• Corpus ID: 238253140

Leveraging power grid topology in machine learning assisted optimal power flow

  title={Leveraging power grid topology in machine learning assisted optimal power flow},
  author={Thomas Falconer and Letif Mones},
—Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although… 

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

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.

Multi-fidelity power flow solver

The results presented herein demonstrate MFNN’s potential and its limits with up to two orders of magnitude faster and more accurate power flow solutions than DC approximation.

Machine Learning for Electricity Market Clearing

—This paper seeks to design a machine learning twin of the optimal power flow (OPF) optimization, which is used in market-clearing procedures by wholesale electricity markets. The motivation for the

Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems

—This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal solutions can be solved with high computational efficiency by the proposed



Optimal Power Flow Using Graph Neural Networks

Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid

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

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.

Machine Learning-Aided Security Constrained Optimal Power Flow

A learning augmented optimization approach is developed to solve the security-constrained optimal power flow (SCOPF) problem, using a multi-input multi-output random forest model to first solve network voltage magnitudes and angles of buses.

Machine Learning for AC Optimal Power Flow

This work presents two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where the optimal generator settings are predicted, and 2) a constraint predictiontask where the set of active constraints in the optimal solution are predicted.

Learning for DC-OPF: Classifying active sets using neural nets

This paper proposes the use of classification algorithms to learn the mapping between the uncertainty realization and the active set of constraints at optimality, thus further enhancing the computational efficiency of the real-time prediction of the optimal power flow.

Graph Neural Networks for Learning Real-Time Prices in Electricity Market

A new graph neural network (GNN) framework for predicting the electricity market prices from solving OPFs innovatively exploits the locality property of prices and introduces physics-aware regularization, while attaining reduced model complexity and fast adaptivity to varying grid topology.

Learning Warm-Start Points For Ac Optimal Power Flow

  • K. Baker
  • Computer Science
    2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
  • 2019
A multi-target approach is utilized to learn approximate voltage and generation solutions to ACOPF problems directly by only using network loads, without the knowledge of other network parameters or the system topology.

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

  • Ahmed S. ZamzamK. Baker
  • Computer Science
    2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
  • 2020
A machine learning approach to optimize the real-time operation of electric power grids finds feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps, resulting in a significant decrease in computational burden for grid operators.

Hot-Starting the Ac Power Flow with Convolutional Neural Networks

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).

Learning an Optimally Reduced Formulation of OPF through Meta-optimization

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.