# GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow

arXiv: https://arxiv.org/abs/2302.08454 · HTML: https://mlsanity.com/paper/2302.08454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Chance Constraints for Improving the Security of AC Optimal Power Flow](https://mlsanity.com/paper/1803.08754/) (2018)
- [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)

## Abstract (arXiv)

The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus brings a lot of uncertainty to power grid operations. This fact makes the conventional model-based CC-OPF problem non-convex and computationally complex to solve. The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy. The proposed approach and developed software can help system operators to effectively perform ED optimization in the presence of large uncertainties in the power grid.