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A distributed power sharing framework among households in microgrids: a repeated game approach

Abstract

In microgrids, the integration of distributed energy resources (DERs) in the residential sector can improve power reliability, and potentially reduce power demands and carbon emissions. Improving the utilization of renewable energy in households is a critical challenge for DERs. In this regard, renewable power sharing is one of the possible solutions to tackle this problem. Even though this solution has attracted significant attention recently, most of the proposed power sharing frameworks focus more on centralized schemes. In contrast, in this paper, the performance of a proposed distributed power sharing framework is investigated. The problem is formulated as a repeated game between households in a microgrid. In this game, each household decides to cooperate and borrow/lend some amount of renewable power from/to a neighboring household, or to defect and purchase the entire demands from the main grid based on a payoff function. The Nash equilibrium of this game is characterized and the effect of the strategies taken by the households on the system is analyzed. We conduct an extensive evaluation using real demand data from 12 households of different sizes and power consumption profiles in Stockholm. Numerical results indicate that cooperation is beneficial from both an economical and environmental perspective and that households can achieve cost savings up to 20 %.

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References

  1. The International Energy Agency (IEA) (2014) World energy outlook. http://www.iea.org/. Accessed Dec 2014

  2. Ipakchi A, Albuyeh F (2009) Grid of the future. Power Energy Mag IEEE 7(2):52–62

    Article  Google Scholar 

  3. Energy Information Administration, US (2014) http://www.eia.gov/todayinenergy/. Accessed Dec 2014

  4. Farhangi H (2010) The path of the smart grid. Power Energy Mag IEEE 8(1):18–28

    MathSciNet  Article  Google Scholar 

  5. Amin SM, Wollenberg BF (2005) Toward a smart grid: power delivery for the 21st century. Power Energy Mag IEEE 3(5):34–41

    Article  Google Scholar 

  6. Gellings CW, Chamberlin JH (1987) Demand-side management: concepts and methods

  7. Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. Power Energy Mag IEEE 5(4):78–94

    Article  Google Scholar 

  8. Barnes J et al (2013) Freeing the grid 2013 best practices in state net metering policies and interconnection procedures. In: Latham NY (ed) Interstate Renewable Energy Council (IREC). http://freeingthegrid.org/wp-content/uploads/2013/11/FTG_2013.pdf. Accessed May 2015

  9. Zhu T, Mishra A, Irwin D, Sharma N, Shenoy P, Towsley D (2011) The case for efficient renewable energy management in smart homes. In: Proceedings of the third ACM workshop on embedded sensing systems for energy-efficiency in buildings, ACM, pp 67–72

  10. Tesla Powerwall (2015) http://www.teslamotors.com/powerwall. Accessed May 2015

  11. Xie X (2012) Vanadium redox-flow battery. Tennessee Valley Authority

  12. REDT. http://www.redtenergy.com/

  13. Alskaif T, Zapata MG, Bellalta B (2015) A reputation-based centralized energy allocation mechanism for microgrids. In: Smart Grid Communications (SmartGridComm), 2015 6th IEEE international conference

  14. Yao J, Venkitasubramaniam P (2015) Optimal end user energy storage sharing in demand response. In: Smart Grid Communications (SmartGridComm), 2015 6th IEEE international conference

  15. Zhu T, Huang Z, Sharma A, Su J, Irwin D, Mishra A, Menasche D, Shenoy P (2013) Sharing renewable energy in smart microgrids. In: Proceedings of the ACM/IEEE 4th international conference on cyber-physical systems, ACM, pp 219–228

  16. Liu T, Tan X, Sun B, Wu Y, Guan X, Tsang DH (2015) Energy management of cooperative microgrids with p2p energy sharing in distribution networks. In: Smart Grid Communications (SmartGridComm), 2015 6th IEEE international conference

  17. Alskaif T, Zapata MG, Bellalta B (2015) Game theory for energy efficiency in wireless sensor networks: latest trends. J Netw Comput Appl 54:33–61

    Article  Google Scholar 

  18. Alskaif T, Zapata MG, Bellalta B (2015) Citizens collaboration to minimize power costs in smart grids: a game theoretic approach. In: SMARTGREENS–4th international conference on smart cities and green ict systems, Lisbon, Portugal, SCITEPRESS–Science and Technology Publications, pp 300–305

  19. Fudenberg D, Maskin E (1986) The folk theorem in repeated games with discounting or with incomplete information. Econometrica 54(3):533–554

    MathSciNet  Article  MATH  Google Scholar 

  20. Friedman JW (1971) A non-cooperative equilibrium for supergames. Rev Econ Stud 38(1):1–12

    Article  MATH  Google Scholar 

  21. Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211(4489):1390–1396

    MathSciNet  Article  MATH  Google Scholar 

  22. Gale D, Shapley LS (1962) College admissions and the stability of marriage. American mathematical monthly, pp 9–15

  23. Nord Pool (2013). http://www.nordpoolspot.com. Accessed May 2015

  24. Brander M, Sood A, Wylie C, Haughton A, Lovell J (2011) Electricity-specific emission factors for grid electricity. Technical paper

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Acknowledgments

This work was partially supported by projects TIN2013-47272-C2-2, TEC2012- 32354 and SGR-2014-881.

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Correspondence to Tarek AlSkaif.

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AlSkaif, T., Zapata, M.G., Bellalta, B. et al. A distributed power sharing framework among households in microgrids: a repeated game approach. Computing 99, 23–37 (2017). https://doi.org/10.1007/s00607-016-0504-y

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  • DOI: https://doi.org/10.1007/s00607-016-0504-y

Keywords

  • Microgrids
  • Game theory
  • Demand side management
  • Distributed energy resources
  • Electricity cost minimization problem
  • Carbon emission reduction strategies

Mathematics Subject Classification

  • 91A20 Multistage and repeated games
  • 91A10 Noncooperative games
  • 91A80 Applications of game theory
  • 68W15 Distributed algorithms
  • 68M14 Distributed systems