How to integrate electric vehicle into urban energy system? - the data structure for integrating urban building, electric vehicles, and land use with linked data approach

Urban energy modelling has been widely used in environmental science, energy policy, and urban planning research. Many studies divide the urban energy model into two categories: urban building energy model (UBEM) and urban transportation and land use energy model (UTEM). UBEM and UTEM focus on simulating and calculating energy consumption in the building and transportation sectors and providing insights for planning respectively. Future energy systems will be more volatile and less controllable with more decentralized electricity generation. Furthermore, the dramatically increased penetration rate of EVs (electric vehicles) will lead a further shift of energy demand. It appears that urban energy modelling practices often focus on specific aspects of energy use so far. EVs have not been well embedded into the urban energy system. It relates to the obstacle of exogenous input data and the shortage of high-quality and quantity data structures. Shared and structured ontologies for urban energy models are lacking so far. The purpose of the research is to explore and summarize the data input of the existing urban energy models and related ontologies, to provide data structure support for developing urban energy ontologies for EVs. The study first summarizes the data input of UBEM. Then the ontologies of EVs and smart grids are concluded to explore the data structure of EVs on the energy models of buildings. Subsequently, the study uses EVs as the linkage between buildings and transport. Lastly, the activity-based model has been utilized to connect the above sectors by distinguishing individual activities with high resolution. In general, the research proposes a framework for integrating electric vehicles in the urban energy system to connect buildings and transport sectors and employing activity-based models to differentiate buildings and transport activities using linked data approach.