A Methodology for Exploring the Interplay Between Battery Design, Data Uncertainty, and Planning Horizon in Multi-Stage Optimization

The performance of multi-stage scheduling for energy storage depends on the interaction between battery design, forecast data characteristics, and the planning horizon. This study presents a systematic methodology to explore how different data profiles-represented through a parameterized model capturing amplitude, seasonality, and forecast uncertainty-interact with the capacity-to-power ratio of batteries to determine optimal planning horizons. We describe the generation of both simple theoretical datasets and complex datasets resembling real electricity prices, photovoltaic generation, and load profiles, illustrating how forecast uncertainty can be incorporated in scheduling models. Using multi-stage model predictive control we outline a framework to map the relationships between data characteristics, battery types, and planning horizons. This manuscript focuses on the methodology and framework; experimental validation and results will be reported in a forthcoming version. The proposed approach provides a foundation for the preliminary design and operational planning of different types of energy storage systems under uncertainty.