# Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

@inproceedings{Fioretto2019PredictingAO, title={Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods}, author={Ferdinando Fioretto and Terrence W.K. Mak and Pascal Van Hentenryck}, booktitle={AAAI Conference on Artificial Intelligence}, year={2019} }

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often needed to be solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter…

## 85 Citations

### High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

- Engineering, Computer ScienceArXiv
- 2020

This paper proposes an integration of deep neural networks and Lagrangian duality to capture the physical and operational constraints of the AC Optimal Power Flow and produces highly accurate approximations whose costs are within 0.01% of optimality.

### Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality

- Computer ScienceGLOBECOM 2022 - 2022 IEEE Global Communications Conference
- 2022

A deep neural network is developed to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations and the fast decoupled power flow solver is adopted to further reduce the computational time.

### A learning-augmented approach for AC optimal power flow

- Computer Science, Engineering
- 2021

### Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow

- Computer ScienceIEEE Transactions on Power Systems
- 2021

A novel approach that combines deep learning and robust optimization techniques is proposed that predicts directly the SCOPF implementable solution and results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%.

### Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow

- Computer ScienceArXiv
- 2020

A novel approach that combines deep learning and robust optimization techniques is proposed that predicts directly the SCOPF implementable solution and results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%.

### Learning to Solve the AC-OPF Using Sensitivity-Informed Deep Neural Networks

- Computer ScienceIEEE Transactions on Power Systems
- 2022

Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.

### Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks

- Computer Science2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- 2020

Numerical tests showcase that sensitivity-informed deep learning can enhance prediction accuracy in terms of mean square error (MSE) by 2-3 orders of magnitude at minimal computational overhead.

### DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

- Computer ScienceIEEE Systems Journal
- 2022

An efficient Deep Neural Network approach, DeepOPF, is developed to ensure the feasibility of the generated solution of the AC-OPF problem, by employing a penalty approach in training the DNN.

### Fast DC Optimal Power Flow Based on Deep Convolutional Neural Network

- Engineering2022 IEEE 5th International Electrical and Energy Conference (CIEEC)
- 2022

The optimal power flow is the cornerstone of the operation and management of electric power systems. However, the stochastic and intermittent uncertainty due to the proliferation of renewable energy…

### Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings

- EngineeringArXiv
- 2020

This approach constitutes the first learning-based approach that successfully respects the full non-linear AC-OPF equations and reports a 12x increase in speed and a 40% increase in robustness compared to a traditional solver.

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