Abstract
Surrogate models are used to reduce the computational effort required to simulate complex systems. The power grid can be considered as such a complex system with a large number of interdependent inputs. With artificial neural networks and deep learning, it is possible to build high-dimensional approximation models. However, a large data set is also required for the training process. This paper presents an approach to sample input data and create a deep learning surrogate model for a low voltage grid. Challenges are discussed and the model is evaluated under different conditions. The results show that the model performs well from a machine learning point of view, but has domain-specific weaknesses.
Highlights
As a safety-critical infrastructure, the power grid is not suitable for testing new algorithms or other technologies
This paper presents an approach to sample input data and create a deep learning surrogate model for a low voltage grid
This paper proposes to replace whole low voltage (LV) grids with a single surrogate model built with a deep neural network
Summary
As a safety-critical infrastructure, the power grid is not suitable for testing new algorithms or other technologies. The open-source tool pandapower (Thurner et al 2018), a joint development of the University of Kassel and Fraunhofer Institute for Energy Economics and Energy System Technology, provides steady state power system modeling, analysis and optimization with load flow calculations and several predefined benchmark grids. The authors used the frequency dependent network equivalent method to build a generic model for real time dynamic simulation. Their model achieves high accuracy in several simulations. The authors aim to provide a comparison regarding accuracy and performance, and regarding authenticity and robustness Both works present promising results and especially for large-scale simulation it is important to reduce the time for PF calculations and increase their robustness.
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