This paper proposes a new collaborative pricing scheme for a power-transportation coupled network based on the variational inequality (VI) approach. In the proposed scheme, nodal electricity prices and congestion tolls on roads and at charging stations are considered to coordinate the coupled networks in order to minimize the operational cost of the whole system. The prices are determined by a second-order cone-based AC power flow model and a mixed user equilibrium model, respectively. A collaborative pricing model (CPM) is then built based on the two models and the interactions between them. In order to avoid the intractability of the developed non-convex model, the CPM is transformed into the VI formulation. With proven existence and uniqueness of solutions of the VI formulation, a new prediction-correction algorithm is proposed to accelerate the solution of the CPM problem, which is guaranteed to converge to the optimal solution. The proposed models and algorithm are verified using case studies on a real-world test system. The results show that the proposed pricing scheme can reduce the operational cost and the proposed algorithm shows improved convergence and higher computation efficiency compared with the existing algorithms.

The high proliferation of electric vehicles has intensified the interdependency between traffic networks (TNs) and power distribution networks (PDNs). Accordingly, this article proposes a decentralized optimization framework for the multiarea optimal traffic-power flow (OTPF) problem, in which the models of PDNs and TNs are established separately in a distributed manner. A multistage distributionally robust optimization (MDRO) model is formulated for PDNs to address wind power uncertainty, with the introduction of a traffic assignment problem to describe the distribution of traffic flows in TNs. Furthermore, an improved alternative direction method of multipliers (I-ADMM) algorithm is developed to solve the multiarea OTPF problem. Numerical results from a three-area traffic-power coupled system demonstrate that the proposed MDRO model bears a cost $89.50 lower than that of the multistage robust optimization model, while the I-ADMM algorithm yields a solving time only one-third that of the traditional ADMM.

The development of DC distribution network technology has provided a more efficient way for renewable energy accommodation and flexible power supply. A two-stage stochastic scheduling model for the hybrid AC/DC distribution network is proposed to study the active-reactive power coordinated optimal dispatch. In this framework, the wind power scenario set is utilized to deal with its uncertainty in real time, which is integrated into the decision-making process at the first stage. The charging/discharging power of ESSs and the transferred active/reactive power by VSCs can be adjusted when wind power uncertainty is observed at the second stage. Moreover, the proposed model is transformed into a mixed integer second-order cone programming optimization problem by linearization and second-order cone relaxation techniques to solve. Finally, case studies are implemented on the modified IEEE 33-node AC/DC distribution system and the simulation results demonstrate the effectiveness of the proposed stochastic scheduling model and solving method.