Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets.

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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, 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. CCS CONCEPTS• Mathematics of computing → Probabilistic algorithms; • Computing methodologies → Model development and analysis.

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