Advertisement

Power System Analysis: Competitive Markets, Demand Management, and Security

  • Anibal Sanjab
  • Walid Saad
Reference work entry

Abstract

In recent years, the power system has undergone unprecedented changes that have led to the rise of an interactive modern electric system typically known as the smart grid. In this interactive power system, various participants such as generation owners, utility companies, and active customers can compete, cooperate, and exchange information on various levels. Thus, instead of being centrally operated as in traditional power systems, the restructured operation is expected to rely on distributed decisions taken autonomously by its various interacting constituents. Due to their heterogeneous nature, these constituents can possess different objectives which can be at times conflicting and at other times aligned. Consequently, such a distributed operation has introduced various technical challenges at different levels of the power system ranging from energy management to control and security. To meet these challenges, game theory provides a plethora of useful analytical tools for the modeling and analysis of complex distributed decision making in smart power systems.

The goal of this chapter is to provide an overview of the application of game theory to various aspects of the power system including: i) strategic bidding in wholesale electric energy markets, ii) demand-side management mechanisms with special focus on demand response and energy management of electric vehicles, iii) energy exchange and coalition formation between microgrids, and iv) security of the power system as a cyber-physical system presenting a general cyber-physical security framework along with applications to the security of state estimation and automatic generation control. For each one of these applications, first an introduction to the key domain aspects and challenges is presented, followed by appropriate game-theoretic formulations as well as relevant solution concepts and main results.

Keywords

Smart grid Electric energy markets Demand-side management Power system security Energy management Distributed power system operation Dynamic game theory 

Notes

Acknowledgements

This work was supported by the US National Science Foundation under Grants ECCS-1549894 and CNS-1446621.

References

  1. Abur A, Exposito AG (2004) Power system state estimation: theory and implementation. Marcel Dekker, New YorkGoogle Scholar
  2. Couillet R, Perlaza SM, Tembine H, Debbah M (2012) Electrical vehicles in the smart grid: a mean field game analysis. IEEE J Sel Areas Commun 30(6):1086–1096Google Scholar
  3. El Rahi G, Sanjab A, Saad W, Mandayam NB, Poor HV (2016) Prospect theory for enhanced smart grid resilience using distributed energy storage. In: Proceedings of the 54th annual Allerton conference on communication, control, and computing, Sept 2016Google Scholar
  4. Glover JD, Sarma MS, Overbye TJ (2012) Power system analysis and design, 5th edn. Stamford, Cengage LearningGoogle Scholar
  5. Gomez-Exposito A, Conejo AJ, Canizares C (2009) Electric energy systems: analysis and operation. CRC Press, Boca RatonGoogle Scholar
  6. Guan X, Ho Y-C, Pepyne DL (2001) Gaming and price spikes in electric power markets. IEEE Trans Power Syst 16(3):402–408Google Scholar
  7. Horowitz SH, Phadke AG, Niemira JK (2013) Power system relaying, 4th edn. John Wiley & Sons Inc, Chichester, West SussexGoogle Scholar
  8. Law YW, Alpcan T, Palaniswami M (2015) Security games for risk minimization in automatic generation control. IEEE Trans Power Syst 30(1):223–232Google Scholar
  9. Li T, Shahidehpour M (2005) Strategic bidding of transmission-constrained GENCOs with incomplete information. IEEE Trans Power Syst 20(1):437–447Google Scholar
  10. Maharjan S, Zhu Q, Zhang Y, Gjessing S, Başar T (2013) Dependable demand response management in the smart grid: a Stackelberg game approach. IEEE Trans Smart Grid 4(1): 120–132Google Scholar
  11. Maharjan S, Zhu Q, Zhang Y, Gjessing S, Başar T (2016) Demand response management in the smart grid in a large population regime. IEEE Trans Smart Grid 7(1):189–199Google Scholar
  12. Nanduri V, Das T (2007) A reinforcement learning model to assess market power under auction-based energy pricing. IEEE Trans Power Syst 22(1):85–95Google Scholar
  13. Saad W, Han Z, Poor HV (2011) Coalitional game theory for cooperative micro-grid distribution networks. In: IEEE international conference on communications workshops (ICC), June 2011, pp 1–5Google Scholar
  14. Saad W, Han Z, Poor HV, Başar T (2012) Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag 29(5):86–105Google Scholar
  15. Sanjab A, Saad W (2016a) Data injection attacks on smart grids with multiple adversaries: a game-theoretic perspective. IEEE Trans Smart Grid 7(4):2038–2049Google Scholar
  16. Sanjab A, Saad W (2016b) On bounded rationality in cyber-physical systems security: game-theoretic analysis with application to smart grid protection. In: IEEE/ACM CPS week joint workshop on cyber-physical security and resilience in smart grids (CPSR-SG), Apr 2016, pp 1–6Google Scholar
  17. Sauer PW, Pai MA (1998) Power system dynamics and stability. Prentice Hall, Upper Saddle RiverGoogle Scholar
  18. Wood AJ, Wollenberg BF (2012) Power generation, operation, and control. John Wiley & Sons, New YorkGoogle Scholar
  19. Zhu Q, Başar T (2011) Robust and resilient control design for cyber-physical systems with an application to power systems. In: 50th IEEE conference on decision and control and European control conference, Dec 2011, pp 4066–4071Google Scholar
  20. Zhu Q, Başar T (2015) Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems. IEEE Control Syst Mag 35(1):46–65MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Virginia TechBlacksburgUSA

Personalised recommendations