CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing

Yuan, Chen; Yao, Ziyan; Dai, Jiangpeng; Dai, Renchang; Wang, Zhiwei; Huang, G.

doi:10.1109/pesgm.2018.8586367

Cited by 33 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous works have explored the usage and feasibility of the graph database to naturally represent power systems and apply graph computing to energy management systems in power grids [15]. In this subsection, two main parts of graph computing, i.e.…”

Section: A Graph Database and Graph Computingmentioning

confidence: 99%

Graph Computing based Distributed State Estimation with PMUs

Liang

Yuan

Zhang

et al. 2020

2020 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

Power system state estimation plays a fundamental and critical role in the energy management system (EMS). To achieve a high performance and accurate system states estimation, a graph computing based distributed state estimation approach is proposed in this paper. Firstly, a power system network is divided into multiple areas. Reference buses are selected with PMUs being installed at these buses for each area. Then, the system network is converted into multiple independent areas. In this way, the power system state estimation could be conducted in parallel for each area and the estimated system states are obtained without compromise of accuracy. IEEE 118-bus system and MP 10790-bus system are employed to verify the results accuracy and present the promising computation performance.

show abstract

Section: A Graph Database and Graph Computingmentioning

confidence: 99%

Graph Computing based Distributed State Estimation with PMUs

Liang

Yuan

Zhang

et al. 2020

2020 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous works have explored the usage and feasibility of the graph database to naturally represent power systems and apply graph computing to energy management systems in power grids [18]. In this subsection, two main parts of graph computing, i.e.…”

Section: A Graph Database and Graph Computingmentioning

confidence: 99%

Graph Computing based Distributed Fast Decoupled Power Flow Analysis

Yuan

Liang

Feng

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

Power flow analysis plays a fundamental and critical role in the energy management system (EMS). It is required to well accommodate large and complex power system. To achieve a high performance and accurate power flow analysis, a graph computing based distributed power flow analysis approach is proposed in this paper. Firstly, a power system network is divided into multiple areas. Slack buses are selected for each area and, at each SCADA sampling period, the inter-area transmission line power flows are equivalently allocated as extra load injections to corresponding buses. Then, the system network is converted into multiple independent areas. In this way, the power flow analysis could be conducted in parallel for each area and the solved system states could be guaranteed without compromise of accuracy. Besides, for each area, graph computing based fast decoupled power flow (FDPF) is employed to quickly analyze system states. IEEE 118-bus system and MP 10790-bus system are employed to verify the results accuracy and present the promising computation performance of the proposed approach.

show abstract

“…In previous research, the graph computing techniques were developed for a wide variety of power system analysis, including power flow calculation [16]- [18] (including both AC systems and AC-DC hybrid systems), topology analysis [27], state estimation [28], and real-time EMS framework [29], etc.…”

Section: Graph-computing-based Platform For Power Flow Calculatimentioning

confidence: 99%

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

et al. 2019

Self Cite

View full text Add to dashboard Cite

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS). INDEX TERMS Power system security, voltage stability monitoring, voltage stability index, graph computing, demand response, security enhancement, local Thevenin index.

show abstract

CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing

Cited by 33 publications

References 7 publications

Graph Computing based Distributed State Estimation with PMUs

Graph Computing based Distributed State Estimation with PMUs

Graph Computing based Distributed Fast Decoupled Power Flow Analysis

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

Contact Info

Product

Resources

About