<p>SimStadt is an urban simulation tool in development during various research projects since 2015. Up to now the potential of photovoltaic, solar thermal energy and building heating demands can be assessed at the level of individual buildings. SimStadt uses the City Geography Mark-up Language (CityGML), which describes 3D urban building models.</p>
<p>Work has been conducted to extend SimStadt tool with a new FWE assessment extension. This work introduces a new workflow, which evaluates regional bioenergy potentials and its impact on water demand based on geographical information system (GIS)-based land use data, satellite maps on local crop types and soil types, and conversion factors from biomass to bioenergy. The actual annual biomass yield of crops is assessed through an automated process considering the factors of local climate, crop type, soil, and irrigation. The crop biomass yields are validated with historic statistical data, with deviation less than 7% in most cases. Additionally, the resulting bioenergy potentials yield between 10.7 and 12.0 GWh/ha compared with 13.3 GWh/ha from other studies. </p>
<p>Set of input and resulting maps for Marbach, Ludwigsburg county, Baden-Württemberg, is shown below. (a) Digital landscape model (DLM) map in polygons with land use; (b) satellite map in raster with crop type; (c) soil map; and (d) overlay of (a) and (b).</p>
<p>For the newly established workflow on regional bioenergy potentials, most of the predefined modules are not applicable due to the fact that the input data is land use polygons instead of building geometries, the exception being the import module that can read CityGML files regardless of the type of objects (building or land use polygon) and the weather model that imports the meteorological data in TMY3 format generated by Meteonorm for the specific region in hourly or monthly resolution. The meteorological data are stored in SimStadt and can be called in later steps. </p>
<p>To model bioenergy potentials more accurately than by using static values for all crops, a new module “YieldGenerator” was developed. Another module, “BiomassProcessor” then processes all land use polygons. Users can modify parameters, such as the annual forest wood energetic use rate, the share of energy crops such as corn and rapeseed that are actually used for energetic purposes, or the grass land energy usage rate. Further input parameters can also be imported from an XML configuration file step. The module analyses each land field polygon, tagged with a certain type of vegetation and soil. Therefore, the module was able to find the corresponding biomass yield of the crop on the soil, the possible bioenergy usages and bioenergy conversion coefficient from the XML configuration file. It then calculated the corresponding technical bioenergy energy potential, with the output being exported to a CSV file.</p>
<p>In Fig. 4, a small area of county Ludwigsburg, Marbach, was visualized with 3D buildings and satellite map. The red area represents area with very long biomass density, either with buildings, roads or water bodies. Each polygon has homogeneous density over all area because a polygon is assumed to be covered by one type of crop. Except for red build-up area, road and river, the only vegetation cover land type is farming land in this map. Different colours represent different biomass potential brought by different crop types.</p>
<p>A small area of county Ludwigsburg, Marbach, was visualized with 3D buildings and satellite map. The red area represents area with very long biomass density, either with buildings, roads or water bodies. Each polygon has homogeneous density over all area because a polygon is assumed to be covered by one type of crop. Except for red build-up area, road and river, the only vegetation cover land type is farming land in this map. Different colours represent different biomass potential brought by different crop types.</p>
<p>The detailed descripotion and findings of this workflow can be find in the following two open-sourced papers, which are funded by IN-Source project.</p>
<p>Bao, K.; Padsala, R.; Coors, V.; Thrän, D.; Schröter, B. A Method for Assessing Regional Bioenergy Potentials Based on GIS Data and a Dynamic Yield Simulation Model. Energies 2020, 13, 6488. DOI: https://doi.org/10.3390/en13246488</p>
<p>Bao K, Padsala R, Coors V, Thrän D, Schröter B (2020): GIS-Based Assessment of Regional Biomass Potentials at the Example of Two Counties in Germany. In European Biomass Conference and Exhibition Proceedings, pp. 77–85. DOI: 10.5071/28thEUBCE2020-1CV.4.15.</p>
<p>Bao, K.; Padsala, R.; Coors, V.; Thrän, D.; Schröter, B. A Method for Assessing Regional Bioenergy Potentials Based on GIS Data and a Dynamic Yield Simulation Model. Energies 2020, 13, 6488. DOI: https://doi.org/10.3390/en13246488</p>
<p>Bao K, Padsala R, Coors V, Thrän D, Schröter B (2020): GIS-Based Assessment of Regional Biomass Potentials at the Example of Two Counties in Germany. In European Biomass Conference and Exhibition Proceedings, pp. 77–85. DOI: 10.5071/28thEUBCE2020-1CV.4.15.</p>
