New framework maps battery scheduling performance under uncertainty

This paper introduces a framework for analyzing battery scheduling under various uncertainties, including data, battery design, and planning horizons. It uses parametrized synthetic datasets to explore how these factors jointly affect revenue performance in energy storage optimization. The study highlights that increased forecast uncertainty systematically shortens the optimal planning horizon, indicating reduced value of long-term information when forecasts are unreliable. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Provides a framework for optimizing energy storage planning under uncertainty, potentially improving efficiency in energy markets.

RANK_REASON This is a research paper published on arXiv detailing a new framework for analysis. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

• arXiv
• Jaime De Miguel-Rodríguez

• paper
• other