The increasing global spread of electric vehicles (EVs) has introduced significant interdependence between transportation and power networks. Most previous studies on coupled networks focus on the formation of equilibrium states between transportation and distribution networks but neglect diminishing the adverse influences of the interaction, especially the voltage quality problems caused by large and fluctuating charging loads. This paper constructs a dynamic interaction model of coupled networks considering both economic and safe operation. First, the dynamic traffic assignment problem is established considering both departure time and route choices, and a dynamic queuing model is designed to describe the EV queuing behaviors at fast charging stations (FCSs). Then, the charging load fluctuation toll is designed as an ancillary toll at FCSs and the bus voltage deviation penalty is considered in optimal power flow to guide the interaction of two networks and tackle the voltage deviation issue. Moreover, the equilibrium state is designed as a fixed-point problem and the solution’s existence is proved under mild assumptions. Third, linearization and convex relaxation techniques are used to improve computational efficiency and a Monte Carlo simulation technique is developed to evaluate the influence of uncertain travel demands on coupled networks. Numerical simulations of coupled networks interaction analyses in deterministic and uncertain conditions are presented.

Thermostatically controlled loads (TCLs) have huge thermal inertia and are promising resources to promote consumption of renewable energy sources (RESs) for carbon reduction. Thus, this paper employs the virtual power plant (VPP) to regulate TCLs to address problems caused by RESs. Specifically, a two-stage VPP scheduling framework based on multi-time scale coordinated control of TCLs is proposed to address forecast errors of variable RES power output. In the first stage (hour time scale), TCLs are controlled as virtual generators to mitigate forecast errors between hour-ahead and day-ahead RES power. In the second stage (minute time scale), TCLs are regulated as virtual batteries to mitigate forecast errors between intra-hour and hour-ahead RES power. To respect wills and preferences of end-users, a transactive energy (TE) market within VPP is built to guide TCL behaviors via the price mechanism. Moreover, a stochastic VPP schedule using the Wasserstein-metric-based distributionally robust optimization method is developed to consider RES power uncertainties, and its solution process is transformed into a computationally tractable mixed-integer linear programming problem based on the affine decision rule and duality theory. The proposed method is effectively validated by comparison with robust optimization and stochastic optimization. Simulation results demonstrate the proposed two-stage VPP scheduling method employs TCL flexibilities more comprehensively to mitigate RES output power forecast errors in VPP operations.