Practical Risk Modelling of Future Electricity Distribution Networks

  • David Clements

Student thesis: Phd


Distribution networks are currently undergoing a period of profound change. Driven by changing customer behaviour, new technologies becoming available, and above all economic pressure network operators are adopting new "smart grid" technologies. These smart grid technologies have the potential to increase network capacity significantly, but could compromise network performance. Studies often focus on individual smart grid technologies, ignoring how the network might change around the smart grid components. On a more fundamental level, some contributions to network performance that are not new, but hard to pin down, such as staff levels are not yet fully understood. However, understanding the impact of new smart grid technologies require understanding how the network is currently operated. This thesis shows how staff levels can affect both network reliability and resilience, affecting the network through both differing maintenance levels and limits on the number of network assets that can be repaired simultaneously. The impact of new smart grid technologies, real time ratings of underground cables and energy storage are considered. This thesis shows that while the use of these smart grid technologies has some negative consequences. However, those negative consequences are comparable to those experienced due to "normal" asset ageing. Industry is comfortable with the consequences of asset ageing, having experienced those consequences for decades. Newer smart grid technologies however are less well understood, and unknowns are frequently overestimated. A tool is described in this thesis that can quantify the effects of changing network designs, staffing levels or adding smart solutions to the network. A better understanding and quantification of the effects of each of these factors will allow network operators to invest more efficiently, saving society at large precious resources that could be dedicated to other goals.
Date of Award31 Dec 2019
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorPierluigi Mancarella (Supervisor) & Luis(Nando) Ochoa (Supervisor)


  • Simulation
  • Distribution Networks
  • Reliability

Cite this