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Peer-to-Peer Multi-Energy Trading Among Heterogeneous Building Prosumers via Asynchronous distributed Algorithm

IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1590-1603页

作者： Jin, Xiaolong Wang, Xiaoyu Jia, Hongjie Mu, Yunfei Wu, Qiu-Wei Wei, Wei Tianjin Univ Key Lab Smart Grid Minist Educ Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China StateGrid Jibei Elect Power Co Ltd Res Inst Dept Smart Grid & New Energy Beijing 100045 Peoples R China

Smart buildings in the integrated community energy system (ICES) are normally equipped with distributed energy resources (DERs), thereby creating building prosumers with both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers can bring higher economic benefits for them. Therefore, a P2P multi-energy trading scheme among building prosumers is proposed, which fully explores the flexibility of buildings' heating loads based on the thermal dynamics of buildings with different thermal insulation properties. Each building prosumer is heterogeneous in terms of its computation and communication infrastructures. This results in a heavy computation burden with the traditional centralized method. To improve the computational efficiency for P2P trading among heterogeneous building prosumers, an asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed to enable each prosumer to trade energy asynchronously instead of waiting for the trading information from others with poor infrastructure. This asynchronous procedure integrated with the prediction and anomaly detection steps can further accelerate the convergence speed of P2P trading. Simulation results verify the effectiveness of the proposed trading scheme and the feasibility and solution optimality of the proposed algorithm.

关键词： Buildings Heating systems Resistance heating Water heating Optimization Indexes Insulation Electricity distributed algorithms Convex functions P2P multi-energy trading buildings' heating loads heterogeneous asynchronous distributed algorithm ADMM

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A Hybrid Multi-Agent System Approach for distributed Composite Convex Optimization Under Unbalanced Directed Graphs

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1267-1279页

作者： Wang, Zhu Wang, Dong Xu, Xiaopeng Lian, Jie Dalian Univ Technol Sch Control Sci & Engn Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China

This paper studies a distributed composite convex optimization problem for multi-agent systems over an unbalanced directed graph. The global objective function is the sum of local cost functions with known mathematical expressions and local cost functions with unknown ones. Due to the particularity of the local cost function, a hybrid multi-agent system composed of continuous-time dynamic agents and discrete-time dynamic agents is employed to solve such a problem. Also, because the local cost function may not be differentiable, a distributed algorithm based on subgradient and gradient-free oracle is proposed. Given some general assumptions, the developed algorithm almost surely converges to an approximately optimal solution. In addition, theoretical analysis indicates that the proposed algorithm possesses the same convergence rate as the existing stochastic gradient-free descent approaches under similar problem settings. Finally, a numerical example is provided to demonstrate the effectiveness of the findings.

关键词： Cost function Mathematical models distributed algorithms Multi-agent systems Heuristic algorithms Approximation algorithms Convergence Topology Directed graphs Vectors Composite convex optimization hybrid multi-agent systems unbalanced directed graphs distributed gradient-like algorithm

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An Improved distributed Nesterov Gradient Tracking Algorithm for Smooth Convex Optimization Over Directed Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2738-2745页

作者： Lin, Yifu Li, Wenling Zhang, Bin Du, Junping Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

This article explores the problem of distributed optimization for functions that are smooth and nonstrongly convex over directed networks. To address this issue, an improved distributed Nesterov gradient tracking (IDNGT) algorithm is proposed, which utilizes the adapt-then-combine rule and row-stochastic weights. A main novelty of the proposed algorithm is the introduction of a scale factor into the gradient tracking scheme to suppress the consensus error. By the estimate sequence approach, the dynamics of the error due to the unbalance of directed networks is analyzed and it is shown that a sublinear convergence rate can be achieved with a vanishing step size. Numerical results suggest that the performance of IDNGT is comparable to that of the centralized Nesterov gradient descent algorithm.

关键词： Vectors Convergence Convex functions Topology Telecommunications distributed algorithms Cost function Catalysts Symbols Standards Directed networks distributed optimization gradient tracking Nesterov smooth function

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High Probability Convergence of Clipped distributed Dual Averaging With Heavy-Tailed Noises

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2624-2632页

作者： Qin, Yanfu Lu, Kaihong Xu, Hang Chen, Xiangyong Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Jiangxi Univ Sci & Technol Inst Permanent Magnet Levitat Technol & Rail Trans Ganzhou 341000 Peoples R China Linyi Univ Sch Automat & Elect Engn Linyi 276005 Peoples R China

In this article, the problem of distributed optimization subject to a convex set is studied by employing a multiagent system, where each agent only has access to a stochastic gradient of its own cost function, and exchanges local information with its neighbors through a time-varying digraph. To handle this problem, a stochastic distributed algorithm based on the clipped distributed strategy and the dual averaging strategy is proposed. Of particular interest follows that we study the high probability convergence of the stochastic distributed algorithm under the assumption that the stochastic gradient follows a heavy-tailed distribution. Heavy-tailed distributions often causes extreme values, which bring difficulties in achieving the high probability convergence. Under mild assumptions on the graph and cost functions, we prove that the difference between the cost function value generated by the algorithm and its minimum value converges with high probability. Finally, a simulation experiment is presented to demonstrate the effectiveness of our theoretical results.

关键词： Heavily-tailed distribution Convergence Noise Stochastic processes distributed algorithms Cost function Lightly-tailed distribution Vectors Noise measurement Random variables Clipped distributed strategy distributed optimization heavy-tailed noises high probability stochastic gradients

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distributed predefined-time dynamic weighted average tracking control for nonlinear multi-agent systems

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NONLINEAR DYNAMICS 2025年 1-18页

作者： Dai, Hao Su, Chuxiong Yan, Li Fang, Xinpeng Chang, Jing Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China Taiyuan Univ Technol Sch Math Taiyuan 030024 Peoples R China

This paper proposes two novel distributed predefined-time algorithms to solve the dynamic weighted average tracking control problem of first-order multi-agent systems. The first algorithm, a distributed accurate predefined-time algorithm, guarantees that the system state precisely tracks the time-varying weighted average signal within a predefined time. The second, a distributed bounded predefined-time algorithm, ensures that tracking errors converge to a bounded region within a predefined time. Additionally, this paper introduces a novel globally predefined-time stability theory for nonlinear systems. Leveraging this theory, we conduct a thorough analysis of the predefined-time convergence of our proposed algorithms. Finally, the effectiveness and superiority of our algorithms are validated through rigorous performance evaluations and engineering applications.

关键词： distributed algorithms Predefined-time stability Dynamic weighted average tracking Nonlinear multi-agent systems

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Privacy-Preserving distributed Nash Equilibrium Seeking for Noncooperative Games With Masked Interactive Information

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 351-360页

作者： Cai, Xin Xinjiang Univ Sch Elect Engn Urumqi 830017 Peoples R China

The privacy preservation problem in distributed Nash equilibrium (NE) seeking for noncooperative games is studied in this article. Different from adding random noises to the transmitted data in estimators for some global information, a dynamical privacy preservation method is used to both protect each agent's strategy and avoid reconstructing the privacy of agents. Thus, a novel distributed algorithm is proposed with the masked interactive information to seek NE. Moreover, a discrete-time communication scheme is designed based on an event-triggering rule without the Zeno behavior for the multiagent noncooperative game. The accurate convergence of the designed distributed algorithm is established, rather than the bounded convergence of the differential privacy-based algorithms. The performance of the designed algorithm with communication delay is also analyzed. Cournot competition with distributed energy resources is taken as an example to verify the proposed algorithm.

关键词： Games Privacy Convergence Heuristic algorithms Noise distributed algorithms Differential privacy Event-triggered communication multiagent systems Nash equilibrium (NE) noncooperative games privacy preservation

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Trustworthy distributed Average Consensus Based on Locally Assessed Trust Evaluations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 371-386页

作者： Hadjicostis, Christoforos N. Dominguez-Garcia, Alejandro D. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

This article proposes a distributed algorithm for average consensus in a multiagent system under a fixed bidirectional communication topology, in the presence of untrustworthy (malicious) agents (nodes) that may try to influence the average consensus outcome by manipulating their updates. The proposed algorithm converges asymptotically to the average of the initial values of the trustworthy (nonmalicious) nodes, which we refer to as the trustworthy average, as long as the underlying topology that describes the information exchange among the trustworthy nodes is connected. We first present a distributed iterative algorithm that assumes that each node receives (at each iteration or periodically) side information about the trustworthiness of the other nodes, and it uses such trust assessments to determine whether or not to incorporate messages received from its neighbors, as well as to make proper adjustments in its calculation depending on whether a previously trustworthy neighbor becomes untrustworthy or vice-versa. We show that, as long as the trust assessments for each trustworthy node eventually reflect correctly the status (trustworthy or untrustworthy) of its neighboring nodes, the algorithm guarantees asymptotic convergence to the trustworthy average. We subsequently discuss how the proposed algorithm can be enhanced with functionality that enables each node to obtain trust assessments about its neighbors by utilizing information that it receives from its two-hop neighbors at infrequent, perhaps randomly chosen, time instants.

关键词： Topology Multi-agent systems distributed algorithms Convergence Bidirectional control Stochastic processes Heuristic algorithms distributed averaging distributed trust assessment fault-tolerant consensus resilience multiagent systems trustworthy computation

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Deconfliction of Centralized and distributed ADMS and DERMS Applications

Deconfliction of Centralized and Distributed ADMS and DERMS ...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Anderson, Alexander Fisher, Andrew Poudel, Shiva Vasios, Orestis Black, Gary Reiman, Andrew Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9798350352528

Ongoing distribution grid modernization efforts are introducing an increasing number of advanced applications granted control of field devices. An emerging challenge is the deconfliction problem of coordinating conflicting control commands issued by multiple applications to the same device. As an alternative to custom, centralized grid orchestration techniques, the concept of distributed deconfliction is introduced. Conflicts between centralized and distributed applications are decomposed based on definition of local distributed control areas based on the network topology. This work introduces a distributed deconfliction architecture derived from prior work in centralized deconfliction and distributed application architecture. Setpoint-informed optimization and objective-informed multi-criteria decision-making are compared as two possible numerical techniques for selection of final device setpoints. © 2025 IEEE.

关键词： common information model distributed algorithms distributed power generation edge computing power distribution control

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Efficient bounded timestamping from standard synchronization primitives

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distributed COMPUTING 2025年 1-39页

作者： Bashari, Benyamin Jamadi, Ali Woelfel, Philipp Univ Calgary Comp Sci 2500 Univ Dr Calgary AB T2N 1N4 Canada

Bounded timestamping systems (Israeli and Li in Proceedings of the 28th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp 371-382, 1987;Dolev and Shavit in SIAM J Comput 26 (2):418-455, 1997) allow a temporal ordering of events in executions of concurrent algorithms. They are a fundamental and well-studied building block used in many shared-memory algorithms (Haldar and Vit & aacute;nyi in J ACM, 49 (1):101-126, 2002;Afek et al. in ACM Trans Program Lang Syst 16:939-953, 1994;Abrahamson in Proceedings of the 7th ACM symposium on principles of distributed computing (PODC), pp 291-302, 1988;Bashari and Woelfel in Proceedings of the 40th ACM symposium on principles of distributed computing (PODC), pp 545-555, 2021). A concurrent bounded timestamping system keeps track of m timestamps, which is usually greater or equal to the number of processes in the system, n. A process may, at any point, obtain a new timestamp, and later determine a total order of all process's most recent timestamps. Known bounded timestamping algorithms (Dolev and Shavit in SIAM J Comput 26(2):418-455, 1997;Dwork and Waarts in J ACM 46(5):633-666, 1999;Dwork et al. in SIAM J Comput 28(5):1848-1874, 1999;Gawlick et al. in Theory of computing and systems (ISTCS), pp 171-183, 1992;Israeli and Pinhasov in distributed algorithms, pp 95-109, 1992;Haldar and Vit & aacute;nyi in J ACM 49(1):101-126, 2002) do not scale well in the number of processes as getting a new timestamp takes at least Omega(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Omega (n)$$\end{document} steps. Moreover, a lower bound by Israeli and Li (Proceedings of the 28th annual IEEE symposium on foundations of computer science (FOCS), pp 371-382, 1987) implies that timestamps need to be represented by Omega(m)\documentclass[12pt]{minim

关键词： Timestamping Shared memory Concurrency distributed algorithms

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Near-optimal dispersion on arbitrary anonymous graphs

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2025年 152卷

作者： Kshemkalyani, Ajay D. Sharma, Gokarna Univ Illinois Dept Comp Sci Chicago IL USA Kent State Univ Dept Comp Sci Kent OH 44240 USA

Given an undirected, anonymous, port-labeled graph of n memory-less nodes, m edges, and degree Delta, we consider the problem of dispersing k <= n robots (or tokens) positioned initially arbitrarily on the nodes of the graph to exactly k different nodes, one on each node. The objective is to simultaneously minimize time and memory requirement at each robot. The best previously known algorithm solves this problem in O(min{m,k Delta}.log l) time storing O(log(k+Delta)) bits at each robot, where l <= k/2 is the number of nodes with multiple robots positioned on them in the initial configuration. In this paper, we present a novel multi-source DFS traversal algorithm solving this problem in O(min{m,k Delta}) time with O(log(k+Delta)) bits at each robot. The memory complexity of our algorithm is already asymptotically optimal and the time complexity is asymptotically optimal for the graphs of constant degree Delta=O(1). The result holds in both synchronous and asynchronous settings. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/).

关键词： distributed algorithms Multi-agent systems Mobile robots Local communication Dispersion Exploration Time and memory complexity

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