Research on Smart Grid Platform System Based on Cloud Computing

This paper proposes a smart grid platform system based on cloud computing, combined with a distributed deep reinforcement learning (DRL) optimization algorithm, to improve the efficiency of power grid management and optimize dispatching. The system architecture adopts a hierarchical design of data layer, service layer, interface layer, presentation layer, and access layer to ensure efficient data processing and system scalability. Through the distributed computing architecture, the algorithm fully utilizes the computing power of cloud computing to optimize power grid load dispatching and fault prediction. In the experimental part, the system verifies the effectiveness of the platform in power dispatching and fault prediction through simulation tests on a typical medium-sized power grid data set. In terms of load dispatching, the algorithm based on deep reinforcement learning improves dispatching efficiency by 20% and reduces power loss by 15% compared with traditional methods. In terms of fault prediction, the recall rate of the system model reaches 92%, and the accuracy rate is 89%, which is 7% higher than the traditional SVM algorithm. Additionally, the system response speed has been significantly optimized, and the response time after the state change of power grid equipment has been shortened by 25%.