  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
