Digital Twin-Driven State Estimation and Control for EV Adjustable Capability under Constraint Drift

With the rapid growth of electric vehicles (EVs), EV flexibility has become a key resource for enhancing distribution network resilience and facilitating renewable energy integration. However, the time-varying physical constraints of distribution networks, the dynamic response of electricity market incentives, and the uncertainty in EV user behavior make it difficult to accurately estimate adjustable capability in real time. To address this problem, this paper proposes a Digital Twin (DT)-driven state estimation and control framework for online state estimation of EV adjustable capability under constraint drift. First, a multi-dimensional physical model is established by coupling distribution-network security constraints, electricity-market incentive constraints, and EV operation constraints. Second, a digital twin historical scenario library is constructed to support scenario similarity matching, state-feature fusion, and drift-intensity quantification. Third, a hierarchical state-drift estimation method is designed to distinguish single-constraint drift from multi-constraint interactive drift. Finally, a twin-driven boundary evolution and distributed control execution strategy is developed by combining adaptive state estimation, two-stage warm start, and the alternating direction method of multipliers. Case studies based on charging-pile operational data in Tianjin show that the proposed method can dynamically track constraint drift, update EV adjustable boundaries, and provide high-precision state estimates for distribution-network dispatch.