# Solar Power driven EV Charging Optimization with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.09479 · HTML: https://mlsanity.com/paper/2211.09479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)

## Built on by (most cited first, 1 of 1)

- [Optimal Scheduling of Electric Vehicle Charging with Deep Reinforcement Learning considering End Users Flexibility](https://mlsanity.com/paper/2310.09040/) (2023)

## Abstract (arXiv)

Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to address the challenge of domestic EV charging while prioritizing clean, solar energy consumption. Real Time-of-Use tariffs are treated as a price-based Demand Response (DR) mechanism that can incentivize end-users to optimally shift EV charging load in hours of high solar PV generation with the use of Deep Reinforcement Learning (DRL). Historical measurements from the Pecan Street dataset are analyzed to shape a flexibility potential reward to describe end-user charging preferences. Experimental results show that the proposed DQN EV optimal charging policy is able to reduce electricity bills by an average 11.5% by achieving an average utilization of solar power 88.4