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De-Risking Grid Code Compliance for a 50 MW Grid-Forming BESS

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Academia, Energy

10 / 28 / 2026

De-Risking Grid Code Compliance for a 50 MW Grid-Forming BESS

The Customer

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) bridges academic research and industrial practice through its Institute for Electrical Energy Systems. For the Neilston Greener Grid Park project, FAU worked closely with Fluence Energy GmbH, a global leader in energy storage technology and the project’s BESS supplier and power plant controller developer, to validate the complete 50 MW grid-forming BESS against grid code requirements before commissioning.

The Challenges

  • Limited flexibility when requirements or controller parameters evolve during development.
  • High costs associated with repeated site testing campaigns.
  • Long feedback cycles between development, integration, and validation teams.
  • Difficulty reproducing severe grid disturbances in a controlled and repeatable manner.
  • Increased project risk due to the late discovery of control and integration issues.

The OPAL-RT Solution

  • CHIL Testing with HYPERSIM: A comprehensive CHIL platform based on HYPERSIM reproduced demanding grid-code scenarios in a controlled and repeatable laboratory environment.
  • Real Hardware Integration: The platform integrated the actual PPC, physical measurement hardware, converter control implementations, and the simulated transmission network.
  • High-Fidelity Real-Time Modeling: A detailed switching converter model was developed in HYPERSIM and benchmarked against the real device to accurately capture converter dynamics.
  • Automated Testing Workflow: HYPERSIM’s Python API automated grid-code test sequences, scenario configuration, test campaigns, data collection, and compliance reporting.

The Results

  • Reduced Technical Risk: Potential control and integration issues were identified earlier, reducing the likelihood of costly late-stage corrections.
  • Increased Confidence: Validating the full control architecture under realistic conditions increased confidence in compliance readiness.
  • Greater Test Coverage: Scenarios difficult or impossible to reproduce in the field could be executed repeatedly and consistently in the laboratory.
  • Accelerated Validation Workflow: HYPERSIM real-time simulation and Python-based automation reduced manual effort and enabled efficient, large-scale test campaigns.
  • Enhanced Scalability: The reusable framework can support future grid-forming BESS developments and growing compliance-testing needs.

 

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