In this paper, a generalized formulation for intelligent
energy management of amicrogrid is proposed using artificial
intelligence techniques jointly with linear-programming-based
multiobjective optimization. The proposed multiobjective intelligent
energy management aims to minimize the operation cost and
the environmental impact of a microgrid, taking into account its
preoperational variables as future availability of renewable energies
and load demand (LD). An artificial neural network ensemble
is developed to predict 24-h-ahead photovoltaic generation and
1-h-ahead wind power generation and LD. The proposed machine
learning is characterized by enhanced learning model and generalization
capability. The efficiency of the microgrid operation
strongly depends on the battery scheduling process, which cannot
be achieved through conventional optimization formulation. In
this paper, a fuzzy logic expert system is used for battery scheduling.
The proposed approach can handle uncertainties regarding to
the fuzzy environment of the overall microgrid operation and the
uncertainty related to the forecasted parameters. The results show
considerable minimization on operation cost and emission level
compared to literature microgrid energy management approaches
based on opportunity charging and Heuristic Flowchart (HF)
battery management.