Synthesis of an evolutionary Fuzzy multi-objective energy management system for an electric boat

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.