Comparing Machine Learning and Optimization Approaches for the N − k Interdiction Problem Considering Load Variability
| dc.contributor.author | Owen Aquino, Alejandro | |
| dc.contributor.author | Harris, Rachel | |
| dc.contributor.author | Kody, Alyssa | |
| dc.contributor.author | Molzahn, Daniel | |
| dc.date.accessioned | 2022-12-27T19:05:22Z | |
| dc.date.available | 2022-12-27T19:05:22Z | |
| dc.date.issued | 2023-01-03 | |
| dc.description.abstract | Power grids must be operated, protected, and maintained such that a small number of line failures will not result in significant load shedding. To identify problematic combinations of failures, we consider an N-k interdiction problem that seeks the set of k failed lines (out of N total lines) that result in the largest load shed. This is naturally formulated as a bilevel optimization problem with an upper level representing the attacker that selects line failures and a lower level modeling the defender's generator redispatch to minimize the load shedding. Compounding the difficulties inherent to the bilevel nature of interdiction problems, we consider a nonlinear AC power flow model that makes this problem intractable with traditional solution approaches. Furthermore, since the solutions found at a particular load condition may not generalize to other loading conditions, operators may need to quickly recompute these worst-case failures online to protect against them during operations. To address these challenges, we formulate and compare the performance of three simplified methods for solving the N-k interdiction problem: a state-of-the-art optimization approach based on a network-flow relaxation of the power flow equations and two newly developed machine learning algorithms that predict load sheds given the state of the network. | |
| dc.format.extent | 10 | |
| dc.identifier.doi | 10.24251/HICSS.2023.340 | |
| dc.identifier.isbn | 978-0-9981331-6-4 | |
| dc.identifier.other | 8c819e1c-ad09-4b34-926f-1e4378a1d23a | |
| dc.identifier.uri | https://hdl.handle.net/10125/102971 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Proceedings of the 56th Hawaii International Conference on System Sciences | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Resilient Networks | |
| dc.subject | bilevel optimization | |
| dc.subject | interdiction | |
| dc.subject | neural networks | |
| dc.subject | n − k | |
| dc.title | Comparing Machine Learning and Optimization Approaches for the N − k Interdiction Problem Considering Load Variability | |
| dc.type.dcmi | text | |
| prism.startingpage | 2766 |
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