  Recent proliferation of electric vehicle (EV) charging events has brought
prominent stress over power grid operation. Due to the stochastic and volatile
EV charging behaviors, the induced charging loads are extremely uncertain,
posing modeling and control challenges for grid operators and charging
management. Generating EV charging scenarios would aid via synthesizing a
myriad of realistic charging scenarios. To this end, we propose a novel
denoising Diffusion-based Charging scenario generation model DiffCharge, which
is capable of generating a broad variety of realistic EV charging profiles with
distinctive temporal properties. It is able to progressively convert the simply
known Gaussian noise to genuine charging time-series data, by learning a
parameterized reversal of a forward diffusion process. Besides, we leverage the
multi-head self-attention and prior conditions to capture the temporal
correlations and unique information associated with EV or charging station
types in real charging profiles. Moreover, We demonstrate the superiority of
DiffCharge on extensive real-world charging datasets, as well as the efficacy
on EV integration in power distribution grids.
