Energy management of multi-carrier microgrids including electric vehicles: A self-structuring type-3 fuzzy approach

This paper studies a microgrid comprising wind and solar systems, battery storage units, flywheels, diesel generators, and multi-carrier energy (MCE) systems, which are designed for simultaneous electricity and heat production. Additionally, electric vehicles (EVs) are considered by incorporating Vehicle-to-Grid (V2G) technology, into the frequency-tuning operations. The frequency control of the microgrid is achieved by taking into account the gas network and its peak consumption. The load distribution in the gas network is simultaneously considered alongside electric charge distribution, ensuring nonlinear frequency control. This integration is achieved using an adaptive non-singleton (NS) type-3 (T3) fuzzy logic controller (FLC). In the introduced controller, there is no need for predefined models of various units and related equations that represent the relations of frequency deviation with different factors. The FLC parameters, including fuzzy set parameters, rule parameters, and level of slices, are adaptively tuned using a nonlinear Square Root Cubature Kalman Filter (SCF). Also, the Gaussian fuzzy sets consider and model the sensor noise and measurement errors of the inputs. The designed controller uses a small number of rules and is quickly adopted with system conditions. Furthermore, the fully self-structuring scheme improves the system’s resilience against disturbances and uncertainties. The results demonstrate that the suggested approach achieves a 16%/22% improvement in root-mean-square (RMS) values, and a 79%/81% reduction in maximum frequency deviation in comparison to conventional FLC.