple aggregators, both symmetric and asymmetric game-theoretic approaches have been widely employed. Thus, the aggregation and scheduling of distributed resources via game-theory-enhanced distributed control offer effective solutions for the optimized operation of large-scale distribution power systems.

This Special Issue invites original research papers and review articles focusing on control and optimization techniques for flexible resources in modern distribution grids, especially those addressing theoretical or practical aspects of symmetry. Applied case studies are particularly welcome. Topics of interest include, but are not limited to, the following:

  • Symmetry/asymmetry in spatiotemporal deployment characteristics of distributed energy resources;
  • Application of symmetric/asymmetric game theory for coordinated dispatching of heterogeneous aggregators;
  • Distributed/decentralized control of flexible energy resources under asymmetric grid conditions;
  • Advanced control strategies considering asymmetric operating conditions in power grids;
  • Data fusion and state estimation techniques using multi-source measurement data with asymmetric structures;
  • Asymmetric and hierarchical control architectures for coordinated operation of flexible resources integrated across different voltage levels;
  • Service restoration strategies for active distribution networks under symmetric/asymmetric fault conditions.

Prof. Dr. Hui Gao
Dr. Xiao Xu
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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry 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

  • power system aggregation
  • coordinated control strategies
  • decentralized control architectures
  • distributed control algorithms
  • flexible energy resource management
  • power system optimization
  • game-theoretic approaches

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Published Papers (4 papers)

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23 pages, 3919 KB  
Article
A Graph Reinforcement Learning-Based Charging Guidance Strategy for Electric Vehicles in Faulty Electricity–Transportation Coupled Networks
by Yi Pan, Mingshen Wang, Haiqing Gan, Xize Jiao, Kemin Dai, Xinyu Xu, Yuhai Chen and Zhe Chen
Symmetry 2026, 18(4), 591; https://doi.org/10.3390/sym18040591 - 30 Mar 2026
Viewed by 332
Abstract
To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module [...] Read more.
To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article
(This article belongs to the Special Issue Symmetry with Power Systems: Control and Optimization)
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34 pages, 6990 KB  
Article
Enhancing Active Distribution Network Resilience with V2G-Powered Pre- and Post-Disaster Coordination
by Wuxiao Chen, Zhijun Jiang, Zishang Xu and Meng Li
Symmetry 2026, 18(3), 523; https://doi.org/10.3390/sym18030523 - 18 Mar 2026
Viewed by 295
Abstract
With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to [...] Read more.
With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article
(This article belongs to the Special Issue Symmetry with Power Systems: Control and Optimization)
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32 pages, 2455 KB  
Article
Symmetry-Inspired Comparative Evaluation of Metaheuristic Algorithms for Optimized Control of Distributed Generation Microgrids with Active Loads
by Hafiz Arslan Khan, Muhammad Salman Fakhar, Syed Abdul Rahman Kashif, Ahmed Ali and Akhtar Rasool
Symmetry 2026, 18(3), 463; https://doi.org/10.3390/sym18030463 - 9 Mar 2026
Viewed by 313
Abstract
Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical [...] Read more.
Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical Particle Swarm Optimization (PSO), have settling time and voltage ripple minimization constraints, indicating possible improvement scopes. This research addresses this gap by employing advanced metaheuristic algorithms such as Accelerated Particle Swarm Optimization (APSO), Accelerated Particle Swarm Optimization with variable α (APSO α), Accelerated Particle Swarm Optimization with Normal Distribution (APSO_G), Rayleigh Distribution Accelerated Particle Swarm Optimization (RDAPSO), Rayleigh Distribution Accelerated Particle Swarm Optimization with variable α (RDAPSO α), and the Dragonfly Algorithm (DA). The algorithms were tested for their performance by using CEC Standard Benchmark functions from 2017, 2019, and 2022, providing a basis for rigorous and symmetrical testing and validation. The optimized RDAPSO α algorithm showed a significant reduction in voltage ripple, which was reduced from 4 V to 0.47 V, with an 88.25% reduction. It also showed a 46.32% improvement in settling time, which was reduced from 184.2 ms to 98.9 ms compared to PSO. A detailed statistical analysis was conducted to enhance the reliability and symmetry of the outcomes using Multivariate Analysis of Variance (MANOVA), the Mann–Whitney U test, the Friedman test, and the Bonferroni test. The results show that RDAPSO α offers a significant edge over the rest of the algorithms, with improvements that can be declared statistically superior in optimizing microgrids with improved symmetry in performance. Full article
(This article belongs to the Special Issue Symmetry with Power Systems: Control and Optimization)
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18 pages, 2215 KB  
Article
A Dynamic Evaluation Method for Pumped Storage Units Adapting to Asymmetric Evolution of Power System
by Longxiang Chen, Yuan Wang, Hengyu Xue, Lei Deng, Ziwei Zhong, Xuan Jia, Shuo Feng and Jun Xie
Symmetry 2025, 17(11), 1900; https://doi.org/10.3390/sym17111900 - 7 Nov 2025
Cited by 1 | Viewed by 523
Abstract
As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the [...] Read more.
As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the symmetry issues in PSU evaluation methods by proposing an innovative approach based on evolutionary combination weighting and cloud model theory, thereby adapting to the long-term asymmetric evolution of the power system. First, the subjective and objective weights of indicators at all levels for PSU are obtained using the analytic hierarchy process (AHP) and the entropy weight method (EWM). Then, the optimal combination coefficients for subjective and objective weights are determined through game theory, achieving symmetry and balance between the subjective and objective weights. Subsequently, dynamic correction of the indicator weights is realized using a designed evolutionary response function, enabling the weights to evolve dynamically in response to the asymmetric development of the power system. Finally, the cloud model is employed to characterize the randomness and fuzziness of evaluation boundaries, which enhances the adaptability of the evaluation process and the interpretability of results. The simulation results show that, when considering the long-term asymmetric evolution of the power system, the expected score deviations of secondary indicators are approximately 4.7%, 1.3%, 3.5%, and 7.7%, respectively, with an overall score deviation of about 6.4%. The proposed method not only achieves symmetry and balance between subjective and objective factors in traditional evaluation but also accommodates the asymmetric evolution requirements of the power system. Full article
(This article belongs to the Special Issue Symmetry with Power Systems: Control and Optimization)
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