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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Kallitsis, Michael G. Michailidis, George Devetsikiotis, Michael Univ Michigan Dept Stat Ann Arbor MI 48109 USA

ISBN: (纸本)9781457717024

We introduce a framework for optimal resource allocation in smart grids. We consider two components of the smart grid;the power distribution network and the data communication network. By defining suitable utility functions, the power and bandwidth resources are optimally allocated. This requires the solution of the, so called, local public goods problem, in mathematical economics terminology. We propose an iterative, distributed algorithm for its solution. The algorithm is scalable for deployment in large networks since it requires only O(N) messages per network user per iteration, where N is the number of users. Moreover, it is guaranteed to converge, does not require revelation of private information from each user and all algorithm actions can be realized by programmable smart devices of the smart grid.

关键词： Bandwidth Delay Niobium Optimization Proposals Resource management Smart grids bandwidth allocation communication network externalities data communication data communication network decentralized algorithm distribution networks iterative methods iterative

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A consensus-based decentralized algorithm for non-convex optimization with application to dictionary learning 40

A consensus-based decentralized algorithm for non-convex opt...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wai, Hoi-To Chang, Tsung-Hui Scaglione, Anna School of Elect. Comp. and Energy. Eng. Arizona State University TempeAZ United States Dept. of Elect. and Comp. Eng. National Taiwan Univ. of Science and Technology Taiwan

ISBN: (纸本)9781467369978

In handling massive-scale signal processing problems arising from 'big-data' applications, key technologies could come from the development of decentralized algorithms. In this context, consensus-based methods have been advocated because of their simplicity, fault tolerance and versatility. This paper presents a new consensus-based decentralized algorithm for a class of non-convex optimization problems that arises often in inference and learning problems, including 'sparse dictionary learning' as a special case. For the proposed algorithm, we provide sufficient conditions for convergence to a stationary point. Numerical results demonstrate the efficacy of the proposed algorithm and provide evidence that validates our convergence claim. © 2015 IEEE.

关键词： decentralized algorithm dictionary learning non-convex optimization

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A fully decentralized algorithm to timestamping transactions in a peer-to-peer environments

A fully decentralized algorithm to timestamping transactions...

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International Conference on Machine and Web Intelligence

作者： Hemam, Sofiane Mounine Hidouci, Khaled Walid LCSI Laboratory University Centre of Khenchela CUK Khenchela Algeria LCSI Laboratory High School of Computer Science ESI Algiers Algeria

ISBN: (纸本)9781424486113

In this paper, we investigate a decentralized approach to timestamping transactions in a replicated database, under partial replication in Peer-To-Peer (P2P) environments. In order to solve problems of concurrent updates and node failures, we propose an architecture based on quorums, this architecture allows assigning a unique timestamp to each distributed transaction, to select the servers replicas and to coordinate the distributed execution of the transaction. © 2010 IEEE.

关键词： Partial replication decentralized algorithm distributed transaction multivariable systems p2p peer-to-peer computing peer-to-peer environment replicated database replicated database replicated databases timestamping transaction transaction processing

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A decentralized trading algorithm for an electricity market with generation uncertainty

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APPLIED ENERGY 2018年 218卷 520-532页

作者： Bahrami, Shahab Amini, M. Hadi Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

The uncertainties in renewable power generators and the proliferation of price-responsive load aggregators make it a challenge for independent system operators (ISOs) to manage the energy trading in the power markets. Hence, a centralized framework for the energy trading market may not be remained practical for the ISOs mainly due to violating the privacy of different entities, i.e., load aggregators and generators. It can also suffer from the high computational burden in a market with a large number of entities. Instead, in this paper, we focus on proposing a decentralized energy trading framework enabling the ISO to incentivize the entities toward an operating point that jointly optimize the cost of load aggregators and profit of the generators, as well as the risk of shortage in the renewable generation. To address the uncertainties in the renewable resources, we apply a risk measure called the conditional value-at-risk (CVaR) with the goal of limiting the likelihood of high renewable generation shortage with a certain confidence level. Then by considering the risk attitude of the ISO and the generators, we develop a decentralized energy trading algorithm with some control signals that properly coordinate the entities toward the market operating point of the ISO's centralized approach. Simulation results on the IEEE 30-bus test system show that the proposed decentralized algorithm converges to the solution of the ISO's centralized problem in a timely fashion. Furthermore, the load aggregators can help their consumers reduce their electricity cost by 18% on average through managing their loads using locally available information. Meanwhile, the generators can benefit from 17.1% increase in their total profit through decreasing their generation cost.

关键词： Renewable Energy Resources Price-responsive load aggregator Power market Conditional value-at-risk (CVaR) Generation uncertainty Controllable load decentralized algorithm

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A consensus-based decentralized algorithm for non-convex optimization with application to dictionary learning

A consensus-based decentralized algorithm for non-convex opt...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： H. T. Wai T. H. Chang A. Scaglione Sch. of Electr. Comp. & Energy. Eng. Arizona State Univ. Tempe AZ USA

ISBN: (纸本)9781467369985

关键词： decentralized algorithm dictionary learning non-convex optimization Convex function decentralized Non-Convex Dictionaries as Topic Sufficient conditions Learning Problems algorithms Converge

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A negotiation-based incentive mechanism for efficient Transmission Expansion Planning considering generation investment equilibrium in deregulated environment

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APPLIED ENERGY 2025年 386卷

作者： Guo, Hui Xiao, Yunpeng Pinson, Pierre Wang, Xiuli Zhang, Likai Wang, Xifan Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England

The current Transmission Expansion Planning (TEP) incentive mechanisms are inadequate. They either fail to ensure revenue sufficiency or achieve socially optimal investment. The non-negligible coordination between TEP and Generation Expansion Planning (GEP) in the deregulated environment introduces more computational challenges to the TEP problem. This paper proposes a novel negotiation mechanism that enables Generation Companies (GenCos) and Load-Serving-Entities (LSEs) to negotiate TEP strategies with Transmission Companies (TransCo) directly. The negotiation process is modeled based on the Nash Bargaining theory. We explore the intertwined relationship between TEP and GEP through a bi-level, single-leader-multi-follower model. We transform the upper-level problem for better tractability and introduce a modified Proximal-Message-Passing (PMP) decentralized algorithm to achieve generation investment equilibrium at the lower level. We then utilize an iterative solving approach to coordinate the two levels. The feasibility and efficiency of this mechanism and methodologies are demonstrated using an IEEE 24-bus test system. The numerical results verify that our mechanism ensures revenue sufficiency and achieves socially optimal TEP strategies comparable to state-ofthe-art mechanisms. Additionally, our mechanism maintains transmission network user privacy, aligns the benefits of TransCo with those of transmission network users, and ensures a fair allocation of TEP costs and risks. The proactive participation of market players enabled by the negotiation mechanism can promote the transformation towards new market systems by mitigating the stranded cost issue. Moreover, our decentralized algorithm effectively addresses the non-cooperative nature of GEP, and the computational efficiency analysis justifies the model's scalability and practicality.

关键词： Transmission Expansion Planning Incentive mechanism design Nash Bargaining theory Generation investment equilibrium decentralized algorithm

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Performing accelerated convergence in decentralized economic dispatch over dynamic directed networks

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第6期362卷

作者： Lv, Yunshan Xiong, Hailing Zhang, Fuqing Dong, Shengying Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Chongqing Coll Mobile Commun Coll Big Data Chongqing 401520 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China

This article delves into the economic dispatch problem (EDP) within smart grids, specifically exploring it in time-varying directed networks. The objective is to allocate generation power efficiently among generators to fulfill load demands while minimizing the total generation cost, adhering to local capacity constraints. Each generator carries its unique local generation cost, and the total cost is calculated by summing these individual costs. To this aim, a novel algorithm (ADED-TVD) Accelerated decentralized Economic Dispatch algorithm is introduced, which is suitable for Time-Varying Directed networks well. ADED-TVD takes inspiration from the parameter momentum accelerated technique to improve the convergence with different parameters resulting in different momentum (Nesterov or heavy-ball) methods. In addition, ADED-TVD lies in time-varying directed communication networks, where theoretical evidence of linear convergence towards the optimal dispatch is offered. Also, explicit bounds for the step-size and momentum parameters are obtained. Finally, simulations that delve into various aspects of EDP in smart grids are presented.

关键词： Economic dispatch problem decentralized algorithm Momentum acceleration Time-varying directed networks Linear convergence

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decentralized non-convex online optimization with adaptive momentum estimation and quantized communication

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COMPLEX & INTELLIGENT SYSTEMS 2025年第4期11卷 1-18页

作者： Lv, Yunshan Xiong, Hailing Zhang, Fuqing Dong, Shengying Dai, Xiangguang Southwest Univ Coll Comp & Informat Sci Tiansheng St Chongqing 400715 Peoples R China Chongqing Coll Mobile Commun Coll Big Data Jiari St Chongqing 401520 Peoples R China Southwest Univ Coll Elect & Informat Engn Tiansheng St Chongqing 400715 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Fucheng St Beijing 100101 Peoples R China Chongqing Three Gorges Univ Sch Three Gorges Artificial Intelligence Tianxing St Chongqing 404020 Peoples R China

In this work, we consider the decentralized non-convex online optimization problem over an undirected network. To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. The proposed algorithm not only can effectively reduce the data transmission volume but also contribute to improved convergence. Theoretical analysis proves that the proposed algorithm can achieve sublinear dynamic regret under appropriate step-size and quantization level, which matches the convergence of the decentralized online algorithm with exact-communication. Extensive simulations are given to demonstrate the efficacy of the algorithm.

关键词： Non-convex online optimization decentralized algorithm Quantized communication Adaptive momentum estimation Sublinear regret

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A spatiotemporal decomposition algorithm for fully decentralized dynamic economic dispatch in a microgrid

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 185卷 106361-106361页

作者： Zhu, Jianquan Zhu, Tao Mo, Xiemin Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China Guangdong Power Exchange Ctr Guangzhou 510030 Peoples R China

In this paper, we present a spatiotemporal decomposition solution approach to the fully decentralized dynamic economic dispatch (DED) problem in a microgrid. Our approach divides the centralized DED problem into a series of sub-problems in the spatiotemporal dimensions and relies on multiple agents to solve those sub-problems. The proposed method requires no central operator intervention, preserving the decision independence and information privacy of each unit. Approximate value functions are used to describe the interaction among those sub-problems. With the approximate value functions, one agent not only knows the impact of its decision on the decisions of other agents in the same period, but also knows the impact of this decision on the decisions of its subsequent periods. Unlike the existing value function update strategy, which updates the state variables and value functions in one direction, we update the state variables and value functions in two directions based on a forward-push-back strategy. In this manner, the time-delayed problem can be solved, and the iteration speed of the algorithm is greatly improved. Moreover, the proposed algorithm does not require parameter tuning and has good accuracy and adaptability. Numerical simulations for multiple cases demonstrate the effectiveness of the proposed algorithm.

关键词： Dynamic economic dispatch Microgrid decentralized algorithm Spatiotemporal decomposition Forward-push-back strategy

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decentralized Plug-in Electric Vehicle Charging Selection algorithm in Power Systems

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IEEE TRANSACTIONS ON SMART GRID 2012年第4期3卷 1779-1789页

作者： Wen, Chao-Kai Chen, Jung-Chieh Teng, Jen-Hao Ting, Pangan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 804 Taiwan Natl Kaohsiung Normal Univ Dept Optoelect & Commun Engn Kaohsiung 802 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804 Taiwan Ind Technol Res Inst Hsinchu 310 Taiwan

This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits. The optimal PEV-charging selection problem requires an exhaustive search for all possible combinations of PEVs in a power system, which cannot be solved for the practical number of PEVs. Inspired by the efficiency of the convex relaxation optimization tool in finding close-to-optimal results in huge search spaces, this paper proposes the application of the convex relaxation optimization method to solve the PEV-charging selection problem. Compared with the results of the uncontrolled case, the simulated results indicate that the proposed PEV-charging selection algorithm only slightly reduces user convenience levels, but significantly mitigates the impact of the PEV-charging on the power system. We also develop a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner, i.e., the binary charging decisions (charged or not charged) are made locally by each vehicle. Using the proposed distributed optimization algorithm, each vehicle is only required to report its power demand rather than report several of its private user state information, mitigating the security problems inherent in such problem. The proposed decentralized algorithm only requires low-speed communication capability, making it suitable for real-time implementation.

关键词： Charging decentralized algorithm optimization problem plug-in hybrid electric vehicle smart grid

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