Name
Abstract | ||
|---|---|---|
The typical five-minute computational latency of current Energy Management Systems (EMSs) can leave and has left system operators blind to the system state created by events that lead to cascading outages, system separation and/or system collapse. In this paper, we present a graph-computing based EMS that allows the EMS computational cycle time to be reduced to the SCADA sampling cycle, about five seconds. This is achieved in part by taking advantage of both nodal and hierarchical parallelism, to achieve high-speed graph-based state estimation, power flow, and contingency analysis applications. A production-mode case study on an existing large-scale system verifies the advantages and performance of graph computing, demonstrating that the graph-based EMS is more than 20 times faster than a contemporary commercial EMS. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TSG.2019.2943815 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
Energy management,Power systems,Relational databases,Parallel processing,Data models,Data structures | Journal | 11 |
Issue | ISSN | Citations |
3 | 1949-3053 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renchang Dai | 1 | 0 | 1.35 |
| Guangyi Liu | 2 | 223 | 36.37 |
| Zhiwei Wang | 3 | 1 | 1.79 |
| Bowen Kan | 4 | 0 | 1.01 |
| Chen Yuan | 5 | 27 | 12.30 |