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TEMPerature Optimisation for Low Temperature District Heating across Europe

Periodic Reporting for period 3 - TEMPO (TEMPerature Optimisation for Low Temperature District Heating across Europe)

Reporting period: 2021-04-01 to 2022-03-31

The TEMPO – Temperature Optimisation for Low Temperature District Heating across Europe – project develops innovations that enable district heating networks to operate at lower temperatures. By decreasing the temperature in the systems, it reduces heat losses and allows a higher share of renewable and excess heat. The developed innovations are demonstrated and implemented in 2 district heating networks which cover 90% of the district heating market types. The following objectives were defined to achieve this challenge:
• Development and demonstration of innovations for low-temperature district heating networks.
• Quantification of the benefits of the solutions through demonstration in 2 demonstration sites.
• Empowerment of end users in low-temperature district heating networks.
• Development of innovative business models and demonstrate their replication potential for the roll-out of sustainable and economically viable DH networks across the EU.
• Guarantee EU-wide market uptake of solutions packages by developing an exploitation and replication plan

The TEMPO consortium consists of research institutes, two full-scale demonstration networks in the city of Windsbach (Germany) and the City of Brescia (Italy) as well as leading District Heating energy specialists and industrial manufacturers. They cover new innovations in fault detection in substations and buildings, visualization tools, smart control of DH networks, innovative piping systems, optimization of building installations and innovative decentralized buffers.

The implementation of a mixing station in the Brescia demo site resulted in a reduction of the heat distribution losses by 4% on average. 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. By controlling the mixing station supply temperature, the peak energy could be reduced by 30% to 50%. 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.

The decentralized buffer tanks at the Windsbach demo site 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. 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%. After some anomalous behaviour was detected, faults were corrected and the average return temperatures were reduced by 7.8 K.

All demonstrations have delivered highly relevant lessons for further market roll out of these technologies that will be made accessible as part of the exploitation and replication activities.
During the last reporting period of the TEMPO project, the final development of the innovations was made, and they were successfully tested and integrated at the demo sites. The work progressed at large towards the main objectives of the project, the testing and evaluation of the innovations at the demo sites, delivering the final deliverables, organising the final event and planning the exploitation and replication actions to bring the TEMPO innovations to the market.

Despite the large number of tests (and the unknown outcome/direction of these test results) still to be performed after the last reporting period, the consortium succeeded in following the test plans as scheduled including a detailed follow-up of the testing campaigns. No further delays in the demonstrations were encountered.

Furthermore, the project also focussed on including feedback from the different stakeholders of the project e.g. Building managers, solution providers, district heating companies, etc. For the German demo site, the involvement of an end user and district heating operator was incorporated in the demo site video. For the Italian demo site, the building managers were informed about worst performing buildings via a leaflet describing the faults in their buildings and the corrective measures. Follow-up conversation with the building managers were setup to discuss the way these faults could be eliminated in the future. In this phase special attention was given to the way the building managers were approached to get a much as possible their contribution and involvement and without jeopardising the normal operation/business model of a district heating company.

Moreover, some of the key exploitable results in this project have already been applied in commercial and research contracts ensuring a substantiated entry in the market. This has led to new ways of cooperation and contributed substantially to the further development and deployment of district heating networks in Flanders, Germany, Italy, Austria and at the European scale.
TEMPO brings additional state of the art algorithms and models to a nearly market ready level by testing and validation in real demonstration sites. The fault detection algorithms and models provide an automated system that can detect faults in substations so that manual time-consuming interventions will be much more eliminated and will be planned more effective. The return temperature minimization is achieved by an optimal control algorithm using model predictions to determine the optimal control action per building, i.e. the optimal secondary supply temperature profile, that minimizes the primary return temperature of the building. In practice, the algorithm relies on a model of the building’s thermal zone, hydronic heat emission system and district heating substation and can predict the building’s room temperature depending on the secondary supply temperature and the thermal boundary conditions such as solar irradiation, outdoor temperature etc. The corresponding primary flow rate and return temperature are also predicted by this model.

The algorithm for the coordinated charging of the decentralized buffers has been changed to a reinforcement learning (RL) algorithm implementation due to numerous technical challenges and the limitations of the underlying buffer charging system. This was possible because we had to switch to an ON/OFF control of the buffers instead of a modulating control (which would lead to too exploding state spaces in an RL setting). RL is an area of machine learning in which an intelligent agent (the buffers) take actions in an environment to maximize their cumulative reward.

The insights in the smart controller and the harvesting of the thermal flexibility of the building thermal mass and the district heating network can pave the way for new companies/products/processes and can on their turn increase the competitiveness of district heating companies. The potential for such cross-fertilisation is increased in TEMPO since several project partners are currently involved in other research and demonstration projects or commercial offers.
Picture of the TEMPO mixing station in the A2A demonstator in Brescia, Italy
PIcture of the final result for building heat load control
Picture of the TEMPO test plans and the several test periods