  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, a surrogate model for a low
voltage grid was built 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 question. 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.
