Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

In this manuscript, a genetic algorithm (GA) is proposed for the power management (PM) of a grid integrated hybrid distributed generation (DG) system. The hybrid distribution generation (DG) system incorporates photovoltaic (PV), wind turbine (WT), fuel cell (FC) and battery. The power fluctuations are produced in the distributed generation system, because the hybrid resource utilization and the generation of power is changeable. The major purpose of the proposed control method is “to control the power flow (PF) of active with reactive energy amid the source and grid side. The proposed GA-based power control system is mainly utilized for optimizing active with reactive power flow controllers. By controlling charge with discharge of battery, the proposed system met the energy requirement of the charge and managed the sensitivity of the charge. The proposed method provides an optimal power flow in DG systems. To evaluate the management of PF, the equality with inequality constraints is determined that is used to specify the accessibility of renewable energy sources (RES), the demand for electricity and storage components load level. The security of power system is improved with the help of proposed control system. Moreover, the battery is used to allow the renewable energy system and maintain a stable power output. The proposed method is activated in MATLAB / Simulink work site and the performance is compared with existing methods. The statistical analysis of mean, median and standard deviation (SD) are also analyzed for proposed with existing methods. The proposed technique mean value is 1.5784, median is 1.4892, SD is 0.5883.