This chapter explores the coordinated charging dispatching strategies of electric vehicles (EVs) within a competitive electricity market ecosystem from the viewpoint of EV aggregators, aiming to facilitate large-scale EV integration into power grids. A two-stage energy management system is constructed for EV charging, aligned with the operational guidelines of the Guangdong electricity market. This framework encompasses a day-ahead bidding stage and a real-time charging dispatching stage. During the day-ahead phase, the aggregators formulate optimal energy bids by accounting for various uncertainties inherent to EV charging dynamics, e.g., variability in user arrival/departure timings and initial state of charge, alongside volatilities in market-clearing prices. To hedge against potential financial penalties, this stage integrates projections of future real-time market scenarios. Subsequently, in the real-time phase, a rolling-horizon optimization engine dynamically recalibrates charging protocols to adhere closely to pre-submitted day-ahead schedules, thereby minimizing deviation-related settlement costs. To accommodate the diverse preferences of individual EV users, two distinctive operational modes are engineered: Centralized Management Mode (CMM) and Distributed Incentive Mode (DIM). Under the CMM, the aggregator exerts direct control over charging operations in return for preferential electricity rates. The DIM mode grants users autonomy over their devices, incentivizing them to respond to real-time price signals independently. For simulation purposes, stochastic processes are employed to generate realistic EV charging profiles. Additionally, four distinct real-time price trajectories are modeled by using multi-class support vector machines. Case study results validate that aggregators can generate substantial revenues by capitalizing on the flexibility of EV charging assets, with bidding strategies being primarily dictated by the price differentials between day-ahead and real-time markets. The proposed methodology effectively aligns actual EV load profiles with intended bidding volumes, significantly reduces the computational burden associated with large-scale optimization, and underscores significant market opportunities available to the aggregators in modern energy trading ecosystems.