Proactive Resilience Enhancement of Power Systems with Link Transmission Model-Based Dynamic Traffic Assignment Among Electric Vehicles

Abstract

The rapid development of electric vehicles (EVs) is strengthening the bi-directional interactions between electric power networks (EPNs) and transportation networks (TNs) while providing opportunities to enhance the resilience of power systems towards extreme events. To quantify the temporal and spatial flexibility of EVs for charging and discharging, a novel dynamic traffic assignment (DTA) problem is proposed. The DTA problem is based on a link transmission model (LTM) with extended charging links, depicting the interaction between EVs and power systems. It models the charging rates as continuous variables by an energy boundary model. To consider the evacuation requirements of TNs and the uncertainties of traffic conditions, the DTA problem is extended to a two-stage distributionally robust version. It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs. The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers. Case studies are performed on two test networks. The effectiveness is verified by the numerical results, e.g., reducing the load shedding amount without increasing the unmet traffic demand.