检索结果-内蒙古大学图书馆

distributed Fixed-Time Optimal Resource Management for Microgrids

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第1期20卷 404-412页

作者： Liu, Li-Ning Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This article studies the problem of distributed cooperative resource management for Microgrids. In order to maximize the social welfare, a distributed fixed-time consensus algorithm is first proposed to integrate economic dispatch and demand response, which optimally assigns the energy between generation participants and load participants. Based on the designed algorithm, each participant is able to quickly obtain its optimal operation and only requires partial calculation and communication without a central control coordination, thus it offers high flexibility, strong robustness, and better privacy. Moreover, compared with the existing distributed optimization algorithms, the proposed algorithm simultaneously possesses the advantages of fixed-time consensus, no initialization conditions, and supporting switching topology, etc. Furthermore, it is proved that each participant can achieve consensus in a fixed-time manner and converge to the global optimal point if the parameters of the algorithm satisfy some conditions. Finally, numerical simulations in the 6-Bus and the IEEE 30-Bus systems are provided to validate the effectiveness of the presented algorithm.

关键词： Microgrids Resource management Convergence Communication networks Switches distributed algorithms Costs distributed algorithm fixed-time resource management demand response microgrids cyber-physical system

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving distributed Economic Dispatch of Microgrids: A Dynamic Quantization-Based Consensus Scheme With Homomorphic Encryption

引用

IEEE TRANSACTIONS ON SMART GRID 2023年第1期14卷 701-713页

作者： Chen, Wei Liu, Lu Liu, Guo-Ping City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn Hong Kong Peoples R China Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen 518055 Peoples R China

This paper is concerned with the privacy-preserving distributed economic dispatch problem (ED) of microgrids. A homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the least cost while preventing sensitive information leakage during the entire communication process. For ease of data encryption, a novel estimator-like dynamic quantizer is first constructed, where the information to be transmitted is converted into a series of finite-level codewords. Then, a sufficient condition is derived, taking advantage of mathematical induction and the properties of matrix norms, to ensure that the quantization output is unsaturated and exact consensus is reached. Furthermore, by means of the additive homomorphic property of the Paillier algorithm to embed secrecy in pairwise interaction dynamics, the confidential communication strategy is adopted to ensure that the distributed algorithm converges to the optimal value without disclosing private or sensitive state information of agents. Finally, case studies are provided to illustrate the feasibility and validity of the adopted privacy-preserving ED scheme in IEEE 39-bus power systems.

关键词： Microgrids Quantization (signal) Cryptography Convergence Heuristic algorithms Privacy distributed algorithms Microgrids economic dispatch consensus algorithm privacy preservation dynamic quantization homomorphic cryptography

来源：评论

学校读者我要写书评

暂无评论

Fairness-Aware distributed Energy Coordination for Voltage Regulation in Power Distribution Systems

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第3期14卷 1866-1880页

作者： Poudel, Shiva Mukherjee, Monish Sadnan, Rabayet Reiman, Andrew P. Pacific Northwest Natl Lab Richland WA 99354 USA

The accelerating deployment of solar photovoltaics into low-voltage distribution networks can cause reverse power flow and overvoltage problems. However, if coordinated properly, the real and reactive power flexibility of these resources enables distribution operators to manage their networks more efficiently. Existing literature is rich in droop-based control (Volt-Watt and Volt-VAr) and optimization-based distributed energy coordination for four-quadrant control of photovoltaics to prevent overvoltage issues. While optimal coordination can effectively mitigate overvoltage, it treats resources at sensitive parts of the grid unfairly. To address this concern, we propose a distributed optimal power flow formulation that incorporates fairness in curtailing photovoltaic generation and utilizes the reactive power capability of smart inverters. Fair curtailment of photovoltaic systems is studied with aggregation at each of two layers in a distribution network: 1) area-level fairness and 2) feeder-level fairness to demonstrate the flexibility of the proposed approach in resource aggregation. To explore the trade-off, the fairness-aware control actions are compared against the performance of a centralized controller that aims to maximize the aggregate PV generation without incorporating fairness. Simulation results show that introducing area-level fairness increased curtailment by 1.11 percentage points, and feeder-level fairness increased curtailment by 4.7 percentage points compared to a fairness-agnostic control.

关键词： distributed algorithms photovoltaic systems power distribution quadratic programming voltage control

来源：评论

学校读者我要写书评

暂无评论

A distributed parallel optimization algorithm via alternating direction method of multipliers

引用

IET CONTROL THEORY AND APPLICATIONS 2023年第7期17卷 896-905页

作者： Liu, Ziye Guo, Fanghong Wang, Wei Wu, Xiaoqun Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Zhejiang Univ Technol Dept Automat Hangzhou Peoples R China Wuhan Univ Sch Math & Stat Wuhan Hubei Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Alternating direction method of multipliers (ADMM) has been widely used for solving the distributed optimisation problems. This paper proposes a novel distributed ADMM algorithm to solve the distributed optimisation problems consisting of convex cost functions under an undirected connected graph. The proposed algorithm adopts the concepts of predecessors and successors in the distributed sequential ADMM algorithm, but changes the sequential updating manner to a parallel one, which allows the agents to update their local states and dual variables in a completely distributed and parallel manner. This brings some benefits when solving large-scale optimisation problems. Variational inequality is applied to analyse the convergence of agents' states. It is proved that the states of all the agents converge to the optimal point, and the global cost function converge to the optimal value at a rate of O(1/k)$O(1/k)$. Numerical experiments are given to show the effectiveness and suitability of the proposed algorithm.

关键词： distributed algorithms optimisation

来源：评论

学校读者我要写书评

暂无评论

Fed-SAD: A secure aggregation federated learning method for distributed short-term load forecasting

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第22期17卷 5090-5100页

作者： Li, Hexiao Wu, Sixing Wang, Ruiqi Guo, Yiguo Li, Jianbin North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan Shandong Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

The distributed and privacy-preserving attributes of fine-grained smart grid data create obstacles to data sharing. As a result, federated learning emerges as an effective strategy for collaborative training in distributed load forecasting. However, poisoning attacks can interfere with training in the federated learning aggregation process, making it challenging to ensure the accuracy and safety of the global model in load forecasting. Therefore, the authors propose a secure aggregation federated learning method based on similarity and distance (Fed-SAD) for distributed load forecasting. The server determines approximate global model parameters based on the similarity of the model parameters of the participants and aggregates the global model parameters using a distance-based weighting method. Fed-SAD effectively reduces the interference of poisoning attacks by securely aggregating models in short-term load forecasting. Experimental results show that using the Fed-SAD results in mean absolute percentage error (MAPE) reduction for certain participants by 19% on sign flipping attacks, MAPE reduction by 15% on additive noise attacks, and MAPE reduction by 4.5% without attack, compared to the Federated Average Aggregation algorithm. Furthermore, Fed-SAD consistently maintains robustness and has excellent attack detection accuracy.

关键词： computational Intelligence distributed algorithms power grids

来源：评论

学校读者我要写书评

暂无评论

ADABA: improving the balancing between runtime and accuracy in a new distributed version of the alpha-beta algorithm

引用

ARTIFICIAL INTELLIGENCE REVIEW 2023年第5期56卷 4255-4293页

作者： Paiva Tomaz, Lidia Bononi Silva Julia, Rita Maria Prandini Faria, Matheus Prado Fed Inst Triangulo Mineiro Uberaba Brazil Univ Fed Uberlandia Uberlandia MG Brazil

The satisfactory performance of the intelligent agents conceived to solve several real-life problems requires efficient decision-making algorithms. To address issues with high state-spaces within reasonable runtime limits, these algorithms are distributed according to some approaches that can be either synchronous or asynchronous, where the former guarantee the same results as their corresponding serial versions through synchronization points, which causes the undesirable effects of communication overhead and idle processors. To mitigate this, the asynchronous approaches reduce the message exchanges in such a way as to accelerate the runtime without too much compromising the response accuracy. The challenge of enhancing the minimax technique through pruning makes Alpha-Beta a relevant case study in parallelism research. Young Brothers Wait Concept (YBWC) and Asynchronous Parallel Hierarchical Iterative Deepening (APHID) are highlighted among the existing Alpha-Beta distributions. Knowing that APHID proved to be more suitable than YBWC to operate in distributed memory and that shared memory architectures are scarcely available due to their high costs, the primary motivation here is to implement the Asynchronous distributed Alpha-Beta Algorithm (ADABA), which increases the accuracy and performance of APHID through the enhancement of the slaves' task ordering policies, the communication process between the processors and the window's updating strategy. Experiments fulfilled through tournaments involving ADABA-based and APHID-based Checkers agents proved that the player based on the best ADABA version reached, approximately, a victory rate 95% superior and a runtime two times faster than the APHID-based player, keeping the same response accuracy level of its opponent.

关键词： distributed algorithms Machine learning Alpha-beta search algorithm Asynchronous parallel algorithms ADABA APHID

来源：评论

学校读者我要写书评

暂无评论

Efficient Algorithm for Approximating Nash Equilibrium of distributed Aggregative Games

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4375-4387页

作者： Xu, Gehui Chen, Guanpu Qi, Hongsheng Hong, Yiguang Univ Chinese Acad Sci Sch Math Sci Beijing 101408 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China JD Explore Acad Beijing 100176 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Dept Control Sci & Engn Shanghai 200070 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

In this article, we aim to design a distributed approximate algorithm for seeking Nash equilibria (NE) of an aggregative game. Due to the local set constraints of each player, projection-based algorithms have been widely employed for solving such problems actually. Since it may be quite hard to get the exact projection in practice, we utilize inscribed polyhedrons to approximate local set constraints, which yields a related approximate game model. We first prove that the NE of the approximate game is the epsilon -NE of the original game and then propose a distributed algorithm to seek the epsilon -NE, where the projection is then of a standard form in quadratic optimization with linear constraints. With the help of the existing developed methods for solving quadratic optimization, we show the convergence of the proposed algorithm and also discuss the computational cost issue related to the approximation. Furthermore, based on the exponential convergence of the algorithm, we estimate the approximation accuracy related to epsilon . In addition, we investigate the computational cost saved by approximation in numerical simulation.

关键词： Games Approximation algorithms Heuristic algorithms Optimization Standards distributed algorithms Convergence -Nash equilibrium (NE) aggregative game approximation distributed algorithm

来源：评论

学校读者我要写书评

暂无评论

distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5310-5321页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China

This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and communicates with its neighbors to seek for the global optimum. In this article, we develop a modified variance reduction technique to deal with the variance introduced by stochastic gradients. Combining gradient tracking and variance reduction techniques, this article proposes a distributed stochastic algorithm, gradient tracking algorithm with variance reduction (GT-VR), to solve large-scale non-convex finite-sum optimization over multiagent networks. A complete and rigorous proof shows that the GT-VR algorithm converges to the first-order stationary points with O(1/k) convergence rate. In addition, we provide the complexity analysis of the proposed algorithm. Compared with some existing first-order methods, the proposed algorithm has a lower O(PM epsilon b;(1)) gradient complexity under some mild condition. By comparing state-of-the-art algorithms and GT-VR in numerical simulations, we verify the efficiency of the proposed algorithm.

关键词： Signal processing algorithms Optimization Convergence Random variables distributed algorithms Machine learning distributed databases Complexity analysis distributed algorithm non-convex finite-sum optimization stochastic gradient variance reduction

来源：评论

学校读者我要写书评

暂无评论

distributed Multiarmed Bandits

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第5期68卷 3025-3040页

作者： Zhu, Jingxuan Liu, Ji SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

This article studies a distributed multiarmed bandit problem with heterogeneous observations of rewards. The problem is cooperatively solved by N agents assuming each agent faces a common set of M arms yet observes only local biased rewards of the arms. The goal of each agent is to minimize the cumulative expected regret with respect to the true rewards of the arms, where the mean of each arm's true reward equals the average of the means of all agents' observed biased rewards. Each agent recursively updates its decision by utilizing the information from its neighbors. Neighbor relationships are described by a time-dependent directed graph G(t) whose vertices correspond to agents and whose arcs depict neighbor relationships. A fully distributed bandit algorithm is proposed, which couples the classical distributed averaging algorithm and the celebrated upper confidence bound bandit algorithm. It is shown that for any uniformly strongly connected sequence of G(t), the algorithm achieves guaranteed regret for each agent at the order of O(log T).

关键词： Robot sensing systems distributed algorithms Program processors Random variables Manipulators Social networking (online) Process control Cooperative control machine learning agents and autonomous systems network analysis and control

来源：评论

学校读者我要写书评

暂无评论

distributed Nash Equilibrium Seeking in Games With Partial Decision Information: A Survey

引用

PROCEEDINGS OF THE IEEE 2023年第2期111卷 140-157页

作者： Ye, Maojiao Han, Qing-Long Ding, Lei Xu, Shengyuan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

Nash equilibrium, as an essential strategic profile in game theory, is of both practical relevance and theoretical significance due to its wide penetration into various fields, such as smart grids, wireless communication networks, and networked mobile vehicles. In particular, distributed Nash equilibrium seeking strategies have recently attracted increasing attention because they show remarkable advantages in relaxing the requirement of a central node for information broadcasting or full observation of players' actions. This article aims to provide a survey of distributed Nash equilibrium seeking in games with partial decision information, in which players can only exchange information with their neighbors and their objective functions may explicitly depend on all players' actions. First, fundamental problem descriptions on distributed Nash equilibrium seeking are presented. Second, related results on distributed Nash equilibrium seeking in general multiplayer games, aggregative games, and multicluster games are reviewed, respectively, where representative continuous-and discrete-time methods are explained in detail. Third, two practical applications, including collaborative control for a network of mobile sensors and energy consumption control in smart grids, are provided to demonstrate the applicability of distributed Nash equilibrium seeking strategies. Finally, some promising directions are suggested for future research.

关键词： Games Nash equilibrium Linear programming Smart grids Vaccines Interference distributed algorithms Aggregative games distributed networks general multiplayer games multicluster games Nash equilibrium seeking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：