On Valuation of Smart Grid Architectures: An Enterprise Engineering Perspective

  • Iván S. Razo-ZapataEmail author
  • Anup Shrestha
  • Erik Proper
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 287)


This paper presents the initial design of a method to value smart grid (SG) architectures from a business point of view. The proposed design relies on the use of the Smart Grid Architecture Model (SGAM) and an adapted version of Bedell’s method to assess the strategic importance and effectiveness of SG elements. As an attempt to automate such valuation, we also propose the use of a survey and a decision support system (DSS) that can determine the overall value of SG architectures.


Smart grid Enterprise architecture Valuation Decision support system 


  1. 1.
    Borenstein, S.: The private and public economics of renewable electricity generation. J. Econ. Perspect. 26(1), 67–92 (2012)CrossRefGoogle Scholar
  2. 2.
    Bush, S.F.: Communication-Enabled Intelligence for the Electric Power Grid. IEEE Press, London (2014)CrossRefGoogle Scholar
  3. 3.
    CEN-CENELEC-ETSI. Smart grid coordination group: Smart grid reference architecture, European Committee for Standardization, Brussels, Belgium, Technical report (2012)Google Scholar
  4. 4.
    Chan, S.H., et al.: Decision motivation and its antecedents. Information & Management (2017)Google Scholar
  5. 5.
    DISCERN Project. WP8 D8.1 Business Case on Use Cases and Sensitivity Analysis (2017). Accessed 27 Feb 2017
  6. 6.
    Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37(2), 337–355 (2013)Google Scholar
  7. 7.
    Technology Roadmap: How2Guide for Smart Grids in Distribution Networks, International Energy Agency (IEA). Technical report OECD/IEA (2015)Google Scholar
  8. 8.
    Next Generation Wind and Solar Power - From Cost to Value, International Energy Agency (IEA). Technical report, OECD/IEA (2016)Google Scholar
  9. 9.
    Klashner, R., Sabet, S.: A DSS Design Model for complex problems: Lessons from mission critical infrastructure. Decis. Support Syst. 43(3), 990–1013 (2007)CrossRefGoogle Scholar
  10. 10.
    Peffers, K., et al.: A design science research methodology for information systems research. J. Manage. Inf. Syst. 24(3), 45–77 (2007)CrossRefGoogle Scholar
  11. 11.
    Power, D.J., Sharda, R.: Model-driven decision support systems: concepts and research directions. Decis. Support Syst. 43(3), 1044–1061 (2007)CrossRefGoogle Scholar
  12. 12.
    Quartel, D., Steen, M.W., Lankhorst, M.: Using enterprise architecture and business requirements modeling. In: 14th IEEE International Enterprise Distributed Object Computing Conference (EDOC), pp. 3–13 (2010)Google Scholar
  13. 13.
    Schuurman, P., Egon, W.B., Philip, P.: Calculating the importance of information systems: the method of Bedell revisited (2008)Google Scholar
  14. 14.
    Ueckerdt, F., et al.: System LCOE: what are the costs of variable renewables? Energy 63, 61–75 (2013)CrossRefGoogle Scholar
  15. 15.
    U.S. Energy Information Administration. Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2016, Technical report, August 2016Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Iván S. Razo-Zapata
    • 1
    Email author
  • Anup Shrestha
    • 2
  • Erik Proper
    • 1
  1. 1.Luxembourg Institute of Science and Technology (LIST)Esch-Sur-AlzetteLuxembourg
  2. 2.School of Management and EnterpriseUniversity of Southern QueenslandToowoombaAustralia

Personalised recommendations