Modeling Curtailment in Germany: How Spatial Resolution Impacts Line Congestion

TL;DR

This study examines how spatial aggregation in energy system models affects the accuracy of simulated renewable energy curtailment and network congestion in Germany, highlighting the trade-offs between model resolution and computational efficiency.

Contribution

It demonstrates that reducing network resolution can still accurately capture curtailment effects while improving computational performance, and proposes a measure for assessing spatial assignment errors.

Findings

High network resolution overestimates curtailment due to demand and capacity misallocation.

Reducing network nodes decreases congestion and curtailment estimates.

A new measure helps identify optimal spatial resolution for modeling.

Abstract

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.