In a district heating network comprising dozens of substations, a small number of improperly adjusted delivery points were reducing the overall efficiency of the system. Dyneo enabled the automatic identification of these critical substations and the prioritization of maintenance interventions. The result: a reduction of up to 15°C in the most problematic return flows, and an overall reduction of 5 to 10°C in the network return temperature.
In a large district heating network, Pareto’s law holds true without exception: 20% of the substations account for 80% of heat losses. But identifying them remained difficult without the right diagnostic tool.
The practical implications for the operator:
- The return temperature of the system is too high, which reduces the efficiency of the generation units (heat pumps, cogeneration).
- Increased heat loss in the distribution network.
- Limited connection capacity: it is not possible to add new subscribers without upgrading the infrastructure.
- Teams are being deployed on inefficient rounds due to a lack of objective prioritization of tasks.
- Failure to meet the EED criteria for classification as an efficient network.
.jpeg)
Dyneo has connected all of its substations to its suite of algorithms for continuous thermal and hydraulic analysis:
Behavioral analysis: For each substation, the platform continuously analyzes return temperatures, power demand, and load response dynamics, and compares them to expected behavior based on actual grid conditions.
Automatic scoring: Each substation is assigned a score reflecting its impact on the overall return temperature. Any deviations are identified (hot loop, stuck valve, incorrectly configured control, undersized heat exchanger).
Prioritized work list: Technicians receive a weekly list of the 5 to 10 substations that need to be addressed first.
Post-intervention monitoring: The platform automatically measures the impact of each intervention and confirms that operations have returned to normal.
Up to a 20°C reduction in the return temperature of the substations identified as the most critical.
An overall reduction of 10°C in the network return temperature and a direct improvement in production efficiency.
Reduction in heat loss of 8 to 15%, depending on the network configuration.
Significant operational time savings: teams focus on the 20% of substations responsible for 80% of the losses, rather than conducting exhaustive inspections.
Ability to connect new subscribers without expanding the infrastructure, thanks to the freed-up capacity.
Compliance with EED criteria for qualification as an efficient district heating system under European regulations.
