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A novel aggregation method for virtual power plant based on virtual contribution theory and community detection algorithm

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ENERGY REPORTS 2025年 13卷 3265-3278页

作者： Zhao, Jing Du, Songhuai Dong, Min Dong, Yanjun Zhao, Jingtong Su, Juan China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China State Grid Shaanxi Elect Power Co Ltd Chengcheng Power Supply Co Chengcheng Shaanxi Peoples R China

The rapid development of power system has significantly increased the number and proportion of distributed energy resources (DERs) and flexible loads (FLs), presenting substantial challenges to the safe and stable operation and economic dispatch of distribution network (DN). Virtual Power Plant (VPP) technology addresses these challenges by integrating DERs and FLs for unified control and dispatch. However, existing VPP aggregation methods suffer from issues including ignoring power loss, using fixed aggregation ranges and quantities, and relying on a single evaluation indicator. This paper presents a novel VPP aggregation method based on Virtual Contribution Theory (VCT) and community detection algorithm (CDA). First, a graph network-based electrical intensity model is developed using VCT, which fully considers the impact of electrical intensity on the power transmission paths between nodes, resolving the issue of insufficient power loss management during aggregation and transactions. Next, an improved CDA algorithm is introduced to dynamically identify the optimal number and configuration of VPPs, overcoming the limitations of traditional methods in reflecting electrical dynamics, parameter selection, and local optimization. To guide and evaluate VPP aggregation, a comprehensive index system is established, focusing on modularity based on electrical intensity, power balance capability, and aggregation stability-addressing gaps in current aggregation performance evaluation. Finally, simulation results using the IEEE 69-bus system demonstrate that: 1) The proposed method reduces overall power loss by 17.07% by allowing VPP to bear only a small portion of branch losses in the power market. 2) By optimizing modularity, this method uniquely determines the aggregation range and the number of VPPs, with stable and optimal results. 3)The proposed method performs consistently across various operating scenarios in terms of modularity, power balance, and aggregation stabilit

关键词： Virtual power plant aggregation Power loss Virtual contribution theory community detection algorithm Power market

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Density-Peak-Based Overlapping community detection algorithm

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第4期9卷 1211-1223页

作者： Sun, Liping Ye, Tao Sun, Jian Duan, Xiaoyu Luo, Yonglong Anhui Normal Univ Sch Comp & Informat Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China Nanjing Univ Software Inst Nanjing 210023 Peoples R China

Overlapping community detection is essential for revealing the hidden structure of complex networks. In this work, we present an overlapping community detection algorithm that selects community centers adaptively based on density peaks. The proposed algorithm, called the density-peak-based overlapping community detection (DPOCD) algorithm, defines point link strength and edge link strength to construct distance matrix. Unlike the density peaks clustering algorithm, by which cluster centers are selected manually, the DPOCD algorithm uses the linear fitting method to select community centers. To evaluate the feasibility of the presented algorithm, we compared it with other advanced methods on artificial synthetic network and real complex network datasets. The experimental results demonstrate that our method achieves excellent performance in large-scale complex networks and the robustness of the algorithm.

关键词： Complex networks Clustering algorithms detection algorithms Sun Optimization Image edge detection Robustness Adaptive selection of cluster centers community detection algorithm density peak overlapping community

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Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based community detection algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第2期37卷 1612-1622页

作者： Yang, Yang Sun, Yichao Wang, Qi Liu, Fusuo Zhu, Ling Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China NARI Grp Corp Dept Elect Network Anal Nanjing 210097 Peoples R China

Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach.

关键词： detection algorithms Power grids Partitioning algorithms Voltage measurement Power measurement Sensitivity Sun community detection algorithm electrical distance hierarchical clustering algorithm voltage control power network partition modularity pilot bus

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A sub-assembly division method based on community detection algorithm

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2022年第10-11期35卷 1133-1150页

作者： Zheng, Yu Chen, Liang Jiang, Peng Cheng, Huanchong Shanghai Jiao Tong Univ Sch Mech Engn Room A-715DongChuan Rd 800 Shanghai 200240 Peoples R China

Assembly sequence planning is always a great challenge in complex product assembly. Subassembly division is a way that can validly decrease the complexity of assembly sequence planning problem. Above all, assemblies comprising parts with different connection relationships can be modelled as the weighted graph in which the weighted adjacent list is used to accomplish calculation of the subassembly division. A weighted adjacency list generation method is presented for generating the list quickly. Furthermore, according to the requirements of subassembly division and the connection relationships of actual parts, an optimized community detection algorithm based on Fast Newman algorithm is proposed. The weight assignment of different connection relationships and deviation parameters are introduced to improve the subassembly division results. Finally, the optimized algorithm is applied in the subassembly division process of a practical pumping unit, with the influence of the weight assignment and deviation parameters being analyzed in detail. The results show that the proposed algorithm has a good performance in terms of balance, stability and independence.

关键词： Subassembly division community detection algorithm assembly adjacency list community agglomerative algorithm

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A novel density based community detection algorithm and its application in detecting potential biomarkers of ESCC

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 81卷

作者： Baruah, Bikash Dutta, Manash P. Banerjee, Subhasish Bhattacharyya, Dhruba K. NIT Arunachal Pradesh Dept Comp Sci & Engn Jote India Cotton Univ Dept Comp Sci & Informat Technol Gauhati Assam India Tezpur Univ Dept Comp Sci & Engn Tezpur India

The development of statistically and biologically competent community detection algorithm (CDA) is essential for extracting hidden information from massive biological datasets. This study introduces a novel community index as well as a CDA based on the newly introduced community index. To validate the effectiveness and robustness of the communities identified by the proposed CDA, we compare with six sets of communities identified by well-known CDAs, namely, FastGreedy, infomap, labelProp, leadingEigen, louvain, and walktrap. It is observed that the proposed algorithm outperforms its competing algorithms in terms of several prominent statistical and biological measures. We implement the hardware coding with Verilog, which surprisingly reduces the computation time by 20% compared to R programming while extracting the communities. Next, the communities identified by the proposed algorithm are used for topological and biological analysis with reference to the elite genes, obtained from Genecards, to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC). Finally, we discover that the genes F2RL3, CALM1, LPAR1, ARPC2, and CLDN7 carry significantly high topological and biological relevance of previously established ESCC elite genes. Further the established wet lab results also substantiate our claims. Hence, we affirm the aforesaid genes, as ESCC potential biomarkers.

关键词： community detection algorithm Potential Biomarker Elite Gene Topological Analysis Biological Analysis

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An improved Girvan-Newman community detection algorithm using trust-based centrality

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第4期14卷 3755-3766页

作者： Zahiri, Mahsa Mohammadzadeh, Javad Harifi, Sasan Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran

Accumulative structure or cluster-like shape is one of the important features of social networks. These structures and clusters are communities in a complex network and are fully detectable. Common group behaviors of different communities can be categorized using community detection methods. Categorize behavior allows the study of each part of the network to be done centrally. This paper uses trust-based centrality to detect the communities that make up the network. Centrality determines the relative importance of a node in the graph of social networks. Redefining the trust-based centrality makes it possible to change the position in the analysis of centrality and separates the local central nodes and global central nodes. Then, a trust-based algorithm is proposed to express the strength of trust penetration conceptually between nodes to extract communities in networks. This method has led to the achievement of a flexible and effective community detection method. The proposed algorithm is applied to four benchmark networks. The experiments consist of two independent parts. The first part is to use the proposed algorithm to detect clusters and communities. After that, the algorithm is compared with a Girvan-Newman inspired method. The second part is the implementation of the proposed algorithm with a large number of iterations with the aim of modularity maximization and comparing it with other community detection algorithms. Although, the modularity criterion has been used to validate and compare the solution quality in both independent parts of the experiments. The results show about 1.4-5.2% improvement in community detection.

关键词： community detection algorithm Trust-based centrality Social network Complex network Clustering Girvan-Newman

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Fast convergent average consensus of multiagent systems based on community detection algorithm

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ADVANCES IN DIFFERENCE EQUATIONS 2018年第1期2018卷 1页

作者： Feng, Xinlei Yang, Xiaoli Leshan Normal Univ Coll Math & Informat Sci Leshan Peoples R China Georgia State Univ Dept Math Stat Atlanta GA 30303 USA Leshan Normal Univ Coll Special Educ Leshan Peoples R China

In this paper, we consider fast convergent average consensus based on community detection algorithm. Generally, we know that a small network can have a faster convergence speed than a big one at the same condition. So we divide a multiagent network into several small networks. Firstly, let every small network reach own consensus, and then entire network reach the average consensus. Based on this idea, we present FCWAC algorithm. For the FCWAC algorithm, we obtain the results on the average consensus of first-, second-, and high-order continuous-time multiagent systems. Finally, simulation examples illustrate our theoretical results.

关键词： Consensus Multiagent systems community detection algorithm Continuous-time system

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On convergence rate for multi-agent consensus: A community detection algorithm 32

On convergence rate for multi-agent consensus: A community d...

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32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zhang, Baofeng Chang, Debao Li, Zhanjie Ma, Dan Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538629017

This paper considers the problem of improving the convergence rate for multi-agent consensus via a community detection algorithm used to divide the single layer topology into layers of connected subgraphs. This divided topological graph maintains the constraints of the original topological graph. Combining with the consensus protocol, community detection algorithm improves the convergence rate of multi-agent consensus effectively, and we propose a grouping improvement algorithm and a hierarchical grouping improvement algorithm to verify its feasibility.

关键词： multi-agent community detection algorithm consensus convergence rate

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Assessment of Discrete BAT-Modified (DBAT-M) Optimization algorithm for community detection in Complex Network

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2277-2296页

作者： Aggarwal, Kirti Arora, Anuja Jaypee Inst Informat Technol Dept Comp Sci & Engn & Informat Technol Noida India

The science of networks has made drastic changes in the modeling of complex real-world systems. community detection is one of the most important complex network concepts to divulge the unknown structural patterns of the network and extract unknown information from network structure. Extant literature has revealed that community detection in a complex network is an NP-hard problem. Therefore, an optimized and effective community detection algorithm is the most significant research gap. By employing the concept of optimization and nonlinear network structure, a Discrete BAT-Modified (DBAT-M) algorithm is developed. The modifications in the existing BAT algorithm are incorporated to achieve fast convergence and effective detection of communities in a complex network. The algorithm is compared with the Discrete BAT algorithm and two social network community detection algorithms-Greedy and Label Propagation. The performance evaluation and comparison of DBAT-M is measured using modularity Q index, stability, coverage, and performance evaluation metrics. The community detection results are evident proof of the best performance of the proposed DBAT-M algorithm over other algorithms. Thereby, a Discrete BAT-Modified algorithm is offered which is able to achieve effective and stable community partitioning as compared to existing community detection approaches.

关键词： community detection algorithm Optimization BAT

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A review of clique-based overlapping community detection algorithms

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第8期64卷 2023-2058页

作者： Gupta, Sumit Kumar Singh, Dhirendra Pratap Choudhary, Jaytrilok Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462001 MP India

detection of communities is one of the prominent characteristics of vast and complex networks like social networks, collaborative networks, and web graphs. In the modern era, new users get added to these complex networks, which results in an expansion of application-generated networks. Extracting relevant information from these large networks has become one of the most prominent research areas. community detection tries to reduce the application-generated graph into smaller communities in which nodes within the community are similar. Most of the recent proposals are focused on detecting overlapping communities in the network with higher accuracy. An integral issue in graph theory is the enumeration of cliques in a larger graph. As clique is a group of completely connected nodes which shows the explicit communities means these nodes share the same types of information. Clique-based community detection algorithm utilizing the clique property of the graph also identifies the implicit communities, which is not directly shown in the graph. Many overlapping community detection algorithms are proposed by researchers that rely on cliques. The goal of this paper is to offer a comparative analysis of clique-based community detection algorithms. This paper provides a pervasive survey on research works identifying the cliques in a network for detecting overlapping communities. We bring together most of the state-of-the-art clique-based community detection algorithms into a single article with their accessible benchmark data sets. It presents a detailed description of methods based on K-cliques, maximal cliques, and triad percolation methods and addresses these approaches' challenges. Finally, the comparative analysis of overlapping community detection methodologies is also reported.

关键词： Graph algorithm community detection algorithm Clique-based algorithm Maximal clique-based algorithm Clique percolation method Triad percolation method Overlapping community detection algorithm

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