Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet

Abstract

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.