The uncertainty in solar energy is different from conventional, dispatchable generation fuels and difficult to incorporate into the standard system operating procedures. In the first part of this work, the machine learning algorithm is used to train models based on solar irradiance data and different meteorological weather information to predict the solar irradiance for different cities to validate the forecasting model. Again, the intermittent and inertialess nature of photovoltaic (PV) systems can produce significant power oscillations that can cause significant problems with dynamic stability of the power system and also limit the penetration capacity of PV into the grid. In the second part, it is shown that the residue-based power oscillation damping (POD) controller obviously improves the inter-area oscillation damping. The validity and effectiveness of the proposed controller are demonstrated on the three-machine two-area test system that combines the conventional synchronous generator and flexible alternating current transmission systems (FACTS) device using simulations. This report overall puts an in-depth analysis with regard to the challenges of solar resources with integrating, planning, operating, and particularly the stability of the rest of the power grid, including existing generation resources, customer requirements, and the transmission system itself that will lead to an improved decision making in resource allocations and grid stability.