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The rapid electrification of transportation, driven by stringentdecarbonization targets and supportive policies, poses significant challengesfor distribution system operators (DSOs). When numerous electric vehicles (EVs)charge concurrently, local transformers risk overloading - a problem thatcurrent tariff-based strategies do not adequately address. This paperintroduces an aggregator-based coordination mechanism that shifts EV chargingfrom congested to underutilized periods using a rule-based schedulingalgorithm. Unlike conventional methods that depend on complex real-time pricingsignals or optimization-heavy solutions, the aggregator approach uses a simpleyet effective "laxity" measure to prioritize charging flexibility. To assesstechnical and economic viability, a multi-agent simulation was developed toreplicate residential user behavior and DSO constraints under the use of a 400kVA low-voltage transformer. The results indicate that overloads are completelyeliminated with minimal inconvenience to users, whose increased charging costsare offset by the aggregator at an annual total of under DKK 6000 -significantly lower than the cost of infrastructure reinforcement. This studycontributes by (i) quantifying the compensation needed to prevent large-scaleoverloads, (ii) presenting a replicable, computationally feasible, rule-basedaggregator model for DSOs, and (iii) comparing aggregator solutions to costlytransformer upgrades, underscoring the aggregator's role as a viable tool forfuture distribution systems.

A Multi-Agent, Laxity-Based Aggregation Strategy for Cost-Effective Electric Vehicle Charging and Local Transformer Overload Prevention