Case Study: ane.energy improves day-ahead and intraday trading precision with Dexter Energy forecasting

ane.energy optimizes trading across 2,500 MW with Dexter Energy

ane.energy, a German renewable energy aggregator operating a virtual power plant, faced the challenge of managing the complexity of trading a 2,500 MW portfolio of wind and solar power across short-term day-ahead and intraday markets. Forecast errors due to changing weather conditions directly impacted their trading outcomes, creating a need for highly accurate and timely power production forecasts. To support this, ane.energy partnered with vendor Dexter Energy for its wind and solar power forecasting services.

Dexter Energy implemented a solution using an ensemble of weather models retrained with machine learning on real production data. This provided ane.energy with forecasts that captured both large-scale patterns and local deviations. The critical near-time update feature integrated live asset data, allowing the trading team to adjust positions with precision as market conditions evolved. With more reliable and timely forecasts from Dexter Energy, ane.energy achieved improved trading results and could act with greater precision across short-term markets.

ane.energy