Ricerc@Sapienza

In the last few years, most of the western countries, alerted by the increasing of the populations and energy consumption with the consequent increment of fossil fuel emissions, started to promote and adopt several strategies aimed to an efficient, clean and sustainable production and utilization of the energy, especially in the transportation, the industrial and the domestic field.
One of the most relevant issue in this context is the efficient integration of the Renewable Energy Sources (RES) and Electric Vehicle (EV) public charge stations infrastructures that are both characterized by intermittent stochastic power profiles.
The spreading of such power systems, if are not intelligently managed, would increase the risk of outages, the waste of energy, local faults and accelerate the deterioration of electric infrastructure, in particular, the distribution grid.
Obviously all these circumstances are reflected on the cost of the dispatched kWh.
The need of an electric grid supported by an ICT infrastructure and distributed Energy Storage Systems (ESSs) able to manage such kind of energy production and assuring the power demand, the safety and stability of the electric grid is growing more and more.
Grid connected-Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures.
In this project it is proposed a novel approach based on graph theory in order to represent and manage the MG energy flows. It consists in focusing how much MG energy demand and energy production must be managed in real time respect with can be shifted or more in general controlled with a certain degree of flexibility in a given time horizon. Such model must be able to efficiently support the realization of a MG Energy Management System based on machine learning techniques and optimized through evolutionary algorithm therefore a data driven approach.
Furthermore, different tools and problem assumptions are need to formulate a suitable application scenario.