  As the global energy landscape shifts towards renewable energy and the
electrification of the transport and heating sectors, national energy systems
will include more controllable prosumers. Many future scenarios contain
millions of such prosumers with individualistic behavior. This poses a problem
for energy system modelers. Memory and runtime limitations often make it
impossible to model each prosumer individually. In these cases, it is necessary
to model the prosumers with representatives or in aggregated form. Existing
literature offers various aggregation methods, each with strengths, drawbacks,
and an inherent modeling error. It is difficult to evaluate which of these
methods perform best. Each paper presenting a new aggregation method usually
includes a performance evaluation. However, what is missing is a direct
comparison on the same benchmark, preferably by a neutral third party that is
not associated with any of the compared methods. This paper addresses this gap
by introducing a benchmark to evaluate the end-to-end performance of multiple
aggregation-disaggregation pipelines, specifically focusing on electric
vehicles (EVs). Our study assesses the performance of the common representative
profile (REP) approach, four different versions of the virtual battery (VB)
approach, the Flex Object (FO) method, and the Dependency-based FlexOffer (DFO)
method. The results show that each method has a clear use case. Depending on
the price signal, additional median charging costs of 2%-50% are incurred using
an aggregation method, compared to the optimal charging costs (i.e., the
charging costs resulting from optimizing the EVs directly, without
aggregation). The representative profile approach results in the lowest
additional costs (2%-20%), while the FO and DFO methods allow for error-free
disaggregation, which is advantageous in real-world use cases.
