The rapid electrification of transportation, driven by stringent
decarbonization targets and supportive policies, poses significant challenges
for distribution system operators (DSOs). When numerous electric vehicles (EVs)
charge concurrently, local transformers risk overloading – a problem that
current tariff-based strategies do not adequately address. This paper
introduces an aggregator-based coordination mechanism that shifts EV charging
from congested to underutilized periods using a rule-based scheduling
algorithm. Unlike conventional methods that depend on complex real-time pricing
signals or optimization-heavy solutions, the aggregator approach uses a simple
yet effective “laxity” measure to prioritize charging flexibility. To assess
technical and economic viability, a multi-agent simulation was developed to
replicate residential user behavior and DSO constraints under the use of a 400
kVA low-voltage transformer. The results indicate that overloads are completely
eliminated with minimal inconvenience to users, whose increased charging costs
are offset by the aggregator at an annual total of under DKK 6000 –
significantly lower than the cost of infrastructure reinforcement. This study
contributes by (i) quantifying the compensation needed to prevent large-scale
overloads, (ii) presenting a replicable, computationally feasible, rule-based
aggregator model for DSOs, and (iii) comparing aggregator solutions to costly
transformer upgrades, underscoring the aggregator’s role as a viable tool for
future distribution systems.

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