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distributed Energy Trading: The Multiple-Microgrid Case

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2015年第4期62卷 2551-2559页

作者： Gregoratti, David Matamoros, Javier Ctr Tecnol Telecomunicac Catalunya Castelldefels 08860 Spain

In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm that converges to the optimal solution in a practical number of iterations and with limited communication overhead is proposed. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of a "supply-demand model" and "market clearing." Numerical results are given in terms of the convergence rate of the algorithm and the attained costs for different network topologies.

关键词： distributed convex optimization energy trading smart grid

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distributed Bisection Method for Economic Power Dispatch in Smart Grid

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IEEE TRANSACTIONS ON POWER SYSTEMS 2015年第6期30卷 3024-3035页

作者： Xing, Hao Mou, Yuting Fu, Minyue Lin, Zhiyun Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

In this paper, we present a fully distributed bisection algorithm for the economic dispatch problem (EDP) in a smart grid scenario, with the goal to minimize the aggregated cost of a network of generators, which cooperatively furnish a given amount of power within their individual capacity constraints. Our distributed algorithm adopts the method of bisection, and is based on a consensus-like iterative method, with no need for a central decision maker or a leader node. Under strong connectivity conditions and allowance for local communications, we show that the iterative solution converges to the globally optimal solution. Furthermore, two stopping criteria are presented for the practical implementation of the proposed algorithm, for which sign consensus is defined. Finally, numerical simulations based on the IEEE 14-bus and 118-bus systems are given to illustrate the performance of the algorithm.

关键词： Consensus distributed algorithm distributed convex optimization economic power dispatch smart grid

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distributed Random Projection Algorithm for convex optimization

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2013年第2期7卷 221-229页

作者： Lee, Soomin Nedic, Angelia Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Ind & Enterprise Syst Engn Dept Urbana IL 61801 USA

Random projection algorithm is of interest for constrained optimization when the constraint set is not known in advance or the projection operation on the whole constraint set is computationally prohibitive. This paper presents a distributed random projection algorithm for constrained convex optimization problems that can be used by multiple agents connected over a time-varying network, where each agent has its own objective function and its own constrained set. We prove that the iterates of all agents converge to the same point in the optimal set almost surely. Experiments on distributed support vector machines demonstrate good performance of the algorithm.

关键词： Asynchronous algorithms distributed convex optimization distributed multi-agent system random gossip network

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distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism 3

Distributed Frank-Wolfe under Pipelined Stale Synchronous Pa...

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IEEE International Conference on Big Data

作者： Tran, Nam-Luc Peel, Thomas Skhiri, Sabri EURA NOVA Mt St Guibert France

ISBN: (纸本)9781479999255

Iterative-convergent algorithms represent an important family of applications in big data analytics. These are typically run on distributed processing frameworks deployed on a cluster of machines. On the other hand, we are witnessing the move towards data center operating systems (OS), where resources are unified by a resource manager and processing frameworks coexist with each other. In this context, different processing framework job tasks can be scheduled on the same machine and slow down a worker (straggler problem). Existing work has shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with stragglers. In this paper we propose a model for the integration of the SSP model on a pipelined distributed processing framework. We then apply SSP on a distributed version of the Frank-Wolfe algorithm. We theoretically show its sparsity bounds and convergence under SSP. Finally, we experimentally show that the Frank-Wolfe algorithm applied on LASSO regression under SSP is able to converge faster than its BSP counterpart, especially under load conditions similar to those encountered in a data center OS.

关键词： stale synchronous parallel distributed convex optimization Frank-Wolfe LASSO regression parameter server big data

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distributed Caching in 5G Networks: An Alternating Direction Method of Multipliers Approach 16

Distributed Caching in 5<i>G</i> Networks: An Alternating Di...

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16th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Abboud, Azary Bastug, Ejder Hamidouche, Kenza Debbah, Merouane Supelec Large Networks & Syst Grp LANEAS F-91192 Gif Sur Yvette France Supelec Automat & Control Dept F-91192 Gif Sur Yvette France Huawei France R&D Math & Algorithm Sci Lab Paris France

ISBN: (纸本)9781479919314

We consider the problem of distributed caching in next generation mobile cellular networks (a.k.a., 5G) where densely-deployed small base stations (SBSs) are able to store and deliver users' content accordingly. In particular, we formulate the optimal cache allocation policy as a convex optimization problem where a subset of SBSs have their own i) local cost function which captures backhaul consumption aspects in terms of bandwidth and i i) a set of local network parameters and storage constraints. Given the fact that no coordination involves between SBSs, we then solve this problem distributively using the Alternating Direction Method of Multipliers (ADMM) approach. The proposed ADMM-based algorithm relies on the application of random Gauss-Seidel iterations on the Douglas-Rachford splitting operator, which results in a low-complexity and easy-to-implement solution for SBSs. We examine the convergence of our proposed algorithm via numerical simulations with different parameters of interest such as storage capacity distribution of SBSs, content catalogue size, demand intensity and demand shape. Our numerical results show that the proposed algorithm performs well in terms of convergence and requires less iterations as the number of contents in the catalogue increases.

关键词： 5G distributed caching optimal cache allocation ADMM distributed convex optimization wireless networks

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An ADMM + consensus based distributed algorithm for dynamic economic power dispatch in smart grid 34

An ADMM + consensus based distributed algorithm for dynamic ...

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34th Chinese Control Conference, CCC 2015

作者： Xing, Hao Lin, Zhiyun Fu, Minyue Department of Control Science and Engineering Zhejiang University 38 Zheda Road Hangzhou China College of Electrical Engineering Zhejiang University 38 Zheda Road Hangzhou China School of Electrical Engineering and Computer Science University of Newcastle NSW Australia State Key Laboratory of Industrial Control Technology 38 Zheda Road Hangzhou China

ISBN: (纸本)9789881563897

In this paper, we propose a distributed algorithm for the dynamic economic dispatch problem (DEDP) in a smart grid scenario. Different from the static economic dispatch problem (SEDP), the DEDP aims at minimizing the aggregate operating costs of a group of generators over a time period with ramp rate constraints. The proposed algorithm is based on the average consensus algorithm on undirected graphs and the alternating direction method of multipliers (ADMM). Our algorithm is distributed in the sense that no leader or master nodes are needed, while all the nodes (generators) conduct local computation and merely communicate with their neighbors. Convergence analysis shows that the proposed algorithm converges to the optimal solution. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： distributed convex optimization Dynamic Economic Dispatch Economic Dispatch Smart Grid

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A new approach to distributed charging control for plug-in hybrid electric vehicles 33

A new approach to distributed charging control for plug-in h...

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33rd Chinese Control Conference (CCC)

作者： Mou, Yuting Xing, Hao Lin, Zhiyun Fu, Minyue Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781479947249

Plug-in hybrid electric vehicles (PHEV) tend to become more widespread in the next decades. However, large penetration of PHEVs will overload the distribution system. In smart grid, the charging of PHEVs can be controlled to reduce the peak load, which is known as demand side management (DSM). In this paper, we focus on the impact of PHEV charging on low-voltage transformers. The objective is to flatten the load curve of low-voltage transformers, while each consumer's requirement for their PHEV to be charged to the required level by their specified time is satisfied. We first formulate it as a convex optimization problem and then propose a distributed water-filling-based algorithm to solve it. Proofs of optimality and numerical simulations are given to demonstrate the effectiveness of our algorithm.

关键词： Smart Grid Demand Side Management Plug-in Hybrid Electric Vehicle Water Filling distributed convex optimization

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Sample-Based Zero-Gradient-Sum distributed Consensus optimization of Multi-Agent Systems 11

Sample-Based Zero-Gradient-Sum Distributed Consensus Optimiz...

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11th World Congress on Intelligent Control and Automation

作者： Liu, Jiayun Chen, Weisheng Xidian Univ Sch Math & Stat Xian Shanxi Peoples R China

ISBN: (纸本)9781479958252

This paper addresses distributed convex optimization problem for continuous-time multi-agent systems via sampled control over undirected networks. A novel sampled-based Zero-Gradient-Sum (ZGS) distributed optimization algorithm is proposed which are induced from ZGS algorithm by using periodic sampling technology and zero-order hold circuit. A new Lyapunov-based approach is used to give convergence analysis of the proposed algorithm. By choosing proper sampling period and gain parameter, we prove that each agent of the system converges asymptotically to the unknown optimal to illustrate the effectiveness of the algorithm proposed in this paper.

关键词： Multi-agent system distributed convex optimization sampled data Zero-Gradient-Sum

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distributed Subgradient Method for Multi-Agent optimization with Communication Delays

Distributed Subgradient Method for Multi-Agent Optimization ...

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32nd Chinese Control Conference (CCC)

作者： Liu Jun Li Dequan Yin Zhixiang Anhui Univ Sci & Technol Sch Sci Huainan 232001 Peoples R China

ISBN: (纸本)9781479900305

We study the distributed subgradient method for multi-agent optimization problem, where multiple agents try to cooperatively optimize the sum of their local convex objective functions but subject to communication delays. By augmenting delay nodes in communication network, the optimization problem with communication delays is converted into the optimization problem without communication delays. Meanwhile, the corresponding adjacency matrix associated with the augmented communication network may be stochastic and all its diagonal entries are not necessarily positive. Thus, this weakens some typical requirements for the adjacency matrix in literature. Then with the help of the related result of non-reversible Markov theory, we prove that the convergence of the proposed optimization algorithm can still be guaranteed provided that communication delays are upper bounded. The obtained results show that communication delays induce more updated errors. Finally, an example is given to demonstrate the effectiveness of the optimization algorithm.

关键词： distributed convex optimization communication delay subgradient method multi-Agent system stochastic matrix

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Gossip Algorithms for convex Consensus optimization Over Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2011年第12期56卷 2911-2918页

作者： Lu, Jie Tang, Choon Yik Regier, Paul R. Bow, Travis D. Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA

In many applications, nodes in a network desire not only a consensus, but an optimal one. To date, a family of subgradient algorithms have been proposed to solve this problem under general convexity assumptions. This technical note shows that, for the scalar case and by assuming a bit more, novel non-gradient-based algorithms with appealing features can be constructed. Specifically, we develop Pairwise Equalizing (PE) and Pairwise Bisectioning (PB), two gossip algorithms that solve unconstrained, separable, convex consensus optimization problems over undirected networks with time-varying topologies, where each local function is strictly convex, continuously differentiable, and has a minimizer. We show that PE and PB are easy to implement, bypass limitations of the subgradient algorithms, and produce switched, nonlinear, networked dynamical systems that admit a common Lyapunov function and asymptotically converge. Moreover, PE generalizes the well-known Pairwise Averaging and Randomized Gossip Algorithm, while PB relaxes a requirement of PE, allowing nodes to never share their local functions.

关键词： distributed consensus distributed convex optimization gossip algorithms networked dynamical systems

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