Virtual Testing for Energy Systems

Validate software and hardware controllers, compare operating strategies and explore system behavior with GreenCity simulation models.

Model-Based Testing

From Simulation Model to Virtual Test Bench

A GreenCity model describes the physical behavior of your energy system: generators, consumers, storages, grids, buildings, HVAC equipment, e-mobility infrastructure and control-related interfaces. Exported as an FMU, this model can be coupled with external software, AI algorithms, process controls or PLC hardware.

The FMU provides virtual sensor values and receives control variables from the system under test. This makes it possible to evaluate control behavior, compare strategies and test edge cases without risk to the real plant.

Typical Coupling Options

Software controllers and energy management systems
AI-based optimization algorithms
PLCs, DDCs and automation hardware
Web-based operation and visualization environments
Scalable simulation frameworks for large variant studies

Virtual Control Testing

Software-in-the-Loop Testing

Test Control Algorithms Before Deployment

Software-in-the-Loop testing connects the GreenCity FMU to a software controller, such as a Java, Python or cloud-based energy management algorithm. The controller interacts with the simulated energy system as if it were connected to a real plant.

This enables early development and validation of control logic under reproducible conditions. Different controller versions can be tested against the same scenarios, making performance transparent and comparable.

Software-in-the-Loop test bench overview

Benefits of SiL Testing

Faster and safer controller development
Early testing of control functions before field deployment
Reproducible test conditions for different algorithms
Evaluation of average, extreme and fault scenarios
KPI-based comparison of controller performance
Visualization of benefits such as energy savings, cost reduction or improved operating behavior
Automatic parameterization of controllers via the test bench
Higher-quality algorithms before delivery to end customers

Example Applications

Energy management for industrial sites
Optimization of heat pumps, CHP units and thermal storages
Charging and load management for e-mobility
PV, battery and grid interaction
Predictive and model-predictive control
Regression testing after software updates or system extensions

Are you interested in learning more about Software-in-the-Loop testing for your specific application?

Real Hardware Testing

Hardware-in-the-Loop Testing

Connect Real Automation Hardware to a Virtual Energy System

Hardware-in-the-Loop testing connects real controller hardware, such as PLCs or DDCs, to a GreenCity digital twin. The controller under test exchanges signals with the FMU-based simulation through fieldbus, communication protocols or analog and digital I/O.

This allows control engineers to perform commissioning-like tests at their desk. Control quality, reliability and fault behavior can be analyzed long before the controller is connected to the actual plant.

Hardware-in-the-Loop test bench overview

Benefits of HiL Testing

Virtual commissioning and early fault detection
Desk-based controller tests and regression tests
Reduced on-site commissioning effort
Safe testing of critical or rare operating conditions
Analysis of control quality and reliability under multiple scenarios
Location-independent testing for distributed development teams
Faster feedback loops for developers
Increased system quality and customer satisfaction

Example Applications

Creation of new control algorithms during product development
Continuous tests of controller algorithms
Pre-configuration of algorithms for specific plants

Are you interested in learning more about Hardware-in-the-Loop testing for your specific application?

Advanced Applications

Beyond SiL and HiL

Observer-Based Control and Virtual Sensors

A simulation model can be used as an observer model alongside the real system. By comparing simulated and measured variables, internal states can be estimated that are difficult or impossible to measure directly. This enables advanced control strategies, virtual sensors and deeper insight into system behavior.

Large-Scale Variant Studies

With scalable simulation frameworks, thousands of model variants can be scheduled, simulated and stored. This enables decentralized simulation, parameter studies and systematic comparison of design alternatives across different locations, tariffs, weather data or operating strategies.

Are you interested in learning more about advanced use cases of our GreenCity models for your specific application?