Solar Power driven EV Charging Optimization with Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize electric vehicle charging schedules, prioritizing solar energy use and reducing electricity costs in residential settings.

Contribution

It introduces a novel DQN-based EV charging policy that effectively shifts charging to high solar generation periods, enhancing renewable energy utilization.

Findings

Reduces electricity bills by 11.5% on average.

Achieves 88.4% solar power utilization.

Demonstrates effectiveness using real-world data.

Abstract

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