MD1: Structural and Dynamic Modelling of Integrated Energy Systems

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

Research Outputs

Journal A mathematical model for elasticity using calculus on discrete manifolds
2018; Mathematical Methods in the Applied Science; Dassios, I.K., Keeffe, G.O. and Jivkov, A.
Journal Stability Analysis of Power Systems with Inclusion of Realistic-Modeling of WAMS Delays
2018; IEEE Transactions on Power Systems; Liu, M., Dassios, I., Tzounas, G., and  Milano, F.
Journal Blockchain Electricity Trading Under Demurrage
2018; IEEE Transactions on Smart Grid; Cuffe, P. and Devine, M.
Journal Caputo and related fractional derivatives in singular systems
2018; Applied Mathematics and Computation; Dassios, I.K. and Baleanu, D.
Journal A practical formula of solutions for a family of linear non-autonomous fractional nabla difference equations
2018; Journal of Computational and Applied Mathematics; Dassios, I.K.
Journal Stability of Bounded Dynamical Networks with Symmetry
2018; Symmetry; Dassios, I.K.
Conference  Modelling, Simulation and Hardware-in-the-Loop of Virtual Synchronous Generator Control in Low Inertia Power System
2018; 20th IEEE Power Systems Computation Conference (PSCC 2018), Ireland; Chen, J., Liu, M., O'Loughlin, C., Milano, F. and O'Donnell, T.
Journal A deterministic approach to locating series flow-controllers within transmission systems to alleviate congestion
2017; Electric Power Systems Research; Cuffe, P. and Keane, A.
Journal Validating Two Novel Equivalent Impedance Estimators
2017; IEEE Transactions on Power Systems; Cuffe, P. and Milano, F.
Journal Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
2017; Physical Review Letters; Onaga, T., Gleeson, J.P. and Masuda, N.


Professor Federico Milano
Funded Investigator, Professor, School of Electrical & Electronic Engineering
01 7161844
Dr Paul Cuffe
Funded Investigator, Lecturer/Assistant Professor, School Of Electrical and Electronic Engineering
+353 1 7161732
Dr Ioannis Dassios
Senior Researcher
Taulant Kerci
PhD Researcher
Arash Beiranvand
PhD Researcher
Weilin Zhong
PhD Researcher