Price Aware Power Split Control in Heterogeneous Battery Storage Systems

TL;DR

This paper introduces a unified framework for optimal battery dispatch and internal power management in heterogeneous BESS, integrating market signals and physical constraints to improve economic and operational performance.

Contribution

It compares model-based LP and model-free RL approaches for BESS optimization, highlighting their respective advantages in efficiency, savings, and adaptability.

Findings

RL achieved 10% higher system efficiency than LP.

LP yielded 33% greater cumulative savings.

LP maintained lower SOC imbalance, RL improved temperature balance.

Abstract

This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery energy storage systems (BESS). This novel approach integrates both market-based (price-aware) signals and physical system constraints to simultaneously optimize (1) external energy dispatch and (2) internal heterogeneity management of BESS, enhancing its operational economic value and performance. This work compares both model-based Linear Programming (LP) and model-free Reinforcement Learning (RL) approaches for optimization under varying forecast assumptions, using a custom Gym-based simulation environment. The evaluation considers both long-term and short-term performance, focusing on economic savings, State of Charge (SOC) and temperature balancing, and overall system efficiency. In summary, the long-term results show that the RL approach achieved 10% higher system efficiency compared to LP, whereas the latter yielded 33% greater cumulative savings. In terms of internal heterogeneity, the LP approach resulted in lower mean SOC imbalance, while the RL approach achieved better temperature balance between strings. This behavior is further examined in the short-term evaluation, which indicates that LP delivers strong optimization under known and stable conditions, whereas RL demonstrates higher adaptability in dynamic environments, offering potential advantages for real-time BESS control.