A Strategic Urban Grid Planning Tool to Improve the Resilience of Smart Grid Networks

  • Eng Tseng LauEmail author
  • Kok Keong Chai
  • Yue Chen
  • Alexandr Vasenev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 921)


The unresponsive and poor resilience of the traditional city architecture may cause instability and failure. Therefore, strategical positioning of new urban electricity or city components do not only make the city more resilient to electricity outages, but also a step towards a greener and a smarter city. Money and resilience are two conflicting goals in this case. In case of blackouts, distributed energy resources can serve critical demand to essential city components such as hospitals, water purification facilities, fire and police stations. In addition, the city level stakeholders may need to envision monetary saving and the overall urban planning resilience related to city component changes. In order to provide decision makers with resilience and monetary information, it is needed to analyze the impact of modifying the city components. This paper introduces a novel tool suitable for this purpose and reports on the validation efforts through a stakeholder workshop. The outcomes indicate that predicted outcomes of two alternative solutions can be analyzed and compared with the assistance of the tool.


Stakeholder workshop System design Monetary cost Grid resilience Smart grid 



This work was partially supported by the Joint Program Initiative (JPI) Urban Europe via the project IRENE (Improving the Robustness of Urban Electricity Network). Grant Reference: ES/M008509/1. Further information about project IRENE is available in the weblink:


  1. 1.
    Amado, M., Poggi, F.: Solar urban planning: a parametric approach. Energy Procedia 48, 1539–1548 (2014)CrossRefGoogle Scholar
  2. 2.
    Argonne National Laboratory (ANL): Resilient infrastructure capabilities (2016). Accessed 19 Jan 2017
  3. 3.
    Barjis, J.: Collaborative, participative and interactive enterprise modeling. In: Filipe, J., Cordeiro, J. (eds.) ICEIS 2009. LNBIP, vol. 24, pp. 651–662. Springer, Heidelberg (2009). Scholar
  4. 4.
    Bennett, B.: Understanding, Assessing, and Responding to Terrorism: Protecting Critical Infrastructure and Personnel. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  5. 5.
    Bollinger, L.A.: Fostering climate resilient electricity infrastructure (2015). Accessed 06 Dec 2016
  6. 6.
    DNV GL: Microgrid optimizer - a holistic operational simulation tool to maximize economic value or electrical power reliability (2016).. Accessed 05 Dec 2016
  7. 7.
    Dugan, R., McGranaghan, M.: Sim city. IEEE Power Mag. 9(5), 74–81 (2011)CrossRefGoogle Scholar
  8. 8.
    ETAP Grid: Power technologies international (2015). (2015). Accessed 19 Jan 2017
  9. 9.
    IEC: White paper - microgrids for disaster preparedness and recovery with electricity continuity and systems. Technical report, IEC WP Microgrids, Switzerland (2014)Google Scholar
  10. 10.
    IRENE: D2.2 - Root causes identification and societal impact analysis. Technical report (2016)Google Scholar
  11. 11.
    IRENE: D3.1 - System architecture design, supply demand model and simulation. Technical report (2016)Google Scholar
  12. 12.
    Jung, O., et al.: Towards a collaborative framework to improve urban grid resilience. In: Proceedings of 2016 IEEE International Energy Conference (ENERGYCON), 4–8 April, pp. 1–6. IEEE (2016).
  13. 13.
    Lau, E.T., Chai, K.K., Chen, Y., Vasenev, A.: Towards improving resilience of smart urban electricity networks by interactively assessing potential microgrids. In: Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems (SmartGreens 2017), Porto, Portugal, 22–24 April 2017, pp. 1–8 (2017).
  14. 14.
    Le, A., Chen, Y., Chai, K.K., Vasenev, A., Montoya, L.: Assessing loss event frequencies of smart grid cyber threats: encoding flexibility into FAIR using bayesian network approach. In: Hu, J., Leung, V.C.M., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds.) Smart Grid Inspired Future Technologies. LNICST, vol. 175, pp. 43–51. Springer, Cham (2017). Scholar
  15. 15.
    lp\_solve: Introduction to lp\_solve (2015). Accessed 19 Oct 2016
  16. 16.
  17. 17.
    Stauffer, N.: The microgrid - a small-scale flexible, reliable source of energy (2012). Accessed 19 Jan 2017
  18. 18.
    Vasenev, A., Montoya Morales, A.L.: Analysing non-malicious threats to urban smart grids by interrelating threats and threat taxonomies. In: Proceedings of 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy, 12–15 September 2016, pp. 1–4. IEEE (2016)Google Scholar
  19. 19.
    Vasenev, A., Montoya Morales, A.L., Ceccarelli, A.: A Hazus-based method for assessing robustness of electricity supply to critical smart grid consumers during flood events. In: Proceedings of the 11th International Conference on Availability, Reliability and Security, ARES 2016, Salzburg, Austria, 31 August–02 September 2016, pp. 223–228. IEEE (2016)Google Scholar
  20. 20.
    Vasenev, A., Montoya, L., Ceccarelli, A., Le, A., Ionita, D.: Threat navigator: grouping and ranking malicious external threats to current and future urban smart grids. In: Hu, J., Leung, V.C.M., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds.) Smart Grid Inspired Future Technologies. LNICST, vol. 175, pp. 184–192. Springer, Cham (2017). Scholar
  21. 21.
    Zubelzu, S., Alvarez, R., Hernandez, A.: Methodology to calculate the carbon footprint of household land use in the urban planning stage. Land Use Policy 48, 223–235 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eng Tseng Lau
    • 1
    Email author
  • Kok Keong Chai
    • 1
  • Yue Chen
    • 1
  • Alexandr Vasenev
    • 2
  1. 1.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  2. 2.Faculty of Engineering, Mathematics and Computer ScienceUniversity of TwenteEnschedeThe Netherlands

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