ANN Based Solar Power Forecasting in a Smart Microgrid System for Power Flow Management

Under the current energy scenario, the growth of the existing grid is purely depending on the microgrids. Solar PV and Wind energy system are the two prominent microgrid sources that are widely and wisely accepted. The existence of the non-linear loads in the power system is indelible, but it is very necessary to avoid the power quality issues caused by them. These issues become worse during microgrid integration. Now Power system utilities are promoting the microgrid integration from the prosumers. The excess electricity will be purchased by the utility with a feed-in tariff which is acceptable by both the prosumer and the utility. So the microgrid integration with the grid should be done economically. Since the microgrids are variable power sources, they are hardly predictable in the nearby future. One of the major hindrances for proper grid integration is that solar power is unpredictable in nature. Power forecasting aids in economic integration, along with intelligent power flow management in the integrated grid system. In this paper, a Fuzzy Logic Controller(FLC) based intelligent power flow management is implemented for a smart microgrid system with two different microgrids. An Artificial Neural Network (ANN) based solar Power forecasting is also done using MATLAB/Simulink. Active Power sharing, Reactive Power compensation, and Harmonic Current eliminations are realized using a knowledge-based algorithm.