Input data, models and results supporting the journal article. Quantifying energy transport by electric vehicles: A Monte Carlo and optimization framework for flexible energy communities.
Wat erin zit
This dataset supports a study that quantifies how much energy electric vehicles (EVs) physically carry between locations (home and workplace) and how this transported energy can be exploited by a flexible energy community (FlexEC). It combines computational modelling and simulation: a Monte Carlo model of commuting EVs generates trips, stops, energy consumption, charging sessions and the energy transported between locations (minute resolution, aggregated to 15 minutes), which is then fed into a rule-based FlexEC model and a linear-programming (Pyomo) FlexEC model with economic and hierarchical objectives for smart-charging and vehicle-to-grid (V2G) scenarios. The dataset includes input time series (mobility parameters, building load, PV generation and electricity prices), the Python code of the three modelling steps, and the resulting 15-minute aggregated simulation and optimisation outputs used in the associated journal article (Smart Energy, 2026). Data are provided as CSV/Excel files, code as Python scripts and Jupyter notebooks.
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