FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging Energy Flexibility

Abstract

Electric vehicles (EVs) and renewable energy sources (RES) are vital components of sustainable energy systems, yet their uncoordinated integration can pose substantial challenges to grid stability, such as unmanaged peak loads and energy balance issues. Vehicle-to-Grid (V2G), offer a promising solution to address these challenges by enabling bidirectional energy flow between EVs and the grid. As such, EVs can be used in advances Demand Response (DR) strategies to optimize energy use and mitigate the intermittency of renewable generation. To reach such advantages, optimization algorithms need data on EV energy flexibility, such as charging patterns and usage preferences. However, data collection remains constrained by challenges such as high costs, user engagement, data privacy concerns, and limited access to open-source datasets on EV energy flexibility. This paper presents FlexiGen an open-source stochastic dataset generator tool designed to overcome the data limitations in EV flexibility for V2G and V1G DR applications. FlexiGen generates synthetic datasets encompassing realist EV usage patterns, behaviours and flexibility scenarios for household and office routines. To generate these datasets, FlexiGen uses a series of configurable probabilistic variables, such as stochastic user routines, traffic conditions, charger types, car average electricity consumption and State of Charge (SoC). The generated datasets include an hourly routine with the EV State of connection, Destination Charger, Estimated Departure Time, Required SOC at Departure, Estimated Arrival Time, and Estimated SOC at Arrival. Accompanying this publication an example dataset is generated for 3 households with 1 EV each, and 1 office building with 3 EVs. The generated dataset is analyzed and discussed on the paper and published alongside the open-source code for FlexiGen tool.