A stochastic deep reinforcement learning agent for grid-friendly electric vehicle charging management

Abstract

Electrification of the transportation sector provides several advantages in favor of climate protection and a shared economy. At the same time, the rapid growth of electric vehicles also demands innovative solutions to mitigate risks to the low-voltage network due to unpredictable charging patterns of electric vehicles. This article conceptualizes a stochastic reinforcement learning agent that learns the optimal policy for regulating the charging power. The optimization objective intends to reduce charging time, thus charging faster while minimizing the expected voltage violations in the distribution network. The problem is formulated as a two-stage optimization routine where the stochastic policy gradient agent predicts the boundary condition of the inner non-linear optimization problem. The results confirm the performance of the proposed architecture to control the charging power as intended. The article also provides extensive theoretical background and directions for future research in this discipline.