Name
Abstract | ||
|---|---|---|
Due to a high penetration of renewable energy, power systems operational planning today needs to capture unprecedented uncertainties in a short period. Fast probabilistic state estimation (SE), which creates probabilistic load flow estimates, represents one such planning tool. This paper describes a graphical model for probabilistic SE modeling that captures both the uncertainties and the power gr... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSG.2017.2749369 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Probabilistic logic,Planning,State estimation,Graphical models,Generators,Mathematical model,Computational modeling | Operational planning,Mathematical optimization,Electric power system,Distributed algorithm,State variable,Graphical model,Maximum a posteriori estimation,Probabilistic logic,Engineering,Message passing | Journal |
Volume | Issue | ISSN |
10 | 1 | 1949-3053 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| qingan | 1 | 122 | 12.38 |
| Rohit Negi | 2 | 1263 | 97.44 |
| Marija D. Ilić | 3 | 237 | 35.82 |