Considering the increasing usage of electric vehicles, the possibility to utilise their extensive combined energy storage within our power grids to increase system flexibility and reduce emissions is becoming a promising area of research. We present a holistic approach for the optimization of district energy systems considering heating, electricity and mobility. Specifically, this optimization maximizes the self-consumption and minimizes the peak load. This work extends on previous studies by integrating mobility into district energy management, applying stochastic programming methods to a linear optimisation framework, and through using an interface to obtain data from real world vehicles for district energy management. Mobility data is created with a chain of three tools: TAPAS (Travel and Activity PAtterns Simulation) and SUMO (Simulation of Urban MObility) for computing daily trip data (e.g. time, duration, energy consumption), and CHARGIN (Consumer Habits based Approach to model Recharging and Grid INtegration). By using residential presence times from the mobility dataset, we determine electricity and heating load profiles with Modelica. Integrating mobility data with the Load Profile Generator (LPG) enables the creation of synthetic, high-resolution load profiles through agent-based behavior simulation that accurately reflect home occupancy. In order to account for natural behavioural variability, we employ a stochastic programming approach using multi-scenario simulation methods. We show how to include such stochastic programming methods into the linear optimisation framework oemof (open energy modeling framework). The holistic energy systems optimization is done with the tool MTRESS (Model Template for Renewable Energy Supply Systems). This tool manages the charging and discharging flows of the EV batteries for the system optimisation. This scenario-aware method can be utilized to plan energy systems that consider uncertainties associated with varying demand profiles, providing a more robust and flexible approach to energy planning. Another novelty of this work is the integration of data from real electric vehicles in the district energy management system. The sensor data transfer is achieved using a standardised V2X module. We investigate how this data can be used to improve the district energy management system and as a direct input for the simulation models. This district level simulation with integrated energy, heat and mobility energy flows is a stepping stone towards the simulation of a district level energy model.