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.

The ongoing shift towards a more decentralized and renewable energy system in Germany requires extensive modifications to existing grids and their operating principlesespecially at the distribution level. Furthermore, the integration of e-mobility will have a significant effect on distribution grids. Smart distribution systems are one way of handling these new supply scenarios. Hence, a self-sustaining monitoring and control system for LV-grids has been developed. It monitors the power flow situation and is able to control the grid if necessary. The system has been implemented in four LV-grids in Germany. The present paper describes the automation system and our initial experience with this smart grid approach.

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

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