Collaborative Planning of Fast Charging Stations with Solar PV in Urban Power and Mobility Systems

Wide deployment of electric vehicles (EVs) requires the investment of new charging infrastructures and brings the security issues on the grid. In this paper, a two-stage collaborative planning strategy is proposed for location selection of fast charging stations (FCSs) to achieve optimal planning and scheduling with guaranteed constraints of power network (PN) and transportation network (TN). In the scheduling model, a mixed-integer linear programming (MILP) model is formulated based on the integration of a PN and a TN, in which an extended road model is established with consideration of various vehicle types, such as EVs and gasoline vehicles (GVs), to achieve optimal operation of the coupled power and transportation systems. In the planning model, FCSs and solar PV systems are established considering both investment and scheduling costs. Finally, the SOS2 method is used to linearize the TN model and the particle swarm-genetic algorithm (PSO-GA) is used to solve the nonlinearity caused by the coupling between the planning model and the scheduling model. The effectiveness and applicability of the proposed planning strategy are validated using the IEEE 33 bus power system and 12 node transportation system.