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graph computing and Its Application in Power Grid Analysis

CSEE Journal of Power and Energy Systems 2022年第6期8卷 1550-1557页

作者： Mike Zhou Jianfeng Yan Qianhong Wu China Electric Power Research Institute Beijing 100192China

Approaches to apply graph computing to power grid analysis are systematically explained using real-world application examples.Through exploring the nature of the power grid and the characteristics of power grid analysis,the guidelines for selecting appropriate graph computing techniques for the application to power grid analysis are outlined.A custom graph model for representing the power grid for the analysis and simulation purpose and an in-memory computing(IMC)based graph-centric approach with a shared-everything architecture are introduced.graph algorithms,including network topology processing and subgraph processing,and graph computing application scenarios,including in-memory computing,contingency analysis,and Common Information Model(CIM)model merge,are presented.

关键词： CIM model merge contingency analysis graph computing in-memory computing network topology processing power grid analysis subgraph processing

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Fast Cycle Structure Detection for Power Grids Based on graph computing

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2204-2213页

作者： Xinqiao Wang Kewen Liu Weijiang Lu Ting Zhao Baohua Zhao Xiaoming Liu the computing&Application Technology Research Division Global Energy Interconnection Research InstituteBeijing 102209China

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid networks.However,detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive.In addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real time.In traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible task.graph computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state estimation.In this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing platform.Two cases are used to test its performance in an actual power grid topology scenario.The results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.

关键词： Cycle detection graph computing power grid distribution grid

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Modeling and application of marketing and distribution data based on graph computing

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Global Energy Interconnection 2022年第4期5卷 448-460页

作者： Kai Xiao Daoxing Li Xiaohui Wang Pengtian Guo China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution businesses.The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks.As a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory.Specifically,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and distribution.In this manner,a novel identification algorithm is proposed based on the collected data for marketing and distribution.Lastly,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads.Furthermore,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.

关键词： Marketing and distribution connection graph data graph computing Knowledge graph Data model

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graph computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 253-263页

作者： Wei Feng Chen Yuan Qingxin Shi Renchang Dai Guangyi Liu Zhiwei Wang Fangxing Li the University of Tennessee 1525 Coleman Road KnoxvilleTennessee 37909USA Global Energy Interconnection Research Institute North America San JoseCalifornia 95134USA

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid systems.In this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed.The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result.First,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data processing.Considering the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis.For each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system states.Furthermore,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson method.With the improved initial estimate,the sequential method can converge in fewer iterations.Consequently,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system.The performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

关键词： AC/DC system distributed parallel computing graph computing initial estimate power flow analysis

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graph computing Based Security Constrained Unit Commitment in Hydro-thermal Power Systems Incorporating Pumped Hydro Storage

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 485-496页

作者： Longfei Wei Guangyi Liu Shen Yan Renchang Dai Yachen Tang Hitachi ABB Power Grids San JoseCAUSA School of Electrical Engineering Xi'an University of TechnologyXi'anChina Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-ChampaignChampaignILUSA Puget Sound Energy BellevueWAUSA Envision Digital Inc. Redwood CityCAUSA

This paper proposes a graph computing based mixed integer programming(MIP)framework for solving the security constrained unit commitment(SCUC)problem in hydro-thermal power systems incorporating pumped hydro storage(PHS).The proposed graph computing-based MIP framework considers the economic operations of thermal units,cascade hydropower stations and PHS stations,as well as their technical impacts towards the network security.First,the hydro-thermal power system data and unit information are stored in a graph structure with nodes and edges,which enables nodal and hierarchical parallel computing for the unit commitment(UC)solution calculation and network security analysis.A MIP model is then formulated to solve the SCUC problem with the mathematical models of thermal units,cascade hydropower stations and PHS stations.In addition,two optimization approaches including convex hull reformulation(CHR)and special ordered set(SOS)methods are introduced for speeding up the MIP calculation procedure.To ensure the system stability under the derived UC solution,a parallelized graph power flow(PGPF)algorithm is proposed for the hydro-thermal power system network security analysis.Finally,case studies of the IEEE 118-bus system and a practical 2749-bus hydro-thermal power system are introduced to demonstrate the feasibility and validity of the proposed graph computing-based MIP framework.

关键词： graph computing hydro-thermal system mixed integer programming network security unit commitment

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Fraud detection on payment transaction networks via graph computing and visualization

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High Technology Letters 2020年第3期26卷 253-261页

作者：孙权 Tang Tao Zheng Jianbin Lin Jiale Zhao Jintao Liu Hongbao School of Computer Science Fudan UniversityShanghai 200433P.R.China China UnionPay Research Institute of Electronic Payment Shanghai 201201P.R.China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240P.R.China

With the fast development of Internet technology,more and more payments are fulfilled by mobile Apps in an electrical way which significantly saves time and efforts for payment.Such a change has benefited a large number of individual users as well as merchants,and a few major players for payment service have emerged in China.As a result,the payment service competition becomes even fierce,and various promotion activities have been launched for attracting more users by the payment service providers.In this paper,the problem focused on is fraud payment detection,which in fact has been a major concern for the providers who spend a significant amount of money to popularize their payment tools.This paper tries the graph computing-based visualization to the behavior of transactions occuring between the individual users and merchants.Specifically,a network analysisbased pipeline has been built.It consists of the following key components:transaction network building based on daily records aggregation;transaction network filtering based on edge and node removal;transaction network decomposition by community detection;detected transaction community visualization.The proposed approach is verified on the real-world dataset collected from the major player in the payment market in Asia and the qualitative results show the efficiency of the method.

关键词： payment fraud detection graph computing graph embedding machine learning

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