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A Finite-Time distributed optimization algorithm for Economic Dispatch in Smart Grids

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2068-2079页

作者： Mao, Shuai Dong, Ziwei Schultz, Paul Tang, Yang Meng, Ke Dong, Zhao Yang Qian, Feng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Potsdam Inst Climate Impact Res Complex Sci Dept D-14412 Potsdam Germany Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.

关键词： Convergence Smart grids Generators Cost function Consensus distributed optimization algorithm economic dispatch finite time smart grids

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distributed algorithm Over Time-Varying Unbalanced Graphs for optimization Problem Subject to Multiple Local Constraints

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 387-402页

作者： Liu, Hongzhe Yu, Wenwu Wen, Guanghui Zheng, Wei Xing Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

This article addresses the optimization problem with its global objective function being composed of multiple convex functions under multiple nonidentical local constraints. The specific purpose is to resolve the studied optimization problem in a distributed manner in the presence of the time-varying unbalanced graph sequence. For this purpose, an efficient distributed discrete-time algorithm is developed over the time-varying unbalanced graph sequence by improving the classical push-pull algorithms. Moreover, for the developed distributed discrete-time algorithm, a rigorous analysis is made of its convergence property to the optimal solution as well as its convergence rate under some standard assumptions. Finally, numerical simulations are carried out to demonstrate the good performance of the designed algorithm.

关键词： optimization Convergence Vectors Directed graphs Network systems Linear programming distributed algorithms Control systems Numerical simulation Standards distributed optimization algorithm improved push-pull framework multiple local constraints time-varying unbalanced graphs

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Asynchronous distributed Nonsmooth Composite optimization via Computation-Efficient Primal-Dual Proximal algorithms

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1595-1609页

作者： Ran, Liang Li, Huaqing Zheng, Lifeng Li, Jun Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This paper focuses on a distributed nonsmooth composite optimization problem over a multiagent networked system, in which each agent is equipped with a local Lipschitz-differentiable function and two possibly nonsmooth functions, one of which incorporates a linear mapping. To address this problem, we introduce a synchronous distributed algorithm featuring uncoordinated relaxed factors. It serves as a generalized relaxed version of the recent method TriPD-Dist. Notably, the considered problem in the presence of asynchrony and delays remains relatively unexplored. In response, a new asynchronous distributed primal-dual proximal algorithm is first proposed, rooted in a comprehensive asynchronous model. It is operated under the assumption that agents utilize possibly outdated information from their neighbors, while considering arbitrary, time-varying, yet bounded delays. With some special adjustments, new asynchronous distributed extensions of existing centralized methods are obtained via the proposed asynchronous algorithm. Theoretically, a new convergence analysis technique of the proposed algorithms is provided. Specifically, a sublinear convergence rate is explicitly derived by showcasing that the iteration behaves as a nonexpansive operator. In addition, the proposed asynchronous algorithm converges the optimal solution in expectation under the same step-size conditions as its synchronous counterpart. Finally, numerical studies substantiate the efficacy of the proposed algorithms and validate their performance in practical scenarios.

关键词： Delays optimization distributed algorithms Convergence Vectors Convex functions Computational intelligence Asynchrony delayed communication nonsmooth convex functions distributed optimization algorithm

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Variable-coupled distributed resource allocations for multi-agent system with limited admissible interaction

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ISA TRANSACTIONS 2025年 157卷 35-45页

作者： Luan, Linhua Qin, Sitian Harbin Inst Technol Dept Math Weihai 264209 Peoples R China

Variable-coupled distributed resource allocations (VCDRAs) involve optimizing resource distribution among interconnected entities, reflecting the complex correlation in practical systems. Based on the consensus technology, this paper proposes a novel distributed optimization algorithm to address the VCDRA in scenarios where agents are confronted with admissible interaction ranges and external disturbances. Admissible interaction refers to agents only transmitting information within a specified range due to limited communication capabilities or resource availability. The proposed distributed optimization algorithm improves existing interaction mechanisms, reducing computational demands and unifying the communication schemes of the multi-agent system, thereby avoiding additional information exchange. Theoretically, it is proven that the algorithm can get the optimal solution of VCDRA with an exponential rate. Compared to existing algorithms for suppressing external disturbances, the robustness of the proposed distributed optimization algorithm no longer relies on the upper bound of external disturbances, allowing it to remain effective even in the presence of unbounded disturbances. Finally, smart grids and wireless communications applications demonstrate the convergence and robustness of the developed distributed optimization algorithm, further proving its superiority in practical applications.

关键词： Variable-coupled distributed resource allocation Multi-agent system distributed optimization algorithm Admissible interaction range External disturbances

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An overview of distributed economic dispatch of microgrids: advances and challenges

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2025年第1期13卷

作者： Sun, Lei An, Wenjing Chen, Yangkai Zhao, Peifeng Ding, Derui Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai Peoples R China

The integration of renewable energy provides a fresh boost for the development of power systems. Economic dispatch (ED) is necessary to achieve optimal power allocation while meeting practical physical constraints and ensuring economic benefits and production safety. Traditional centralized ED methods are not always able to meet the ED demands of microgrids that mostly use distributed power sources. Consequently, distributed ED (i.e. DED) schemes are receiving more research attention because of their high reliability, scalability and uniformity in communication and computation loads. The latest theoretical advancements in DED algorithms are reviewed in this paper to give a comprehensive review. The survey focuses on design considerations of DED optimization algorithms for both discrete-time and continuous-time implementation scenarios and cutting-edge engineering applications by incorporating practical operational constraints or privacy-preserving mechanisms. Furthermore, deep insights are provided to reveal the intrinsic mechanism and exceptional performance of various DED algorithms. A brief outline of prospective research directions is provided based on the reviewed literature, which includes DED issues with non-convex and multi-objective functions, fast DED algorithms and DED algorithms without initialization.

关键词： Microgrids distributed economic dispatch distributed optimization algorithm engineering constraints privacy-preserving mechanisms

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Predefined-time distributed optimization algorithms for a class of resource allocation problem

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第12期361卷

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

In this paper, two predefined -time distributed optimization algorithms are proposed to solve a class of resource allocation problem (RAP) with equation constraint. One is a distributed predefined continuous -time optimization algorithm and the other is an accelerated distributed predefined discrete iterative optimization algorithm. Compared with existing finite -time and fixed -time distributed optimization algorithms, the proposed algorithms can solve the RAP in any given time. The core of the optimization algorithms is to design two predefined -time multi -agent weighted consensus algorithms. Especially in the design of discrete algorithm, the performance of optimization algorithm has been greatly improved through information composite interaction. Finally, the proposed algorithms are used to solve the problem of power resource allocation in power grids, and five sets of simulation instances are provided to verify the effectiveness and advantages of the proposed algorithms.

关键词： distributed optimization algorithm Resource allocation Equality constraint Convex optimization Predefined-time convergence

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distributed safe trajectory optimization for large-scale spacecraft formation reconfiguration

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ACTA ASTRONAUTICA 2024年 214卷 125-136页

作者： Chen, Junyu Wu, Baolin Sun, Zhaobo Wang, Danwei Harbin Inst Technol Res Ctr Satellite Technol Harbin 150001 Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

This paper presents a distributed trajectory optimization algorithm for the reconfiguration of large-scale spacecraft formation, which uses a low thrust propulsion system to safely guide the spacecraft formation to a desired formation. Formation reconfiguration is formulated as a trajectory optimization problem with complex constraints, and the relative motion is accurately described by a nonlinear relative dynamics considering J2 perturbation. The collision avoidance constraint is convexified into a tangent plane to ensure the accuracy of the solution. The resulting nonlinear trajectory optimization problem is solved by applying the hp-adaptive pseudospectral method to convert it into a nonlinear programming problem. In order to overcome the disadvantage of huge computation of centralized algorithm considering collision avoidance, "predicted trajectory" is introduced to transmit information between spacecraft, and the parallel computing is implemented. Finally, a numerical simulation is given to verify the computational efficiency and collision avoidance effectiveness of the proposed distributed algorithm.

关键词： Large-scale spacecraft formation distributed optimization algorithm Trajectory optimization Collision avoidance Autonomous reconfiguration

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distributed continuous-time optimization in multi-agent networks with undirected topology 15

Distributed continuous-time optimization in multi-agent netw...

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IEEE 15th International Conference on Control and Automation (ICCA)

作者： Fu, Zao Zhao, You Wen, Guanghui Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Math Nanjing 211189 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3000 Australia

ISBN: (纸本)9781728111643

By using combined tools from smooth approximation technique and exact penalty method, a smooth distributed continuous-time algorithm is designed in this paper to solve a kind of convex problem in multi-agent networks with undirected topology. One of the remarkable features of this paper lies in the fact that a convergence rate O ( 1=t(2)) could be yielded by using the proposed distributed algorithm if, some suitable conditions are satisfied. Specifically, by using the smooth approximation method, a kind of distributed algorithm is proposed to deal with the optimization problem with non-smooth cost functions or cost functions having nonLipschitz gradient. The asymptotic convergence property and the convergence rate of the proposed distributed algorithm are analyzed under certain mild conditions. A numerical simulation is conducted to validate the effectiveness of the theoretical analysis.

关键词： Continuous-time optimization multi-agent network smooth approximation approach distributed optimization algorithm

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An optimization Scheduling Method for Integrated Energy Microgrid Based on Heavy-Ball Acceleration distributed optimization 8

An Optimization Scheduling Method for Integrated Energy Micr...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Li, Haoran Li, Zhijiang Zhao, Haoran Liu, Kailong Shandong University School of Electrical Engineering Jinan China Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798331523558

The utilization of distributed renewable energy sources such as photovoltaic and wind power is characterized by uncertainty and volatility, posing significant challenges to the reliability and stability of integrated energy microgrids. Compared to centralized optimization methods, distributed optimization methods exhibit lower computational and communication burdens, along with enhanced flexibility, reliability, and scalability. This paper introduces heavy-ball acceleration technology into the traditional Alternating Direction Method of Multipliers (ADMM) algorithm. This approach not only ensures the stability of node voltages but also reduces network losses and accelerates the convergence speed of the algorithm. Firstly, the second-order cone relaxation technique is utilized to convexify the optimization problem of the integrated energy microgrid model, thereby improving the solution accuracy and enabling direct solution by the ADMM algorithm. Subsequently, an ADMM algorithm model incorporating heavy-ball acceleration technology is proposed for real-time optimization scheduling and power allocation in the integrated energy microgrid. Finally, simulation calculations are performed based on an improved IEEE 33-node integrated energy microgrid system, with an analysis conducted on the voltage stability of each node, network losses, and model convergence. The simulation results demonstrate that the improved algorithm can reduce network losses and improve the solution accuracy and convergence speed of the system while maintaining the basic stability of node voltages. This has certain guiding significance for practical engineering applications. © 2024 IEEE.

关键词： distributed optimization algorithm Integrated Energy Microgrid Optimal Dispatch Renewable Energy

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distributed dynamic task allocation for unmanned aerial vehicle swarm systems:A networked evolutionary game-theoretic approach

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Chinese Journal of Aeronautics 2024年第6期37卷 182-204页

作者： Zhe ZHANG Ju JIANG Haiyan XU Wen-An ZHANG College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China

Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative *** an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the *** paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the ***,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy ***,a PayOff-based Time-Variant Log-linear Learning algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann *** former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of *** simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.

关键词： Task allocation Unmanned Aerial Vehicles(UAV) Game theory Log-linear learning distributed optimization algorithm

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