Philipp Goergens

Bad data detection and handling in distribution grid state estimation using artificial neural networks

This research project addresses the problem of erroneous measurements for distribution grid state estimation (DGSE) by using bad data detection. A method based on artificial neural networks is developed and tested in combination with DGSE. The necessary steps in order to create the neural networks are presented and training parameters are investigated. The method replaces identified measurement errors with new estimates. The developed method is validated by simulation on the basis of an electric distribution grid. It is shown that the bad data correction method detects and correctly identifies single and multiple erroneous measurement values. Furthermore, the ability of the developed method to handle different types of measurement errors and the impact on the quality of the state estimation result are examined. The method improves the quality of the DGSE result and reduces the state estimations probability to diverge.

Determination of the potential to provide reactive power from distribution grids to the transmission grid using optimal power flow

This paper addresses the subject of determining the potential of distribution grids to provide reactive power to the transmission grid. A method based on optimal power flow is developed in order to determine the reactive power potential Day-Ahead taking meteorological data into account that decisively affects the contribution of distributed energy resources. The developed method is validated by simulations on the basis of a realistic low voltage grid with load measurements and meteorological (forecast and measurements) data. It can be summarized that the forecast of the reactive power potential that can be provided is of low quality due to forecast errors when the inverter of the of distributed energy resources is operated according to guidelines. By exploiting the theoretical potential of PVs inverter to a greater extent the reactive power potential is rather high and the forecast errors are less significant. The method enables for an improved predictability for transmission system operators with respect to reactive power balancing in the future and the results give first indications about the reactive power potential from the distribution grid for the transmission grid.

Optimal use of decentralized methods for volt/var control in distribution networks

This paper presents results of a heuristic optimization method for parameterization of decentral reactive power control, which can be used to improve static voltage quality in distribution grids. With an optimized Q(V)-curve it is possible to significantly reduce the additional reactive energy and accomplish lower losses in the grid while keeping the voltage within the specified limits. In this paper results for Q(V)-control and optimized cosφ(P)-curves with optional voltage regulated distribution transformers are presented. It is shown that the gradient of a Q(V)-curve should be as steep as possible. The optimization method also shows positive impact of bigger reactive power capability of each DG unit. Individual Q(V)-parameters for a group of distributed generators can significantly improve the efficiency of Q(V). The comparison with results using Optimal Power Flow shows a significant gap between optimal decentralized methods and coordinated optimization. Using coordinated optimization it is possible to reduce the yearly reactive energy of all DG units by over 95%.

Netzdienlicher Einsatz von Speichersystemen in Verteilungsnetzen

Im Rahmen dieses Beitrags wird der netzdienliche Einsatz von dezentralen Speichersystemen in Verteilungsnetzen analysiert. Dabei stellt das gesamte Niederspannungsnetz den Betrachtungsrahmen dar und es erfolgt eine Optimierung von systemischen Kenngrosen durch eine Flexibilisierung der Last bzw. Einspeisung mittels Speichersystemen. Anhand von zwei Niederspannungstypnetzen und zwei zukunftigen Last- und Erzeugungsszenarien werden unterschiedliche Dimensionierungen und Platzierungen von Speichersystemen untersucht. Dabei stehen insbesondere die Unterschiede zwischen einem Einsatz vieler Heimspeichersysteme versus einzelner groser dimensionierter Netzspeicher im Fokus der Untersuchungen. Zudem werden das Optimierungsmodell und die Vorgehensweise dargestellt und exemplarische Untersuchungen diskutiert.