Computational Methods for Nonlinear Power Operational Problems: Convex Reformulations and Near-Linear Time Algorithms
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About this item
| Description: |
Lavaei, J
Tuesday 8th January 2019 - 11:30 to 12:30 |
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| Created: | 2019-01-09 11:38 |
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| Collection: | The mathematics of energy systems |
| Publisher: | Isaac Newton Institute |
| Copyright: | Lavaei, J |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Somayeh Sojoudi (UC Berkeley), Richard Zhang (UC Berkeley), Salar Fattahi (UC Berkeley), Igor Molybog (UC Berkeley), Ming Jin (UC Berkeley), SangWoo Park (UC Berkeley)In this talk, we will study a set of nonlinear power optimization and decision-making problems, namely power flow, optimal power flow, state estimation and topology error detection. We will propose different conic relaxation and approximation techniques to solve these nonconvex problems. We will prove that such conic problems could be solved in near linear time due to intrinsic properties of real-world power networks. We will offer case studies on systems with as high as 14,000 nodes. |
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