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Authors: Antonino Capillo ; Enrico De Santis ; Fabio Mascioli and Antonello Rizzi

Affiliation: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Rome, Italy

Keyword(s): Energy Management System, Fuzzy System, Evolutionary Computation, Genetic Algorithm, Electric Vehicle, e-Boat.

Abstract: Even though it is known that Renewable Energy Sources (RESs) are necessary to face Climate Change and pollution, technology is still in a developement phase, aiming at improving energy exploitation from RESs, as these type of sources suffer from low energy density and variability over time. Thus, proper ICT infrastructures equipped with a robust software, i.e., Energy Management System (EMS), are needed to ensure that Renewable Energy (RE) does not go to waste. Relatively small local electrical grids called Microgrids (MGs) represent the EMS ecosystem, since their main features are the proximity between generation and loads and the presence of Energy Storage Systems (ESSs) adopted to recover surplus energy. The Vehicle-to-Grid (V2G) paradigm helps to realize the Smart City, which in substance is an interconnection of MGs hosting electrical vehicles for an efficient energy management at a larger scale. In this context, e-boats have only recently been considered. Hence, in this work a Multi-Objective (MO) EMS is synthesized for an e-boat docked in a small Microgrid (PV generator and ESS) with the aim of maximizing the charging time of the e-boat ESS and spending as little as possible both for energy purchase and also in terms of ESS wear. A Fuzzy Inference System - Hierarchical Genetic Algorithm (FIS-HGA) is used to achieve the Pareto Front, with the HGA that is in charge of optimizing the FIS parameters. Results laid to a balanced trade-off between the two objectives, since the e-boat ESS is almost fully charged in a reasonable time and with a low cost, compatible with people transportation. Last but not least, the inference process of a FIS is easily interpretable, in the perspective of an Explainable AI. (More)

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Paper citation in several formats:
Capillo, A.; De Santis, E.; Mascioli, F. and Rizzi, A. (2022). Synthesis of an Evolutionary Fuzzy Multi-objective Energy Management System for an Electric Boat. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - FCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 199-208. DOI: 10.5220/0011527800003332

@conference{fcta22,
author={Antonino Capillo. and Enrico {De Santis}. and Fabio Mascioli. and Antonello Rizzi.},
title={Synthesis of an Evolutionary Fuzzy Multi-objective Energy Management System for an Electric Boat},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - FCTA},
year={2022},
pages={199-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011527800003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - FCTA
TI - Synthesis of an Evolutionary Fuzzy Multi-objective Energy Management System for an Electric Boat
SN - 978-989-758-611-8
IS - 2184-3236
AU - Capillo, A.
AU - De Santis, E.
AU - Mascioli, F.
AU - Rizzi, A.
PY - 2022
SP - 199
EP - 208
DO - 10.5220/0011527800003332
PB - SciTePress