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

@article{Pan2020DeepOPFAF, title={DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems}, author={Xiang Pan and Minghua Chen and Tianyu Zhao and Steven H. Low}, journal={ArXiv}, year={2020}, volume={abs/2007.01002} }

The AC-OPF problem is the key and challenging problem in the power system operation. When solving the AC-OPF problem, the feasibility issue is critical. In this paper, we develop an efficient Deep Neural Network (DNN) approach, DeepOPF, to ensure the feasibility of the generated solution. The idea is to train a DNN model to predict a set of independent operating variables, and then to directly compute the remaining dependable variables by solving the AC power flow equations. While this…

## 19 Citations

### 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.

### 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.

### 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-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.

### 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.

### Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads

- Computer ScienceArXiv
- 2022

An AC-OPF formulation-ready dataset called TAS-97 is constructed that contains realistic network information and realistic bus loads from Tasmania’s electricity network and it is found that the realistic loads in Tasmania are correlated between buses and they show signs of an underlying multivariate normal distribution.

### Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions

- Computer Science
- 2020

A neural network is trained to emulate an iterative solver in order to cheaply and approximately iterate towards the optimum, and it is shown that the proposed method can solve “difﬁcult” AC OPF solutions that cause DC-warm started algorithms to diverge.

### Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems

- Computer ScienceArXiv
- 2021

A “preventive learning” framework to systematically guarantee DNN solution feasibility for problems with convex constraints and general objective functions, and proposes a new Adversary-Sample Aware training algorithm to improve DNN’s optimality performance without sacrificing feasibility guarantee.

### Teaching Networks to Solve Optimization Problems

- Computer ScienceArXiv
- 2022

This paper proposes to replace the iterative solvers altogether with a trainable parametric set function that outputs the optimal arguments/parameters of an optimization problem in a single feed-forward.

### Security Enhancement in Power System Using FACTS Devices and Atom Search Optimization Algorithm

- EngineeringEAI Endorsed Trans. Energy Web
- 2021

The Atom Search Algorithm (ASO) is utilized to compute the precise optimal placement of the FACTS device and the performances are evaluated by severity index, Line Overload Sensitivity Index (LOSI), voltage, voltage deviation, power loss, fitness function, and the fuel cost.

## References

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### 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: Deep Neural Network for DC Optimal Power Flow

- Computer Science2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
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Simulation results of IEEE test cases show that DeepOPF always generates feasible solutions with negligible optimality loss, while speeding up the computing time by two orders of magnitude as compared to conventional approaches implemented in a state-of-the-art solver.

### 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.

### 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.

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A deep learning approach to the Optimal Power Flow problem that exploits the information available in the prior states of the system, as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF.

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- 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%.

### 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.

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

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- 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.

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A neural network that predicts the binding status of constraints of the system is used to generate an initial reduced OPF problem, defined by removing the predicted non-binding constraints, which leads to a classifier that significantly outperforms conventional loss functions used to train neural network classifiers.

### Hot-Starting the Ac Power Flow with Convolutional Neural Networks

- Computer ScienceArXiv
- 2020

This paper proposes a framework to obtain the initial bus voltage magnitude and phase values that decrease the solution iterations and time for the NR based ACPF model, using the dc power flow results and one dimensional convolutional neural networks (1D CNNs).