Accounting for Subsystem Aging Variability in Battery Energy Storage System Optimization

TL;DR

This paper develops an optimization framework for battery energy storage systems that accounts for subsystem aging heterogeneity, significantly improving operational revenue and efficiency.

Contribution

It introduces a degradation-cost-aware optimization approach that explicitly models subsystem aging variability, enhancing decision-making and economic outcomes.

Findings

Ignoring subsystem heterogeneity can lead to infeasible plans and revenue loss.

Accurate aging modeling and cost consideration improve operational accuracy.

The fully informed scenario yields 21% higher revenue per unit of SOH loss.

Abstract

This paper presents a degradation-cost-aware optimization framework for multi-string battery energy storage systems, emphasizing the impact of inhomogeneous subsystem-level aging in operational decision-making. We evaluate four scenarios for an energy arbitrage scenario, that vary in model precision and treatment of aging costs. Key performance metrics include operational revenue, power schedule mismatch, missed revenues, capacity losses, and revenue generated per unit of capacity loss. Our analysis reveals that ignoring heterogeneity of subunits may lead to infeasible dispatch plans and reduced revenues. In contrast, combining accurate representation of degraded subsystems and the consideration of aging costs in the objective function improves operational accuracy and economic efficiency of BESS with heterogeneous aged subunits. The fully informed scenario, which combines aging-cost-aware optimization with precise string-level modeling, achieves 21% higher revenue per unit of SOH loss compared to the baseline scenario. These findings highlight that modeling aging heterogeneity is not just a technical refinement but may become a crucial enabler for maximizing both short-term profitability and long-term asset value in particular for long BESS usage scenarios.