This study proposes a novel approach to power flow control in on-grid hybrid microgrids by combining AC-DC microgrids with a modified unified phase-to-phase power controller (UIPC). The AC microgrid and the DC microgrid represent a hybrid system that is normally connected to the grid. Instead of power converters connected in parallel, these microgrids are connected to one another via a modified UIPC. The first contribution of this thesis is a modification of the UIPC standard, which makes it possible to control the energy exchange in AC-DC microgrids with fewer power converters than three are required in each phase of the conventional UIPC structure. LPC control methods use an artificial neural network (ANN) controller. Using the H∞ filter method, the ANN tool is optimized to eliminate format errors in the membership functions. The DC microgrid is used to supply the LPC with the required DC voltage via the BPC. After successfully powering the DC microgrid with a photovoltaic instrument, LPC #039; The intermediate circuit voltage changes. For the DC- -BPC, the NDO-MS-SMC (Strate Strong Absolute Multi-Mass Slip Mode Control) strategy is available to stabilize the DC link jitter, which is based on the observation of non-linear disturbances. The simulation results demonstrate the effectiveness of the proposed energy management strategy in combination with the flow control technique for smart grids in Advanced UIPC. An artificial neural network (ANN) is used to adapt UIPC converters; this is proposed in this study as a comparable extension.