Assessment of Interruption Costs in Electric Power Systems using the Weibull-Markov Mode

Modern competitive electricity markets do not ask for power systems with the highest possible technical perfection, but for systems with the highest possible economic efficiency. Higher efficiency can only be reached when accurate and flexible analysis tools are used. In order to relate investment costs to the resulting levels of supply reliability, it is required to quantify supply reliability in a monetary way.This can be done by calculating the expected interruption costs. Interruption costs evaluation, however, cannot be done correctly in all cases by methods which are based on the commonly used homogenous Markov model and is time consuming when using a Monte-Carlo simulation. It was the objective of this thesis to find a new way for calculating interruption costs which would combine the speed and precision of the analytic Markov method with the flexibility and correctness of the Monte Carlo simulation. A new calculation method was found, based on a new stochastic model. This new model was called the "Weibull-Markov" model and is described in detail in this thesis. The new model and methods have been implemented in a computer program and the speed and accuracy of the calculation method was tested in various projects and by comparison with Monte-Carlo simulations. It is shown in this thesis that disregarding the effects of the probability distribution of the interruption duration can lead to large errors, up to 40% and more, in the calculated expected interruption costs. An estimation of the possible error has been made for a large number of published customer interruption cost functions. The actual error in specific reliability calculations is hard to estimate. It is however clear that this error cannot be simply ignored. The use of the new Weibull-Markov model and the reliability assessment methods do not significantly slow down the calculation speed, offer more flexibility in reliability worth assessment and produce more accurate results. They can be used in all areas of power system reliability assessment which have always been the exclusive domain of homogenous Markov modeling

Bus Split Distribution Factors

The linearised DC loadflow is a fast way to calculate the load flow. However, until now changes in topology due to busbar splitting cannot be dealt with in an efficient way. In this study, we introduce the Bus Split Distribution Factors (BSDF) which enable an efficient way to compute the effects of busbar splitting on the DC load flow. The derivation of the BSDF formulas is based on modelling the busbar coupler as a branch with vanishing reactance and by using transformed LODF formulas. In times of the energy transition the BSDF approch might be especially helpful since optimal grid topology control is still a largely unexploited form of flexibility for system operators due to the complex combinatorial nature of grid topology reconfiguration. With the BSDF approach much faster screening of topological remedial actions (including substation reconfigurations) in congestion management applications is possible. </p

Detecting semantic priming at the single-trial level

Contains fulltext : 111318.pdf (publisher's version ) (Open Access)Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N = 12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.7 p