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With the proliferation of electric vehicles, the electrical distributiongrids are more prone to overloads. In this paper, we study an intelligentpricing and power control mechanism based on contextual bandits to provideincentives for distributing charging load and preventing network failure. Thepresented work combines the microscopic mobility simulator SUMO with electricnetwork simulator SIMONA and thus produces reliable electrical distributionload values. Our experiments are carefully conducted under realistic conditionsand reveal that conditional bandit learning outperforms context-freereinforcement learning algorithms and our approach is suitable for the givenproblem. As reinforcement learning algorithms can be adapted rapidly to includenew information we assume these to be suitable as part of a holistic trafficcontrol scenario.

Charging control of electric vehicles using contextual bandits considering the electrical distribution grid