text: 489,135,1467934898728148995,2894532745,Priya L. Donti,"In our new #NeurIPS2021 paper, we provide one of the first approaches to address N-k SCOPF, a core problem for the operation of power grids, at realistic scale.  Paper: <LINK> Joint work with Aayushya Agarwal, @neerajssp2, @LarryPileggi, and @zicokolter 1/ <LINK> N-k SCOPF aims to schedule power generation in a way that is robust to k potential equipment failures. While it's become increasingly important to solve (see, e.g., recent blackout events in the UK and Texas), N-k SCOPF is in practice prohibitively expensive to solve at scale. 2/ We propose a heuristic approach to address N-k SCOPF at scale. Our approach entails rewriting N-k SCOPF as a continuous minimax (attacker-defender) optimization problem, and solving it efficiently using insights from adversarial robustness and implicit layers in deep learning. 3/ <LINK> We use our approach to address N-3 SCOPF on a realistic-size (4622 bus) system, and show that it significantly reduces the number of feasibility violations (by a factor of 3-4x) compared to state-of-the-art baselines, while taking only 21 minutes to run on a standard laptop. 4/ <LINK> If you’re interested in chatting further about this work, Aayushya and I will be presenting our poster at #NeurIPS2021 on Tue, Dec 7 from 11:30am-1pm Eastern. Hope to see you there!  <LINK>  5/5 <LINK> @nandofioretto @neerajssp2 @LarryPileggi @zicokolter Thanks! And yes, exactly :) It's a bit of a play on the name of our method, which is CAN∂Y",https://arxiv.org/abs/2111.06961,"In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored. In this work, we combine innovations from these areas to tackle the problem of N-k security-constrained optimal power flow (SCOPF). N-k SCOPF is a core problem for the operation of electrical grids, and aims to schedule power generation in a manner that is robust to potentially k simultaneous equipment outages. Inspired by methods in adversarially robust training, we frame N-k SCOPF as a minimax optimization problem - viewing power generation settings as adjustable parameters and equipment outages as (adversarial) attacks - and solve this problem via gradient-based techniques. The loss function of this minimax problem involves resolving implicit equations representing grid physics and operational decisions, which we differentiate through via the implicit function theorem. We demonstrate the efficacy of our framework in solving N-3 SCOPF, which has traditionally been considered as prohibitively expensive to solve given that the problem size depends combinatorially on the number of potential outages. ","Adversarially Robust Learning for Security-Constrained Optimal Power