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

arXiv: https://arxiv.org/abs/2210.07013 · HTML: https://mlsanity.com/paper/2210.07013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.09374/) (2019)

## Built on by (most cited first, 2 of 2)

- [Collective Large-scale Wind Farm Multivariate Power Output Control Based on Hierarchical Communication Multi-Agent Proximal Policy Optimization](https://mlsanity.com/paper/2305.10161/) (2023)
- [Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits](https://mlsanity.com/paper/2308.00218/) (2023)

## Abstract (arXiv)

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.