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 interact with the capacity-to-power ratio of batteries to determine optimal planning horizons. Data profiles are represented through a parameterized model capturing amplitude, seasonality, and forecast uncertainty. This model is described in relation to the generation of both simple theoretical datasets and more complex ones resembling real electricity prices, photovoltaic generation, and load profiles. 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 various data and uncertainty profiles.