VALUES heat– Geodata-based and automated identification of potential heating networks

During the project VALUES heat – geodata-based and automated identification of potential heating networks, data modelling was used to determine street section specific heating network potential areas throughout Germany on behalf of Westenergie AG.

Motivation

Municipal heat planning is becoming mandatory in more and more federal states. As a result, the relevance of local and district heating networks for energy supply is increasing. It is not possible to draw conclusions about the potential for heating networks from data at district or municipal level. Smaller-scale data is required, preferably at individual building level.

In recent years, FfE’s Geodata Lab has summarized freely available and comprehensive data on the building stock and its heat demand into a building model. This model cannot replace detailed planning of heating networks, but it does form the basis for a comprehensive assessment of interesting potential heating networks and can be used in advance of a detailed analysis.

Project Objectives and structure

As a first step, the suitability of the building data set for determining potential heating networks was to be tested in a pilot project based on 10 pilot municipalities. Specific routes should be planned for these heating networks, infrastructure costs should be determined using a cost model and potential heat sources discussed.

Following an evaluation of the results of this data-driven analysis as part of a workshop, the methodology developed should be rolled out throughout Germany if successful.

Methodology

In addition to the availability of suitable heat sources, a favourable ratio of heat sales and heat transport infrastructure is particularly important for the suitability of areas for heating networks in order to be able to operate heating networks economically and sensibly.
Two variables are primarily used here: The heat demand density indicates the heat demand/expected heat sales per year and unit area (e.g. MWh/(ha*a)). Alternatively, the heat demand density (also heat line density) describes the heat demand per metre (m) of installed route length. Due to the higher accuracy, this measure is used for the modelling described.

The availability of suitable heat sources could not be mapped as part of the project due to data being difficult to access. As there are a variety of options available for providing heat, it was not considered a limiting factor.

Schematically, the methodology for identifying potential heating networks consists of the following steps:

1. randomized identification of connected buildings
2. assignment of the connected buildings to the nearest street section
3. identification of search areas based on the resulting heat occupancy density of the street sections
4. consolidation of the identified search areas into potential heating networks
5. design of the relevant components of the heating networks
6. cost calculation

In addition to heat demand information from the FfE building model, the FfE heat pump traffic light was also used to identify possible buildings connected to the potential heating network. This estimates the potential of various heat pump technologies for residential buildings. Depending on the scenario, different connection probabilities are used for the individual buildings, which are based on their suitability for heat pumps, building use and ground space.

For each building identified as connected, the nearest street section is identified within a defined radius, which depends on the total heat demand of the respective building, and the building is assigned to this section. Subsequently, the sum of the heat demand of the connected buildings and the geometric length is used to calculate the heat occupancy density in MWh/(m*a) of each street section.

In the next step, road sections with a sufficiently high heat occupancy density span areas with a size de-pendent on the respective heat line density. These include neighboring road sections and are combined into preliminary potential heat networks, the search areas, by means of geometric intersection. In order to generate the final potential heating networks, these search areas are consolidated using an iterative pro-cess. Figure 2 shows an example of the road sections of an area that are located in a potential heating network, colored in red.

Based on the heat line densities and a distribution of the heat flow classes of the pipelines, determined using a routing method, the pipelines, pumping station, heat transfer stations, house connection lines, heat generators and energy source requirements are finally dimensioned for a standard case. Finally, a cost calculation is carried out which, in addition to depreciation and energy source costs, also includes standard maintenance costs and imputed interest.

Since the connection behavior of potential heating network customers is difficult to predict, three scenarios were developed for this purpose, which differ in terms of the probability of connecting the buildings to the heating network.

Results

The result is a heating network potential area for the whole of Germany, broken down by street section. In addition to the heating network geometries, the data generated also includes a building-specific model of the heat demand and a cost calculation for each heating network for a typical heat generator park.

The data model created in the course of the project has been continuously developed further since then and has been used in numerous projects.