An intelligent controller for power flow management in a smart Microgrid system with power quality enhancement

Abstract

The widespread popularity of renewable and sustainable sources of energy like solar and wind newlinecalls for the integration of renewable energy sources into electrical power grids for sustainable newlinedevelopment. Microgrids minimize power quality issues in the main grid by acting as active newlinefilter supports, furnishing reactive power compensation, harmonic mitigation, and load balancing newlineat the Point of Common Coupling(PCC). The reliability issues faced by standalone DC newlinemicrogrids can be managed by interlinking microgrids with the main power grid. An Artificial newlineIntelligence based Icosand#981;(phi) Control Algorithm for Power Sharing and Power Quality Improvement newlinein Smart Microgrid Systems is proposed here to render the microgrid integrated power system newlinemore intelligent. The proposed controller considers various uncertainties caused by load variations, newlinethe state of charge of the battery of microgrids, and power tariffs based on the availability newlineof power in microgrids. This work presents a detailed analysis of the integration of wind and newlinesolar microgrids with the grid for dynamic power flow management to improve power quality newlineand reduce the burden, thereby strengthening the central grid. A smart grid system with newlinemultiple Smart Microgrids coupled with Renewable Energy Sources(RES) with tariff control newlineand judicious power flow management is simulated, targeting power-sharing and power quality newlineimprovement. RES plays a pivotal role in broadening the supply of inexhaustible energy with newlineless carbon emission, and with proper power, forecasting can help overcome its intermittent newlinenature and aid in intelligent power flow management. Power forecasting of two microgrids, newlineone with a solar source and another with a wind source, is performed using Artificial Neural newlineNetwork(ANN). The regression plot and the error histogram obtained show the accuracy of newlinethe ANN controller. The simulation of power flow management of the smart microgrid system newlineis tested and analyzed, and the proposed system is compared with other existing systems.