  This paper presents a novel physics-informed diffusion model for generating
synthetic net load data, addressing the challenges of data scarcity and privacy
concerns. The proposed framework embeds physical models within denoising
networks, offering a versatile approach that can be readily generalized to
unforeseen scenarios. A conditional denoising neural network is designed to
jointly train the parameters of the transition kernel of the diffusion model
and the parameters of the physics-informed function. Utilizing the real-world
smart meter data from Pecan Street, we validate the proposed method and conduct
a thorough numerical study comparing its performance with state-of-the-art
generative models, including generative adversarial networks, variational
autoencoders, normalizing flows, and a well calibrated baseline diffusion
model. A comprehensive set of evaluation metrics is used to assess the accuracy
and diversity of the generated synthetic net load data. The numerical study
results demonstrate that the proposed physics-informed diffusion model
outperforms state-of-the-art models across all quantitative metrics, yielding
at least 20% improvement.
