Technological Innovations

The TEMPO – Temperature Optimisation for Low-Temperature District Heating across Europe – project developed and demonstrated technical innovations in two demo sites (Brescia, Italy and Nuremberg, Germany) that enable district heating networks to operate at lower network temperatures. By decreasing the temperature in the networks, heat losses are reduced and allows a higher share of renewable and excess heat to be used as heat sources.

A description of every innovation and the main findings of our project is presented in the following section. More information on the findings can be found in the Deliverables section of this website.

1. A supervision ICT platform for detection and diagnosis of faults in district heating substations

75% of all district heating substations perform sub-optimally, resulting in on average 15-20°C higher return temperature than necessary. In substations with suboptimal behaviour, the water from the DH network is cooled insufficiently and as a result the return temperature to the DH network will be higher than needed. A detection and diagnosis ICT platform was developed and will serve to detect faults and deviations causing suboptimal behaviour in consumer substations.

Main findings

In the H2020 TEMPO project a supervision ICT platform was developed that detects suboptimal behaviour and tested using larger external datasets. Clustering mechanism were applied to evaluate the data and this was expanded with geographical clustering as well as improved ways to manage automated selection of cluster solutions.  More details in the reports: D4.3 (Brescia) and D3.3 (Nuremberg).


2. Visualisation tools for expert users

Expert visualisation is about making underlying data accessible to users by transforming it into information that the expert can use in his or her daily work and operations. An important part of this is developing a data-driven process for filtering, sorting and ranking analysis results in a way that people can work with in practice. In TEMPO, we created a process based on the separation between core performance metric visualisers and combined performance metric visualisers. The former relates to core performance metrics such as different aspects of overflow, energy signatures and other metrics and how they dynamically evolve over time. Combined performance metrics combine underlying core metrics into more advanced metrics or even models. An example of this is how to use the dynamics of different core metrics to identify poorly controlled substations or to use automated multi-dimensional analysis based on the contour map concept to provide visual decision support.

Example of contour maps. To the left is a well performing substation, while the right shows a poorly performing substation.

Main findings

Additional innovations relating to visualisation is the ability to easily manage and integrate datasets on the fly. This has been shown to be an important tool in supporting the practical use of these type of visualisation systems. In TEMPO an online program manager scripting engine was integrated in the graphical user interface together with online integration manager. This is further described in “D4.3 Report – Integrated Improved Innovations in A2A Network”.

A screenshot from the online program manager scripting engine embedded in the graphical user interface.


Part of the systems have been evaluated by expert users in the Brescia demonstration case. This input provided valuable input for further development during the project and beyond. The results of this evaluation are described in more detail in “D5.3: Performance Assessment Report Monitoring Season 2021-2022”.

3. Smart district heating network controller, to balance supply and demand and minimise the return temperature

The smart controller tries to control the heat demand in the network, in order to optimize the temperature levels in the network. The controller developed in TEMPO builds further on the STORM-controller (link to storm DHC website), developed in a previous Horizon 2020 project. Three different control strategies were identified, which all were implemented and tested in the demonstration sites.

Return temperature optimization

By sending heat demand control signals to the buildings, it is possible to lower the return temperature to the network. By doing this in a coordinated way, the average return temperature can be decreased, or the temperature reduction can be concentrated so that the low temperature front hit the production unit on appropriate moments (such as times when the electricity price is high in case of CHP).

Coordinated buffer charging

In this case, the objective is to charge the decentralised buffers in such a way that the peak loads in the network is minimized. This control strategy has been specifically developed for the Enerpipe case.

Supply temperature optimization

With this control strategy, the water in the network pipes itself is used as a virtual storage buffer: by increasing the temperature of the water in the supply pipes when excess heat is available, heat can be stored for a limited time. Doing so, the unlocked flexibility can be offered to achieve a certain objective for the network, such as shaving off the consumption peaks. The controller balances the demand of heat to fluctuating renewable and residual heat sources. Furthermore, the controller can further reduce the return temperature by influencing the demand behaviour at the consumer side and coordinating this on a network level.

Main findings:

The smart district heating controller enabled a reduction of the primary return temperature of 0.7 K and up to 2 K by controlling the secondary supply temperature. At the same time, the peak energy was reduced by 60% to 70%. By controlling the mixing station supply temperature, the peak energy could be reduced by 30% to 50%. The smart district heating controller for smart buffer charging allowed a theoretical peak reduction of 35%. The smart district heating controller for CHP optimization based on forecasts of the electricity price and the forecast of the total heat load allowed a theoretical improvement of the electricity revenues by 8%. More details in the reports: D4.3 (Brescia) and D3.3 (Nuremberg).


4. Innovative piping system

Substations have bypasses (for conform reasons) that cause hot supply water to be injected in the return pipes, thereby increasing the return temperatures. The innovative piping system eliminates these bypasses in substations and ensures that flow and return temperatures in the network are kept at the lowest temperature level required by the end customer.

Main findings

Read the scientific article “Pipe Sizing for Novel Heat Distribution Technology” by Halmstad University.


5. Optimisation of the building installation

The performance of district heating substations is influenced by the building installation behind the substations, which often suffers from faults. To optimise the return temperature, faults in the building installation must be minimised. The innovation “optimisation of the building installations” combines two related functions, which are the simulation-based fault detection and the diagnosis for secondary systems and the guide for auditing building installations.

 Main findings

Simulation-based fault detection and diagnosis is based on data-driven algorithms which have been trained on simulation data and are applied on monitoring data from the individual substations. The predictions made by the algorithms for a given period are summarized in an HTML document along with metrics calculated for the period.

Further on, a set of easy to understand short reports for the buildings were created, including key information of the actual performance of the building with regards to the return temperature, the contractual reference return temperatures, and the return temperature of good performing buildings.

The automated prediction of secondary side faults in combination with an evaluation of performance metrics allowed to identify poorly performing buildings very efficient. Remediating the “low-hanging fruits” of the identified faults would yield in a reduction of return temperature by 1.4K.

6. Decentralised buffers

Decentralised buffers at the building side will cut the power peaks in the network, thereby allowing reduction of the network pipes dimensions. Especially for rural areas where the piping represents a relative high investment, this concept can enhance the economic feasibility of district heating networks.

Main findings

The decentralized buffer tanks allowed to reduce the connection capacity rates for new standard single-family houses from today’s 30 kW down to theoretically 7.5 kW. Further on, smaller DH pipe dimensions enable cost savings for the DH network of around 11% of the yearly total costs. More details on the relevant report D3.3.