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As the proportion of total generation by renewable sources compared to non-renewable sources increases, the relative inertial stability provided by large rotating generators in electricity grids is found to shrink and is not being replaced by sources such as photovoltaic and wind power, which are already known for their inherent variability. This leads to electricity generation systems being less stable, less flexible, and less adequate in applications with a high diversity factor, and literature shows that the penetration of renewable energy sources in distribution-generation/microgrid system frequently presents several technical and economic challenges in their usual applications. This work examines how increased renewable energy penetration impacts the distribution-generation system and suggests approaches and measures for tackling the challenges that are associated with it.

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Safety is an important aim in designing safe-critical systems. To design such systems, many policy iterative algorithms are introduced to find safe optimal controllers. Due to the fact that in most practical systems, finding accurate information from the system is rather impossible, a new online training method is presented in this paper to perform an iterative reinforcement learning based algorithm using real data instead of identifying system dynamics. Also, in this paper the impact of model uncertainty is examined on control Lyapunov functions (CLF) and control barrier functions (CBF) dynamic limitations. The Sum of Square program is used to iteratively find an optimal safe control solution. The simulation results which are applied on a quarter car model show the efficiency of the proposed method in the fields of optimality and robustness.