Intelligent guarantee power supply decision method based on reinforcement learning algorithm

Traditional power supply decision methods rely on fixed and rigorous mathematical models, which are difficult to accurately capture the characteristics and changing patterns of new loads, resulting in low prediction accuracy. Therefore, a decision model for guaranteeing power supply is constructed based on an improved proximal policy optimization algorithm, to study the intelligent guarantee power supply decision method. The experimental results show that the stability of the proximal policy optimization algorithms is generally high in all scenarios, especially in fault or anomaly scenarios and low load demand scenarios, which exceeds 110%. Its loss value decreases with the increase of training iterations. At 60 iterations, its loss value reaches the optimal value of 100 and then tends to stabilize. The research results indicate that the intelligent power supply strategy has good feasibility. This decision method helps to improve the stability, efficiency, intelligence level, and ability to respond to emergencies of the power grid.