# DNN-based Policies for Stochastic AC OPF

arXiv: https://arxiv.org/abs/2112.02441 · HTML: https://mlsanity.com/paper/2112.02441/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow](https://mlsanity.com/paper/1910.01213/) (2019)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Learning for DC-OPF: Classifying active sets using neural nets](https://mlsanity.com/paper/1902.05607/) (2019)
- [Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks](https://mlsanity.com/paper/2103.14779/) (2021)
- [Statistical Learning For DC Optimal Power Flow](https://mlsanity.com/paper/1801.07809/) (2018)
- [Learning to Solve Network Flow Problems via Neural Decoding](https://mlsanity.com/paper/2002.04091/) (2020)
- [Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks](https://mlsanity.com/paper/2007.03728/) (2020)
- [Designing reactive power control rules for smart inverters using support vector machines](https://mlsanity.com/paper/1903.01016/) (2019)
- [Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach](https://mlsanity.com/paper/2105.00429/) (2021)
- [Stochastic AC Optimal Power Flow: A Data-Driven Approach](https://mlsanity.com/paper/1910.09144/) (2019)
- [Two-Timescale Stochastic Dispatch of Smart Distribution Grids](https://mlsanity.com/paper/1608.05269/) (2016)
- [Efficient Polynomial Chaos Expansion for Uncertainty Quantification in Power Systems](https://mlsanity.com/paper/1910.06498/) (2019)
- [Ergodic Energy Management Leveraging Resource Variability in Distribution Grids](https://mlsanity.com/paper/1508.00654/) (2015)
- [Chance-Constrained AC Optimal Power Flow -- A Polynomial Chaos Approach](https://mlsanity.com/paper/1903.11337/) (2019)
- [Deep Learning for Reactive Power Control of Smart Inverters under Communication Constraints](https://mlsanity.com/paper/2007.05868/) (2020)
- [Optimal Power Flow with Weighted Chance Constraints and General Policies for Generation Control](https://mlsanity.com/paper/1504.00057/) (2015)

## Built on by (most cited first, 7 of 7)

- [End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch](https://mlsanity.com/paper/2304.11726/) (2023)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2207.10781/) (2022)
- [Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach](https://mlsanity.com/paper/2410.03085/) (2024)
- [DiffOPF: Diffusion Solver for Optimal Power Flow](https://mlsanity.com/paper/2510.14075/) (2025)
- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2402.11365/) (2024)
- [Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration](https://mlsanity.com/paper/2608.25095/) (2026)

## Abstract (arXiv)

A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF formulations consider simple control policies such as affine policies that are mathematically simple and resemble many policies used in current practice. Motivated by the efficacy of machine learning (ML) algorithms and the potential benefits of general control policies for cost and constraint enforcement, we put forth a deep neural network (DNN)-based policy that predicts the generator dispatch decisions in real time in response to uncertainty. The weights of the DNN are learnt using stochastic primal-dual updates that solve the SOPF without the need for prior generation of training labels and can explicitly account for the feasibility constraints in the SOPF. The advantages of the DNN policy over simpler policies and their efficacy in enforcing safety limits and producing near optimal solutions are demonstrated in the context of a chance constrained formulation on a number of test cases.