We launched another significant enhancement to the Re-Twin Energy modeling engine: the integration of EEG subsidy mechanics into revenue simulations. This update enables developers, investors, and asset owners to accurately assess the financial viability of their systems under the German Renewable Energy Act (EEG).
Why This Matters
The EEG subsidy plays a crucial role in determining project revenues for both behind-the-meter (BTM) and front-of-the-meter (FTM) setups. By integrating this support mechanism into our platform, we provide users with a more complete and regulatory-compliant view of their project’s revenue potential.
For BTM systems, where PV and storage are tightly coupled on-site, correct modeling of EEG implications is essential to avoid compliance risks. For FTM projects, the EEG market premium can be a critical component in securing financing and long-term profitability.
What’s New in the Modeling Logic?
Our EEG simulation module now includes:
- Market Premium Calculations
We account for the EEG market premium by simulating the spread between the reference value and actual market prices, including 6-hour negative price thresholds.
- Battery Operation Rules
The logic reflects key EEG constraints, including:
- No double compensation: EEG premium is paid once per kWh, even if stored and sold later.
- PV-only charging: Stored energy must originate from PV to qualify for the subsidy.
- Separate metering requirements: Clear distinction between PV generation, battery activity, and grid interactions is mandatory.
- Revenue Optimization Scenarios
Our simulations evaluate storage dispatch strategies that comply with EEG regulations while optimizing returns, particularly important for load shifting and peak shaving.
How You Benefit
With this new feature, you can:
- Precisely understandt total project revenues under EEG support
- Make informed investment and operational decisions
Want to see what this means for your project?
Log in to the platform and test the new feature, or get in touch for a personalized demo:
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