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Recent proliferation of electric vehicle (EV) charging events has broughtprominent stress over power grid operation. Due to the stochastic and volatileEV charging behaviors, the induced charging loads are extremely uncertain,posing modeling and control challenges for grid operators and chargingmanagement. Generating EV charging scenarios would aid via synthesizing amyriad of realistic charging scenarios. To this end, we propose a noveldenoising Diffusion-based Charging scenario generation model DiffCharge, whichis capable of generating a broad variety of realistic EV charging profiles withdistinctive temporal properties. It is able to progressively convert the simplyknown Gaussian noise to genuine charging time-series data, by learning aparameterized reversal of a forward diffusion process. Besides, we leverage themulti-head self-attention and prior conditions to capture the temporalcorrelations and unique information associated with EV or charging stationtypes in real charging profiles. Moreover, We demonstrate the superiority ofDiffCharge on extensive real-world charging datasets, as well as the efficacyon EV integration in power distribution grids.

DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model