Defining a synthetic data generator for realistic electric vehicle charging sessions
This provides realistic synthetic data for EV charging analysis, addressing a domain-specific bottleneck for researchers and practitioners in energy grid management.
The researchers tackled the lack of publicly available electric vehicle (EV) charging session data by developing a synthetic data generator (SDG) that models inter-arrival times with an exponential distribution and connection times with Gaussian mixture models, resulting in realistic synthetic data trained on a large real-world dataset.
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.