  The increasing demand for direct electric energy in the grid is also tied to
the increase of Electric Vehicle (EV) usage in the cities, which eventually
will totally substitute combustion engine Vehicles. Nevertheless, this high
amount of energy required, which is stored in the EV batteries, is not always
used and it can constitute a virtual power plant on its own. Bidirectional EVs
equipped with batteries connected to the grid can therefore charge or discharge
energy depending on public needs, producing a smart shift of energy where and
when needed. EVs employed as mobile storage devices can add resilience and
supply/demand balance benefits to specific loads, in many cases as part of a
Microgrid (MG). Depending on the direction of the energy transfer, EVs can
provide backup power to households through vehicle-to-house (V2H) charging, or
storing unused renewable power through renewable-to-vehicle (RE2V) charging.
V2H and RE2V solutions can complement renewable power sources like solar
photovoltaic (PV) panels and wind turbines (WT), which fluctuate over time,
increasing the self-consumption and autarky. The concept of distributed energy
resources (DERs) is becoming more and more present and requires new solutions
for the integration of multiple complementary resources with variable supply
over time. The development of these ideas is coupled with the growth of new AI
techniques that will potentially be the managing core of such systems. Machine
learning techniques can model the energy grid environment in such a flexible
way that constant optimization is possible. This fascinating working principle
introduces the wider concept of an interconnected, shared, decentralized grid
of energy. This research on Reinforcement Learning control strategies for
Electric Vehicles and Renewable energy sources Virtual Power Plants focuses on
providing solutions for such energy supply optimization models.
