Authors:
Emanuele Ferrandino
;
Antonino Capillo
;
Fabio Massimo Frattale Mascioli
and
Antonello Rizzi
Affiliation:
Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
Keyword(s):
Smart Grid, Electric Vehicles, Bidirectional Fast Charge, Renewable Energy Source, Vehicle-to-Grid, Grid-to-Vehicle, Microgrid, Nanogrid, Energy Management System, Fuzzy Logic, Evolutionary Computing, Hierarchical Genetic Algorithm.
Abstract:
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. Thus, single day training and test sets with a very short time step are chosen, with the aim of reducing the computational cost, without affecting accuracy. A convex optimization algorithm is applied for benchmark tests. Results show that the EMS succesfully performs the EV energy flows optimization. It is remarkable that the EMS achieves good performances when tested on different days than the one it has been trained on, further reducing the computationa
l cost.
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