# Data-Driven Stochastic AC-OPF using Gaussian Processes

arXiv: https://arxiv.org/abs/2402.11365 · HTML: https://mlsanity.com/paper/2402.11365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 121 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [Solutions of DC OPF are Never AC Feasible](https://mlsanity.com/paper/1912.00319/) (2019)
- [Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems](https://mlsanity.com/paper/1705.00105/) (2017)
- [DNN-based Policies for Stochastic AC OPF](https://mlsanity.com/paper/2112.02441/) (2021)
- [Importance sampling approach to chance-constrained DC optimal power flow](https://mlsanity.com/paper/2111.11729/) (2021)
- [Security Constrained Optimal Power Flow with Distributionally Robust Chance Constraints](https://mlsanity.com/paper/1508.06061/) (2015)
- [Entropy Penalized Semidefinite Programming](https://mlsanity.com/paper/1802.04332/) (2018)
- [Learning a Generator Model from Terminal Bus Data](https://mlsanity.com/paper/1901.00781/) (2019)
- [Large-Scale Sequential Learning for Recommender and Engineering Systems](https://mlsanity.com/paper/2205.06893/) (2022)
- [Ranking-Based Physics-Informed Line Failure Detection in Power Grids](https://mlsanity.com/paper/2209.01021/) (2022)

## Abstract (arXiv)

The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The proposed approach aims to address this limitation and demonstrate its empirical efficiency through applications to multiple IEEE test cases. To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model. The full Gaussian process (GP) approach is capable of learning a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The proposed approach uses various approximations for GP-uncertainty propagation. The full GP CC-OPF approach exhibits highly competitive and promising results, outperforming the state-of-the-art sample-based chance constraint approaches. To further improve the robustness and complexity/accuracy trade-off of the full GP CC-OPF, a fast data-driven setup is proposed. This setup relies on the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty.