This chapter provides a comprehensive exploration of the critical challenges and advanced solutions associated with the optimal planning, scheduling, and market participation of plug-in electric vehicles (PEVs) within active distribution networks and competitive electricity markets, while also addressing the emerging complexities of power-transportation coupling. The research is motivated by the significant uncertainties in PEV charging behaviors, which pose threats to grid safety and economics. The book is organized to first tackle these foundational issues through three primary research domains. Initially, it addresses the need for accurate spatiotemporal charging demand forecasting and the coordinated planning of charging facilities with distributed energy sources, proposing novel methodologies like time-electricity quantity analysis and Markov models to capture the stochastic nature of PEV usage. Subsequently, the focus shifts to the operational strategies of electric vehicle aggregators in electricity markets, where it delves into risk-averse bidding strategies, the design of agency models to secure charging flexibility from users, and the management of multi-entity competition, utilizing frameworks like Stackelberg games and conditional value at risk to balance profitability and uncertainty. Finally, the book explores the cutting-edge field of fast charging station optimization within coupled power-transportation networks, explicitly incorporating users’ bounded rationality into traffic flow and charging behavior models. It introduces innovative concepts such as bounded rationality dynamic user equilibrium models and three-layer game-theoretic pricing strategies to guide station operations and enhance grid interaction. Furthermore, the scope extends to the coordinated operation of multi-energy integrated service stations, proposing risk-averse dispatch methods for clusters offering electric, hydrogen, and gas refueling services. Through these interconnected parts, the book systematically develops and validates a suite of analytical, game-theoretic, and stochastic optimization methods, ultimately providing a robust theoretical and practical framework for enabling a secure, efficient, and economically viable integration of electric vehicles into future low-carbon, sustainable energy and transportation systems.