Skip to main content
SpringerLink
Log in

Demand response management of smart grid based on Stackelberg-evolutionary joint game

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

We investigated the real-time pricing demand response management system of multiple microgrids and multiple power users. Accordingly, we have proposed a Stackelberg-evolutionary joint game framework to examine the real-time pricing scheme of multiple microgrids and multiple power users so as to establish equilibrium strategies. Both a non-cooperative game among multiple microgrids and a multi-population evolutionary game among multiple power users were considered. Furthermore, we constructed a Stackelberg game between microgrids and power users to reflect their sequential interaction, wherein the microgrids are leaders, and the power users are followers. We also proved the existence and uniqueness of the Stackelberg equilibrium. Furthermore, we proposed an iterative algorithm to compute the equilibrium strategy and demonstrate the convergence and effectiveness through numerical simulations, which demonstrated that the algorithm could achieve a balance between power supply and demand balance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lu T G, Chen X Y, McElroy M B, et al. A reinforcement learning-based decision system for electricity pricing plan selection by smart grid end users. IEEE Trans Smart Grid, 2021, 12: 2176–2187

    Article  Google Scholar 

  2. Ghosal A, Conti M. Key management systems for smart grid advanced metering infrastructure: a survey. IEEE Commun Surv Tutorials, 2019, 21: 2831–2848

    Article  Google Scholar 

  3. Yang J J, Zhao J H, Luo F J, et al. Decision-making for electricity retailers: a brief survey. IEEE Trans Smart Grid, 2018, 9: 4140–4153

    Article  Google Scholar 

  4. Wan Y N, Qin J H, Yu X H, et al. Price-based residential demand response management in smart grids: a reinforcement learning-based approach. IEEE CAA J Autom Sin, 2022, 9: 123–134

    Article  Google Scholar 

  5. Cheng L F, Yu T. Game-theoretic approaches applied to transactions in the open and ever-growing electricity markets from the perspective of power demand response: an overview. IEEE Access, 2019, 7: 25727–25762

    Article  Google Scholar 

  6. Ding T, Qu M, Amjady N, et al. Tracking equilibrium point under real-time price-based residential demand response. IEEE Trans Smart Grid, 2021, 12: 2736–2740

    Article  Google Scholar 

  7. Cao G C, Fang D B, Wang P Y. The impacts of social learning on a real-time pricing scheme in the electricity market. Appl Energy, 2021, 291: 116874

    Article  Google Scholar 

  8. Aljohani T M, Ebrahim A F, Mohammed O A. Dynamic real-time pricing mechanism for electric vehicles charging considering optimal microgrids energy management system. IEEE Trans Ind Applicat, 2021, 57: 5372–5381

    Article  Google Scholar 

  9. Yang H L, Schell K R. Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets. Appl Energy, 2021, 299: 117242

    Article  Google Scholar 

  10. Estebsari A, Mazzarino P R, Bottaccioli L, et al. IoT-enabled real-time management of smart grids with demand response aggregators. IEEE Trans Ind Appl, 2022, 58: 102–112

    Article  Google Scholar 

  11. Seok H, Kim S P. Pareto efficient incentive-based real-time pricing model for balanced smart grids. IEEE Access, 2022, 10: 2766–2774

    Article  Google Scholar 

  12. Jiang Y N, Zhou K L, Lu X H, et al. Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment. Appl Energy, 2020, 271: 115239

    Article  Google Scholar 

  13. Tang R, Wang S W, Li H X. Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids. Appl Energy, 2019, 250: 118–130

    Article  Google Scholar 

  14. Hayes B, Melatti I, Mancini T, et al. Residential demand management using individualized demand aware price policies. IEEE Trans Smart Grid, 2017, 8: 1284–1294

    Article  Google Scholar 

  15. Mishra M K, Parida S K. A game theoretic approach for demand-side management using real-time variable peak pricing considering distributed energy resources. IEEE Syst J, 2022, 16: 144–154

    Article  Google Scholar 

  16. Lee D, Kundur D. An evolutionary game approach to predict demand response from real-time pricing. In: Proceedings of IEEE Electrical Power and Energy Conference, Ontario, 2015. 197–202

  17. Liu F, Pan L, Yao L. Evolutionary game based analysis for user privacy protection behaviors in social networks. In: Proceedings of IEEE 3rd International Conference on Data Science in Cyberspace, Guangzhou, 2018. 274–279

  18. Hu M, Xiao J W, Cui S C, et al. Distributed real-time demand response for energy management scheduling in smart grid. Int J Electrical Power Energy Syst, 2018, 99: 233–245

    Article  Google Scholar 

  19. Liu W, Gu W, Wang J, et al. Game theoretic non-cooperative distributed coordination control for multi-microgrids. IEEE Trans Smart Grid, 2018, 9: 6986–6997

    Article  Google Scholar 

  20. Yu M M, Hong S H. A real-time demand-response algorithm for smart grids: a Stackelberg game approach. IEEE Trans Smart Grid, 2016, 7: 879–888

    Google Scholar 

  21. Kamyab F, Amini M, Sheykhha S, et al. Demand response program in smart grid using supply function bidding mechanism. IEEE Trans Smart Grid, 2016, 7: 1277–1284

    Article  Google Scholar 

  22. Chai B, Chen J, Yang Z, et al. Demand response management with multiple utility companies: a two-level game approach. IEEE Trans Smart Grid, 2014, 5: 722–731

    Article  Google Scholar 

  23. Li J, Ma G Q, Li T, et al. A Stackelberg game approach for demand response management of multi-microgrids with overlapping sales areas. Sci China Inf Sci, 2019, 62: 212203

    Article  MathSciNet  Google Scholar 

  24. Yao R T, Lu X Q, Zhou H, et al. A novel category-specific pricing strategy for demand response in microgrids. IEEE Trans Sustain Energy, 2022, 13: 182–195

    Article  Google Scholar 

  25. Aguiar N, Dubey A, Gupta V. Network-constrained Stackelberg game for pricing demand flexibility in power distribution systems. IEEE Trans Smart Grid, 2021, 12: 4049–4058

    Article  Google Scholar 

  26. Gao B T, Chen C, Qin Y H, et al. Evolutionary game-theoretic analysis for residential users considering integrated demand response. J Modern Power Syst Clean Energy, 2021, 9: 1500–1509

    Article  Google Scholar 

  27. Xue K P, Yang Q Y, Li S H, et al. PPSO: a privacy-preserving service outsourcing scheme for real-time pricing demand response in smart grid. IEEE Internet Things J, 2019, 6: 2486–2496

    Article  Google Scholar 

  28. Samadi P, Mohsenian-Rad A H, Schober R, et al. Optimal real-time pricing algorithm based on utility maximization for smart grid. In: Proceedings of the 1st IEEE International Conference on Smart Grid Communications, Gaithersburg, 2010. 415–420

  29. Mohsenian-Rad A H, Wong V W S, Jatskevich J, et al. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid, 2010, 1: 320–331

    Article  Google Scholar 

  30. Wood A J, Wollenberg B F. Power Generation Operation and Control. New York: Wiley-Interscience, 1996

    Google Scholar 

  31. Cheng L F, Yu T. Nash equilibrium-based asymptotic stability analysis of multi-group asymmetric evolutionary games in typical scenario of electricity market. IEEE Access, 2018, 6: 32064–32086

    Article  Google Scholar 

  32. Han O Z, Ding T, Bai L Q, et al. Evolutionary game based demand response bidding strategy for end-users using Q-learning and compound differential evolution. IEEE Trans Cloud Comput, 2022, 10: 97–110

    Article  Google Scholar 

  33. Friedman D. On economic applications of evolutionary game theory. J Evolary Economics, 1998, 8: 15–43

    Article  Google Scholar 

  34. Zhu Z Q, Chan K W, Bu S Q, et al. Analysis of evolutionary dynamics for bidding strategy driven by multi-agent reinforcement learning. IEEE Trans Power Syst, 2021, 36: 5975–5978

    Article  Google Scholar 

  35. Lakshmikantham V, Leela S, Martynyuk A A. Stability Analysis of Nonlinear Systems. 2nd ed. Switzerland: Springer International Publishing Switzerland, 2015

    Book  MATH  Google Scholar 

  36. Slotine J E, Li W P. Applied Nonlinear Control. Englewood Cliffs: Prentice Hall, 1991. 61–67

    MATH  Google Scholar 

  37. Ma K, Wang C S, Yang J, et al. Pricing mechanism with noncooperative game and revenue sharing contract in electricity market. IEEE Trans Cybern, 2019, 49: 97–106

    Article  Google Scholar 

  38. Zhang W Y. Game Theory Information Economics. Shanghai: Shanghai People’s Publishing House, 2012

    Google Scholar 

  39. Yates R D. A framework for uplink power control in cellular radio systems. IEEE J Sel Areas Commun, 1995, 13: 1341–1347

    Article  Google Scholar 

  40. Başar T, Olsder G J. Dynamic Noncooperative Game Theory. 2nd ed. New York: Academic Press, 1995

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu, 241000, China

    Jun Li

  2. Key Laboratory of Pure Mathematics and Mathematical Practice, School of Mathematical Sciences, East China Normal University, Shanghai, 200241, China

    Tao Li

  3. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, 2600, Australia

    Daoyi Dong

Authors
  1. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daoyi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Li, T. & Dong, D. Demand response management of smart grid based on Stackelberg-evolutionary joint game. Sci. China Inf. Sci. 66, 182201 (2023). https://doi.org/10.1007/s11432-022-3674-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-022-3674-6

Keywords

Search

Navigation