Case Study: District Heating Simulation

The main focus of our work at heatbeat is the dynamic simulation of district heating and cooling networks. In order to give you an overview of our services, we demonstrate our methods and processes on the following pages using a simple case study. In this case study, we compare the simulated heat losses resulting from different levels of thermal insulation for the pipe network for an example network.

For the data acquisition of a network and registering it into our simulation environment, we can usually import existing technical drawings or datasets from our customer's GIS data. To create a fictitious example network for our case study,we draw a simple network topology for 100 buildings into the district Bleiweiß in Nuremberg.

Location of the heating network with network topology in Nuremberg Bleiweiß district

After transferring the data into our simulation environment, the network topology with all relevant data is available in the form of a mathematical graph. The system efficiently manages all variants of the energy system necessary for the investigation by linking the graph to our databases. The data can then be evaluated and adjusted before it is used for automated model generation and dynamic simulation. By feeding the simulation results back into our data system, the results can be evaluated efficiently. By comparing different variants, we identify the advantages and disadvantages of the variants and derive recommendations for concrete actions to take.

The following network topology is the result of importing the drawing for our example network:

Network topology based on the technical drawing

In addition to the network topology shown here, our simulation environment also manages all data for the supplies and connected buildings relevant for the simulation. In order to estimate the heat demands over the course of a year with an hourly time step, our simulation environment generates individual building models for each of the connected buildings. Utilizing suitable usage profiles and weather data, the heating requirements for each building are simulated in hourly resolution for the course the year.

Hourly heat demand of an apartment building and outside air temperature for one year

In order to estimate the heat demands for providing domestic hot water, our simulation environment also generates stochastic hot water tap profiles for each building based on the buildings' type of usage. Based on the calculation methods of DIN 12831-3, we determine the amount of heat that has tobe drawn from the heating network to provide domestic hot water. Depending on the project-specific requirements, this can also take into account decentralized storage tanks and storage and distribution losses.

For the entire network, the building simulations result in the following heat demands over the course of the year:

Annual space heating and domestic hot water demand