# DeepOPF: Deep Neural Network for DC Optimal Power Flow

@article{Pan2019DeepOPFDN, title={DeepOPF: Deep Neural Network for DC Optimal Power Flow}, author={Xiang Pan and Tianyu Zhao and Minghua Chen}, journal={2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)}, year={2019}, pages={1-6} }

We develop DeepOPF as a Deep Neural Network (DNN) based approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions. We construct and train a DNN model to learn such mapping, then we apply it to obtain optimized operating decisions upon arbitrary load inputs. We adopt…

## 64 Citations

### DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow

- Computer ScienceIEEE Transactions on Power Systems
- 2021

DeepOPF is inspired by the observation that solving SC-DCOPF problems for a given power network is equivalent to depicting a high-dimensional mapping from the load inputs to the generation and phase angle outputs and develops a post-processing procedure based on $\ell _1$-projection to ensure the feasibility of the obtained solution.

### DeepOPF: deep neural networks for optimal power flow

- Computer Science
- 2021

DeepOPF leverages a DNN model to depict the high-dimensional load-to-solution mapping and can directly solve the OPF problem upon given load, excelling in fast computation process and desirable scalability.

### DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility

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

The proposed DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes and theoretically characterize the calibration magnitude necessary for ensuring universal feasibility.

### 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.

### 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.

### 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.

### Model Information-Aided Deep Learning for Corrective AC Power flow

- Computer Science, Engineering2022 IEEE Power & Energy Society General Meeting (PESGM)
- 2022

A novel model information-aided deep learning approach to approximate the corrective AC optimal power flow solution with proper model information with better performances in both training time and accuracy is presented.

### A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

- Computer ScienceIEEE Open Access Journal of Power and Energy
- 2022

This work proposes a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach that achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques.

### Projection-aware Deep Neural Network for DC Optimal Power Flow Without Constraint Violations

- Computer Science, Engineering2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- 2022

This is the first paper that guarantees no constraint violations of DC optimal power flow using deep neural network, and the proposed PA-DNN takes active power demand as an input and has a projection layer at the final layer.

### DeepOPF-V: Solving AC-OPF Problems Efficiently

- EngineeringIEEE Transactions on Power Systems
- 2022

A deep neural network-based voltage-constrained approach to solve AC optimal power flow problems with a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.

## References

SHOWING 1-10 OF 44 REFERENCES

### Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning

- Computer ScienceIEEE Transactions on Cybernetics
- 2020

We show that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model predictive control…

### Statistical Learning for DC Optimal Power Flow

- Computer Science2018 Power Systems Computation Conference (PSCC)
- 2018

An ensemble control policy is proposed that combines several basis policies to improve performance and is based on the observation that the OPF solution corresponding to a certain uncertainty realization is a basic feasible solution, which provides an affine control policy.

### On Computational Issues of Market-Based Optimal Power Flow

- EngineeringIEEE Transactions on Power Systems
- 2007

The deregulated electricity market calls for robust optimal power flow (OPF) tools that can provide a) deterministic convergence; b) accurate computation of nodal prices; c) support of both smooth…

### Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

- Mathematics
- 2017

### Optimal power flow: a bibliographic survey II

- Engineering
- 2012

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of…

### Optimal power flow: a bibliographic survey I

- Engineering
- 2012

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of…

### Learning for Convex Optimization

- Computer Science
- 2018

This work considers the problem of using the information available through this solution process to directly learn the optimal solution as a function of the input parameters, thus reducing the need of solving computationally expensive large-scale parametric programs in real time.

### A generalized quadratic-based model for optimal power flow

- EngineeringConference Proceedings., IEEE International Conference on Systems, Man and Cybernetics
- 1989

A quadratic-based model of a power system is used to formulate a generalized optimum power system flow problem and the generalized algorithm using sensitivity of objective functions with optimal adjustments in the constraints yields a global optimal solution.

### Improved genetic algorithms for optimal power flow under both normal and contingent operation states

- Engineering
- 1997

### A Knowledge-Based Framework for Power Flow and Optimal Power Flow Analyses

- EngineeringIEEE Transactions on Smart Grid
- 2018

A knowledge-based paradigm for PF and OPF analyses is used to extract complex features, hidden relationships, and useful hypotheses potentially describing regularities in the problem solutions from operation data-sets to reduce the complexity of power flow and optimal power flow problems.