  This paper investigates the effects of network constraints in energy system
models at transmission level on renewable energy generation and curtailment as
the network is being spatially aggregated. We seek to reproduce historically
measured curtailment in Germany for the years 2013-2018 using an open model of
the transmission system, PyPSA-Eur. Our simulations include spatial and
temporal considerations, including congestion per line as well as curtailment
per control zone and quarter. Results indicate that curtailment at high network
resolution is significantly overestimated due to inaccurate allocation of
electricity demand and renewable capacities to overloaded sites. However, high
congestion rates of the transmission network decrease as the network is
clustered to a smaller number of nodes, thus reducing curtailment. A measure to
capture errors in the assignment of electricity demand and power plants is
defined and hints towards a preferable spatial resolution. Thus, we are able to
balance the effects of accurate node assignment and network congestion
revealing that a reduced model can capture curtailment from recent historical
data. This shows that it is possible to reduce the network to improve
computation times and capture the most important effects of network constraints
on variable renewable energy feed-in at the same time.
