# Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

arXiv: https://arxiv.org/abs/2208.14814 · HTML: https://mlsanity.com/paper/2208.14814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Learning-based Model Predictive Control for Safe Exploration](https://mlsanity.com/paper/1803.08287/) (2018)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow](https://mlsanity.com/paper/1910.01213/) (2019)
- [DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow](https://mlsanity.com/paper/1910.14448/) (2019)
- [Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks](https://mlsanity.com/paper/2103.14779/) (2021)
- [A Cubic Algorithm for Computing Gaussian Volume](https://mlsanity.com/paper/1306.5829/) (2013)
- [Gaussian Process Learning-based Probabilistic Optimal Power Flow](https://mlsanity.com/paper/2004.07757/) (2020)
- [Importance sampling approach to chance-constrained DC optimal power flow](https://mlsanity.com/paper/2111.11729/) (2021)
- [Chance Constraints for Improving the Security of AC Optimal Power Flow](https://mlsanity.com/paper/1803.08754/) (2018)
- [Stochastic AC Optimal Power Flow: A Data-Driven Approach](https://mlsanity.com/paper/1910.09144/) (2019)
- [Power Grid Reliability Estimation via Adaptive Importance Sampling](https://mlsanity.com/paper/2105.08753/) (2021)
- [Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms](https://mlsanity.com/paper/1706.03241/) (2017)
- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2207.10781/) (2022)

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

- [GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow](https://mlsanity.com/paper/2302.08454/) (2023)

## Abstract (arXiv)

The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.