Optimal Energy Storage Systems Sizing for High Renewable Penetration Systems Considering Mechanistic-Empirical Degradation Model

High penetration of wind and solar power require energy storage systems (ESSs) to provide grid flexibility. However, traditional planning methods often oversimplify battery aging, leading to inaccurate cost estimates. This paper proposes a mixed-integer linear programming (MILP) framework that integrates a physics-based solid electrolyte interphase (SEI) degradation model with life cycle cost analysis. The method linearizes the non-linear SEI growth mechanism to calculate stress-dependent service life and replacement costs directly within the optimization process. This approach allows for the simultaneous optimization of ESS sizing, investment timing and daily dispatch. Validation results show that the SEI-based model significantly reduces prediction errors when extrapolating to high depths of discharge (DoD). In a test distribution system, the integrated framework identifies an optimal operating DoD of 62%, representing an 11% reduction compared to baseline models. This adjustment reduces total system costs by 3% and extends battery service life by 18%. Furthermore, sensitivity analysis demonstrates that overestimating battery lifespan carries a much higher financial risk than underestimating it, suggesting that conservative parameter estimation is preferable for robust planning.