  Efforts in the fight against Climate Change are increasingly oriented towards
new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper
legislation, the European Union (EU) defined the Renewable Energy Community
(REC) as a local electrical grid whose participants share their self-produced
renewable energy, aiming at reducing bill costs by taking advantage of proper
incentives. That action aspires to accelerate the spread of local renewable
energy exploitation, whose costs could not be within everyone's reach. Since a
REC is technically an SG, the strategies above can be applied, and
specifically, practical Energy Management Systems (EMSs) are required.
Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for
REC cost minimization to evaluate its superiority over a local self-consumption
approach. EU technical indications (as inherited from Italy) are diligently
followed, aiming for results that are as realistic as possible. Power flows
between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage
Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A
hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented
with the GA encoding the FIS parameters. Power generation and consumption,
which are the overall system input, are predicted by a LSTM trained on
historical data. The proposed hierarchical model achieves good precision in
short computation times and outperforms the self-consumption approach, leading
to about 20% savings compared to the latter. In addition, the Explainable AI
(XAI), which characterizes the model through the FIS, makes results more
reliable thanks to an excellent human interpretation level. To finish, the HEMS
is parametrized so that it is straightforward to switch to another Country's
technical legislation framework.
