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Optimal Scheduling With Vehicle-to-Grid Regulation Service

IEEE INTERNET OF THINGS JOURNAL 2014年第6期1卷 556-569页

作者： Lin, Junhao Leung, Ka-Cheong Li, Victor O. K. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

In a vehicle-to-grid (V2G) system, aggregators coordinate the charging/discharging schedules of electric vehicle (EV) batteries so that they can collectively form a massive energy storage system to provide ancillary services, such as frequency regulation, to the power grid. In this paper, the optimal charging/discharging scheduling between one aggregator and its coordinated EVs for the provision of the regulation service is studied. We propose a scheduling method that assures adequate charging of EVs and the quality of the regulation service at the same time. First, the scheduling problem is formulated as a convex optimization problem relying on accurate forecasts of the regulation demand. By exploiting the zero-energy nature of the regulation service, the forecast-based scheduling in turn degenerates to an online scheduling problem to cope with the high uncertainty in the forecasts. decentralized algorithms based on the gradient projection method are designed to solve the optimization problems, enabling each EV to solve its local problem and to obtain its own schedule. Our simulation study of 1000 EVs shows that the proposed online scheduling can perform nearly as well as the forecast-based scheduling, and it is able to smooth out the real-time power fluctuations of the grid, demonstrating the potential of V2G in providing the regulation service.

关键词： Charging/discharging scheduling decentralized algorithm electric vehicles (EVs) regulation service vehicle-to-grid (V2G)

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Hierarchical decentralized Optimization Architecture for Economic Dispatch: A New Approach for Large-Scale Power System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第2期14卷 523-534页

作者： Guo, Fanghong Wen, Changyun Mao, Jianfeng Chen, Jiawei Song, Yong-Duan Agcy Sci Technol & Res Expt Power Grid Ctr Singapore 627590 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China

In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent. In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method.

关键词： decentralized algorithm economic dispatch (ED) hierarchical optimization projected gradient virtual agent

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Adaptive allocation of independent tasks to maximize throughput

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007年第10期18卷 1420-1435页

作者： Hong, Bo Prasanna, Viktor K. Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Univ So Calif Dept Elect Engn Los Angeles CA 90089 USA

In this paper, we consider the task allocation problem for computing a large set of equal-sized independent tasks on a heterogeneous computing system where the tasks initially reside on a single computer (the root) in the system. This problem represents the computation paradigm for a wide range of applications such as SETI@ home and Monte Carlo simulations. We consider the scenario where the systems have a general graph-structured topology and the computers are capable of concurrent communications and overlapping communications with computation. We show that the maximization of system throughput reduces to a standard network flow problem. We then develop a decentralized adaptive algorithm that solves a relaxed form of the standard network flow problem and maximizes the system throughput. This algorithm is then approximated by a simple decentralized protocol to coordinate the resources adaptively. Simulations are conducted to verify the effectiveness of the proposed approach. For both uniformly distributed and power law distributed systems, a close-to-optimal throughput is achieved, and improved performance over a bandwidth-centric heuristic is observed. The adaptivity of the proposed approach is also verified through simulations.

关键词： task allocation heterogeneous computing network flow decentralized algorithm throughput

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decentralized Jointly Sparse Optimization by Reweighted l_q Minimization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第5期61卷 1165-1170页

作者： Ling, Qing Wen, Zaiwen Yin, Wotao Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Shanghai Jiao Tong Univ Dept Math MOE LSC Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200030 Peoples R China Rice Univ Dept Computat & Appl Math Houston TX 77005 USA

A set of vectors (or signals) are jointly sparse if all their nonzero entries are found on a small number of rows (or columns). Consider a network of agents {i} that collaboratively recover a set of jointly sparse vectors {x((i))} from their linear measurements {y((i))}. Assume that every agent i collects its own measurement y((i)) and aims to recover its own vector x((i)) taking advantages of the joint sparsity structure. This paper proposes novel decentralized algorithms to recover these vectors in a way that every agent runs a recovery algorithm and exchanges with its neighbors only the estimated joint support of the vectors. The agents will obtain their solutions through collaboration while keeping their vectors' values and measurements private. As such, the proposed approach finds applications in distributed human action recognition, cooperative spectrum sensing, decentralized event detection, as well as collaborative data mining. We use a non-convex minimization model and propose algorithms that alternate between support consensus and vector update. The latter step is based on reweighted l(q) iterations, where q can be 1 or 2. We numerically compare the proposed decentralized algorithms with existing centralized and decentralized algorithms. Simulation results demonstrate that the proposed decentralized approaches have strong recovery performance and converge reasonably fast.

关键词： decentralized algorithm jointly sparse optimization non-convex model

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Joint Node Activation, Beamforming and Phase-Shifting Control in IoT Sensor Network Assisted by Reconfigurable Intelligent Surface

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第11期21卷 9325-9340页

作者： Liu, Yang Shi, Qingjiang Wu, Qingqing Zhao, Jun Li, Ming Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518000 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 637121 Singapore

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors' power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% - 70% power saving compared to the no-RIS case.

关键词： Wireless communication Internet of Things Power demand Array signal processing Wireless sensor networks Wireless fidelity Quality of service IoT sensor network reconfigurable intelligent surface (RIS) group sparsity decentralized algorithm

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decentralized Pricing Mechanism for Traffic and Charging Station Management of EVs in Smart Cities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 5258-5270页

作者： Ghavami, Mahsa Haeri, Mohammad Kebriaei, Hamed Sharif Univ Technol Dept Elect Engn Adv Control Syst Lab Tehran 111554363 Iran Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran *** Iran

Due to the increasing popularity of Electric Vehicles (EVs) and infrastructural limitations, it is vital to manage traffic and Charging Stations (CSs) crowdedness. In Bakhshayesh and Kebriaei (2022), the problem of choosing the route and CSs of EVs is modeled as a non-cooperative game of selfish EVs with probabilistic decision strategies. In this paper, we have proposed a linear pricing policy that ensures global efficiency of the obtained Nash strategies of EVs in Bakhshayesh and Kebriaei (2022) for the Smart City Coordinator (SCC). We model the problem as a hierarchical game with a SCC as the leader and EVs as the followers. The leader aims to design optimal price functions of CSs and Traffic Coordinator (TC) and impose them on the EVs to maximize the social profits of CSs and TC. In response, the followers play a non-cooperative game with coupling constraints to optimally decide on their route and charging destination. Thus, we have a Stackelberg Game (SG) between SCC and EVs and also a Nash game among the EVs in the lower level. Compared to the conventional Nash-based pricing policies, our proposed functional SG formulation enables the possibility of simultaneous and global-optimum management of traffic and CSs' crowdedness. Moreover, we have proposed a two-level decentralized algorithm that preserves the privacy of EVs and have considered a decentralized computation for equilibrium seeking of followers based on the Alternating Direction Method of Multipliers (ADMM) method. Finally, we carry out simulation studies on the transportation network of Sioux Falls City to compare and evaluate the proposed method.

关键词： Pricing mechanism route and destination planning electric vehicle Stackelberg game decentralized algorithm

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Optimal Power Allocation and User Scheduling in Multicell Networks: Base Station Cooperation Using a Game-Theoretic Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2014年第12期13卷 6928-6942页

作者： Zheng, Jianchao Cai, Yueming Liu, Yongkang Xu, Yuhua Duan, Bowen Shen, Xuemin (Sherman) PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

This paper proposes a novel base station (BS) coordination approach for intercell interference mitigation in the orthogonal frequency-division multiple access based cellular networks. Specifically, we first propose a new performance metric for evaluating end user's quality of experience (QoE), which jointly considers spectrum efficiency, user fairness, and service satisfaction. Interference graph is applied here to capture and analyze the interactions between BSs. Then, a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their peer nodes in the adjacent cells in user scheduling and power allocation. The existence of the joint-strategy Nash equilibrium (NE) has been proved, in which no BS player would unilaterally change its own strategy in user scheduling or power allocation. Furthermore, the NE in the formulated game is proved to lead to the global optimality of the network utility. Accordingly, we design an iterative searching algorithm to obtain the global optimum (i.e., the best NE) with an arbitrarily high probability in a decentralized manner, in which only local information exchange is needed. Theoretical analysis and simulation results both validate the convergence and optimality of the proposed algorithm with fairness improvement.

关键词： OFDMA inter-cell interference mitigation QoE BS cooperation potential game Nash equilibrium decentralized algorithm global optimality

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decentralized Expectation Consistent Signal Recovery for Phase Retrieval

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1484-1499页

作者： Wang, Chang-Jen Wen, Chao-Kai Tsai, Shang-Ho Jin, Shi Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 30010 Taiwan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 804 Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu 30010 Taiwan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

In this study, we present a phase retrieval solution that aims to recover signals from noisy phaseless measurements. A recently proposed scheme known as generalized expectation consistent signal recovery (GEC-SR), has shown better accuracy, speed, and robustness than many existing methods. However, sensing high-resolution images with large transform matrices presents a computational burden for GEC-SR, thereby limiting its applications to areas, such as real-time implementation. Moreover, GEC-SR does not support distributed computing, which is an important requirement to modern computing. To address these issues, we propose a novel decentralized algorithm called & x201C;deGEC-SR & x201D;by leveraging the core framework of GEC-SR. deGEC-SR exhibits excellent performance similar to GEC-SR but runs tens to hundreds of times faster than GEC-SR. We derive the theoretical state evolution for deGEC-SR and demonstrate its accuracy using numerical results. Analysis allows quick generation of performance predictions and enriches our understanding on the proposed algorithm.

关键词： Signal processing algorithms Transforms Approximation algorithms Inference algorithms Robustness Bayes methods Phase measurement Phase retrieval Bayes-optimal inference expectation consistent decentralized algorithm distributed processing

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decentralized GRADIENT DESCENT MAXIMIZATION METHOD FOR COMPOSITE NONCONVEX STRONGLY-CONCAVE MINIMAX PROBLEMS

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SIAM JOURNAL ON OPTIMIZATION 2024年第1期34卷 1006-1044页

作者： Xu, Yangyang Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA

Minimax problems have recently attracted a lot of research interests. A few efforts have been made to solve decentralized nonconvex strongly -concave (NCSC) minimax-structured optimization;however, all of them focus on smooth problems with at most a constraint on the maximization variable. In this paper, we make the first attempt on solving composite NCSC minimax problems that can have convex nonsmooth terms on both minimization and maximization variables. Our algorithm is designed based on a novel reformulation of the decentralized minimax problem that introduces a multiplier to absorb the dual consensus constraint. The removal of dual consensus constraint enables the most aggressive (i.e., local maximization instead of a gradient ascent step) dual update that leads to the benefit of taking a larger primal stepsize and better complexity results. In addition, the decoupling of the nonsmoothness and consensus on the dual variable eases the analysis of a decentralized algorithm;thus our reformulation creates a new way for interested researchers to design new (and possibly more efficient) decentralized methods on solving NCSC minimax problems. We show a global convergence result of the proposed algorithm and an iteration complexity result to produce a (near) stationary point of the reformulation. Moreover, a relation is established between the (near) stationarities of the reformulation and the original formulation. With this relation, we show that when the dual regularizer is smooth, our algorithm can have lower complexity results (with reduced dependence on a condition number) than existing ones to produce a near -stationary point of the original formulation. Numerical experiments are conducted on a distributionally robust logistic regression to demonstrate the performance of the proposed algorithm.

关键词： composite minimax problem nonconvex strongly-concave decentralized algorithm iteration complexity

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A decentralized Renewable Generation Management and Demand Response in Power Distribution Networks

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2018年第4期9卷 1783-1797页

作者： Bahrami, Shahab Amini, M. Hadi Shafie-Khah, Miadreza Catalao, Joao P. S. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Beira Interior Ctr Mech & Aerosp Sci & Technol P-6201001 Covilha Portugal Univ Porto INESC TEC P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Univ Lisbon Inst Super Tecn INESC ID P-1049001 Lisbon Portugal

The stochastic nature of the renewable generators and price-responsive loads, as well as the high computational burden and violation of the generators' and load aggregators' privacy can make the centralized energy market management a big challenge for distribution network operators. In this paper, we first formulate the centralized energy trading as a bilevel optimization problem, which is nonconvex and includes the entities' optimal strategy to the price signals. We tackle the uncertainty issues by proposing a probabilistic load model and studying the down-side risk of renewable generation shortage. To address the nonconvexity of the centralized problem, we apply convex relaxation techniques and design proper price signals that guarantee zero relaxation gap. It enables us to address the privacy issue by developing a decentralized energy trading algorithm. For the sake of comparison, we use the dual decomposition and proximal Jacobian alternating direction method of multipliers for the algorithm design. Extensive simulations are performed on different standard test feeders to compare the CPU time of the proposed algorithm with the centralized approach and evaluate its performance in increasing the load aggregators' and generators' profit. Finally, we compare the impact of load and generation uncertainties on the optimality of the results.

关键词： Price-responsive load renewable generation bi-level optimization decentralized algorithm uncertainty

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