Ricerc@Sapienza

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 growing penetration of non-programmable energy sources will largely contribute to intensify the renewable capacity firming issues. Providing a higher systems flexibility, i.e. the ability to match the supply and the demand sides as much as possible, is the main challenge to cope with, by adopting new energy planning paradigms. In this framework, different combined strategies, aiming at efficiently integrating that large amount of variable RES (VRES), have to be implemented.

The paper proposes real case of application of a smart integration of Electrical Vehicles (EVs) charging stations with Dispersed Generation (DG) that can guarantee the maximum exploitation of the system and its sustainability. The case study is a real distribution grid of e-distribuzione, the main Italian Distribution System Operator (DSO). Different kinds of EVs charging systems and spread scenarios are hypothesized and different Optimal Power Flow (OPF) models, with different objective function, reflecting different needs of DSO, have been implemented and tested.