• Corpus ID: 226277979

Deep learning architectures for inference of AC-OPF solutions

@article{Falconer2020DeepLA,
  title={Deep learning architectures for inference of AC-OPF solutions},
  author={Thomas Falconer and Letif Mones},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03352}
}
We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain, for both convolutional and graph NNs. The performance of the NN architectures is compared for regression (predicting optimal generator set-points) and classification (predicting the active set… 
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References

SHOWING 1-10 OF 23 REFERENCES

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.

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.

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).

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

Stochastic AC Optimal Power Flow: A Data-Driven Approach

The climate mitigation opportunity behind global power transmission and distribution

Inefficient transmission and distribution (T&D) infrastructure that results in losses as electricity travels from supplier to customer contributes to compensatory power generation and therefore to

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.

Meta-Optimization of Optimal Power Flow

A meta-optimizer is proposed that is used to initialize interior-point solvers and can significantly reduce the number of iterations to converge to optimality.

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.

Environmental/economic dispatch incorporating renewable energy sources and plug-in vehicles

Transportation and electricity industries are considered as major sources of greenhouse gases (GHGs) emission. Different methods have been proposed to deal with the increasing rate of the emission,