Identification Method of Energy Storage Operating Conditions based on Random Forest

Abstract

Accurate and effective identification of the operating characteristics and application modes of electrochemical energy storage systems (EESS) is of great significance for the safe and reliable operation of large-scale EESS in power generation, transmission and distribution. The paper proposes a method for identifying the operating conditions of EESS based on the random forest algorithm. Analyzing and characterizing the typical operating conditions, such as frequency regulation, PV-EESS combination, Wind-EESS output schedule and smoothing, and peak shaving, the method introduces the random forest algorithm to build the identification model of EESS operating conditions. The variable importance measures, training model and test analysis have been completed. The results show that the method has an identification accuracy of more than 95% for the five typical operating conditions, which can effectively identify the EESS operating conditions.