# Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons

arXiv: https://arxiv.org/abs/2604.15360 · HTML: https://mlsanity.com/paper/2604.15360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [GP-VAE: Deep Probabilistic Time Series Imputation](https://mlsanity.com/paper/1907.04155/) (2019)
- [Regularization via Mass Transportation](https://mlsanity.com/paper/1710.10016/) (2017)
- [Reinforcement Learning of the Prediction Horizon in Model Predictive Control](https://mlsanity.com/paper/2102.11122/) (2021)
- [Stochastic Model Predictive Control with Discounted Probabilistic Constraints](https://mlsanity.com/paper/1807.07465/) (2018)
- [On discount functions for economic model predictive control without terminal conditions](https://mlsanity.com/paper/2405.14361/) (2024)
- [Optimal Placement and Sizing of Distributed Battery Storage in Low Voltage Grids using Receding Horizon Control Strategies](https://mlsanity.com/paper/1609.07128/) (2016)
- [Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity](https://mlsanity.com/paper/2003.06652/) (2020)
- [Time adaptivity in model predictive control](https://mlsanity.com/paper/2009.01332/) (2020)
- [Linearly discounted economic MPC without terminal conditions for periodic optimal operation](https://mlsanity.com/paper/2205.03118/) (2022)
- [Dynamic Rolling Horizon-Based Robust Energy Management for Microgrids Under Uncertainty](https://mlsanity.com/paper/2307.05154/) (2023)
- [How long is long enough? Finite-horizon approximation of energy storage scheduling problems](https://mlsanity.com/paper/2411.17463/) (2024)
- [Hybrid Adaptive Robust Stochastic Optimization Model for the Design of a Photovoltaic Battery Energy Storage System](https://mlsanity.com/paper/2509.19054/) (2025)
- [On The Detection of Minimum Forecast Horizon For Real-Time Scheduling of Energy Storage Systems in Smart Grid](https://mlsanity.com/paper/2511.12029/) (2025)

## Abstract (arXiv)

This study presents a controlled parametric framework for analyzing energy storage planning under uncertainty in a multi-stage model predictive control setting. The framework enables a broad and systematic exploration through parametrized generation of synthetic datasets in the context of energy price arbitrage. It facilitates the study of the joint effects of battery characteristics, signal structure, forecast uncertainty, and planning horizon on revenue performance in energy storage optimization, which are rarely considered together. The analysis is driven by two objectives. First, it characterizes how these interacting factors influence operational revenue and its sensitivity to planning horizon selection, including economic losses caused by deviations from optimal horizons. This provides guidance on expected horizon ranges and their impact on revenue and computational cost. Second, it enables a compact parametrization of the relationships between battery properties, data characteristics, forecast uncertainty, and horizon-dependent performance, providing a basis for future modelling of optimal planning horizon length. Results show that the framework captures consistent structural dependencies across configurations and provides meaningful guidance for horizon selection under uncertainty. In particular, increasing forecast uncertainty systematically reduces the optimal planning horizon across battery types, reflecting the diminishing value of long-term information under increasingly unreliable forecasts. Comparison with real market data shows that the parametrization reproduces the main qualitative trends of optimal horizon behavior, suggesting its potential as a lightweight surrogate for more complex simulation-based analysis.