Enhancing Resilience of Networked Microgrids Using V2G: A Distributed Model Predictive Control Approach With Non-Intrusive Battery Surrogate Model

Enhancing the resilience of networked microgrids (NMGs) through model predictive control-based vehicle-to-grid (V2G) strategies faces a key challenge due to the lack of real-time perception of electric vehicle (EV) batteries' states, making it difficult for the MPC to accurately predict and utilize V2G responses. To address this gap, this paper proposes a non-intrusive battery surrogate model (NBSM) based on an RC equivalent circuit. The NBSM effectively emulates an EV battery dynamics and enables the distributed model predictive control (DMPC) approach to predict states of EV batteries. It enables real-time V2G control informed by the EV response modeled by the NBSM, maintaining power tracking errors below 10%. Moreover, it provides a physical interpretation of the commonly used first-order inertia element in V2G response modeling. Building on this model, two resilience-oriented metrics-capturing frequency recovery and EV support sustainability-are formulated to quantify V2G contributions. These metrics are then embedded into a DMPC framework, yielding a frequency control strategy with tunable resilience objectives. Simulation results demonstrate that the proposed strategy led to an 11.2% improvement in frequency nadir (from -0.4071 Hz to -0.3615 Hz) and a 36.2% promotion in the resilience metric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {R}_{f}$</tex-math></inline-formula>. The proposed controller maintains robust performance across varying prediction horizons and weight settings. This scalable V2G framework effectively bridges the EV physical layer and grid dispatch layer, supporting resilience of NMGs.