Transforming to low-temperature operation with a Digital Twin
High forward temperatures in district heating grids not only lead to significant energy losses but also impede the integration of green energy sources. The business case for the heat pump or geothermal source decreases heavily if the system operates at high temperatures. Therefore, lowering the forward temperature is a top priority for district heating grids. A real-time Digital Twin can help district heating companies to transform their grid to low-temperature operation.
Many district heating companies see the benefit of switching to lower forward temperatures. It optimises energy usage, reduces carbon emissions, and ensures the system is prepared for long-term sustainability changes with renewable sources.
Disadvantages of high forward temperatures
Energy losses and high cost: High-temperature heating systems typically experience heat losses of around 15% during operation. This results in wasted heat, which can be very expensive if supplied by fossil fueled sources. This puts a financial strain on heating companies and negatively impacts their environmental footprint.
Obstacle to integrating renewable sources: High temperatures make it difficult to incorporate renewable energy sources in the heating system. For instance, heat pumps or solar thermal sources operate more efficiently at lower temperature levels. Lowering temperature by a few degrees can reduce the operational costs of the heat pump by more than 20%. Only by lowering the forward temperature, district heating grids can really unlock the potential of green energy and reduce reliance on fossil fuels.
Most district heating operators acknowledge the need to reduce forward temperatures. However, they often feel that they already are doing everything to operate the grid at the lowest possible temperature.
A real-time Digital Twin can help in reducing the temperature even more, while still delivering affordable and reliable heat. How does this work?
How a real-time Digital Twin can help the Transition to further Low-Temperature
The following five elements give new insights into how and where to lower temperatures:
Gathering Real-Time Sensor Data: Utilising actual sensor data from the grid is essential for accurate analysis and optimisation. Historical information alone cannot provide the necessary insights into real-time system behavior.
Automated data cleaning and validation: In cases where grids are not fully equipped with smart meters, machine learning models are employed to fill in the missing data. This ensures a comprehensive and reliable dataset for analysis.
Thermohydraulic Solvers: Predictions based on data only make sense if they conform to the physical reality. So, building thermohydraulic solvers allows for the computation of temperatures throughout the entire grid and helps optimise the flow of heat, improving the overall system efficiency.
Demand Forecasting: Advanced weather forecasting combined with demand predictions per area assists in anticipating heating requirements. This enables the district heating operator to proactively adjust temperatures to match the changing needs of consumers and environmental conditions.
Live Set Points: To bring the value from data to operations, the real-time Digital Twin provides live setpoints for network temperatures. These set points are adapting dynamically to changes in demand and external factors – ensuring running the grid without unnecessary margins.
Making Insights Accessible with a Clear Interface
It is equally important to ensure that the information derived from the analysis is easily accessible to operators in control rooms.
At Gradyent, we address this need by providing a user-friendly interface in the cloud, that visualises the entire system in real-time. This enables operators to monitor all elements, analyse network behavior and make informed decisions based on real-time insights and setpoints.
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Gradyent's real-time Digital Twin Platform drastically improves the management of district heating grids and ensures the user can reach the lowest forward temperature possible. It enables the integration of new sustainable sources and reduces the amount of heat losses across the network to a minimum.
The results are real-time recommendations on how to optimise one's heating system's behavior in different scenarios.
It empowers district heating companies to optimise, decarbonise, and grow, with measurable results, for example realising 10-20% higher efficiency of renewable sources.
Additional resources
To learn more about how our real-time Digital Twin Platform can help to reduce forward temperature and future-proof a district heating grid, visit the Digital Twin page.
We've successfully applied our Digital Twin and its 6 modules to heating grids across Europe, amongst others Wien Energie GmbH, E.ON, and Eneco - read more about it in Customer Stories.
Setting up a Digital Twin doesn't need to take a long time. It could be done in 4 to 6 months —and in most cases, with the data that the district heating company already has. Explore how our Digital Twin supported Wien Energie by reducing forward temperature by 4 degrees here. After connecting the first network in just a few weeks, Wien Energie has worked with the Gradyent Digital Twin Platform for temperature optimisation in various grids.