Defining a synthetic data generator for realistic electric vehicle charging sessions

TL;DR

This paper introduces a synthetic data generator for realistic electric vehicle charging sessions, enabling better analysis and planning in electricity grids despite limited real-world data availability.

Contribution

The paper presents a novel synthetic data generator for EV charging sessions that models inter-arrival times and connection durations using statistical distributions trained on real data.

Findings

Generated data closely matches real-world EV charging patterns

The SDG enables improved analysis of EV load and flexibility

Realistic data supports better grid management strategies

Abstract

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customers, etc. Yet, the limited availability of such EV sessions data hinders further development in these fields. Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions. Our SDG assumes the EV inter-arrival time to follow an exponential distribution. Departure times are modeled by defining a conditional probability density function (pdf) for connection times. This pdf for connection time and required energy is fitted by Gaussian mixture models. Since we train our SDG using a large real-world dataset, its output is realistic.