Within flow-based market coupling, the EU's preferred method for calculating cross-border trading capacities, recent regulatory changes stipulate minimum trading capacities, so-called minRAMs which have to be provided to electricity markets. Effectively, high predicted flows on considered electricity grid elements have to be reduced to reserve a minimum of the elements' capacities for cross-zonal trading. This analysis investigates if the adjustments made to meet this criterion, in the form of augmented trading domains, lead to higher amounts of curative congestion management. To quantify the effect of increasing minRAMs on overall welfare, the markets and grids of Central Western Europe are analyzed during two representative weeks of 2016. The results show the increasing market coupling welfare is more than offset by rising congestion management costs, leading to net welfare losses. In the best case, the generation plus congestion management costs within Central Western Europe rise by 7.25% when increasing the minRAMs from the current 20%–45% and a minRAM of 70% is 6.28% more expensive compared to a minRAM of 20%. The analysis derives policy recommendations for implementing the minRAM stipulation, with a particular focus on a cost-minimizing selection of generation shift keys, in general as well as situation-dependent terms.

Cyber security is an emerging field in the IT-sector. As more devices are connected to the internet, the attack surface for hackers is steadily increasing. Network-based Intrusion Detection Systems (NIDS) can be used to detect malicious traffic in networks and Machine Learning is an up and coming approach for improving the detection rate. In this thesis the NIDS Zeek is used to extract features based on time and data size from network traffic. The features are then analyzed with Machine Learning in Scikit-Learn in order to detect malicious traffic. A 98.58% Bayesian detection rate was achieved for the CICIDS2017 which is about the same level as the results from previous works on CICIDS2017 (without Zeek). The best performing algorithms were K-Nearest Neighbors, Random Forest and Decision Tree. / IT-säkerhet är ett växande fält inom IT-sektorn. I takt med att allt fler saker ansluts till internet, ökar även angreppsytan och risken för IT-attacker. Ett Nätverksbaserat Intrångsdetekteringssystem (NIDS) kan användas för att upptäcka skadlig trafik i nätverk och maskininlärning har blivit ett allt vanligare sätt att förbättra denna förmåga. I det här examensarbetet används ett NIDS som heter Zeek för att extrahera parametrar baserade på tid och datastorlek från nätverkstrafik. Dessa parametrar analyseras sedan med maskininlärning i Scikit-Learn för att upptäcka skadlig trafik. För datasetet CICIDS2017 uppnåddes en Bayesian detection rate på 98.58% vilket är på ungefär samma nivå som resultat från tidigare arbeten med CICIDS2017 (utan Zeek). Algoritmerna som gav bäst resultat var K-Nearest Neighbors, Random Forest och Decision Tree.