Schedler, SteffenSteffenSchedlerMeilinger, StefanieStefanieMeilingerClees, TanjaTanjaClees2024-01-302024-01-302023https://publica.fraunhofer.de/handle/publica/459460A building's energy storage demand depends on a variety of factors related to the specific local conditions such as building type, self-sufficiency-rate and grid connection. Here we present a method to classify buildings of an urban building portfolio according to these criteria, in a newly developed bottom-up procedure. The algorithm uses publicly available building data such as building use, ground floor area, ridge height, solar roof potential, and population statistics. In addition, it considers the local gas grid (GG) as well as the district heating (DH) network. The building classification is developed for identifying typical building situations that can be used in a defined scenario to estimate the demand for residential energy storage capacity. In our case, the algorithm shall identify potential implementation of private photovoltaic(PV)-metal-hydride-storage(MHS) systems, for three scenarios, into the urban infrastructure for the city of Cologne. Since the data sets used are available for many German or European metropolitan areas, the method developed with the assumptions presented in this work, can be used for classification of other urban and semi-urban areas including the assessment of their grid infrastructure.enpresentation