Network-Aware Electric Vehicle Coordination for Value Stacking in Smart Power Grid

This thesis optimizes the scheduling and coordination of electric vehicle (EV) charging and discharging through a vehicle-to-everything (V2X) value-stacking framework. It evaluates the impact of prediction errors under energy system uncertainties, develops a distributed and privacy-preserving coordination method based on the Learning-Accelerated Asynchronous Alternating Direction Method of Multipliers (LAA-ADMM), and designs an aggregator-based coordination framework for large-scale EV participation in contingency frequency control ancillary services (FCAS) markets under uncertainty. Simulation results show that the proposed approaches improve the economic performance of EV coordination in distribution systems, delivering benefits to both EV-owning prosumers and grid operators.