MD1: Structural and Dynamic Modelling: Analyse and enhance the resilience and flexibility of combined energy networks

This work package addresses the structural modelling, topological optimization and dynamic behaviour of electric power systems, in light of their interaction with other engineered networks, such as communications systems. This analysis, which will build on complex networks approaches (Pagani et al., 2013), can offer new insights into how, for example, cascading failures propagate through energy networks.

The analysis will not just consider how the connectivity topology of an energy system affects its robustness against failure, but will also articulate ways that its topology can be enhanced. For instance, dynamically removing electricity transmission lines from service can actually decrease network congestion and lower generation costs (Hedman et al., 2011). Such schemes are not in common use, however, with their computational complexity being one impediment. Likewise, uncertainty remains on how removing lines may affect the security and robustness of the power system.

Finally, the impact of communication systems and energy systems on the stability of the electrical grid will be studied. Communication systems are characterized by a time scales of the order of milliseconds to hundreds of milliseconds. On the other hand, heating, gas, and water systems have much longer time scales, i.e., minutes or higher. It is thus sensible to study separately the dynamic interaction of the electric grid with the communication system and other energy carriers. Deterministic and stochastic models will be considered, e.g., grey-box modelling approach as proposed in Nielsen et al., 2000 and Kristensen et al. 2004. A sensitivity analysis of uncertainties and unknown data through relevant statistical methods, e.g., Monte Carlo time domain simulations of stochastic differential equations as discussed in Milano et al., 2013, will be carried out. These analyses will allow definition of (i) the level of detail and dynamics that are prudent to consider for the considered time scales; and (ii) the identification of the most relevant interactions and, whenever relevant, corrective actions, e.g., control strategies, to mitigate arising instabilities.

The red arrows in this network diagram indicate where overloading of power lines could occur in the case2382wp power system, due to short-term fluctuations in (notionally) renewable generator outputs

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