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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yu Bai Wenling Li Bin Zhang School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665402460

In this paper, we propose a distributed incremental quais-Newton (D-IQN) algorithm for multi-area power system state estimation (MASE). Maximum correntropy criterion (MCC) is used in objective function in order to address non-Gaussian noise. Incremental quais-Newton (IQN) is applied to solve state estimation in each area. In the inter-area communication networks, consensus+innovation strategy is adopted to form a distributed pattern. In this way, each area carries out a local state estimation with limited information exchange with its neighboring areas. As a fully distributed algorithm, no central coordinator is needed here. Based on this peer-to-peer communication paradigm, accurate estimation results are obtained and the privacy of each area remains well-preserved. Numerical experiments are carried out on 118-bus systems. The results show that the algorithm is effective for non-Gaussian noise and outperforms other methods such as distributed Broyden-Fletcher-Goldfarb-Shanno (BFGS), Gauss-Newton and WLS method.

关键词： Privacy Linear programming Power systems Peer-to-peer computing Communication networks State estimation distributed algorithms

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An Accelerated Method For Decentralized distributed Stochastic Optimization Over Time-Varying Graphs

An Accelerated Method For Decentralized Distributed Stochast...

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IEEE Conference on Decision and Control

作者： Alexander Rogozin Mikhail Bochko Pavel Dvurechensky Alexander Gasnikov Vladislav Lukoshkin Russian Federation Moscow Institute of Physics and Technology and HSE University Weierstrass Institute for Applied Analysis and Stochastics Germany Institute for Information Transmission Problems RAS HSE University Russian Federation Moscow Institute of Physics and Technology Institute for Information Transmission Problems RAS HSE University Russian Federation Skolkovo Institute of Science and Technology Russia

ISBN: (纸本)9781665436601

We consider a distributed stochastic optimization problem that is solved by a decentralized network of agents with only local communication between neighboring agents. The goal of the whole system is to minimize a global objective function given as a sum of local objectives held by each agent. Each local objective is defined as an expectation of a convex smooth random function and the agent is allowed to sample stochastic gradients for this function. For this setting we propose the first accelerated (in the sense of Nesterov’s acceleration) method that simultaneously attains optimal up to a logarithmic factor communication and oracle complexity bounds for smooth strongly convex distributed stochastic optimization. We also consider the case when the communication graph is allowed to vary with time and obtain complexity bounds for our algorithm, which are the first upper complexity bounds for this setting in the literature.

关键词： Gradient methods Conferences Linear programming Complexity theory distributed algorithms Optimization

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distributed Multi-Period DCOPF via an Auxiliary Principle Problem Algorithm

Distributed Multi-Period DCOPF via an Auxiliary Principle Pr...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Mohannad Alkhraijah Maad Alowaifeer Santiago Grijalva Daniel K. Molzahn School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781728186122

ISBN: (纸本)9781728173450

distributed algorithms provide attractive features for solving Optimal Power Flow (OPF) problems in interconnected power systems compared to traditional centralized algorithms. distributed algorithms help to maintain the control autonomy and data privacy of subsystems, which is particularly relevant in competitive markets and practical control system implementations. This paper analyzes a distributed optimization algorithm known as the “Auxiliary Principle Problem” to solve multiperiod distributed DCOPF problems with distributed energy resources including energy storage systems. The proposed approach enables multiple interconnected systems with their own sub-objectives to share their resources and to participate in an electricity market without implicitly sharing information about their local generators or internal network parameters. The paper also shows how the proposed approach can enable future microgrids to coordinate their operation, reduce the total operational cost, and avoid internal constraint violations caused by unscheduled flows (USF) while maintaining the subsystems' autonomy. We use an 11-bus test system consisting of two interconnected subsystems to evaluate the proposed approach and analyze the impact of USF.

关键词： Microgrids Tools Electricity supply industry Power systems distributed algorithms Optimization Load flow

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An Accelerated Second-Order Method for distributed Stochastic Optimization

An Accelerated Second-Order Method for Distributed Stochasti...

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IEEE Conference on Decision and Control

作者： Artem Agafonov Pavel Dvurechensky Gesualdo Scutari Alexander Gasnikov Dmitry Kamzolov Aleksandr Lukashevich Amir Daneshmand Moscow Institute of Physics and Technology Russia Weierstrass Institute for Applied Analysis and Stochastics Germany Institute for Information Transmission Problems RAS and HSE University Russian Federation School of Industrial Engineering Purdue University USA Moscow Institute of Physics and Technology Institute for Information Transmission Problems RAS HSE University Russian Federation Skolkovo Institute of Science and Technology Russia

ISBN: (纸本)9781665436601

We consider centralized distributed algorithms for general stochastic convex optimization problems which we approximate by a finite-sum minimization problem with summands distributed among computational nodes. We exploit statistical similarity between the summands and the whole sum to construct a distributed accelerated cubic-regularized Newton’s method that achieves lower communication complexity bound for this setting and improves upon existing upper bound. Further, we use this algorithm to obtain convergence rate bounds for the original stochastic optimization problem and compare our bounds with the existing ones in several regimes when the goal is to minimize the number of communication rounds and improve the parallelization when increasing the number of workers.

关键词： Upper bound Conferences Minimization Approximation algorithms Convex functions Complexity theory distributed algorithms

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distributed Generalized Nash Equilibrium Seeking Algorithm Design for Aggregative Games Over Weight-Balanced Digraphs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019年第3期30卷 695-706页

作者： Deng, Zhenhua Nian, Xiaohong Cent S Univ Sch Informat Sci & Engn Changsha 410004 Hunan Peoples R China

In this paper, two aggregative games over weight-balanced digraphs are studied, where the cost functions of all players depend on not only their own decisions but also the aggregate of all decisions. In the first problem, the cost functions of players are differentiable with Lipschitz gradients, and the decisions of all players are coupled by linear coupling constraints. In the second problem, the cost functions are nonsmooth, and the decisions of all players are constrained by local feasibility constraints as well as linear coupling constraints. In order to seek the variational generalized Nash equilibrium (GNE) of the differentiable aggregative games, a continuous-time distributed algorithm is developed via gradient descent and dynamic average consensus, and its exponential convergence to the variational GNE is proven with the help of Lyapunov stability theory. Then, another continuous-time distributed projection-based algorithm is proposed for the nonsmooth aggregative games based on differential inclusions and differentiated projection operations. Moreover, the convergence of the algorithm to the variational GNE is analyzed by utilizing singular perturbation analysis. Finally, simulation examples are presented to illustrate the effectiveness of our methods.

关键词： Aggregative games distributed algorithms generalized Nash equilibrium (GNE) nonsmooth analysis projected dynamics weight-balanced digraphs

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Optimal Regulation of ‘Source-Grid-Load-Storage’ Interaction Based on State Based Potential Game

Optimal Regulation of ‘Source-Grid-Load-Storage’ Interacti...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Bo Li Weinian Ouyang Tianlun Wang Zhirui Tang Tingwei Chen Zefu Wang System Operation Department of Guangdong Power Grid Corporation Guangzhou China Foshan Power Supply Bureau of Guangdong Power Grid Corporation Foshan China School of Electric Power Engineering South China University of Technology Guangzhou China

Under the new power system, the traditional source-follow-load model has changed. With the access of high penetration of renewable energy, more flexible resources need to be explored from the source-side grid side and load side to increase the consumption of renewable energy. Therefore, the optimization of ‘source-grid-load-storage (SGLS)’ interaction is a hot topic of current research. Because of the different interests belonging to different parties and their willingness to participate, the adoption of a centralized algorithm cannot coordinate the interests of all parties well. The state based potential game (SPG) method can coordinate the consistency of individual interests and subject interests, and has higher solution accuracy and solution efficiency compared with the general potential game (PG). Therefore, this article designs the SGLS interaction optimization model as a SPG problem, solves it using a distributed algorithm, and verifies the effectiveness of the proposed method through simulation.

关键词： Renewable energy sources Optimization methods Games Regulation Power systems distributed algorithms Load modeling

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distributed algorithms for Optimization Problems with Equality Constraints

Distributed Algorithms for Optimization Problems with Equali...

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IEEE Annual Conference on Decision and Control

作者： Ion Matei John S. Baras Palo Alto Research Center (PARC) Palo Alto CA Institute for Systems Research at the University of Maryland College Park MD

ISBN: (纸本)9781467357159

In this paper we introduce two discrete-time, distributed optimization algorithms executed by a set of agents whose interactions are subject to a communication graph. The algorithms can be applied to optimization problems where the cost function is expressed as a sum of functions, and where each function is associated to an agent. In addition, the agents can have equality constraints as well. The algorithms are not consensus-based and can be applied to non-convex optimization problems with equality constraints. We demonstrate that the first distributed algorithm results naturally from applying a first order method to solve the first order necessary conditions for a lifted optimization problem with equality constraints;the solution of our original problem is embedded in the solution of this lifted optimization problem. Using an augmented Lagrangian idea, we derive a second distributed algorithm that requires weaker conditions for local convergence compared to the first algorithm. For both algorithms we address the local convergence properties.

关键词： distributed algorithms Equality Agents Cost functions algorithms original problem discrete-time Optimization algorithms lift

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Peer-to-Peer Non-Bayesian Learning in Finite Time with a Finite Amount of Communication

Peer-to-Peer Non-Bayesian Learning in Finite Time with a Fin...

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IEEE Conference on Decision and Control

作者： Shreyas Sundaram Aritra Mitra Elmore Family School of Electrical and Computer Engineering Purdue University Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider the problem of distributed non-Bayesian learning (or hypothesis testing) where a group of agents interacts over a peer-to-peer network to identify the true state of the world from a finite set of hypotheses, based on a series of stochastic signals that each agent receives. Prior work on this problem has provided distributed algorithms that guarantee asymptotic learning of the true state, with corresponding efforts to improve the rate of learning. In this paper, we first argue that one can readily modify existing asymptotic learning algorithms to enable learning in finite time, effectively yielding arbitrarily large (asymptotic) rates. Furthermore, we show that such finite-time learning can be achieved via a simple algorithm which only requires the agents to exchange a binary vector (of length equal to the number of possible hypotheses) with their neighbors at each time-step. Finally, we show that if the agents know the diameter of the network, our algorithm can be further modified to allow all agents to learn the true state and stop transmitting to their neighbors after a finite number of time-steps.

关键词： Peer-to-peer computing distributed algorithms Testing

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distributed Link Removal Using Local Estimation of Network Topology

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2019年第3期6卷 280-292页

作者： Gusrialdi, Azwirman Qu, Zhihua Hirche, Sandra Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Tech Univ Munich Dept Elect Engn & Informat Technol Inst Informat Oriented Control D-80290 Munich Bavaria Germany

This paper considers the problem of network structure manipulation in the absence of information on the global network topology. In particular, the problem of removing some of links is investigated in order to slow or stop the spread of disease in a network while preserving its connectivity. Existing methods solve this combinatorial problem in a centralized manner and they require the global information of network structure. In this paper, we propose a distributed design algorithm to compute a suboptimal solution to this problem efficiently by mimicking gradient based method, namely by iteratively removing one or multiple links at a time from the network. Specifically, using matrix perturbation analysis we formulate an optimization problem involving the eigenvector associated with the largest eigenvalue of the adjacency matrix and whose solution is equal to a suboptimal solution to the original problem. This strategy also enables us to overcome the combinatorial issue of the problem. distributed algorithms to estimate the eigenvector and to verify network's connectivity are then proposed which facilitate us to solve the new optimization problem. In addition, topological insights into the proposed algorithm and optimality of its solution are also discussed. Finally, the proposed distributed design method is demonstrated and evaluated via several numerical examples.

关键词： Eigenvalues and eigenfunctions Network topology Symmetric matrices Diseases distributed algorithms Perturbation methods Optimization Link removal largest eigenvalue minimization distributed algorithm matrix perturbation

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distributed Optimization Under Adversarial Nodes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第3期64卷 1063-1076页

作者： Sundaram, Shreyas Gharesifard, Bahman Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

We investigate the vulnerabilities of consensus-based distributed optimization protocols to nodes that deviate from the prescribed update rule (e.g., due to failures or adversarial attacks). We first characterize certain fundamental limitations on the performance of any distributed optimization algorithm in the presence of adversaries. We then propose a secure distributed optimization algorithm that guarantees that the nonadversarial nodes converge to the convex hull of the minimizers of their local functions under the certain conditions on the graph topology, regardless of the actions of a certain number of the adversarial nodes. In particular, we provide sufficient conditions on the graph topology to tolerate a bounded number of adversaries in the neighborhood of every nonadversarial node, and necessary and sufficient conditions to tolerate a globally bounded number of adversaries. For situations, where there are up to F adversaries in the neighborhood of every node, we use the concept of maximal F-local sets of graphs to provide lower bounds on the distance-to-optimality of achievable solutions under any algorithm. We show that finding the size of such sets is NP-hard.

关键词： distributed algorithms fault tolerance graph theory machine learning multi-agent systems network security optimization

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