No abstract

In Energy Lab 2.0, the interplay of different forms of energy on different value chains is investigated. Novel concepts to stabilize the volatile energy supply of renewables by the use of storage systems and mainly by applying to‐be‐developed tools and algorithms of the information and communication technology sector are sought. Hence, a key element of Energy Lab 2.0 is the smart energies system simulation and control center. This consists of three parts: a power‐hardware‐in‐the‐loop experimental field, an energy grid simulation and analysis laboratory, and a control, monitoring, and visualization center. For these three labs, big data technologies, advanced control methods, and reliable, safe, and secure software structures are of equal importance. As an example, a data processing pipeline to create power flow simulation models from raw Open Street Map data, statistical databases, and geodata is presented and discussed.

No abstract

No abstract

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation.