Urban Energy Systems
Our vision is the intelligent management of all energy flows within a city: A dynamic and holistic urban energy system.
Against the background of energy policy objectives, changing energy markets, increasing urbanization and stronger networking, we increasingly understand the city as an urban energy system. This view includes not only a collection of individual buildings, distribution networks and power plants, but all the processes, energy flows and facilities necessary to provide different energy services in an urban environment. As the needs for energy services such as space heating, air-conditioning, lighting, transport and communication are increasingly concentrated in urban areas, the description, analysis and optimization of urban energy systems is becoming increasingly important. In this context, the Urban Energy Systems team (UES) is working on current research questions to help municipalities, planning offices, energy supply companies and, last but not least, residents of cities to plan and operate urban energy systems more efficiently and economically.
To this end, the UES team develops database systems and graph models to describe urban energy systems, integral planning tools for analysis and optimization of the entire system, cloud solutions for the networking of different subsystems as well as approaches for visualizing energy flows within these complex systems. Thematic focus is on the role of buildings as well as heating and cooling networks and their potentials for increasing efficiency, storing energy and relieving the electrical networks as well as their interaction with other subsystems in an urban context. For this purpose, the UES team develops, among other things, dynamic component and system models for buildings and networks in the equations-based modeling language Modelica as well as mixed-integer optimization models in Python. In order to reduce the manual effort to a large extent, methods for automated modeling are developed, which allow dynamic system models and efficient optimization models to be created on a common data base in combination with databases and geographic information systems.