Constructing and optimizing a digital intelligent power grid synchronous platform integrating big data and artificial intelligence

With the continuous growth of power demand and the increasingly complex operating environment of power grid, the traditional power grid management model has been difficult to meet the needs of modern management. Therefore, by integrating big data technology and AI (artificial intelligence) algorithm, this study aims to build a high-performance and intelligent power grid management platform, namely, DIPGVRSP (Digital Intelligent Power Grid Virtual-Real Synchronous Platform). During the construction of DIPGVRSP, a layered architecture design is adopted, including data acquisition layer, data processing layer, data analysis layer and application layer. In order to further optimize the performance and efficiency of DIPGVRSP, this study puts forward a number of strategies, including data processing efficiency optimization, intelligent scheduling and optimization, and security and reliability improvement. By introducing parallel processing and distributed computing technology, the data processing speed is significantly improved. At the same time, based on historical data and machine learning model, a predictive scheduling algorithm is developed to realize intelligent scheduling and optimization of power grid. The experimental verification shows that DIPGVRSP based on LSTM has achieved high accuracy in power grid load forecasting, and the forecasting performance is improved by about 20% compared with the traditional linear regression model. This result not only verifies the effectiveness of DIPGVRSP, but also provides strong support for the further popularization and application of digital intelligent power grid technology.