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 intelligent, 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) charge stations infrastructures both characterized by intermittent stochastic behavior.
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.
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.
Grid connected MG are local electric grids able to promote the local energy consumption and to support the stability of the power grid which is connected to.
In this project it is proposed a new model based on graph theory to represent and visualize the MG energy flows. It consists in focusing how much MG energy demand and energy production must be managed in real time with respect to shiftable loads or controllable energy sources, that can be managed with a certain degree of flexibility in a given time horizon. The analysis of such model can drive the synthesis of suitable MG Energy Management Systems, based on soft Computing techniques and optimized through evolutionary algorithms, following a data driven approach.