Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons
For industrial battery operators, this work offers practical guidance to reduce computational costs while maintaining performance by identifying the effective horizon, though the findings are domain-specific.
This study investigates how forecast uncertainty, battery design, and planning horizons interact in battery scheduling under model predictive control, identifying an effective horizon beyond which additional forecast data yields limited benefit. Results provide optimal horizon lengths across various conditions and quantify revenue losses due to forecast uncertainty.
This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data characteristics, forecast uncertainty, planning horizon, and battery c-rate. Synthetic datasets are generated to systematically explore variations in data profiles and uncertainty, enabling parametrization and the construction of relationships that map these characteristics to optimal horizon length. Results reveal the presence of an effective horizon, defined as the look-ahead length beyond which additional forecast information provides limited operational benefit. Accounting for this horizon can reduce computational costs while maintaining optimal performance. The study provides optimal horizon lengths across a broad range of combinations of battery types, uncertainty levels, and data profiles, offering practical guidance for industrial storage operation. It also quantifies revenue losses due to forecast uncertainty, showing that errors can impact performance even for fast batteries. Finally, the framework lays the groundwork for future machine learning approaches that map dataset parametrization to optimal horizons, supporting continuous optimization in industrial settings without heavy computation.