## 6 Conclusions  
In this paper, we developed an integrated simulation framework for cascading outage contingency analysis in combined gas and power system networks and demonstrated the capabilities of the implemented framework by applying it to a realistic, combined electricity and gas transmissions network of an European region. The simulation framework is composed of a transient hydraulic model for the gas system and a steady state AC-OPF model for the power system. Both models, are derived from the physical laws governing the flow of gas and electrical power, respectively. Moreover, the most important facilities  
Figure 17: Load and pressure profile of CBE_1 for scenario 1.  
and their technical constraints are considered. The gas and power system models a coupled through coupling equations describing the fuel gas offfake of GFPPs for power generation and the power supply to LNG terminals, UGS facilities and electric driven compressor stations. The model application was divided into three scenarios, namely, scenario 0 with no disruption, scenario 1 with gas side disruptions and scenario 2 with power side disruptions. The results of these scenarios show how disruption events triggered in one system propagate to the other system. In scenario 1, for instance, three major gas supply stations are disrupted and as a result a number of GFPPs are shut-down due to insufficient fuel gas pressure. This contingency propagates further to other buses in the power system, where load shedding is implemented in order to maintain the voltage levels above the minimum voltage threshold. Similar observations are made in scenario 2, where a drastic reduction in renewable energy generation together with a shutdown of a large power plant triggered a large increase in gas demand of GFPPs, leading to a rapid pressure drop in the gas network and the subsequent shut-down of GFPPs. Eventually, this circumstance increased the stress on the power system leading to minimum bus voltage violations in a couple of buses, which is remedied by applying load shedding at the affected buses. Based on these key findings, it can be concluded that there is a need for close collaboration and coordination between gas and power TSOs. Data concerning pressures, flows, voltages etc., efficiently handled and communicated may introduce resilience on the integrated network. This has to be done via well-structured protocols that inform the other TSO about the grace periods and support that each network may grant the other. The use of models like the one proposed in this study may be of much help for getting part of this information to share with the other operator. We believe it is fair to state that the integrated model allows for detailed fingerprinting and exploration of the effects of disruption in gas and/or power, to a level of detail that is not possible by qualitative, expert analysis. Once the data characterizing a gas and electricity grid have been loaded, experts can perform in-silico experiments at will to investigate the system, determine weak elements, and propose mitigation strategies. In both two scenarios, GFPP_9 and GFPP_10 fail, which merits an investigation into their position in the system. If in more scenarios it is these two plants that fail first, it could be decided to equip these with alternative backup fuel options. In the future, we intend to further develop the simulation framework to implement more simulation options and functionalities into the simulation tool SAInt in order to investigate the effectiveness of different demand and supply side measures to mitigate the consequences of supply disruptions in coupled gas and electric power systems. This work has been developed within the framework of the European Program for Critical Infrastructure Protection (EPCIP) of the European Commission. We express our gratitude to our colleague [PERSON] from the Joint Research Centre--Institute for Energy and Transport, in Petten, Netherlands, for providing the GIS-Data for the presented gas and electric model.