The potential for generation of renewable energy and subsequent storage in the subsurface has been thoroughly investigated for the state of Schleswig-Holstein in Germany because of its unique geographical and geological properties (Fricke 2018; Nolde et al. 2016a). The main focus of the research is to explore and visualise the transition to renewable energy sources and impacts of the use of the geological subsurface for energy storage.
To support communication between researchers from different domains and facilitate outreach activities to regional stakeholders concerned with energy systems as well as the interested public, a prototype for a comprehensive graphical environmental information system (EIS) has been developed, based on a virtual geographic environment (VGE). This EIS allows to explore complex datasets, investigate the interrelation between datasets, and develop a better understanding and insight on the available options for a transition to renewable energy for regional authorities, businesses, and private households.
As a basis for our application, we use an extensive collection of heterogeneous datasets with large variances in structure, resolution and extent. Categories include raster data, vector data, point data and time series data, but also volume datasets used for modelling and simulation of the subsurface. Spatial datasets have been transformed into the same geographic coordinate system, in this case UTM zone 32N (EPSG:25832), either via geographic projection in QGIS (QGIS Development Team 2009) or via geometric transformation using the OpenGeoSys Data Explorer (Rink et al. 2014).
Topographic frame of reference
To provide context for all subsequent datasets, we first created a georeferenced topographic frame of reference covering the German state of Schleswig-Holstein with a size of \(15,763\,km^2\) as well as ca. \(25,000\,km^2\) of marine area in the German part of the North Sea and—to a much smaller extent—the Baltic Sea (see Fig. 1a). Geometrical data for the region of interest has been imported into the OpenGeoSys Data Explorer to create a triangulated surface (Rink et al. 2013) consisting of 980,000 triangles with an average edge length of \(250\,m\). Node elevation is based on a digital elevation model with a resolution of \(25\,m\) (Landesamt für Vermessung und Geoinformation Schleswig-Holstein 2019b). The state has a flat topography with a maximum difference in elevation of \(160\,m\), which is difficult to notice given the total area of Schleswig-Holstein at over \(15,000\,km^2\). To increase the effect of a 3D surface, it is optionally possible to superelevate the surface by applying a scaling factor to the z-components of mesh nodes. Displaying the correct surface elevation is required in the context of subsurface data added to the scene, such as geological layers and simulation results. For a more realistic impression of the scene as well as to allow for better orientation for users, we have added two textures that can be applied to the surface topography: a high-resolution satellite image of Schleswig-Holstein from Google Earth (Google, GeoBasis-DE/BKG ©2009) as well a monochrome map from OpenStreetMap (©OpenStreetMap-contributors). Both maps display a map section that covers the whole of the state at a scale of 1 : 100, 000 and have a raster size of \(8192\times 8192\) pixels. In addition, the municipality area of the four major cities, Kiel, Flensburg, Neumünster and Lübeck (GeoBasis-DE/BKG, ©2021), can be highlighted on the topographic surface (see Fig. 1a) as landmarks.
Subsurface information and gas storage
Subsurface information for this study area relates to the North German Basin. Over the last decades, the geological structure for parts of the basin has been thoroughly investigated and well documented (Baldschuhn et al. 2001; Hese 2012; Jähne-Klingberg et al. 2014; Wolf et al. 2014; Arfai et al. 2014). Gasanzade et al. (2021) combined all available literature data for the subsurface potential assessment into a 3D structural geological model. In this study, only the three main storage formations: the Dogger (Middle Jurassic), the Rhaetian (Upper Keuper) and the Middle Buntsandstein are used for visualisation. The storage formation depth horizon maps are derived from Petrel E&P platform (Schlumberger Ltd 2018) as triangulated surfaces consisting from 200,000 to 950,000 number of triangles and are displayed using the associated colours used in geological maps. Fig. 2a shows the main regional tectonic structures from the Entenschnabel graben systems (Northwestmost) to the Eastholstein block.
Georeferenced datasets illustrating the existing renewable energy production infrastructure for electric power in Schleswig-Holstein (Fig. 1) are given in point and vector data format. In particular, point data sets representing wind turbines and biogas plants plus the locations of transformation stations have been mapped onto the triangulated topographic surface of the federal state. In addition, polygons specifying the spatial extent of solar parks as well as onshore and offshore wind parks have been tessellated. Both, these polygons and the polylines representing power lines, have been subsequently mapped so that they are positioned at the same elevation as the topographic surface (Rink et al. 2014).
In addition to static raster and vector data representing topography and energy system infrastructure, this study also includes 3D dynamic simulation results of compressed air energy storage (CAES) and aquifer thermal energy storage (ATES) facilities. This enables the analysis of geological subsurface dimensions for energy storage applications.
Simulation results of the CAES consist of two technological setups: The first setup has a three-stage compression and a two-stage expansion with heat recuperator in the power plant as implemented in McIntosh (USA). The second concept simulates an adiabatic power plant with three-stage compression and expansion parts. Both setups are designed for a future energy system with a high penetration of renewables (i.e. 76–100%), where 9 vertical storage wells at 1 km depth deliver the required air mass flow rate to generate \(115\,MWh\) electrical energy at the power plant. CAES simulations are carried out via a coupled interface simulator developed by Pfeiffer et al. (2021) and subsequent simulation results are converted into VTK data structures (Schroeder et al. 2006) using a mesh converter proposed by Wang and Bauer (2016). The resulting 3D meshes consist of 210,160 hexahedral elements each and include static depth values as well as total pressure and air saturation for annual storage operations stored in 13 time steps.
The compressed air energy storage facility stores pressurised air in a subsurface porous formation using off-peak power, which is released during peak demand using a turbine for power generation. In this study, we are aiming to integrate all these units for a better surface and subsurface space analysis. All nine vertical wells on top of the anticline storage structure are integrated with a virtual power plant model at the surfaceFootnote 1, which supports appropriate surface and subsurface facility design (see Fig. 2b). In the case of the diabatic CAES setup, the required heat during power generation should be delivered from a reference power plant. This need not necessarily be the designated peak load power plant but could conceptually be achieved via one or more of the existing biogas plants in the area.
Aquifer thermal energy storage
The second dataset shows the temperature distribution during system operation of a high-temperature aquifer thermal energy storage (HT-ATES) simulation model (see Fig. 3). The model is part of a larger study, which analyses HT-ATES systems and their role in future urban heat supply systems (see Kabuth et al. (2017) and https://www.angus-projekt.de.), as they can provide the capacity for heat energy to bridge short-term intervals and seasonal fluctuations using renewable energy and thus increase the overall share of renewables in the heating sector. Such systems are planned for a number of cities in Germany, such as Berlin, Hamburg, or Leipzig (Müller 2021; Fatima et al. 2021; Krawczyk et al. 2019; Scheck-Wenderoth et al. 2017; Kastner et al. 2017; Hassanzadegan et al. 2016). The geological subsurface of these particular models is based on the subsurface of one of the districts of Kiel, the capital city of Schleswig-Holstein. It consists of alternating layers of sand and clay, which is typical for northern German Pleistocene formations. The HT-ATES installation consists of four-well doublets with a warm and cold well each, which are installed into a depth of \(100\,m\) with a screen length of \(20\,m\) across the whole aquifer. The cold and warm wells of each doublet are \(300\,m\) apart, while the doublets have a spacing of \(100\,m\). The aquifer is bounded on top and bottom by glacial till, i.e. clay. During storage charging, ambient temperature water is extracted from the cold well, pumped through a heat exchanger and injected into the warm well. During storage discharge, hot water is extracted from the warm well and brought to the district heating network flow temperature level using a heat pump. The four-well doublet setup reaches storage rates of up to \(12\,MW\) with a storage capacity of \(25\,GWh\). The HT-ATES FEM model consists of 575, 000 hexahedron, pyramid, and prism elements in a \(1600\times 1500\times 205\,m\) model area. The governing differential equations of liquid flow and heat transport are solved using the simulation software OpenGeoSys, a process- and object-oriented open source software (Kolditz and Bauer 2004; Kolditz et al. 2012). Simulation results of this model consist of 3650 time steps and include pressure, temperature, and velocity for all elements.
To give context to heat storage systems using the aforementioned setups, the visualisation study also contains data illustrating the annual building heat demand density. Refurbishment of the existing building stock is necessary to lower heat demand. Likewise, the construction of renewable heating systems that use heat storage technologies is necessary to lower emissions from the energy supply covering the remaining heat demand of the building stock, as the energy demand for heating and hot water provision in buildings has a big share of \(CO_2\) emissions. To achieve this goal, laws for mandatory “heat planning” on municipality level are now being introduced in Germany. Both, location and density of heat demand is an important aspect for heat planning (Nielsen 2014). High detailed spatial data on measured heat demand is rare and often not accessible because it is protected by privacy laws, but can be modelled and mapped using available geodata such as 3D building models, census data, and building typologies (Wate and Coors 2015; Nouvel et al. 2017; DeJaeger et al. 2018; Schwanebeck et al. 2021). A ”heat atlas” can support heat planning on different spatial levels by integrating geodata on heat demand with geodata on heat production and heat storage potential (Möller et al. 2019). A heat density distribution map in \(MWh/(km^2 \times a)\) for Schleswig-Holstein, originally published by Schwanebeck et al. (2021), has been integrated into this study as shown in Fig. 4c.
Visualisation of urban infrastructures
A special focus within this visualisation study is the city of Kiel. Being the capital of the state of Schleswig-Holstein with a population of nearly 250,000 people, this city is of particular interest both from a geographic point of view in terms of energy demand as well as from a visualisation point of view regarding the representation of urban areas. Therefore, the workflow previously presented for the state of Schleswig-Holstein has also been independently applied for a region of interest representing this city: a topographic surface of the area covered by the city has been created, consisting of 30, 000 triangles with an average edge length of \(100\,m\). A high-resolution aerial image with a resolution of \(2439\times 4069\) pixels acquired from Google Earth (Google, GeoBasis-DE/BKG ©2009) can be applied for context, as shown in Fig. 4a. In addition, a raster dataset with a pixel size of one hectare illustrating heat demand density for the city (Fig. 4b) can be used as a texture for this surface.
As a prototypical example, LoD1 data (Landesamt für Vermessung und Geoinformation Schleswig-Holstein 2019a) for all buildings in one district of the city of Kiel has been provided by the federal state’s surveying agency. In this context, “LoD1” (“level of detail 1”) refers to datasets where both the building footprint and the average roof height of each building are specified. For that one district, the dataset consisted of over 30,000 polylines that have been mapped onto the topographic surface and subsequently used to generate almost 3500 models of individual buildings (see Fig. 5).
The next section will introduce our proposed framework and give details on the integrated visualisation of the datasets introduced above.