Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources

TL;DR

This paper introduces a deep reinforcement learning approach for large-scale EV charging coordination in V2G systems with renewable energy, significantly reducing load variance and costs while demonstrating transfer learning capabilities.

Contribution

It presents a novel DRL-based continuous charging strategy for V2G that outperforms uncontrolled charging and exhibits strong transfer learning and scalability.

Findings

Load variance reduced by 97.37%

Charging cost decreased by 76.56%

Effective transfer learning to microgrids and weekly scheduling

Abstract

Due to the increasing popularity of electric vehicles (EVs) and the technological advancement of EV electronics, the vehicle-to-grid (V2G) technique and large-scale scheduling algorithms have been developed to achieve a high level of renewable energy and power grid stability. This paper proposes a deep reinforcement learning (DRL) method for the continuous charging/discharging coordination strategy in aggregating large-scale EVs in V2G mode with renewable energy sources (RES). The DRL coordination strategy can efficiently optimize the electric vehicle aggregator's (EVA's) real-time charging/discharging power with the state of charge (SOC) constraints of the EVA and the individual EV. Compared with uncontrolled charging, the load variance is reduced by 97.37$\%$ and the charging cost by 76.56$\%$. The DRL coordination strategy further demonstrates outstanding transfer learning ability to microgrids with RES and large-scale EVA, as well as the complicated weekly scheduling. The DRL coordination strategy demonstrates flexible, adaptable, and scalable performance for the large-scale V2G under realistic operating conditions.