Mitigating Curtailment Risk
with Smarter Wind and Solar Forecasting

Renewable energy operators face mounting challenges from grid congestion, forced curtailments, and negative pricing events that erode profitability. This white paper explores how hybrid wind and solar forecasting, combining physics-based models with machine learning enables asset owners to anticipate risks, protect revenue, and optimize market participation.

In this white paper you'll learn:

The Triple Threat: How congestion, curtailment, and negative pricing impact renewable asset owners.

Market Case Studies: Real-world examples from California, Spain, and Germany showing curtailment and price volatility.

Forecasting Methods: Pros and cons of physics-based vs. machine learning forecasting models.

Battery Integration: How advanced forecasting optimizes charging and discharging decisions for storage assets.

Metrics That Matter: Why capacity-normalized MAE (cnMAE) is the most effective way to measure renewable forecasting accuracy

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