Architecture Design and Performance Optimization of Data Lake Architecture for Energy Storage Power Station Based on Distributed Computing Framework

Abstract

The rapid proliferation of data within energy storage power stations necessitates robust architectures capable of ingesting, processing, and analyzing diverse datasets to drive operational efficiency and grid stability. This study presents an investigation into the architecture design and performance optimization of a data lake tailored specifically for energy storage power stations, leveraging distributed computing frameworks. The experimental procedure encompasses controlled experiments designed to measure key performance parameters under varying workload conditions. Synthetic and real-world datasets simulate diverse data sources, ingested into the architecture through batch processing or streaming pipelines. Analytical workloads, including data transformation and predictive modeling, stress-test the architecture, while performance metrics such as data ingestion rates, storage utilization, and query latency are meticulously recorded. Statistical analysis sheds light on the architecture's efficiency and scalability, guiding optimization strategies. The findings offer valuable insights into designing scalable, efficient data architectures for energy storage systems, fostering informed decision-making and enhancing grid integration.