Markus Zdrallek

State identification and automatic control of smart low voltage grids

Due to the increasing installation of decentralized generation units and the increasing demand of electrical power on distribution level the low voltage grids in Europe are facing different problems, e.g. deviations of the permitted voltage range or local inner overloads of the grid equipment. To overcome these problems a self-sustaining monitoring and control system for low voltage grids has been developed, which monitors the actual power flow situation and controls individual decentralized generation units and consumer loads if necessary. In this context new approaches for power flow calculation and control intelligence are inevitable. This paper describes a newly developed power flow algorithm to be used for online-monitoring of the grid state. In case of critical grid states identified by this power flow algorithm a control intelligence determines and executes possible strategies for elimination of the critical grid state. The developed algorithms have been tested and validated in comprehensive scenarios in consideration of plausibility, calculation speed and reliability of the results.

First practical experience with smart low voltage grids

Due to the increasing installation of decentralized generation units and the increasing demand of electrical power on distribution level the low voltage grids in Germany are facing different problems, e.g. deviations of the permitted voltage range or local inner overloads of the grid equipment. To overcome these problems a self-sustaining monitoring and control system for low voltage grids has been developed, which monitors the actual power flow situation and controls individual decentralized generation units and consumer loads if necessary. The control system has been implemented into several low voltage grids in Germany. This paper describes first practical experience with the developed smart grid approach.

Economic dispatch of flexibility options for Grid services on distribution level

Due to the ascending capacity of Renewable Energy Sources, new challenges arise in the operation of distribution grids. Existing Smart Grid solutions have shown, that direct control interventions into distributed generation and demand facilities may contribute to ensure a reliable and economic grid operation. In extension of these direct control methods, this paper presents a new approach for an economic dispatch of flexibility options for grid services on distribution level. The developed modelling framework for a local flexibility market offers the possibility to simulate the market behavior. The modular set up of a grid operator model and several unit operator models allows research on the interaction between both domains in order to find the economically optimal operation mode for avoidance of critical grid states. The results of a one year simulation of a distribution grid are showing the advantageousness of the system.

Medium and low voltage control strategies for a smart distribution grid

The proceeding changes in the energy supply and consumption are challenging the distribution grids and the operators. Conventional grid enhancement or the use of smart grid solutions are possible approaches for encountering the future tasks. In this paper a comprehensive smart grid solution for medium- and low-voltage grids is analysed and possible control strategies are presented. In former cooperation, the authors developed a smart grid system for the low voltage level. Currently, there are several research activities for a medium voltage smart grid system, leading to an integrated approach by coupling these systems. In order to achieve a maximum of synergetic effects, both solutions have to be united in a structure, which is essential for the systems functionality as it represents a concept for an overall distribution grid automation. Autarkic, coordinated, hierarchic or agent-based control strategies are possible. The differences and advantages of chosen concepts to be implemented for field tests in near future are presented.

Real-time state identification for MV-grids on autonomous agent platforms

The authors of the present paper have developed a decentralized automation concept of LV-grids in recent work. When considering MV- or LV-grids, one of the most interesting aspects is a detailed overview of the current grid state almost in real time. Furthermore, grid state identification is the primary requirement for any continuative surveillance or control method. In contrast to state estimation on HV-level, a whole lot of different methods have been developed for calculation of grid states on MV- or LV-level within a sparse measurement environment related to cost minimization for measurement devices. However, most methods have not been developed for or tested on a decentralized automation system. The scope of the present paper is a comprehensive screening of available grid state identification methods and an adaption of these approaches with respect to the use within a decentralized automation system for MV-grids. In particular, five established methods for MV-grid state identification are applied to a representative 11-node MV-grid in order to compare the accuracy of the calculated grid state.

Smart distribution grids for Germany's Energiewende

The German “Energiewende” constitutes a major challenge for the energy supply system. The present paper shows that the increasing number of volatile distributed generators and large consumers requires a coordination to avoid an expensive grid expansion. After summarizing the basic application areas of smart grid systems, a specific implementation of a decentralized smart grid approach is presented. This solution is able to estimate the grid state via a cost efficient minimal set of sensors. Via a three-stage control algorithm deviations from the permitted grid states can be remedied autonomously. Finally, future development potentialities like an EV-charging add-on and a system expansion to medium voltage grids are discussed.

Self-detection of New Photovoltaic Power Plants Using a Low Voltage Smart Grid System

A rising amount of today’s distribution grids are equipped with smart grid systems to face the problems arising from the increasing use of decentralized and renewable energy sources. In comparison to conventional reinforced grids smart grid systems need to be maintained to stay up to date. Especially the installed photovoltaic (PV) power is a very important parameter for the system. Self-learning smart grid systems would reduce the maintenance efforts. A huge step towards this is a self-detection of new PV power plants in the grid. In this paper three methods to detect unknown PV plants in distribution grids are introduced. They are tested and validated in a use case. Additionally the influence of undetected PV power plants to the accuracy of the grid state identification is considered. Because of the huge impact of different factors only rudimentary results are presented and further investigations are focused.

Impact of improved models on the reliability calculation of offshore wind farms

Reliability calculation programs for electrical power systems are available since the early 80s and have been since then continuously developed e.g. [1] [2]. However, these programs are designed for conventional power supply systems which consist of predominantly consumers. Through the development of electrical power systems in Europe and particularly the establishment of many new offshore wind farms, these lead to an enormous change in the power supply grids (infeed grids). Therefore it is a substantial need for adaptation of these programs to determine the reliability of the overall power system in a realistic way. In this paper, additionally to the offshore specific reliability indices, three new models are presented. These models are especially created for offshore wind farms and their characteristics. These models are respectively: the weather influence model, the power infeed control model and the reliability modeling of the wind turbine generator (WTG). Based on one exemplary wind farm design, the developed models are compared with the conventional models. This work presents the impact of the different improved models on the reliability results. The importance and the necessity of these established models in order to reach more realistic and environmental depended reliability analysis results are hereby illustrated.

Sensitivity analysis of offshore wind farm topology based on reliability calculation

In order to reduce the revenue losses for the offshore wind farm (OWF) operators, it is necessary to ensure the operational availability of the entire OWF. This requires an accurate investigation of the reliability of the individual components of OWF as well as of the complete system. For this reason, reliability calculations will play a major role in the planning and optimization of the network infrastructure. Due to the OWF specific grid construction and the environment-relevant factors such as: long distance to shore, water depth and rough environmental conditions for maintenance activities, new offshore specifics reliability models were evolved. The newly developed models take into account the necessity of the winds potential, weather conditions, control of power generation, automatic functions in case of failure of the OWF grid and the wind turbine generator (WTG) itself. By means of these models, this paper focuses on the availability of various OWF design concepts. These variants consider the differences within inter array layouts, substation configurations and WTG concepts. Their impacts on the reliability results are substantiated by calculations, which make possible to identify the advantages and disadvantages of each OWF design concept.

Einheitliches und durchgängiges Engineering von Steuerungslösungen für hybride Energiesysteme und -netze mittels Energie-Agenten

Zusammenfassung Im Rahmen des vom BMWi geförderten Projekts Agent.HyGrid werden vereinheitlichte Energie-Agenten als Steuerungslösung für hybride Energiesysteme und -netze entwickelt und auf ihre Anwendbarkeit hin untersucht. Hierbei werden sowohl vereinheitlichte Daten- und Verhaltensmodelle entwickelt als auch ein Referenz- und Entwicklungsprozess entworfen. Mit diesem durchgehenden Entwicklungsprozess soll die Anwendung der Energie-Agenten, angefangen von der Planungs- und Simulationsphase bis hin zum realen Einsatz im physischen Vor-Ort-System, ermöglicht werden. Als Energie-Agent wird dabei grundsätzlich ein autonomes, dezentral operierendes Software-System verstanden, das unabhängig von der Sparte bzw. vom Energieträger auf mehreren Ebenen eines Energie-Verteilnetzes eingesetzt werden kann. Durch die Kombination unterschiedlicher Energieträger sollen insbesondere volatile Energieerzeugungsanlagen, wie Windkraft- und Solaranlagen, besser ins Energienetz integriert werden. Die Herausforderungen des Projekts liegen vor allem in der vereinheitlichten Modellierung unterschiedlicher Energieträger und -umwandlungsanlagen sowie in der Überbrückung der Lücke zwischen Simulation und der realen Anwendung vor Ort. Dieser Artikel stellt die bisherigen Erkenntnisse in Hinblick auf die Anwendung des Entwicklungsprozesses und des einheitlichen Datenmodells dar.