Data-analytics identification of heterogeneous component criticality in coupled transportation-power systems considering bidirectional cascading effects

The rapid development of electric vehicles (EVs) significantly accelerates the integration of the power grid and transportation network, which makes the bi-directional cascading effects in the coupled transportation-power system (CTPS). During the cascading effects propagation process, components can exhibit distinct and heterogeneous criticalities. However, existing research on critical component identification is conducted from the perspective of an isolated system, where the bi-directional cascading effects are seldom considered. To address this, this paper proposes a data-analytics identification framework for the component criticality in CTPS considering the bi-directional cascading effects. Firstly, a co-simulation method for the cascading effect is developed, where dynamic re-dispatch features of both the power grid and the transportation network are modelled. Thus, massive CTPS cascading effect data under different anticipated scenarios are simulated. Then, a data-analytics method based on the stochastic approach for link-structure analysis (SALSA) algorithm is developed to evaluate the heterogeneous criticalities for components. In this method, indicators for the cascading effect propagation path risk are analyzed considering both the probability and the impacts on the systems. Finally, numerical studies verify the effectiveness of the proposed method, where the identification results can effectively support prevention for critical components and risk management of the coupled system.