An Efficient Hybrid Technique for Power Flow Management in Smart Grid with Renewable Energy Resources

Abstract

ABSTRACT In this paper, an optimal power flow management of a hybrid renewable energy source (HRES) with a hybrid approach is proposed. Here, the proposed method is the consolidation of Improved Bear Smell Search (IBSS) and Sparrow Search Algorithm (SSA); hence, it is named as IB4SA technique. Here, the IBSS generates the voltage source inverter control signals based on the power exchange variety between the source side and load side. In the proposed work, the Bear Smell Search (BSS) is incorporated by crossover and mutation function, therefore it is named as IBSS. The multi-objective function is designed by the grid needed active with reactive power varieties generated based on the available source power. SSA process guarantees the detection of online control signals using a parallel implementation against active with reactive power varieties. The control method based on the proposed approach improves the control parameters of the power controller under power flow variations. The power flow management of the smart-grid system is controlled using the proposed technique based on variations in the parameters of the source and load side. The proposed technique is responsible for controlling energy sources using both renewable energy sources and energy storage devices, in order to generate the power requirement by the grid. The proposed approach is executed in MATLAB/Simulink work site and the performance is analyzed with the existing approaches. The statistical analysis for Case 1, 2, and 3 using proposed as well as existing approaches are analyzed. In Case 1, using the proposed technique the parameter of with mean represents 0.528, median represents 0.512 and standard deviation represents 0.033. In Case 2, using the proposed technique the parameter of with mean implies 0.671, median implies 0.656, and standard deviation implies 0.026.