Ricerc@Sapienza

In this paper we investigate a public Fast Charge (FC) station nanogrid equipped with a Photovoltaic (PV) system and an Energy Storage System (ESS) using second-life Electric Vehicle (EV) batteries. Since the nanogrid is intended for installation in urban areas, it is designed with a very limited connection with the grid to assure peak shaving and encourage PV autoconsumption.

The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their impact on the SG. As a consequence, an EV can be seen as a Nanogrid (NG) whose energy flows are optimized by an Energy Management System (EMS). In this work, an EMS for an electric boat is synthesized by a Fuzzy Inference System-Hierarchical Genetic Algorithm (FIS-HGA). The electric boat follows cyclic routes day by day.

The large-scale penetration of renewable energy sources is forcing the transition towards
the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new
methodologies for the dynamic energy management of distributed energy resources and foster to form
partnerships and overcome integration barriers. The prediction of energy production of renewable energy
sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool

The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand.

Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently integrate the generation from Distributed Energy Resources, to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System (ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and ?rst order Takagi-Sugeno rules.

The efficient integration of Renewable Energy Sources (RES) in the actual electrical grid has gained recently a high attention in the Smart Grids (SGs) research topic. The evolution of existing electric distribution networks into SGs can be accomplished gradually and conveniently through the installation of local grid-connected Microgrids (MGs), usually installed nearby the RESs and provided by Energy Storage Systems (ESSs). Each MG is in charge to manage connected RES, assuring the local power demand, as well as the safety and stability of the electric grid.

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS).

Microgrids (MGs) development is one of the most pursued solution for the electric grid modernization into smart grids, as an effective approach to achieve the European Funding Program Horizon 2020 targets. In particular, residential grid-connected MGs demand the active role of the customer into the electric market, the fulfilment of Demand Response (DR) services, and a local control of the distribution energy balance.

Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures. They are conceived to intelligently integrate the generation from Distributed Energy Resources (DERs), to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well.