How To Level Up Districts In Bus Simulator 21
Alke Stilwell
The importance of climate protection and sustainability is steadily increasing all over the world. However, there is a large potential for reducing emissions in the heating demand reduction and renewable heat supply of buildings that needs to be addressed. Therefore, a method was developed within the scope of this work that allows local decision-makers such as energy supply companies, project developers and the public sector to calculate, evaluate and compare different scenarios to make buildings and city districts more sustainable based on few and widely available input data. It includes both the determination of the heat demand and measures for its reduction as well as the selection and simulation of centralised and decentralised supply systems. A combination of different methods from the fields of geoinformatics, heuristic decision-making and object-oriented modelling is used. The latter forms a focal point in the work with the development of a data model for energy system components to enable automatic simulation. The applicability as well as the transferability of the method is shown in several case studies. Based on the simulations results, which can be related to CO2 emissions as well as costs, recommendations for the implementation of measures can be given and implemented.
Anthropogenic climate change and the associated increase in temperature worldwide is now recognised by most governments around the world and is seen as one of the greatest challenges for humanity in the coming decades. In recent years, targets have been set worldwide for the reduction of greenhouse gas emissions and the use of fossil fuels, as well as for increasing energy efficiency. In order to achieve, as committed by the European Union and the Federal State of Germany, CO2 emission reductions of 65% compared to 1990 until 2030 and become climate-neutral by 2045 (European Comission 2019; Bundesministerium fuer Umwelt Naturschutz nukleare Sicherheit 2019; Die Bundesregierung 2021) the implementation of measures in form of laws, ordinances and subsidies must be further enforced and incentivized, especially in the building sector. The German goal to have a nearly climate-neutral building stock by 2050 can only be achieved by significantly increasing the refurbishment rate and therefore reducing the heating demand in buildings. In relation to the entire building stock, the heating demand has the largest share of the building energy demand in Germany (Bundesministerium fr Wirtschaft Energie 2019).
In order to be able to find and evaluate suitable measures and pathways to achieve the ambitious goals, suitable energy modelling tools are needed. In recent years, there have been numerous and diverse approaches to the development of urban simulation tools and methods (Li et al. 2017; Sola et al. 2018; Allegrini et al. 2015). They can be classified in top-down and bottom-up approaches. According to Swan and Ugursal (Swan and Ugursal 2009), top-down approaches calculate the energy demand in buildings based on indicators such as energy price, climate and gross domestic product. Bottom-up approaches however focus on the energy demand of individual buildings and scale it up to represent e.g. a whole city or country. This approach can be further divided into physical and statistical methods. Statistical methods can be based on measurement data, surveys or data from energy suppliers. For detailed analysis, a very large amount and ideally high temporal and spatial resolution of these data is needed (Fumo and Rafe Biswas 2015; Beccali et al. 2004). In the physical bottom-up method, buildings are evaluated based on their physical properties and interactions with the environment, e.g. through solar radiation. Various data sets are needed for the physical simulation (Reinhart and Cerezo Davila 2016):
Most of the available tools and methods for urban building energy modelling must be individually adapted to the respective application and analysis. For this, the user needs a high degree of knowledge about the tool itself, which often includes more than basic knowledge of programming with the respective programming language, as well as domain knowledge in the context of urban building energy simulation. In addition, a lot of data and information about the use case is required, which can often only be obtained in a time-consuming manner or is not available at all and must therefore be replaced by assumptions and/or archetypes. Alternatively, there are calculation methods that provide results for energy demand and supply based on simple indicators or highly simplified calculations and assumptions. They do not, or only insufficiently, take into account the individual circumstances of the neighbourhood under investigation. Mostly they do not deal with individual buildings, but create aggregated balances for the entire neighbourhood. This means that it is not possible to localise needs and, if necessary, derive spatially differentiated supply solutions. Furthermore, many of the existing tools are strongly oriented towards only one area of urban building energy simulation. They often have their focus either in energy demand simulation and do not, or only in a simplified manner, consider supply simulation, and vice versa.
The following hypothesis can be derived from the research questions: Neighbourhoods have different energy demand profiles. Only through their detailed knowledge with a high spatial resolution is it possible to design and calculate sustainable energy systems. The results of this work can support climate protection managers or other decision makes of municipalities who want to introduce a climate protection concept and therefore need to calculate and compare different scenarios for heat supply. Developers of neighbourhood projects who want to make a suitable selection from the multitude of possible alternatives for implementation should also be addressed.
Concluding from the problem definition and the literature research in the previous chapters, as well as the open research questions, the framework of the required methods and their application is set out. In the following, the methodological procedure is described in detail within the framework of this thesis.
To determine the heat demand of neighbourhoods, the heating and domestic hot water demand must first be calculated for each building. This requires simulation models that automatically determine the demand for many buildings in hourly resolution. For this purpose, 3D building models in CityGML format (Biljecki et al. 2016) for the representation of the building geometry is used and heating and domestic hot water demand simulations are carried out in SimStadt (Weiler et al. 2019) and DHWcalc (Jordan Vajen 2005). Each building in the CityGML model must have the building attributes year of construction and function in order to be simulated in SimStadt. Based on these attributes, specific data from a building physics (based on (Loga et al. 2015)) and an usage library are used for the heat demand simulation e.g. the typical materials of different construction elements of buildings from a certain year or parameters like occupancy density and schedules for different usages like residential, retail or education.
Often, however, the information for these two attributes is not available. This can be due to the lack of documentation altogether or due to data protection regulations which leads to available data not being passed on to third parties. Furthermore, it is possible that data is outdated, contains errors or is incomplete.
Especially in the area of non-residential buildings, there is a large variety in the naming of the building function. For example, the designation of a building used as a supermarket with the attribute commercial is not wrong, yet using the attribute supermarket instead could access more specific data from the libraries.
In order to gather this more detailed data, PointsOfInterest (POI) from the online platform OpenStreetMap (OSM) are investigated and compared to the attributes in the existing CityGML file (Weiler et al. 2018). The level of detail and the actuality of the data from OSM varies, however, in urban areas the information density is rather high (Haklay 2010). The information from both data sets is combined via an intersection of the point with the building floor plan via the geographical location, then the OSM data is fed back to the CityGML file via an SQL database.
To solve the problem of missing information on yearOfConstruction, two different data sources and methods on how to assign the missing yearOfConstruction to the buildings are investigated (Zirak et al. 2019). In order to enrich the CityGML files with data on the yearOfConstruction, information both from the census database of the German Census 2011 and the German residential building typology IWU (Loga et al. 2015) are used. The Census presents data for individual municipalities, whereas the IWU database contains average values for the whole of Germany. Data from the municipality can be used as a reference to compare and evaluate the two other sources.
Based on those sustainable heat supply systems for a zero carbon future, a qualitative methodology was developed in order to select possible supply technologies for sustainable districts based on the individual framework conditions and constraints, which are usually already available at the beginning of the planning process. Possible restrictions for the different supply variants are defined. These can be spatial as well as geological or strategic and legal parameters. The restrictions are defined based on general planning practice and expert knowledge. These framework conditions are queried via a checklist, shown as a flow chart in Fig. 1. It shows a cut-out of the complete flow chart that comprises of a total of 16 different technology options. Here pictured is as an example only the part for district heating and the connection to gas-based technologies.
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