The increasing penetration of electric vehicles (EVs) and emerging dynamic wireless charging techniques have strengthened the coupling between traffic networks and power distribution networks. This increased coupling necessitates greater coordination between the two networks. This paper proposes a multi-period optimal traffic and power flow model that considers time-varying electricity and traffic demands. The distribution of traffic flow is represented by a semi-dynamic traffic assignment (SDTA) model, which considers flow propagation between adjacent periods. Combined second-order cone, convex hull, and McCormick envelope relaxations are employed to convexify the power and traffic flow model. Optimization-based bound tightening (OBBT) method combined with a heuristic sequential bound tightening (SBT) method is employed to improve the tightness of the relaxation. The modeling of multi-period scheduling provided by the SDTA model is thoroughly compared with that provided by the conventional static traffic assignment model. In addition, the proposed traffic and power flow model is employed to conduct congestion analysis of the coupled networks. Numerical results on two test systems demonstrate both the spatial and temporal impacts of congestion on each of the coupled networks. Moreover, numerical results verify that the proposed OBBT-SBT-based convex relaxation is sufficiently tight.

The increasing penetration of electric vehicles (EVs) results in growing interdependency between power and transportation networks. This paper proposes a hybrid economic-emission dispatch model to coordinate the operation of both networks toward social optima. Specifically, carbon emission from gasoline vehicles (GVs) is quantified by the macroscopic emission model, while the emission from EVs is indirectly characterized by that emitted from fossil power plants. To enhance regulation efficiency and improve social welfare, a differentiated pricing scheme is proposed to independently regulate GV traffic flow, EV traffic flow, and EV charging flow. Logarithmic transformation, combined with an accuracy-aware adaptive piecewise linearization (AAPWL) approximation method, is developed to reformulate the original nonlinear model into a tractable mixed-integer quadratic constrained programming. Numerical results from two test systems demonstrate the relationship between economic dispatch and emission dispatch. Meanwhile, the effectiveness and superiority of the proposed pricing scheme and the AAPWL method are validated.

The growing penetration of electric vehicles results in interdependence between power and transportation networks, in which the coordination among drivers and network operators is highly desirable for raising the respective benefits. Considering the self-interest behavior of each entity, this article develops a Nash–Stackelberg–Nash game framework to model their noncooperative interactions. Specifically, the upper level power and transportation system operators determine monetary incentives independently to achieve the respective economic-emission dispatch, while each lower level driver reacts to the incentive and makes route/charging choice to minimize his own travel cost. To mitigate the upper bounding issue in the existing tariff-based incentive scheme (i.e., the upper bound of the tariff needs to be specified manually), we propose a subsidy-based method to evoke the self-discipline of the upper level operators and to facilitate public acceptance. The bilevel model is transformed into two single-level subproblems through the Karush–Kuhn–Tucker conditions, and a Gauss–Seidel iterative algorithm is developed to identify an operational equilibrium. Numerical experiments demonstrate the performance of the noncooperative operation and the efficacy of the subsidy-based scheme by comparing them with the cooperative operation and the tariff-based scheme, respectively.

With the proliferation of electrical vehicles (EVs), tariff-based monetary incentives (e.g., charging service fees, electricity prices) have been widey adopted by network operators to efficiently manage EV charging demands. The imposed tariff would enforce drivers to bear an extra travel expense for satisfying the charging demand distributions favored by network operators. Accurately modeling users' responsive behavior to price signals is a prerequisite for properly designing the tariff and achieving the desired goal. The present work bridges the gap of existing studies by proposing an augmented user equilibrium (AUE) model that captures EV users' equity-aware decision makings and closed-loop commuting behavior. The model enables network operators to consider 1) the acceptability of EV travelers on regulation policies and 2) the elasticity of EV charging demand as well its rebound effect. Based on the AUE model, we further investigate the optimal pricing and energy sharing strategy of the charging network operator, who aims to maximize his/her profit by tapping the flexibility of EVs and scheduling energy transactions among charging stations while respecting the operating constraints of power and transportation networks. Finally, a relaxation based iterative algorithm is devised to efficiently solve the mathematical problem with complementarity conditions. Numerical results on two test systems validate the effectiveness and merits of the proposed models and methods.

The increasing prevalence of electric vehicles (EVs) and emerging dynamic wireless charging techniques have increased the interdependence between the power and transportation systems. This paper investigates a new nexus scenario—the electrified highway network (EHN) and power transmission network (PTN). The independent but interrelated operation of the two networks is analyzed. On the EHN side, a combined charging–driving navigation (CCDN) model, which rigorously considers EV travel speed and charge/discharge behaviors, is proposed and formulated as an irregular dynamic programming problem. A chronological search algorithm is designed to derive the optimal charging–driving decision sequences. Economic operation of the PTN is formulated as an direct current optimal power flow problem. Hourly Location Marginal Prices (LMPs) and charge/discharge demands are exchanged between the two networks. The above LMP-based interaction forms a Nash-type game, in which both networks aim to minimize their own operation costs. A best-response decomposition algorithm combined with a continuous LMP method is developed to identify the equilibrium state. Numerical results demonstrate the effectiveness of the CCDN model in modeling EV charging–driving behaviors in the EHN. Moreover, the results show that under certain conditions, mobility of EVs along the EHN can economically relieve the transmission congestion and reduce the operation costs of both networks.