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distributed optimization Over Time-Varying Networks With Minimal Connectivity

IEEE CONTROL SYSTEMS LETTERS 2020年第3期4卷 536-541页

作者： Wu, Xuyang Lu, Jie ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China

Many existing distributed optimization algorithms are applicable to time-varying networks, whereas their convergence results are established under the standard $B$ -connectivity condition. In this letter, we establish the convergence of the Fenchel dual gradient methods, proposed in our prior work, under a less restrictive and indeed minimal connectivity condition on undirected networks, which, referred to as joint connectivity, requires the infinitely occurring agent interactions to form a connected graph. Compared to the existing distributed optimization algorithms that are guaranteed to converge under joint connectivity, the Fenchel dual gradient methods are able to handle nonlinear local cost functions and nonidentical local constraints. We also demonstrate the effectiveness of the Fenchel dual gradient methods over time-varying networks satisfying joint connectivity via simulations.

关键词： distributed optimization multi-agent optimization time-varying networks Fenchel duality

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distributed optimization Framework for In-Network Data Processing

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IEEE-ACM TRANSACTIONS ON NETWORKING 2019年第6期27卷 2432-2443页

作者： Nazemi, Sepideh Leung, Kin K. Swami, Ananthram Imperial Coll London Dept Elect Elect Engn London SW7 2BT England Comunnere Ltd London EC1V 7EN England US Army Res Lab Adelphi MD 20783 USA

In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimizing the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimization problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimization problem. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.

关键词： optimization Data aggregation Energy consumption Wireless sensor networks distributed databases Cloud computing In-network processing quality of information data aggregation distributed optimization non-convex optimization data reduction rate trade-off energy efficiency

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distributed optimization for joint peer-to-peer electricity and carbon trading among multi-energy microgrids considering renewable generation uncertainty

ENERGY CONVERSION AND ECONOMICS

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ENERGY CONVERSION AND ECONOMICS 2024年第2期5卷 116-131页

作者： Hou, Hui Wang, Zhuo Zhao, Bo Zhang, Leiqi Shi, Ying Xie, Changjun Dong, ZhaoYang Yu, Keren Wuhan Univ Technol Sch Automat Wuhan Peoples R China Wuhan Univ Technol Shenzhen Res Inst Shenzhen Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Jurong West Singapore ESG Future Fdn Melbourne Vic Australia

The increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low-carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer-to-peer (P2P) electricity and carbon trading in multi-energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value-at-risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision-making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

关键词： conditional value-at-risk distributed optimization electricity and carbon trading multi-energy microgrid peer-to-peer

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distributed optimization Using the Primal-Dual Method of Multipliers

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第1期4卷 173-187页

作者： Zhang, Guoqiang Heusdens, Richard Univ Technol Sydney Sch Comp & Commun Ultimo NSW 2007 Australia Delft Univ Technol Dept Microelect Circuits & Syst Grp NL-2628 CD Delft Netherlands

In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is shown that a saddle point of the constructed function provides an optimal solution of the original problem. Further under both the synchronous and asynchronous updating schemes, PDMM has the convergence rate of O(1/K) (where K denotes the iteration index) for general closed, proper, and convex functions. Other properties of PDMM such as convergence speeds versus different parameter-settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.

关键词： ADMM distributed optimization PDMM sublinear convergence

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distributed optimization Operation of Multi-microgrid with Electricity-carbon Trading

Distributed Optimization Operation of Multi-microgrid with E...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Song, Fengchen Liu, Kaicheng Wang, Luhao Zhao, Shengnan Lv, Quanpeng Wang, Linlin Univ Jinan Sch Elect Engn China Elect Power Res Inst State Key Lab Power Grid Safety Beijing 100192 Peoples R China

ISBN: (纸本)9798350339345

This paper proposes a distributed optimization operation method for multi-microgrids (MMGs) with electricity-carbon trading. First, a bi-level cooperative dispatch model of distribution networks and MMGs is established. The distribution locational marginal price (DLMP) is used to guide MMGs participating in electricity trading. Second, the ladder carbon trading is introduced in MMGs to achieve low carbon production. The alternating directional multiplier method (ADMM) is applied to calculate the proposed lower model and coordinate peer-to-peer (P2P) electricity trading among MMGs. Finally, the upper and lower models are solved iteratively based on the information of pass-through trading quantities and DLMP between the distribution network and MMGs, which realizes the coupling electricity-carbon trading of the MMGs as well as reflects the win-win situation of both low-carbon and economic aspects.

关键词： carbon trading multi-microgrids DLMP distributed optimization

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distributed optimization with Multiple Linear Equality Constraints and Convex Inequality Constraints 31

Distributed Optimization with Multiple Linear Equality Const...

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31st Chinese Control And Decision Conference (CCDC)

作者： Lin, Wen-Ting Wang, Yan-Wu Xiao, Jiang-Wen Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang Peoples R China

ISBN: (纸本)9781728101057

This paper proposes a distributed discrete-time algorithm for optimization problem with global multiple constraints. Based on the projected primal-dual method, a fully distributed algorithm is proposed, and is proved to converge to the optimal solution of the optimization problem under certain sufficient condition with respect to the step-size. Numerical example is given to verify the efficacy of the proposed algorithm.

关键词： distributed optimization multiple constraints global optimization

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distributed optimization in Learning Algorithms with Parsimony in Communications

Distributed Optimization in Learning Algorithms with Parsimo...

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作者： Ren, Jineng University of Minnesota

学位级别：Ph.D.

As we have seen, today machine learning and big data technologies are transforming both our daily life and economies fundamentally. An important factor that fuels the progress of learning algorithms is the abundance of data generated everyday. In many scenarios, including internet of things, intelligent transportation systems, mobile/edge computing, and smart grids, the datasets are often generated and stored locally in different locations. Traditional centralized (concentrated) algorithms, however, are facing challenges in these settings because they usually require much higher computation cost on a single machine, more communications for collecting raw local data, and are more vulnerable to possible failure of the host. Therefore the distributed learning and optimization algorithms, which are essentially exempted from those problems, are becoming promising alternatives that attract growing interest in recent years. Generally speaking, distributed algorithms describe the approaches that solve problems in a collaborative manner over multiple agents (machines, nodes, computation units or cores) based on communications among them. The main theme of this work is the identification of efficient and effective ways to exploit distributed procedures and communication structures in this type of settings and applications. The first part of this work contains the discussions of a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under an asynchronous protocol. As we know, an important criterion for preferable distributed algorithms in latency and communication sensitive applications is that they can complete tasks fast with as less communication resources as possible. Thus in this part we present an asynchronous efficient distributed algorithm with reduced waiting time based on the updates utilizing local higher-order information and investigate the theoretical guarantee for the convergen

关键词： communication efficiency communication patterns convexity and nonconvexity distributed optimization

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distributed optimization for Generation Scheduling of Interconnected Microgrids

Distributed Optimization for Generation Scheduling of Interc...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Li, Yansong Liu, Nian Wu, Chenshuo Zhang, Jianhua North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467381321

Microgrids will make fuller consumption of renew able energy by connecting with each other. On that scenario, the generation schedule is supposed to be optimized on view of the total interconnected microgrids (IMGs), which may infringe the privacy of MG. A distributed optimization method for generation scheduling of MIGs using the alternating direction method of multipliers (ADMM) is proposed in this paper, so that the MC won't need to share their own information of sources and loads and so on, but only the expected exchanging power information. The case confirms the efficiency of the distributed optimization method compared to the centralized one.

关键词： interconnected microgirds autonomy distributed optimization generation schedule

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distributed optimization in sensor networks

Distributed optimization in sensor networks

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3rd International Symposium on Information Processing in Sensor Networks

作者： Rabbat, M Nowak, R Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)1581138466

Wireless sensor networks are capable of collecting an enormous amount of data over space and time. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. The distributed algorithms are based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data. Applying results from the theory of incremental subgradient optimization, we show that for a broad class of estimation problems the distributed algorithms converge to within an epsilon-ball around the globally optimal value. Furthermore, bounds on the number incremental steps required for a particular level of accuracy provide insight into the trade-off between estimation performance and communication overhead. In many realistic scenarios, the distributed algorithms are much more efficient, in terms of energy and communications, than centralized estimation schemes. The theory is verified through simulated applications in robust estimation, source localization, cluster analysis and density estimation.

关键词： distributed optimization incremental subgradient methods energy-accuracy tradeoff

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distributed optimization for High-order Multi-agent Systems via Event-triggered Control 40

Distributed Optimization for High-order Multi-agent Systems ...

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40th Chinese Control Conference (CCC)

作者： Tang, Yutao Liu, Huaihui Li, Ruonan Zhu, Kui Beijing Univ Posts & Telecommun Sch Artif Intelligence Beijing 100876 Peoples R China

ISBN: (纸本)9789881563804

This paper discusses the distributed output optimization problem for a group of linear multi-agent systems via event-triggered control. In this problem, each agent has an individual objective function and is required to reach the minimal solution of the sum of these objective functions. By inserting a dynamic compensator to generate this optimal point, we develop a novel distributed event-triggered controller to solve this problem, where the control inputs only update at some time instants specified by us. An example is given to verify the effectiveness of our designs.

关键词： distributed optimization multi-agent high-order system event-triggered control

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