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Homomorphic Encryption-Based Resilient distributed Energy Management Under Cyber-Attack of Micro-Grid With Event-Triggered Mechanism

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IEEE TRANSACTIONS ON SMART GRID 2024年第5期15卷 5115-5126页

作者： Zhang, Huifeng Yu, Chengqian Zeng, Meilian Ye, Tao Yue, Dong Dou, Chunxia Xie, Xiangpeng Hancke, Gerhard P. Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Jiangsu Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa

Privacy disclosures and malevolent data intrusions targeting adversarial agents pose significant menaces to cyber-physical systems, a reality that extends to the intricate realm of micro-grid energy management. This paper proposes a homomorphic encryption based resilient distributed algorithm with an event-triggered mechanism to address this problem. Due to the potential information disclosure issue, exchange information is encrypted to an arbitrary neighbor and decrypted with a private key to protect agents. Considering the potential security attacks on adversary agents, an event-trigger based resilient distributed optimization with trusted agents (ETRDO-T) is proposed. It ensures the convergence of distributed algorithms, as well as relives the communication burden caused by homomorphic encryption. The simulation results, it can be seen that even under data attacks from malicious nodes, this method can effectively protect privacy information in information exchange while ensuring the convergence of energy management.

关键词： Homomorphic encryption Energy management Privacy Optimization Cyberattack Telecommunications Information exchange Malicious data attack cyber-physic system homomorphic encryption event-trigger distributed algorithm

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Resilient distributed averaging: Adversary detection and topological insights

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SYSTEMS & CONTROL LETTERS 2024年 191卷

作者： Dibaji, Seyed Mehran Safi, Mostafa Sharifi, Iman MIT Dept Mech Engn Cambridge MA USA Amirkabir Univ Technol Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

In this paper, a distributed averaging algorithm in the presence of adversarial Byzantine agents is proposed. The algorithm is based on a resilient retrieval procedure, where all non-Byzantine nodes send their own initial values and retrieve those of other agents. We establish that the convergence of the proposed algorithm relies on strong robustness of the graph for locally bounded adversaries in a network with communication delays and asynchrony. A topology analysis in terms of time complexity and relation between connectivity metrics is also presented. Simulation results are provided to verify the effectiveness of the proposed algorithms under prescribed graph conditions.

关键词： Averaging Resilient consensus distributed algorithm Fault detection Robust graph Time complexity Byzantine

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V2VDisCS: Vehicle to Vehicle distributed Charge Sharing in Intelligent Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 4960-4974页

作者： Chatterjee, Punyasha Majumder, Pratham Das, Sajal K. Jadavpur Univ Sch Mobile Comp & Commun Kolkata 700032 India Jain Deemed Univ Dept Informat Sci & Engn Bengaluru 560069 India Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. In this paper, the problem of V2V matching for charge sharing is mapped to the classical stable matching problem in bipartite graphs. The problem is formulated using integer linear programming that considers flexible decision making for EVs based on multiple charging criteria and constraints. However, as EVs have limited communication ranges, an EV can't possess knowledge about the entire vehicular network. So we propose two sets of distributed heuristics under the name of Vehicle to Vehicle distributed Charge Sharing (V2VDisCS), which yield a sub-optimal solution with lower computational and message complexities compared to existing distributed solutions. We analyze the average case matching probabilities and prove the sub-optimality of our approach. Simulation studies show that our heuristics outperform the existing distributed approaches in terms of message overhead and matching percentage. They show a comparable result for matching preference with respect to the standard centralized stable matching algorithm.

关键词： Costs Charging stations Charge transfer Servers Integer linear programming Electronic mail Electric vehicles Adaptation models Wireless communication Vehicular ad hoc networks Intelligent transportation system (ITS) electric vehicles (EVs) V2V charge sharing multi-criteria decision making integer linear programming distributed algorithm stable matching

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Cost-Minimizing distributed algorithm for Managing Renewable-Integrated Power Grids

Cost-Minimizing Distributed Algorithm for Managing Renewable...

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IEEE Global Conference on Signal and Information Processing

作者： Sun Sun Min Dongt Ben Liang Dept. of Electrical and Computer Engineering University of Toronto Canada Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology Canada

ISBN: (纸本)9781479975921

We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple renewable generators (RGs) each co-located with storage, and is additionally connected with external markets. An aggregator operates the power grid and aims at minimizing the long-term system cost. We propose a distributed real-time power balancing solution, taking into account the uncertainty of the renewable generation, loads, and energy prices. We demonstrate that our proposed algorithm is asymptotically optimal as the storage capacity increases and the CG ramping constraint loosens. The distributed implementation enjoys a fast convergence rate and enables each RG and the aggregator to make their own decisions.

关键词： distributed algorithm energy storage flexible loads renewable generation stochastic optimization

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Attack-Resilient distributed Nash Equilibrium Seeking of Uncertain Multiagent Systems Over Unreliable Communication Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 6365-6379页

作者： Meng, Qing Nian, Xiaohong Chen, Yong Chen, Zhao Cent South Univ Sch Automat Changsha 410075 Peoples R China

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

关键词： Games Multi-agent systems Communication networks Eigenvalues and eigenfunctions Nash equilibrium Resource management Process control Cyberattack distributed algorithm multiagent systems Nash equilibrium (NE) neuroadaptive control

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distributed Multi-Coalition Games With General Linear Systems Over Markovian Switching Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6157-6168页

作者： Liu, Shuai Wang, Dong Wu, Zheng-Guang Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Chengdu Univ Inst Adv Study Chengdu 610106 Peoples R China

In this paper, the multi-coalition game problem with incomplete information over Markovian switching networks is investigated. Each heterogeneous player involved in the problem is driven by a general linear dynamic and shares information by exploiting the randomly evolving network. All players intend to minimize the cost function of their coalition while achieving output action consensus inside the coalition. Regarding this, we develop a distributed multi-coalition game algorithm based on the proportional-integral dynamic consensus protocol. With graph theory, stochastic processes, and the stability principle, the algorithm is proven to converge exponentially to the Nash equilibrium solution, and the effect of gain parameters on the convergence rate is revealed. In addition, motivated by concerns about typical scenarios in which transition probabilities are inaccessible and the demands of the time-limited task, the discussion is extended to cases including Markovian switching networks with partly unknown transition probabilities and the formulation of a distributed predefined time scheme. Then, the corresponding analytical results are given, respectively. Finally, the effectiveness of the proposed algorithm is verified by numerical simulations.

关键词： distributed algorithm general linear system Markovian switching networks multi-coalition games distributed algorithm general linear system Markovian switching networks multi-coalition games

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distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第7期18卷 1-25页

作者： Deng, Jiayi Huang, Danyang Zhang, Bo Chinese Peoples Liberat Army Gen Hosp Dept Stat & Epidemiol Beijing Beijing Peoples R China Renmin Univ China Beijing Peoples R China

This paper proposes a distributed pseudo-likelihood method (DPL) to conveniently identify the community structure of large-scale networks. Specifically, we first propose a block-wise splitting method to divide largescale network data into several subnetworks and distribute them among multiple workers. For simplicity, we assume the classical stochastic block model. Then, the DPL algorithm is iteratively implemented for the distributed optimization of the sum of the local pseudo-likelihood functions. At each iteration, the worker updates its local community labels and communicates with the master. The master then broadcasts the combined estimator to each worker for the new iterative steps. Based on the distributed system, DPL significantly reduces the computational complexity of the traditional pseudo-likelihood method using a single machine. Furthermore, to ensure statistical accuracy, we theoretically discuss the requirements of the worker sample size. Moreover, we extend the DPL method to estimate degree-corrected stochastic block models. The superior performance of the proposed distributed algorithm is demonstrated through extensive numerical studies and real data analysis.

关键词： Community detection computational efficiency distributed algorithm large-scale networks pseudo-likelihood

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Lattice-Like Blockchain System for Maintaining the Data Privacy in the distributed Computation of Carbon Emission

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IEEE TRANSACTIONS ON SMART GRID 2024年第5期15卷 4961-4973页

作者： Han, Zhuopu Ding, Tao Mu, Chenggang Shen, Xin Li, Jiahao Shahidehpour, Mohammad Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China Yunnan Power Grid Co Ltd Measurement Ctr Kunming 650051 Yunnan Peoples R China IIT ECE Dept Chicago IL 60616 USA

Calculation and deposit of carbon emission flow help optimize the systematic management of power system operation with different types of power generation units. Traditionally, centralized computation of carbon emission flow led to some inherent problems, including poor stability, flexibility and data privacy concerns. In contrast, the existing distributed computation methods for carbon emission fail to protect data privacy and is difficult to ensure data credibility. Therefore, this paper proposes a distributed computation method for carbon emission flow, where a bus classification method and an encryption algorithm are designed to protect data privacy. Then, a lattice-like light blockchain structure is proposed for carbon emission flow calculation result, enhancing efficiency during the deposit process. The proposed lattice-like blockchain breaks through the existing structure with a single chain, as it realizes an asynchronous consensus by significantly improving the uploading speed of the blockchain. Finally, the IEEE 33-bus system is used to verify the reliability and security of the proposed method, interact with the lattice-like blockchain, and enhance the computation performance of the proposed method.

关键词： Carbon dioxide Data privacy Power system stability Load flow Consensus protocol Reliability Power system reliability Carbon emission flow blockchain structure distributed algorithm encryption asynchronous consensus

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distributed optimal power flow of AC/DC distribution networks with integer variables

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 243卷

作者： Zheng, Xiaoyuan Jia, Jianyang Jia, Guanlong Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300401 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Reliab Tianjin 300401 Peoples R China

Due to the nature of integer variables, achieving multi-period optimal power flow (OPF) for AC/DC distribution networks (DNs) with integer variables via distributed algorithm presents significant technical challenges. These challenges primarily arise from two aspects. First, the integer variables lead to the traditional distributed algorithms failing to converge or converging to locally optimal solutions. Second, the binary variables related to topology switching cannot be relaxed to continuous variables, making existing distributed algorithms inapplicable. To address these problems, we propose the following solutions: (1) the primal problem is decomposed into a master problem and multiple subproblems using the generalized Benders decomposition (GBD) algorithm, and an iterative process is defined to obtain the optimal solution;(2) employing the branch- and-bound method (BBM) to avoid uncomputable cutting planes due to integer variables in the subproblem;and (3) using two acceleration techniques to improve efficiency by warming up the BBM and reducing parameter transfer between the master and subproblems. The effectiveness and accuracy of the proposed method are proven by simulation results of a test system, showing that the algorithm converges to the optimal solution infinite time. Additionally, the distributed algorithm provides a practical solution for operators and enhances privacy.

关键词： AC/DC distribution networks distributed algorithm Feeder switches Generalized benders decomposition Branch-and-bound method Optimal power flow

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distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting algorithms

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 179-194页

作者： Ran, Liang Li, Huaqing Zheng, Lifeng Li, Jun Li, Zhe Hu, Jinhui Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Business Vocat Coll Coll Artificial Intelligence Chongqing 401331 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong 999077 Peoples R China

This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators, a novel distributed primal-dual splitting algorithm with full-decision information (Dist_PDS_FuDeIn) is designed, eliminating the need for global step-sizes. Furthermore, to address scenarios where players lack access to all other players' decisions, a local estimation is introduced to approximate the decision information of other players, and a fully distributed primal-dual splitting algorithm with partial-decision information (Dist_PDS_PaDeIn) is then proposed. Both algorithms enable the derivation of new distributed forward-backward-like extensions. Theoretically, a new analytical approach for convergence is presented, demonstrating that the proposed algorithms converge to the variational GNE of games, and their convergence rates are also proven, provided that uncoordinated step-sizes are positive and less than explicit upper bounds. Moreover, the approach not only generalizes the forward-backward splitting technique but also improves convergence rates of several well-known algorithms. Finally, the advantages of Dist_PDS_FuDeIn and Dist_PDS_PaDeIn are illustrated through comparative simulations.

关键词： Games Convergence Costs Information processing Heuristic algorithms Standards Lyapunov methods distributed algorithm generalized Nash equilibria game theory operator splitting

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