  Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems
(FIS) are nowadays widely adopted as hybrid techniques in commercial and
industrial environment. In this paper we present an interesting application of
the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing
decision making for power flow management tasks in the proposed microgrid model
equipped by renewable sources and an energy storage system, taking into account
the economical profit in energy trading with the main-grid. In particular, this
study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for
tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover
a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform
decision making in the microgrid. The HGA rationale focuses on a particular
encoding scheme, based on control genes and parametric genes applied to the
optimization of the FIS parameters, allowing to perform a reduction in the
structural complexity of the RB. This approach will be referred in the
following as fuzzy-HGA. Results are compared with a simpler approach based on a
classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned,
while the number of fuzzy rules is fixed in advance. Experiments shows how the
fuzzy-HGA approach adopted for the synthesis of the proposed controller
outperforms the classic fuzzy-GA scheme, increasing the accounting profit by
67\% in the considered energy trading problem yielding at the same time a
simpler RB.
