Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large-scale wind power penetration

Integration of highly volatile wind generation causes reliability and grid issues for system operator (SO). Plug-in electric vehicles (PEVs) are mobile distributed source of active power that provides opportunity to use their battery storage for wind integration. The coordinated integration of wind volatility and PEVs fleet is studied under security-constrained unit commitment (SCUC) model. In this regard, a stochastic SCUC with PEVs considering wind integration and line contingency is proposed. Wind volatility and PEVs driving behavior uncertainty is modeled through Monte Carlo simulations (MCS) of large number of scenarios with associated probabilities. This scenario has been reduced by Kantorovich distance (KD) matrix-based backward reduction technique. Moreover, pre-line and post-line contingency AC optimal power flow is used for network constraints in SCUC (AC SCUC). Due to consideration of N-1 security criteria and wind power scenarios, the proposed model is mixed integer nonlinear programming (MINLP), which is computationally heavy and is thus solved by a two-stage programming Benders decomposition (BD) approach. Different case studies are examined on modified IEEE reliability test system (RTS). Comparative analysis explores the impact on overall operational costs, PEV cost, wind curtailment, and locational marginal price (LMP) for congestion management. Simulation results validate that the proposed model is technoeconomically suitable for large-scale wind power penetration.