Advantages of a dynamic Smart Grid training tool for DSO control centre staff

Abstract

The integration of renewable energy sources (RES) in European electrical networks contributes to the climate targets prescribed by the European Union. However, the increasing integration of RES into all network voltage levels gives rise to some significant side effects which must be taken into account early. To explore these effects a static planning mode is not sufficient. Because of the rather complex interaction of fluctuating RES and regulations the authors recommend the use of a dynamic training simulator to precisely identify the future grid problems. This paper describes the changed requirements and the associated challenges. The focus is put on new and emerging situations and scenarios caused by a high level of RES in Distribution System Operator (DSO) grids, such as load flow reversal, voltage rise and dip problems, maintenance scheduling taking weather conditions into account, regulation of RES in-feed in emergency situations, etc. Possibly the staff in DSO control centres did not experienced such situations before. With the transformation of distribution systems into Smart Grids, these tasks will become important to keep the power system stable. The paper also describes scenarios which are taken from the DSO grid of VNB RMN Grid Company in Darmstadt, Germany. The scenarios have been derived by forecasting the grid structure of the year 2020, taking future loads and distributed RES into account. The forecasted data have been implemented in a dynamic power system simulator and linked to a standard SCADA (Supervisory Control And Data Acquisition) system forming a training system. Main training goal is to prepare the DSO staff as soon as possible to solve the new and emerging Smart Grid problems. The training allows a risk-free increase of knowledge and skills of the staff on how to solve the problems. Real time and dynamic training needs the dynamic modelling of RES components such as wind farms, photovoltaic plants, etc for integration in the training system. Together with the results of modelling, the paper describes as well some of the Smart Grid scenarios and the first experience of a DSO control centre staff training as the lessons to be learned.