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This work deals with the design of a Fuzzy Logic Control (FLC) based Energy Management System (EMS) for smoothing the grid power profile of a grid-connected electro-thermal microgrid. The case study aims to design an Energy Management System (EMS) to reduce the impact on the grid power when renewable energy sources are incorporated to pre-existing grid-connected household appliances. The scenario considers a residential microgrid comprising photovoltaic and wind generators, flat-plate collectors, electric and thermal loads and electrical and thermal energy storage systems and assumes that neither renewable generation nor the electrical and thermal load demands are controllable. The EMS is built through two low-complexity FLC blocks of only 25 rules each. The first one is in charge of smoothing the power profile exchanged with the grid, whereas the second FLC block drives the power of the Electrical Water Heater (EWH). The EMS uses the forecast of the electrical and thermal power balance between generation and consumption to predict the microgrid behavior, for each 15-minute interval, over the next 12 hours. Simulations results, using real one-year measured data show that the proposed EMS design achieves 11.4% reduction of the maximum power absorbed from the grid and an outstanding reduction of the grid power profile ramp-rates when compared with other state-of-the-art studies.

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The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.