Life Cycle Cost Modeling and Financial Benefit Assessment of Energy Storage Systems Incorporating Digital Twins

INTRODUCTION: Energy storage systems are increasingly evaluated under dynamic operating conditions in which battery degradation, efficiency decay, maintenance events, replacement timing, outage losses, and residual value jointly affect investment returns. However, conventional life-cycle cost and financial assessment methods mainly depend on static annual assumptions, making it difficult to explain how operational states are transformed into cost events and how these events change cash flow. OBJECTIVES: This paper proposes a digital twin-driven economic health assessment model for life-cycle cost modeling and financial benefit evaluation of energy storage systems. Its main contribution is an economic state space that synchronizes SOC, SOH, temperature, depth of discharge, cycle count, conversion efficiency, alarm records, maintenance status, and life-cycle stage labels with cost-event generation. METHODS: A window-level state mapping mechanism is further developed to identify available capacity, degradation level, efficiency loss, maintenance and replacement triggers, outage costs, and residual value recovery, and to aggregate them into annual cost streams. Based on the same cost streams and scenario revenue parameters, NPV, IRR, PBP, and LCOS are calculated through a unified financial evaluation chain. RESULTS: Experiments on an industrial energy storage project show that the proposed model achieves an SOH mean absolute error of 0.82%, a total life-cycle cost error of 2.18%, an NPV of 7.18 million yuan, and an LCOS of 0.533 RMB·kWh⁻¹ under the cost-aware dynamic operating strategy. CONCLUSION: The results demonstrate that the model improves the traceability and adaptability of energy storage economic assessment and provides analytical decision support for dispatch and maintenance planning; automatic actuation of the physical asset is outside the scope of the present study.