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The penetration of distributed generation (DG) in the grid has been dramatically increasing in recent years. The aggregated effect of DG units can lead to more line congestions and voltage problems in rural areas. Active Network Management (ANM) enables real-time supervision/control of the DG units to curtail the production in case of any congestion(s). The amount of curtailment for each DG unit should be assessed before accepting any new connection to the grid (in planning phase). In this paper, an efficient methodology to assess the risk(s) of accepting to connect a new DG unit is presented. The proposed methodology is based on an importance sampling scheme systematically targeting electrically challenging scenarios. The results provide the expected value (and also the distribution) of the risk of curtailment for each DG unit.

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Severe weather-related events are the natural disasters that challenge the reliability of power systems in the vulnerable areas every year. Multiple failures of power system elements due to these events and also activation of protections due to electrical instability cause severe problems like major load shedding or blackout. Traditional reliability assessment and network planning standards do not consider the impacts of such multiple outages, which often occur in a very short period of time stressing the system beyond its security boundaries. Therefore, new methods to assess the resilience of a power system, that is defined as the ability of the system to withstand and rapidly recover from these High-Impact Low-Probability (HILP) events, are meaningful to develop. The analysis of HILP events can be performed either by static or dynamic approaches. The static approach is not able to take into account the times of occurrence of failures, transient behaviour of system variables (e.g. voltages and frequency) and performance of the protections and control systems. To address these deficiencies, dynamic simulation is applied in this work. The objective of this paper is to apply a probabilistic dynamic approach in order to estimate the resilience of a power system via the calculation of the associated risk indices in case of occurrence of windstorms. The Eurostag software is applied for dynamic simulation with the use of variable time steps in order to increase efficiency of the computational module.