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

@article{Chatzos2020HighFidelityML, title={High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow}, author={Minas Chatzos and Ferdinando Fioretto and Terrence W.K. Mak and Pascal Van Hentenryck}, journal={ArXiv}, year={2020}, volume={abs/2006.16356} }

The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while satisfying the underlying physical and operational constraints. It is non-convex and NP-hard, and computationally challenging for large-scale power systems. Motivated by the increased stochasticity in generation schedules and increasing penetration of renewable sources, this paper explores a deep learning approach to…

## 15 Citations

### 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.

### 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.

### OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets

- Computer Science2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
- 2022

The OPF-Learn package for Julia and Python is developed, which uses a computationally efficient approach to create representative datasets that span a wide spectrum of the AC OPF feasible region, improving machine learning model performance.

### Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

- Computer Science, EngineeringIEEE Control Systems Letters
- 2022

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared…

### A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow

- Computer Science
- 2020

This paper presents a Quasi-Newton method which performs iterative updates for candidate optimal solutions without having to calculate a Jacobian or approximate Jacobian matrix and utilizes a deep neural network with feedback.

### Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

- Engineeringe-Energy
- 2021

This work proposes PROjected Feasibility (PROF), a method to enforce convex operational constraints within neural policies, which incorporates a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible.

### Smart grid dispatch powered by deep learning: a survey

- EngineeringFrontiers of Information Technology & Electronic Engineering
- 2022

Power dispatch is a core problem for smart grid operations. It aims to provide optimal operating points within a transmission network while power demands are changing over space and time. This…

### 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.

### Self-Supervised Primal-Dual Learning for Constrained Optimization

- Computer ScienceArXiv
- 2022

Primal-Dual Learning is proposed, a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference and is remarkably close to the ALM optimization.

### DeepOPF-FT: One Deep Neural Network for Multiple AC-OPF Problems With Flexible Topology

- Computer ScienceIEEE Transactions on Power Systems
- 2023

The idea is to embed the discrete topology representation into the continuous admittance space and train a DNN to learn the mapping from (load, admittance) to the corresponding OPF solution.

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