Summary
AI-powered forecasting and scenario modeling optimized battery storage participation across energy and balancing markets. The system continuously analyzed market signals, grid conditions, and asset constraints to automate trading and dispatch decisions. This improved forecast precision, captured market volatility, and increased overall trading performance. The approach integrated physical infrastructure with predictive software to maximize asset value in dynamic power markets.