Optimizing Distribution Grid Operations with Deep Reinforcement Learning for a Novel Approach to Managing Renewable Energy Integration

Renewable energy sources can be integrated into the distribution grids with new methodologies, where innovative methods shall be applied to optimize energy management and stability for a grid. In this research work, Deep Reinforcement Learning (DRL) is used, and especially the model Deep Deterministic Policy Gradient (DDPG), which shall be used to tackle the challenge of balancing supply and demand for energy to maximize renewable resources. These include energy production, consumption, and grid status as derivatives of the dataset for the DRL model. The application of this model to case study allows remarkable enhancements in Renewable energy utilization (REU), grid stability (GS), and energy balance accuracy (EBA). Peak REU of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 7. 0 \%}$</tex>, GS of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 3 \%}$</tex>, and EBA of 98.5 % can be obtained by using the DDPG model, which is much superior to the traditional approaches to energy management. The result of the work showed that the model was able to handle real-time renewable energy resources appropriately, thereby reducing the consumption of traditional energy resources and enhancing the reliability of the grid. Besides its importance as a contribution to the knowledge of academia on the applications of DRL in energy management, it also has practical importance for utilities and grid operators who are seeking to implement advanced technologies in improving energy distribution.