Investigating Balance Responsible Party Mismanagement through Comprehensive Schedule Analysis: Day-Ahead to Day-After

Effective balancing group management is vital for ensuring a stable electricity supply. However, significant deviations often occur without clear explanations, challenging grid reliability. This study introduces a comprehensive methodology that integrates correlation analysis, feature-based analysis, and linear regression to examine forecast discrepancies across day-ahead, intraday, and day-after timeframes. Utilizing daily and monthly Key Performance Indicators (KPIs), we analyzed over two million schedule notifications from 928 balance responsible parties within the 50Hertz Transmission System Operator (TSO) region. Our rigorously validated approach provides a sophisticated top-down evaluation of management behaviors, identifying underlying patterns and anomalies with enhanced accuracy. Aligning with the latest advancements in data analytics, this methodology aims to improve energy balance management by effectively detecting anomalies in daily forecast schedules for both production and consumption.