This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on N−1 criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically.

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.