Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm

The integration of solar power into smart grids necessitates efficient solar tracking to maximize energy capture. Conventional methods like Single-Axis Trackers (SAT) and Dual-Axis Trackers (DAT) have limitations, including high maintenance costs, limited adaptability, and inefficiency under variable weather conditions. These drawbacks result in suboptimal energy generation, hindering the performance of smart grids. The proposed Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm (SGST-DRLA) addresses these issues by utilizing a Deep Reinforcement Learning (DRL) approach to dynamically optimize solar panel orientation. Unlike static control strategies of SAT and DAT, SGST-DRLA continuously learns from realtime data, adjusting the tracker’s position to maximize sunlight exposure and energy capture. This adaptive method enhances tracking precision and reduces operational costs. SGST-DRLA significantly improves performance metrics, achieving a $0.25 \%$ increase in energy capture efficiency, a $\mathbf{0. 2 0 \%}$ reduction in operational costs, and a $\mathbf{0. 3 0 \%}$ improvement in adaptability to varying weather conditions compared to traditional methods. This approach not only optimizes solar tracking in smart grids but also supports sustainable and efficient renewable energy integration, making it a viable solution for overcoming the limitations of existing tracking systems.