A hybrid policy gradient and rule-based control framework for electric vehicle charging

Recent years have seen a significant increase in the adoption of electric vehicles, and investments in electric vehi-cle charging infrastructure and rooftop photo-voltaic installations. The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices. Exploiting this flexibility, however, requires smart control al-gorithms capable of handling uncertainties from photo-voltaic generation, electric vehicle energy demand and user's behaviour. This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building. The control objective is to maximize self-consumption of locally generated electricity and consequently, minimize the elec-tricity cost of electric vehicle charging. The performance of the proposed framework is evaluated on a real-world data set from EnergyVille, a Belgian research institute. Simulation results show that the proposed control frame-work achieves a 62 . 5% electricity cost reduction compared to a business-as-usual or passive charging strategy. In addition, only a 5% performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.

Automated summary

This paper introduces a control framework combining reinforcement learning and rule-based control to coordinate the charging of electric vehicles in an office building, aiming to maximize self-consumption of locally generated electricity and minimize the electricity cost of electric vehicle charging.