Parallel Processing of Power System Operation Data Based on MapReduce Algorithm

The transition to large-scale new energy systems has greatly increased the complexity, volume, and multidimensionality of power data in the cloud, making data management more challenging and potentially risking the safe and stable operation of power systems. To tackle this, the paper proposes a power grid data analysis method using the MapReduce algorithm for efficient data processing. Results show that, even with large data volumes, processing efficiency is maintained at around 30 minutes, with stability and accuracy preserved as data volume grows. This method allows for parallel analysis of the grid and provides valuable insights for future data processing in power grid operations.