Intelligent load frequency control in an isolated wind-solar PV-micro turbine-diesel based micro-grid using V2G integration

Abstract

Abstract Modern power systems need more intelligence and flexibility to maintain and control a generation load balance from subsequent serious disturbances due to the emerging of more renewable energy sources. This problem is becoming more significant today because of the increasing number of micro-grids (MGs). MGs usually use renewable energies in electrical production those fluctuate naturally. So, fluctuation and usual uncertainties in power systems cause the conventional controllers to be less efficient to provide a proper load frequency control (LFC) performance for a wide range of operating condition. Therefore, this thesis presents an intelligent control technique which is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture for an isolated wind-Solar PV-micro turbine-diesel based micro-grid (MG) system using Vehicle-to-Grid (V2G) integration. Accordingly, the V2G technology, the electric vehicle (EVs) may act as mobile energy storage units that could be a better solution for the inadequate LFC capacity and thereby to improve the frequency stability in an isolated MG. The performance of the proposed intelligent controller (ANFIs) is compared with conventional proportional-integral-derivative (PID) controller, Interval type-1 (IT1) Fuzzy controller and Interval type-2 (IT2) Fuzzy controller design methods. The results show that ANFIS based neuro-fuzzy LFC controller is having less settling time and improve dynamic responses for the considered MG system