This dissertation is to study the interplay between large-scale electric vehicle (EV) charging and the power system. We address three important issues pertaining to EV charging and integration into the power system: (1) charging station placement, (2) pricing policy and energy management strategy, and (3) electricity trading market and distribution network design to facilitate integrating EV and renewable energy source (RES) into the power system. For charging station placement problem, we propose a multi-stage consumer behavior based placement strategy with incremental EV penetration rates and model the EV charging industry as an oligopoly where the entire market is dominated by a few charging service providers (oligopolists). The optimal placement policy for each service provider is obtained by solving a Bayesian game. For pricing and energy management of EV charging stations, we provide guidelines for charging service providers to determine charging price and manage electricity reserve to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on the power system. Two algorithms --- stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) are applied to derive the pricing and electricity procurement strategy. We design a novel electricity trading market and distribution network, which supports seamless RES integration, grid to vehicle (G2V), vehicle to grid (V2G), vehicle to vehicle (V2V), and distributed generation (DG) and storage. We apply a sharing economy model to the electricity sector to stimulate different entities to exchange and monetize their underutilized electricity. A fitness-score (FS)-based supply-demand matching algorithm is developed by considering consumer surplus, electricity network congestion, and economic dispatch.