New technologies and methodologies for smart grid applications cannot be tested in the real power grid, since it is a safety-critical infrastructure, therefore simulation and co-simulation is utilized. Simulation models itself can rely on quite complex calculations and therefore slow down the simulation. But even less complex models can lead to performance issues when used in large numbers in large-scale setups. The use of surrogate models is one way to improve the performance of simulation systems when the simulation models are slow, but the performance gain diminishes, when the simulation models are already quite fast. This abstract presents a new PhD project, which proposes a method to combine several simulation models into one surrogate model using correlations and other interdependencies of the simulation models. The goal is to further improve the performance gain not only for slower, but also for less complex simulation models, thus enable even larger simulation setups.

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.

The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems. In a recent work, we built a surrogate model for a low voltage grid using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these questions. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.

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Surrogate models have proved to be a suitable replacement for complex simulation models in various applications. Runtime considerations, complexity reduction, and privacy concerns play a role in the decision to use a surrogate model. The choice of an appropriate surrogate model though is often tedious and largely dependent on the individual model properties. A tool can help to facilitate this process. To this end, we present a surrogate modeling process supporting tool that simplifies the process of generation and application of surrogate models in a co-simulation framework. We evaluate the tool in our application context, energy system co-simulation, and apply it to different simulation models from that domain with a focus on decentralized energy units.