Accounting for Subsystem Aging Variability in Battery Energy Storage System Optimization

TL;DR

This work tackles subsystem aging variability in battery energy storage systems by introducing a degradation-cost-aware optimization framework that models intra-system aging heterogeneity. It couples a high-fidelity digital twin (SimSES) with an ECM-based optimizer in a rolling-horizon dispatch to evaluate four scenarios across two parallel strings. Results show that acknowledging aging differences improves schedule reliability and revenue, with aging-cost-aware, fully informed optimization yielding up to 21% more revenue per unit of SOH loss than a baseline. The study highlights practical gains for long-duration BESS operation and points to future work on balancing SOH across subunits to sustain asset value. All mathematics are presented with explicit notation, including $SOC$, $SOH$, $R_t$, and $q^{loss}$ terms, to support reproducibility and integration into downstream tools.

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.

Figures (5)

• Figure 1: The proposed simulation framework for evaluating the influence of heterogeneity among the two battery strings. The two subunits (string A and B) can be optimized and simulated with individual aging characteristics.
• Figure 2: Depiction of the different model accuracies in the scenarios. String A is considered new, while string B is aged. This heterogeneity of initial SOH and $r^{\text{incr}}$ is consistently included in the SimSES simulation. Scenarios I and III assume identical conditions for both strings in the optimization, whereas scenarios II and IV incorporate the string-specific degradation states.
• Figure 3: Exemplary day of operation: SOC trajectories for strings A and B alongside the intraday price signal. While the SOC of string A (dotted blue and green) remains consistent in both scenarios, the SOC of the aged strings B (blue and orange) differs due to different model accuracies.
• Figure 4: Cumulative revenues and SOH degradation of strings A and B in scenarios 1 and 2. While string A performs similarly in both cases due to consistent modeling, string B in scenario II benefits significantly from accurate aging representation, achieving higher revenues with slightly lower degradation compared to string B in scenario I.
• Figure 5: Cumulative revenues and SOH degradation of strings A and B in scenarios III and IV. Scenario IV shows higher performance for the aged string B (orange), with highest revenue due to the combined effect of aging-cost-aware optimization and accurate subsystem modeling. Scenario III, lacking this precision, shows lower performance despite using the same objective function.