Probability statements about the grid

Project state

The grids in Germany are becoming increasingly complex. They must meet new requirements. Probability statements can determine the essence of many future requirements. Deterministic considerations will suffice in many cases. Preconditions such as averages or worst cases are defined. The result is a good approximation. If the result needs to be more accurate, the entire probabilistic problem space needs to be examined. 

This is usually achieved with the Monte Carlo method. It is relatively accurate but requires a lot of computing power. Even simple tasks for existing grids require simplifications. This limits the robustness of the results and, above all, their scalability. Therefore, it is imperative to develop new methods to meet increasing demands.

Possible use: grid expansion planning

Grid expansion planning is a typical application of probabilistic tasks in energy system technology. The reconstruction of the distribution networks into smart grids with generators that are more volatile, decentralised storage systems and smart active equipment in the electrical supply network leads to an increasing level of uncertainty. This affects spatial planning, which deals with the question of where to build new plants. It also affects quantity, which is the question of how many new plants will be built. The third factor is timing: What will the future supply and demand characteristics of the plants be in terms of temporal gradients, as well as maximum and minimum values? These and similar uncertainties must each be modelled using probability distributions. This yields all potentially occurring scenarios for the expansion of renewable energies.

In general, many of the calculations will be redundant. This is the case because the input data are identical or at least very similar. Therefore, new efficient probabilistic methods are needed to map out the entire solution space of grid expansion planning. The PrIME project aims to consider and fundamentally develop methods for probabilistic tasks in the area of energy system technology. Method development will be based on examples of typical application cases of energy system technology. Thus, a high degree of relevancy to practice can be guaranteed for the results, and such methods can yield great potential for application, both in grid planning as well as grid operations management and forecasting, such as Day-Ahead Congestion Forecasts (DACF).

Project duration

01/2015 – 13/2017


Prof. Dr.-Ing. Martin Braun
University of Kassel
Fachgebiet e²n
Wilhelmshöher Allee 73
34121 Kassel
+49 561 804-6202

Basic information

The energy generated in a grid must always be equal to the energy consumed. The ancillary services are tasked with ensuring this.


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