Parallel and distributed solutions for contingency analysis in energy management systems

Alves, Antônio César Baleeiro; Monticelli, A.

doi:10.1109/mwscas.1995.504473

Cited by 11 publications

(2 citation statements)

References 2 publications

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“…To accommodate on-line calculation for large scale electric power system, graph database management system (GDBMS) is adapted to fulfill the power system calculation requirements on complex database store, traversal, concurrent access, flexible expansion and reduction. In contrary to relational database, graph database uses graph structures for semantic queries with nodes and edges to store data [17][18]. Unstructured attributes of node or edge are stored in the node or edge.…”

Section: Graph Database Featuresmentioning

confidence: 99%

Graph Based Power Flow Calculation for Energy Management System

Shi¹,

Dai

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Power flow calculation in EMS is required to accommodate large and complex power system. To achieve a faster than real-time calculation, a graph based power flow calculation is proposed in this paper. Graph database and graph computing advantages in power system calculations are presented. A linear solver for power flow application is formulated and decomposed in nodal parallelism and hierarchical parallelism to fully utilize graph parallel computing capability. Comparison of the algorithm with traditional sequential programs shows significant benefits on computation efficiency. Case studies on practical large-scale systems provide supporting evidence that the new algorithm is promising for online computing for EMS.

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Section: Graph Database Featuresmentioning

confidence: 99%

Graph Based Power Flow Calculation for Energy Management System

Shi¹,

Dai

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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“…Alves et al [8] proposed a parallel and distributed computing architecture for CA. For fast CA, Santos et al [9] developed a socket based client-server model where dynamic load balancing scheme is implemented for improved performance.…”

Section: Previous Workmentioning

confidence: 99%

Real Time Contingency Analysis for Power Grids

Mittal

Hazra

Jain

et al. 2011

Lecture Notes in Computer Science

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Abstract. Modern power grids are continuously monitored by trained system operators equipped with sophisticated monitoring and control systems. Despite such precautionary measures, large blackouts, that affect more than a million consumers, occur quite frequently. To prevent such blackouts, it is important to perform high-order contingency analysis in real time. However, contingency analysis is computationally very expensive as many different combinations of power system component failures must be analyzed. Analyzing several million such possible combinations can take inordinately long time and it is not be possible for conventional systems to predict blackouts in time to take necessary corrective actions. To address this issue, we present a scalable parallel implementation of a probabilistic contingency analysis scheme that processes only most severe and most probable contingencies. We evaluate our implementation by analyzing benchmark IEEE 300 bus and 118 bus test grids. We perform contingency analysis upto level eight (contingency chains of length eight) and can correctly predict blackouts in real time to a high degree of accuracy. To the best of our knowledge, this is the first implementation of real time contingency analysis beyond level two.

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Graph Database and Graph Computing for Power System Analysis

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Parallel and distributed solutions for contingency analysis in energy management systems

Cited by 11 publications

References 2 publications

Graph Based Power Flow Calculation for Energy Management System

Graph Based Power Flow Calculation for Energy Management System

Real Time Contingency Analysis for Power Grids

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