# Computationally Efficient Solutions for Large-Scale Security-Constrained Optimal Power Flow

arXiv: https://arxiv.org/abs/2006.00585 · HTML: https://mlsanity.com/paper/2006.00585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 1 of 1)

- [Adversarially Robust Learning for Security-Constrained Optimal Power Flow](https://mlsanity.com/paper/2111.06961/) (2021)

## Abstract (arXiv)

In this paper, we discuss our approach and algorithmic framework for solving large-scale security constrained optimal power flow (SCOPF) problems. SCOPF is a mixed integer non-convex optimization problem that aims to obtain the minimum dispatch cost while maintaining the system N-1 secure. Finding a feasible solution for this problem over large networks is challenging and this paper presents contingency selection, approximation methods, and decomposition techniques to address this challenge in a short period of time. The performance of the proposed methods are verified through large-scale synthetic and actual power networks in the Grid Optimization (GO) competition organized by the U.S. Advanced Research Projects Agency-Energy (ARPA-E). As many prior works focus on small-scale systems and are not benchmarked using validated, publicly available datasets, we aim to present a practical solution to SCOPF that has been proven to achieve good performance on realistically sized (30,000 buses) networks.