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Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from...
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Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8033

Special Issue Editors

Dr. Bo Zhang

E-Mail Website
Guest Editor
Associate Professor, Institute of Advanced Technology, Nanjing University of Post and Telecommunications, Nanjing 210003, China
Interests: active distribution network and microgrid
Dr. Pei Liu

E-Mail Website
Guest Editor
Department of Electrical Engineerting, Tsinghua University, Beijing 100084, China
Interests: cyber-physical system; demand response; wireless communication; resource allocation
Dr. Yuanai Xie

E-Mail Website
Guest Editor
College of Computer Science, South-Central Minzu University, Wuhan 430074, China
Interests: resource allocation in wireless communication systems
Dr. Haiyan Zhao

E-Mail Website
Guest Editor
Institute of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Interests: localization and optimization
Dr. Weifeng Zhong

E-Mail Website
Guest Editor
Associate Professor, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: smart grid; vehicle-to-grid; vehicular networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the large-scale integration of new energy resources into the grid, power system resource regulation has become a key technology in facilitating the consumption of distributed renewable energy and supporting the realization of the carbon neutrality goals. However, as the proportion of distributed energy resources and flexible loads in the grid gradually increases, their disordered disturbance characteristics cause frequent problems such as the deterioration of power quality and over-limit flow. Due to a lack of active supporting technology for source–network–load–storage resource management, power system security and high-quality and economic operation face huge challenges. To fully "wake up" the regulation ability of controllable resources in the system, it is urgent to apply advanced communication technologies and regulation technologies to the power system so as to enhance the overall viewability and controllability of the system, and make the power system operation more intelligent. However, with the continuous increase in new energy proportion and the gradual deepening of cyber–physical fusion in the systems, it not only intensifies the uncertainty of source-side output and load-side demand in the physical layer but also makes the information uncertainties such as transmission delay, packet loss, and network attacks more prominent. In addition, the two kinds of uncertainties are symmetry and superimposition, which are prone to present the feature of “1 + 1 > 2”, seriously affecting the operation safety, quality and economy of new cyber–physical power systems. Therefore, based on the perspective of information and physical symmetry, it is urgent to explore and study the cyber–physical co-regulation and optimization methods in smart power systems so as to improve the performance of the systems under multiple uncertainties.

Dr. Bo Zhang
Dr. Pei Liu
Dr. Yuanai Xie
Dr. Haiyan Zhao
Dr. Weifeng Zhong
Guest Editors

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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.

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Keywords

  • application of emerging technologies (e.g., federated learning, digital twin, blockchain, metaverse, AIGC, and B5G) in power systems
  • collaborative optimization of energy information resources for cyber–physical convergence of smart power systems
  • cyber–physical–social coupling system technology for smart power systems
  • smart power emergency communication system based on space–air–ground integration
  • attack detection and defense cyber–physical smart power systems
  • key technology for panoramic situation awareness of smart power systems
  • key technology for secure and efficient data transmission in smart power systems
  • key technology for safe/high-quality/economic operation and control of cyber–physical smart power systems
  • key technology for fault location and recovery of smart power systems under extreme disasters
  • key technology of cyber–physical cooperative defense for endogenous security problems of smart power systems
  • key technology of data privacy protection and trusted encryption for smart power systems

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Published Papers (5 papers)

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Research

21 pages, 1123 KiB  
Article
Hallucination Reduction and Optimization for Large Language Model-Based Autonomous Driving
by Jue Wang
Symmetry 2024, 16(9), 1196; https://doi.org/10.3390/sym16091196 - 11 Sep 2024
Viewed by 1299
Abstract
Large language models (LLMs) are widely integrated into autonomous driving systems to enhance their operational intelligence and responsiveness and improve self-driving vehicles’ overall performance. Despite these advances, LLMs still struggle between hallucinations—when models either misinterpret the environment or generate imaginary parts for downstream [...] Read more.
Large language models (LLMs) are widely integrated into autonomous driving systems to enhance their operational intelligence and responsiveness and improve self-driving vehicles’ overall performance. Despite these advances, LLMs still struggle between hallucinations—when models either misinterpret the environment or generate imaginary parts for downstream use cases—and taxing computational overhead that relegates their performance to strictly non-real-time operations. These are essential problems to solve to make autonomous driving as safe and efficient as possible. This work is thus focused on symmetrical trade-offs between the reduction of hallucination and optimization, leading to a framework for these two combined and at least specifically motivated by these limitations. This framework intends to generate a symmetry of mapping between real and virtual worlds. It helps in minimizing hallucinations and optimizing computational resource consumption reasonably. In autonomous driving tasks, we use multimodal LLMs that combine an image-encoding Visual Transformer (ViT) and a decoding GPT-2 with responses generated by the powerful new sequence generator from OpenAI known as GPT4. Our hallucination reduction and optimization framework leverages iterative refinement loops, RLHF—reinforcement learning from human feedback (RLHF)—along with symmetric performance metrics, e.g., BLEU, ROUGE, and CIDEr similarity scores between machine-generated answers specific to other human reference answers. This ensures that improvements in model accuracy are not overused to the detriment of increased computational overhead. Experimental results show a twofold improvement in decision-maker error rate and processing efficiency, resulting in an overall decrease of 30% for the model and a 25% improvement in processing efficiency across diverse driving scenarios. Not only does this symmetrical approach reduce hallucination, but it also better aligns the virtual and real-world representations. Full article
(This article belongs to the Special Issue Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry)
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18 pages, 1244 KiB  
Article
Adaptive Finite-Time Prescribed Performance Control of Nonlinear Power Systems with Symmetry Full-State Constraints
by Xiaohong Cheng, Shuang Liu, Wenbo Wang and Cong Zhang
Symmetry 2024, 16(7), 857; https://doi.org/10.3390/sym16070857 - 6 Jul 2024
Viewed by 955
Abstract
Power system control is commonly based on linear controllers, where linear controllers are designed using a linearized model of the system at a specific operating point. However, when the system’s operating point is changed, the dynamic characteristics of the system shift significantly. At [...] Read more.
Power system control is commonly based on linear controllers, where linear controllers are designed using a linearized model of the system at a specific operating point. However, when the system’s operating point is changed, the dynamic characteristics of the system shift significantly. At this point, linear controllers often fail to meet system stability requirements. Furthermore, the range of state variables in the power system is limited by the objective conditions. In addition, the power system has high-precision constraints on the deviation of the load frequency and so on. Therefore, it is worth designing a finite-time controller that satisfies the prescribed performance and full-state constraints based on the nonlinear model of the power systems. Firstly, the prescribed performance is incorporated into the barrier Lyapunov function to ensure that the tracking error is within the desired accuracy. Then, the tracking strategy is designed based on backstepping and incorporating a first-order filter to ensure that the controlled system’s signals and tracking errors remain bounded in finite time. Finally, two simulations are given to illustrate the effectiveness of the proposed control scheme, confirming that all states keep within the predefined range. Full article
(This article belongs to the Special Issue Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry)
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22 pages, 3961 KiB  
Article
An Equivalent-Perceptional Intertemporal Choice Heuristics Model for Electric Operation Vehicle Charging Behavior
by Yue Han, Yi Quan, Peiwen Li, Bo Fu, Mei Xie and Haiyan Zhao
Symmetry 2024, 16(3), 374; https://doi.org/10.3390/sym16030374 - 20 Mar 2024
Viewed by 1134
Abstract
The inherent stochasticity of electric operation vehicle (EOV) charging poses challenges to the stability and efficiency of regional power distribution networks. Existing charging behavior decision-making models often prioritize revenue considerations, neglecting the influence of multi-time-span characteristics and the potential irrationality of EOV owners. [...] Read more.
The inherent stochasticity of electric operation vehicle (EOV) charging poses challenges to the stability and efficiency of regional power distribution networks. Existing charging behavior decision-making models often prioritize revenue considerations, neglecting the influence of multi-time-span characteristics and the potential irrationality of EOV owners. To address these limitations, this study proposes a comprehensive framework encompassing three aspects. First, operational data are statistically analyzed to reconstruct EOV operation scenarios, establishing a dynamic charging scheme tailored to multi-time-span characteristics. Second, an improved ITCH model is developed using operational equivalent change to incorporate both gains and losses. Third, a WFL framework is employed to integrate the perceptual attenuation of revenue into the ITCH model. Simulation results show that decision-makers (DMs) demonstrate a preference for charging schemes with high equivalent perceived revenues and low time costs. Moreover, when the charging price is doubled, revenue perception attenuation leads decision-makers to postpone their charging behavior. Compared to other models, the equivalent perception intertemporal choice heuristics (EP-ITCH) charging model results in reduced load peaks, valleys, and variances on the grid side. This study highlights the model’s effectiveness and accuracy in optimizing EOV charging infrastructure. Full article
(This article belongs to the Special Issue Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry)
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22 pages, 1870 KiB  
Article
Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods
by Bao Jin, Xiaodong Zhao and Dongmei Yuan
Symmetry 2024, 16(2), 156; https://doi.org/10.3390/sym16020156 - 29 Jan 2024
Cited by 2 | Viewed by 1352
Abstract
False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize [...] Read more.
False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry)
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20 pages, 4024 KiB  
Article
A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems
by Hongfeng Zhang, Xinyu Wang, Lan Ban and Molin Sun
Symmetry 2023, 15(12), 2104; https://doi.org/10.3390/sym15122104 - 23 Nov 2023
Cited by 2 | Viewed by 971
Abstract
The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For [...] Read more.
The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For this reason, this paper presents a novel detection and identification framework for detecting malicious attacks in power systems from the perspective of cyber–physical symmetry. To consider the undetectability of cyber–physical attacks, a physical dynamics detection model using the unknown input observers (UIOs) and cosine similarity theorem is proposed. Through the design of UIO parameters, the influence of attacks on state estimation can be eliminated. A cosine similarity value-based detection criterion is proposed to replace the traditional detection threshold. To further cut down the effects caused by malicious attacks, an observer combination-based attack identification framework is established. Finally, simulations are given to demonstrate that the proposed security method can detect and identify the injected malicious attacks quickly and effectively. Full article
(This article belongs to the Special Issue Cyber–Physical Co-regulation and Optimization Methods in Smart Power Systems from the Perspective of Cyber–Physical Symmetry)
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