Comparing Optimal and Adaptive EV Charging in Smart Cities: MILP vs. Reinforcement Learning

The coordinated scheduling of electric vehicle (EV)
charging is a critical challenge for smart cities, particularly in
high-density infrastructure such as Mobility Hubs (MHs). This
paper evaluates and compares two prominent approaches to the
EV Charging Scheduling Problem (CSP): Mixed-Integer Linear
Programming (MILP) and Reinforcement Learning (RL). We
formulate a shared problem framework and apply both strategies
under two structured scenarios: a small-scale deterministic
benchmark and a medium-scale, realistic deployment with higher
heterogeneity. Results show that MILP achieves optimal cost and
SoC compliance in tractable cases but struggles with scalability.
RL, based on Proximal Policy Optimization (PPO), achieves nearoptimal
performance while scaling to 100 EVs with minimal
computation time. Despite occasional SoC deviations, the RL
agent exhibits robust and adaptive behavior under dynamic
conditions. This study offers actionable insights for selecting
and deploying EV scheduling strategies in real-world urban
environments.