SCARO: Scenario-Conditioned Risk Optimization for Day-Ahead Battery Energy Storage Scheduling
- 1School of Computer and Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
- 2School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Abstract
Day-ahead battery arbitrage requires assessing the return and downside risk of complete charge–discharge cycles. We propose Scenario-Conditioned Risk Optimization (SCARO), which corrects supply and demand forecasts and maps jointly resampled whole-day residuals to price paths. An exact planner identifies adverse outcomes separately for each feasible schedule and combines mean and lower-tail profit to select an operating cycle or no operation. A 182-day rolling evaluation in the Western Inner Mongolia real-time electricity market compares SCARO with 21 baselines for a battery with zero initial and terminal states of charge and at most one daily cycle. The configurations use a single price model (SCARO-S) or ensembles (SCARO-E and SCARO-L), with the ensemble configurations differing in boundary correction. SCARO-S achieves the highest mean profit in 13 of 16 efficiency–cost conditions. At nominal efficiency and zero cycle cost, its mean and worst-month profits exceed the strongest baseline for each metric by 4.72% and 8.76%, respectively; SCARO-L achieves the highest oracle capture. With forecasts held fixed, lower-tail scheduling improves mean profit and loss frequency for SCARO-E and SCARO-L relative to their risk-neutral controls.
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BibTeX
@article{li2026scaro,
title = {{SCARO: Scenario-Conditioned Risk Optimization for Day-Ahead Battery Energy Storage Scheduling}},
author = {Jinchen Li and Xingxing Wang},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100404},
url = {https://www.mdpi.com/2313-0105/12/10/404},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SCARO: Scenario-Conditioned Risk Optimization for Day-Ahead Battery Energy Storage Scheduling
AU - Jinchen Li
AU - Xingxing Wang
PY - 2026
DA - 2026/10/07
JO - Batteries
DO - 10.3390/batteries12100404
UR - https://www.mdpi.com/2313-0105/12/10/404
AB - Day-ahead battery arbitrage requires assessing the return and downside risk of complete charge–discharge cycles. We propose Scenario-Conditioned Risk Optimization (SCARO), which corrects supply and demand forecasts and maps jointly resampled whole-day residuals to price paths. An exact planner identifies adverse outcomes separately for each feasible schedule and combines mean and lower-tail profit to select an operating cycle or no operation. A 182-day rolling evaluation in the Western Inner Mongolia real-time electricity market compares SCARO with 21 baselines for a battery with zero initial and terminal states of charge and at most one daily cycle. The configurations use a single price model (SCARO-S) or ensembles (SCARO-E and SCARO-L), with the ensemble configurations differing in boundary correction. SCARO-S achieves the highest mean profit in 13 of 16 efficiency–cost conditions. At nominal efficiency and zero cycle cost, its mean and worst-month profits exceed the strongest baseline for each metric by 4.72% and 8.76%, respectively; SCARO-L achieves the highest oracle capture. With forecasts held fixed, lower-tail scheduling improves mean profit and loss frequency for SCARO-E and SCARO-L relative to their risk-neutral controls.
SN - 2313-0105
ER -