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A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints

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APPLIED ENERGY 2024年 356卷

作者： Meng, Yuan Qiu, Jing Zhang, Cuo Lei, Gang Zhu, Jianguo Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading;however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multistage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage.

关键词： Virtual power plant Peer-to-Peer energy trading Reactive energy trading Voltage\VAR control distributed algorithm distributed energy resource

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Quantized distributed Estimation With Adaptive Combiner

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2022年 8卷 187-200页

作者： Lao, Xiaoxian Du, Wenfei Li, Chunguang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Zhejiang Peoples R China

distributed estimation over networks has attracted great attention for its wide applicability in source tracking, environmental monitoring, etc. In the problem of distributed estimation, a set of nodes collectively estimates some parameter from noisy measurements. Most distributed algorithms assume that the information shared among nodes has full-precision. However, this assumption is unrealistic as communication channels among nodes have limited bandwidth. Considering this, in this paper, we impose quantization constraints on distributed estimation problem. That is, nodes can only receive quantized information from neighbors. The quantization inevitably brings in errors. Each node has to decide carefully which information provided by the neighbors is more trustworthy since these information could suffer from various degrees of distortion caused by the errors. To make good use of the aggregated information, we propose an adaptive combination strategy for quantized distributed estimation algorithms. We derive sufficient conditions for convergence of the resulting algorithm through mean-square analysis. The advantages of the proposed adaptive combiner over fixed combiners are illustrated experimentally.

关键词： distributed estimation distributed algorithm diffusion quantization adaptive combiner

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Experimental assessment of a JANUS-based consensus protocol

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COMPUTER NETWORKS 2024年 244卷

作者： Wengle, Emil Erstorp, Elias Strandell Lidstrom, Viktor Varagnolo, Damiano Dong, Hefeng Norwegian Univ Sci & Technol NTNU Dept Elect Syst NO-7491 Trondheim Norway Stockholm Univ SU Dept Geol Sci Stockholm Sweden Swedish Def Res Agcy FOI Stockholm Sweden Norwegian Univ Sci & Technol NTNU Dept Engn Cybernet NO-7491 Trondheim Norway

This paper proposes a distributed, JANUS-based protocol that enables an underwater acoustic network to reach consensus on arbitrary local opinions as numeric state variables. An envisioned scenario where nodes shall agree on parameters describing the acoustic environment is used to evaluate the protocol. The scenario exemplifies the protocol's potential in future applications where nodes use the environment description to decide on appropriate modulation and coding schemes. The evaluation is based on numerical simulations and sea experiments in a challenging acoustic environment. The numerical simulations allowed examining the performance for different parameter values regarding the timing of transmission events and state transitions in the finite state machine implementation of the protocol. The best parameter configuration was used in the sea experiments conducted in a bay in the Baltic Sea. The experiments comprised several deployments of five to six commercial modems. Results from the experiments show that the protocol can achieve a consensus up to 89% of the time in the tested environment, and up to 96% of the time if the state variables are permitted to differ by one discretisation step maximum across the network. In addition, if the network separates due to environmental conditions, connected components appear to achieve consensus more often when the links are more reliable. Finally, it is shown that when different consensus processes are active in parallel, packets from one process do not interfere with the opinions in different processes, besides the existing probability of packet loss due to packet collision.

关键词： JANUS Underwater sensor network distributed algorithm

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A pruning extreme learning machine with L2,1/2 regularization for multi-dimensional output problems

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第2期15卷 621-636页

作者： Dai, Yunwei Zhang, Yuao Wu, Qingbiao Zhejiang Univ Sch Math Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Hangzhou City Univ Sch Informat & Elect Engn 51 Huzhou St Hangzhou 310015 Zhejiang Peoples R China

As a fast algorithm for training single-hidden layer feedforward neural networks, extreme learning machine (ELM) has been successfully applied to various classification and regression problems. In recent years, regularization techniques have been widely used in ELM to improve its stability, sparsity and generalization capability. In order to determine the appropriate number of hidden layer nodes, the ELM regularized by l(1/2) quasi-norm (L-1/2-ELM) was developed to prune the redundant hidden nodes. However, in multi-dimensional output tasks, L-1/2-ELM only removes redundant weights of hidden nodes but cannot guarantee the sparsity at the node level. In this paper, we present the L-2,L-1/2-ELM, which is regularized by L-2,L-1/2 quasi-norm to achieve sparsity in multi-dimensional output problems. With the generalization of L-1/2 regularization to L-2,L-1/2 regularization, L-2,L-1/2-ELM can prune the corresponding hidden nodes by setting some rows of the output weight matrix to zero. Since the proximal operator corresponding to L-2,L-1/2 regularization has a closed-form solution, the powerful alternating direction method of multipliers (ADMM) is employed to achieve a fast solution of L-2,L-1/2-ELM. Furthermore, to face the challenge of distributed computing, we extend L-2,L-1/2-ELM to its distributed version, namely DL2,1/2-ELM. DL2,1/2-ELM is solved by the consensus ADMM algorithm. Experiments on multi-classification and multi-target regression datasets demonstrate that our proposed algorithms can achieve competitive sparsity without compromising accuracy.

关键词： Extreme learning machine L-2,L-1/2 regularization Sparsity Multi-dimensional output Alternating direction method of multipliers distributed algorithm

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On the game-theoretic analysis of distributed generative adversarial networks

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期37卷 516-534页

作者： Li, Zhongguo Dong, Zhen Chen, Wen-Hua Ding, Zhengtao Loughborough Univ Dept Aeronaut & Automot Engn Loughborough Leics England Univ Manchester Dept Elect & Elect Engn Sackville St Bldg Manchester M13 9PL Lancs England

In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.

关键词： consensus distributed algorithm game-theoretic approach generative adversarial networks minmax optimisation Nash equilibrium zero-sum game

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A distributed Newton-Raphson-based coordination algorithm for multi-agent optimization with discrete-time communication

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NEURAL COMPUTING & APPLICATIONS 2020年第9期32卷 4649-4663页

作者： Li, Yushuai Zhang, Huaguang Huang, Bonan Han, Ji Northeastern Univ Coll Informat Sci & Engn Boston MA 02115 USA

This paper proposes a novel distributed continuous-time Newton-Raphson algorithm for distributed convex optimization problem, where the components of the goal are obtainable at different agents. To accelerate convergence speed, we focus on introducing Newton descent idea in our algorithm and extending it in a distributed setting. It is proved that the proposed algorithm can converge to the global optimal point with exponential convergence rate under weight-balanced directed graphs. Motivated by practical considerations, an event-triggered broadcasting strategy is further developed for each agent. Therein, the implementation of communication is driven by the designed triggered condition monitored by agents. Consequently, the proposed continuous-time algorithm can be executed with discrete-time communication, thus being able to greatly save communication expenditure. Moreover, the strategy is proved to be free of Zeno behavior. Eventually, the simulation results illustrate the advantages of the proposed algorithm.

关键词： Convex optimization Multi-agent Event-triggered distributed algorithm Newton-Raphson

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DCG: An efficient distributed Conjugate Gradient algorithm for solving linear equations in multi-agent networks

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RESULTS IN CONTROL AND OPTIMIZATION 2023年 10卷

作者： Ping, Haodi Wang, Yongcai Wei, Chengcun Xi, Jin Zhang, Taoming Gao, Yong Renmin Univ China Sch Informat 59 Zhongguancun St Beijing 100872 Peoples R China China West Airport Grp Xian 710075 Shaanxi Peoples R China CACC Southwest Design & Res Inst Co LTD Chengdu 610202 Sichuan Peoples R China

distributed algorithms to solve linear equations in multi -agent networks have attracted great research attention and many iteration -based distributed algorithms have been developed. The convergence speed is a crucial factor of distributed algorithms, and it is shown to be dependent on the spectral radius of the iteration matrix. However, the iteration matrix is determined by the network structure and is difficult to pre -tune. Consequently, the iterative -based distributed algorithms converge slowly when the spectral radius is close to 1. In contrast, in centralized optimization, the Conjugate Gradient (CG) is a widely adopted idea to speed up the convergence of the centralized solvers, which can guarantee convergence in fixed steps. In this paper, we propose a distributed implementation of the Conjugate Gradient method, i.e., DCG. DCG is fully distributed, requiring only local communication and local computation. DCG also inherits the characteristic of fast convergence from CG. DCG guarantees to converge in 3Hn rounds, where H is the maximum hop number of the network and n is the number of nodes. The applications of DCG in solving the least square problem and network localization problem in both 2D space and 3D space are analyzed. The applicability of DCG in tracking mobile robot networks is also validated. The results show the convergence speed of DCG is several orders of magnitude faster than the traditional iteration -based distributed methods. In addition, we offer the codes at https://***/Haodi-Ping/DCG.

关键词： distributed algorithm Conjugate gradient Linear equations Network localization Barycentric localization Least square problem

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An Efficient Incentive Mechanism for Federated Learning in Vehicular Networks

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IEEE NETWORK 2024年第5期38卷 189-195页

作者： Qiao, Cheng Zeng, Yanqing Lu, Hui Liu, Yuan Tian, Zhihong Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Fujian Jiangxia Univ Coll Elect & Informat Sci Fuzhou 350108 Peoples R China

As a novel machine learning technique, the promising performance of Federated Learning (FL) paradigms has been well recognized. However, the incentive issue for this framework is still under-explored. Many existing incentive mechanisms are computationally complex and result in increased communication overhead, making them unsuitable for Vehicular Networks. To address this issue, we propose a simple yet efficient incentive mechanism that identifies agents with high-quality resources and motivates them to behave actively in the learning process, such that the final aggregated model is accurate, with a reduced network overhead. In particular, the specific requirements of end-users and the uniqueness of raw data are considered. Our evaluations on both synthetic and real-world datasets demonstrate that the clustering accuracy can be slightly improved (by around 2%), while the network overhead can be significantly decreased by up to 70%, and the convergence time can be reduced by up to 9%.

关键词： Servers Computational modeling Costs Training Task analysis Estimation Delays Federated Learning distributed algorithm Clustering Incentive Mechanism

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Truthful Auction Mechanisms for Dependent Task Offloading in Vehicular Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14987-15002页

作者： Ren, Hualing Liu, Kai Yan, Guozhi Liu, Chunhui Li, Yantao Li, Chuzhao Wu, Weiwei Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Sch Natl Elite Inst Engn Chongqing 400044 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 211102 Peoples R China

This work investigates the truthful auction for dependent task offloading in vehicular edge computing by considering the selfishness and rationality of participating nodes. Specifically, we first illustrate a truthfulness-guaranteed dependent task offloading architecture. Then, we formulate the Truthfulness-Guaranteed Dependent Task Offloading problem, aiming at maximizing the system utility (SU) while ensuring truthfulness and individual rationality in dynamic environments. Further, we design both centralized and distributed auction mechanisms to derive the optimal and approximate solutions, respectively. For centralized auction mechanism, we adopt the branch-and-price algorithm to determine the offloaded nodes, which yields maximum SU. Then, we adopt VCG mechanism to determine the payment of buyers. For distributed auction mechanism, each seller independently chooses the winning bid, and the buyer greedily chooses the offloaded node with maximum utility. Then, a novel payment mechanism regarding the cost of failed buyers is designed to guarantee the truthfulness and individual rationality. Finally, we build the simulation model and conduct the performance evaluation based on realistic vehicular trajectories. The results demonstrate that the proposed distributed auction mechanism achieves performance within approximately 4% of the optimal method, while significantly reducing computational complexity. Additionally, it significantly outperforms other methods in terms of system utility across various task requirements.

关键词： Dependent task offloading truthful auction distributed algorithm vehicular edge computing Dependent task offloading truthful auction distributed algorithm vehicular edge computing

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On Practical Reconstruction of Quality Virtual Backbones in Heterogeneous Wireless Sensor Networks With Faulty Nodes

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第2期21卷 2064-2076页

作者： He, Feng Liang, Jiarong Li, Qingnian Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Nanning Univ Coll Informat Engn Nanning 530200 Guangxi Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China

Frequently, unit disk graphs (UDGs) are used to model homogeneous wireless sensor networks (WSNs), in which each node has the same transmission radius. In some applications, however, different nodes in a WSN have different transmission radii, meaning that a UDG cannot accurately model the WSN. In this case, a disk graph with bidirectional links (DGB) can be used in place of a UDG. Nevertheless, most results reported to date concern the problem of finding minimum fault-tolerant CDSs in UDGs. In this paper, we investigate the minimum fault-tolerant CDS problem for DGBs by reconstructing CDSs for DGBs with faulty nodes. We present a centralized approximation algorithm for CDS reconstruction to address the minimum fault-tolerant CDS problem in given DGBs. The performance ratio (PR) of the presented algorithm is the same as that of the algorithm used to generate the input CDS C . Furthermore, we present a distributed version, which not only can be easily implemented in real situations but also considers CDS size to reduce the network cost. Theoretical analysis shows that the PR of our proposed algorithm is lower than those of other state-of-the-art algorithms for the minimum fault-tolerant CDS problem in given DGBs. In addition, numerical experiments objectively demonstrate that the performance of our algorithm is superior on average to that of its competitors in terms of CDS size, run time and application rate.

关键词： Connected dominating set distributed algorithm wireless sensor network approximation algorithm disk graph with bidirectional links

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