# DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model

arXiv: https://arxiv.org/abs/2308.09857 · HTML: https://mlsanity.com/paper/2308.09857/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2023.

## Built on by (most cited first, 12 of 12)

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models](https://mlsanity.com/paper/2407.13538/) (2024)
- [Generating Synthetic Net Load Data with Physics-informed Diffusion Model](https://mlsanity.com/paper/2406.01913/) (2024)
- [CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios](https://mlsanity.com/paper/2501.14426/) (2025)
- [Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models](https://mlsanity.com/paper/2311.07110/) (2023)
- [Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets](https://mlsanity.com/paper/2506.11281/) (2025)
- [Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging](https://mlsanity.com/paper/2404.10311/) (2024)
- [Fr'echet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids](https://mlsanity.com/paper/2505.08082/) (2025)
- [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024)
- [Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection](https://mlsanity.com/paper/2504.14300/) (2025)
- [Coherent Load Profile Synthesis with Conditional Diffusion for LV Distribution Network Scenario Generation](https://mlsanity.com/paper/2510.12832/) (2025)
- [An LLM-Enabled Frequency-Aware Flow Diffusion Model for Natural-Language-Guided Power System Scenario Generation](https://mlsanity.com/paper/2602.19522/) (2026)

## Abstract (arXiv)

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.