Simulation-Based Validation for Smart Grid Environments: Framework and Experimental Results

  • Wonkyu Han
  • Mike Mabey
  • Gail-Joon Ahn
  • Tae Sung Kim
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 263)


Large and complex systems, such as the Smart Grid, are often best understood through the use of modeling and simulation. In particular, the task of assessing a complex system’s risks and testing its tolerance and recovery under various attacks has received considerable attention. However, such tedious tasks still demand a systematic approach to model and evaluate each component in complex systems. In other words, supporting a formal validation and verification without needing to implement the entire system or accessing the existing physical infrastructure is critical since many elements of the Smart Grid are still in the process of becoming standardized for widespread use. In this chapter, we describe our simulation-based approach to understanding and examining the behavior of various components of the Smart Grid in the context of verification and validation. To achieve this goal, we adopt the discrete event system specification (DEVS) modeling methodology, which allows the generalization and specialization of entities in the model and supports a customized simulation with specific variables. In addition, we articulate metrics for supporting our simulation-based verification and validation and demonstrate the feasibility and effectiveness of our approach with a real-world use case.


Smart grid Discrete event system specification Risk assessment Simulation Validation 



This work was partially supported by grants from the National Science Foundation and the Department of Energy.


  1. 1.
    Garcia, R.C., Contreras, J., van Akkeren, M., Garcia, J.B.C.: A garch forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 20(2), 867–874 (2005)CrossRefGoogle Scholar
  2. 2.
    Mohsenian-Rad, A.-H., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)CrossRefGoogle Scholar
  3. 3.
    Arora, M., Das, S.K., Biswas, R.: A de-centralized scheduling and load balancing algorithm for heterogeneous grid environments. In: Proceedings of the International Conference on Parallel Processing Workshops (ICPPW’02), pp. 499–505 (2002)Google Scholar
  4. 4.
    Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M.: Domestic energy management methodology for optimizing efficiency in smart grids. In: Proceedings of the IEEE Bucharest PowerTech 2009, 1–7 July 2009Google Scholar
  5. 5.
    Metke, A.R., Ekl, R.L.: Security technology for smart grid networks. IEEE Trans. Smart Grid 1(1), 99–107 (2010)CrossRefGoogle Scholar
  6. 6.
    Ericsson, G.N.: Cyber security and power system communication—essential parts of a smart grid infrastructure. IEEE Trans. Power Delivery 25(3), 1501–1507 (2010)CrossRefGoogle Scholar
  7. 7.
    Nist framework and roadmap for smart grid interoperability standards. (2012). Accessed Feb 2012
  8. 8.
    Energy power research institute, real-time pricing—top level. (2012). Accessed Feb 2012
  9. 9.
    Cox, W., Holmberg, D., Sturek, D.: Oasis collaborative energy standards, facilities, and zigbee smart energy. In: Grid-Interop Forum 2011 (2011)Google Scholar
  10. 10.
    Zigbee smart energy 2.0 draft 0.9 public application profile. (2012). Accessed July 2012
  11. 11.
    Cybersecurity working group final three-year plan. (2011). Accessed Apr 2011
  12. 12.
    Electricity subsector cybersecurity capability maturity model (es-c2m2). (2012). Accessed May 2012
  13. 13.
    Lin, J., Sedigh, S., Miller, A.: Modeling cyber-physical systems with semantic agents. In: Proceedings of the IEEE 34th Annual Computer Software and Applications Conference Workshops (COMPSACW) 2010, 13–18 July 2010Google Scholar
  14. 14.
    Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: design and implementation. In: IEEE/PES Power Systems Conference and Exposition (PSCE’09), pp. 1–8 Mar 2009Google Scholar
  15. 15.
    Stevens, F., Courtney, T. Singh, S., Agbaria, A., Meyer, J.R., Sanders, W.H., Pal, P.: Model-based validation of an intrusion-tolerant information system. In: Proceedings of the 23rd IEEE International Symposium on Reliable Distributed Systems (SRDS’04), pp. 184–194 Oct 2004Google Scholar
  16. 16.
    Nicol, D.M., Sanders, W.H., Trivedi, K.S.: Model-based evaluation: from dependability to security. IEEE Trans. Dependable Secure Comput. 1(1), 48–65 (2004)CrossRefGoogle Scholar
  17. 17.
    Jonsson, E., Olovsson, T.: A quantitative model of the security intrusion process based on attacker behavior. IEEE Trans. Softw. Eng. 23(4), 235–245 (1997)CrossRefGoogle Scholar
  18. 18.
    Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press, San Diego (Feb 2000)Google Scholar
  19. 19.
    Allcott, H.: Real time pricing and electricity markets. (2009). Accessed Jan 2009
  20. 20.
    Taylor, Thomas N., Schwarz, Peter M., Cochell, James E.: 24/7 hourly response to electricity real-time pricing with up to eight summers of experience. J. Regul. Econ. 27, 235–262 (2005)CrossRefGoogle Scholar
  21. 21.
    Allcott, H.: Real-time pricing and electricity market design. (2013). Accessed Mar 2013
  22. 22.
    Levelized cost of new generation resources in the annual energy outlook 2013. (2013). Accessed Jan 2013
  23. 23.
    Ghaemi, S., Brauner, G.: User behavior and patterns of electricity use for energy saving. Internationale Energiewirtschaftstagung an der TU Wien, IEWT (2009)Google Scholar
  24. 24.
    Patrick, R.H., Wolak, F.A.: Estimating the customer-level demand for electricity under real-time market prices. Technical report, National Bureau of Economic Research, Washington (Apr 2001)Google Scholar
  25. 25.
    Herriges, J.A., Baladi, S.M., Caves, D.W., Neenan, B.F.: The response of industrial customers to electric rates based upon dynamic marginal costs. Rev. Econ. Stat. 75(3), 446–454 (1993)CrossRefGoogle Scholar
  26. 26.
    Boisvert, R.N., Cappers, P., Goldman, C., Neenan, B., Hopper, N.: Customer response to rtp in competitive markets: a study of niagara mohawk’s standard offer tariff. Energy J. 28(1), 53–74 (2007)CrossRefGoogle Scholar
  27. 27.
    White, J.: 12 steps toward cyber resilience. InfoSecurity Professional INSIGHTS 2(2). (2013)
  28. 28.
    Watson, H.J., Wixom, B.H.: The current state of business intelligence. Computer 40(9), 96–99 (2007)CrossRefGoogle Scholar
  29. 29.
    Varaiya, P.P., Wu, F.F., Bialek, J.W.: Smart operation of smart grid: risk-limiting dispatch. Proc. IEEE 99(1), 40–57 (2011)CrossRefGoogle Scholar
  30. 30.
    Chao, Hung-po: Price-responsive demand management for a smart grid world. Electr. J. 23(1), 7–20 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Laboratory of Security Engineering for Future Computing (SEFCOM)Arizona State UniversityPhoenixUSA
  2. 2.Chungbuk National UniversityCheongju-siSouth Korea

Personalised recommendations