# Chance Constraints for Improving the Security of AC Optimal Power Flow

arXiv: https://arxiv.org/abs/1803.08754 · HTML: https://mlsanity.com/paper/1803.08754/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2207.10781/) (2022)
- [Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes](https://mlsanity.com/paper/2208.14814/) (2022)
- [Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts](https://mlsanity.com/paper/2207.09818/) (2022)
- [GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow](https://mlsanity.com/paper/2302.08454/) (2023)

## Abstract (arXiv)

This paper presents a scalable method for improving the solutions of AC Optimal Power Flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The focus of the paper is on providing solutions that are more robust to short-term deviations, and which optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modelling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.