Multiphysics-embedded co-optimization of data centers and energy storage systems

This paper proposes an aging-aware joint op-timization framework for data center-grid-energy storage systems that dynamically balances operational economy with battery lifetime preservation. Unlike traditional open-loop degradation models, the proposed method embeds the complex P2D battery degradation model into the optimiza-tion loop. A simplified surrogate model is established within the system-level scheduling, and the P2D model is used to collect input parameters for more accurate aging simula-tions. The updated aging parameters are then fed back into the surrogate model, enabling real-time correction and up-dating. The optimization problem incorporates thermal unit generation and ramping costs, state-of-charge and depth-of-discharge constraints for battery energy storage systems (BESS), and power flow limits, producing schedul-ing strategies that adapt to both macro-level grid conditions and micro-level degradation mechanisms. The proposed framework is validated on the IEEE 30-bus system with co-located data center nodes, and four cases are compared to examine the impacts of aging cost modeling and dynamic electrochemical feedback. Results show that enabling stor-age reduces total operating cost by up to 5.21% compared with the baseline, while incorporating aging cost suppresses deep cycling and mitigates battery wear. The dynamic feed-back strategy further reduces daily aging cost by 16.7% rel-ative to the static model and extends projected battery life-time by about 17%, demonstrating its long-term cost-effec-tiveness. Overall, the proposed study establishes a scalable multiphysics-embedded optimization framework that cou-ples system-level dispatch with real-time electrochemical degradation feedback, providing a practical pathway for lifecycle-oriented operation in future power systems with high data center penetration.