Demonstration of a concept for the data management and monitoring of larger-scale DER utilizing a time-series database

In the 21st century, new energy resources are developing at an ever-increasing rate, and many small-scale distributed energy resources (DER) are being installed and interconnected to the electric energy system. One of the attendant problems is handling the large amount of real-time data generated by those DER, which are difficult to process and control. This situation not only adds more overloads to the grid's operation but also poses a challenge to the further implementation of the smart grid.This contribution proposes a concept that uses a time-series database as the central element to manage energy data generated by DER and associated intelligent devices. The state-of-the-art time-series database allows for the implementation of (semi)-standardized data interfaces, e.g., using the programming language Python. Within the proposed energy data management framework, data from different sources can be well handled and become beneficial for the planning and operation of future energy systems.