How the Digital Twin Enables Decision-Making
Climate-friendly energy supply systems are made up of many interconnected components: heat pumps, PV systems, storage units, geothermal systems, dry coolers, combined heat and power units and district heating networks. In most cases it’s not only the performance of individual assets, but how all of them interact across hours, seasons and different operating conditions.
Dynamic simulation models make these interactions visible. They show how load profiles, temperatures, storage states, volume flows and control strategies influence one another. This makes it possible to compare system variants objectively, identify risks at an early stage and test operating strategies before they are implemented in the real system.
Simulation therefore becomes a reliable basis for decision-making in planning, investment, operation and future system expansion. The digital twin takes this one step further: it does not only represent an energy system computationally, but creates a virtual counterpart that allows technical, economic and ecological questions to be investigated in a transparent and traceable way.
Whether used for district energy supply, the design of local heating networks, the control of heat pumps in combination with PV, innovative supply concepts or the further development of district heating networks, digital twins help make complex interdependencies understandable and enable well-founded decisions.
The following five model examples show how digital twins can be applied in different use cases. Throughout all phases of a project, the GreenCity simulation library helps create such models efficiently and within the required timeframe. GreenCity is used in SimulationX and supports the development of dynamic simulation models for complex energy systems.
Variants Comparison: Probing for Performance (click here to read)
Dimensioning: Sizing for Success (coming soon)
Control: Designing and Testing Algorithms (coming soon)
Operation: Validating Supply Reliability (coming soon)
Expansion: Anticipating Problems Early (coming soon)