Large-scale Ev Charging Coordination for Grid-aware Valley-filling

The rapid proliferation of Electric Vehicles (EVs) presents a significant challenge to the stability of existing power distribution networks, primarily due to the risk of severe demand peaks from uncoordinated charging. This paper proposes a hybrid two-layer optimization framework to manage large-scale EV charging, ensuring grid stability while minimizing costs. The framework integrates a micro-simulation layer-which stochastically models realistic driving patterns and State of Charge (SoC) to determine heterogeneous energy demands-with a macro-optimization layer. This macro-layer utilizes a Particle Swarm Optimization (PSO) algorithm to solve a multi-objective problem. The optimization objectives are twofold: (1) minimizing the total electricity procurement cost for the aggregator, and (2) maximizing the grid load factor (i.e., load flattening or "valley-filling") by minimizing the variance of the total load profile. The model is validated through a large-scale case study of an urban area with 2.2 million EVs, segmented into "Home" and "Workplace" charging clusters. We utilize realistic technical specifications for battery capacity, energy efficiency, and charger power derived from a specific manufacturer's fleet (e.g., VinFast). Simulation results demonstrate that the proposed strategy successfully avoids peak-hour charging and strategically distributes the EV load across both low-price (night-time) and medium-price (midday) periods. This multi-objective approach achieves significant valley-filling, resulting in a much flatter total load profile compared to a naive cost-only optimization, thereby enhancing grid reliability and reducing operational expenditures.