Quantifying the energy supply potential of company fleets as part of implementing bidirectional charging systems for electric vehicles is crucial for future grid planning. Bidirectional charging has two key advantages. Firstly, it allows optimised load control and, secondly, it enables the provision of electrical energy e.g. back to the power grid. The introduction of the technology thus presents a significant opportunity for companies with large vehicle fleets, such as bus operators, city cleaning services, and logistics companies, to optimise their energy demand from the grid and provide ancillary services. Especially fleet operators with scheduled routes and repetitive charging patterns offer charging windows where the time that an electric vehicle is plugged into a charging station can be used to shift, as well as reduce the electrical load, or even to discharge the vehicle and still fulfil the requirements for the next trip. Furthermore, in the event of a power outage, energy from a fleet could be made available in order to ensure the continued functionality of critical infrastructure.

To realise the potential, profiles of company fleets of the total energy stored and the energy that can be released at different times without affecting the normal operation of the company are required. This information is not directly accessible and must be calculated or estimated using data from different systems.

Therefore, the study presents an analysis of the charging behaviour observed among different fleet operators, in addition to a methodology for calculating the available energy profile over a period of one week, with a precision of 15 minute intervals. Open Charge Point Protocol (OCPP) data from a Charge Point Management System (CPMS) combined with data from a fleet management system is used to develop the energy supply profiles. Four divergent information states are taken into account. In the simplest case, a charging process is carried out at a DC charging point, providing information on the vehicle's state of charge. The amount of energy stored can then be calculated at any time using the vehicle's authentication token or the vehicle's battery information stored in the fleet management system. However, if the charging process takes place at an AC charging point where no information about the state of charge is transmitted and no assignment to the vehicle or battery capacity is possible, a method based on historical charging patterns is required. To validate the developed methodology, data from three different fleet operator types are analysed, the different information states are evaluated and the possible inaccuracy of the individual methods is assessed.

The aim of the study is to provide grid planners, companies, and policymakers with a method to evaluate the potential of company fleets for grid services. The realization of a smart grid aims to maximize electromobility potential, promote data-based decision-making, and make grid expansion more efficient. The study offers valuable insights into integrating electric vehicles into the energy system and their role in the energy transition.