Skip to main content
SpringerLink
Log in

A projection neural network for optimal demand response in smart grid environment

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper presents a smart grid model that fully considers the power consumption types of users and the features of electricity price. A satisfaction function is added into the bill function to balance the user experience of electricity usage and the consumption of load. For the purpose of minimizing the electricity bill of all users, a single-layer projection neural network (PNN) is used, which is proven to be global convergence. And the simulation results reveal that the effectiveness and peculiarities of the proposed PNN.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Albadi MH, El-Saadany EF (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78(11):1989–1996

    Article  Google Scholar 

  2. Wang Z, Leng Y, Wu Q (2015) Review of smart grid demand response with clean energy access. Chin J Power Sources 39(8):1798–1800

    Google Scholar 

  3. Aghaei J, Alizadeh MI (2013) Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 18:64–72

    Article  Google Scholar 

  4. Aalami HA, Moghaddam MP, Yousefi GR (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87(1):243–250

    Article  Google Scholar 

  5. Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 1(1):82–88

    Article  Google Scholar 

  6. Wang J, Liu C, Ton D, Zhou Y, Kim J, Vyas A (2011) Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power. Energy Policy 39(7):4016–4021

    Article  Google Scholar 

  7. Wang H, Huang T, Liao X, Abu-Rub H, Chen G (2016) Reinforcement learning in energy trading game among smart microgrids. IEEE Transactions on Industrial Electronics. doi:10.1109/TIE.2016.2554079

    Google Scholar 

  8. Chen Z, Wu L, Fu Y (2012) Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans Smart Grid 3(4):1822–1831

    Article  Google Scholar 

  9. Li C, Yu X, Yu W, Chen G, Wang J (2016) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 99:1–12

    Google Scholar 

  10. Wen S, Yu X, Zeng Z, Wang J (2016) Event-triggering load frequency control for multi-area power systems with communication delays. IEEE Trans Ind Electron 63(2):1308–1317

    Article  Google Scholar 

  11. Maharjan S, Zhu Q, Zhang Y, Gjessing S, Basar T (2016) Demand response management in the smart grid in a large population regime. IEEE Trans Smart Grid 7(1):189–199

    Article  Google Scholar 

  12. Li C, Yu X, Yu W, Huang T, Liu Z (2015) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inform 68:1–11

    Google Scholar 

  13. Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Energy Rev 59:166–178

    Article  Google Scholar 

  14. Nosratabadi SM, Hooshmand RA, Gholipour E (2016) Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy. Appl Energy 59:590–606

    Article  Google Scholar 

  15. Siano Pierluigi (2014) Demand response and smart grids—a survey. Renew Sustain Energy Rev 30:461–478

    Article  Google Scholar 

  16. Li N, Chen L, Low SH (2011) Optimal demand response based on utility maximization in power networks. 2011 IEEE Power and Energy Society General Meeting, pp 1–8

  17. Atwa YM, El-Saadany EF, Salama MMA, Seethapathy R (2010) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25(1):360–370

    Article  Google Scholar 

  18. Urias MEG, Sanchez EN, Ricalde LJ (2015) Electrical microgrid optimization via a new recurrent neural network. IEEE Syst J 9(3):945–953

    Article  Google Scholar 

  19. Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52(3):141–152

    MATH  Google Scholar 

  20. Tank DW, Hopfield JJ (1986) Simple neural optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans Circuits Syst 33(5):533–541

    Article  Google Scholar 

  21. Li H, Liao X, Chen G, Hill DJ, Dong Z, Huang T (2015) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks. Neural Netw 66:1–10

    Article  Google Scholar 

  22. He X, Yu J, Huang T, Li CD, Li CJ (2014) Neural networks for solving Nash equilibrium problem in application of multiuser power control. Neural Netw 57(9):73–78

    Article  MATH  Google Scholar 

  23. Liu Q, Guo Z, Wang J (2012) A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Netw 26:99–109

    Article  MATH  Google Scholar 

  24. Xia Y, Wang J (2004) A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans Neural Netw 15(2):318–328

    Article  Google Scholar 

  25. Hu X, Zhang B (2009) An alternative recurrent neural network for solving variational inequalities and related optimization problems. IEEE Trans Syst Man Cybern Part B (Cybern) 39(6):1640–1645

    Article  Google Scholar 

  26. He X, Li CD, Huang T, Li CJ, Huang J (2014) A recurrent neural network for solving bilevel linear programming problem. IEEE Trans Neural Netw Learn Syst 25(4):824–830

    Article  Google Scholar 

  27. Li C, Yu X, Huang T, Chen G, He X (2016) A generalized Hopfield network for nonsmooth constrained convex optimization: Lie derivative approach. IEEE Trans Neural Netw Learn Syst 27(2):308–321

    Article  MathSciNet  Google Scholar 

  28. He X, Huang T, Li C, Che H, Dong Z (2015) A recurrent neural network for optimal real-time price in smart grid. Neurocomputing 149:608–612

    Article  Google Scholar 

  29. Zeng Z, Cichocki A, Cheng L, Xia Y, Hu X (2012) Guest editorial special issue on neurodynamic systems for optimization and applications. IEEE Transactions on Neural Networks and Learning Systems 27(2):210–213

    Article  MathSciNet  Google Scholar 

  30. He X, Li CD, Huang T, Li CJ (2014) Neural network for solving convex quadratic bilevel programming. Neural Netw 51(3):17–25

    MATH  Google Scholar 

  31. Cheng L, Hou ZG, Lin Y, Tan M, Zhang WC, Wu FX (2011) Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans Neural Netw 22(5):714–726

    Article  Google Scholar 

  32. He X, Huang T, Yu J, Li C, Li C (2016) An inertial projection neural network for solving variational inequalities. IEEE Trans Cybern. doi:10.1109/TCYB.2016.2523541

    Google Scholar 

  33. Xia Y, Leung H, Wang J (2002) A projection neural network and its application to constrained optimization problems. IEEE Trans Circuits Syst I Fundam Theory Appl 49(4):447–458

    Article  MathSciNet  MATH  Google Scholar 

  34. Fu W, Jing Z, Luo Z, Huang X, Wu Y (2015) A time-of-use pricing based control scheme for intelligent household appliances. Power Syst Technol 39(3):717–723

    Google Scholar 

  35. Yang P, Tang G, Nehorai A (2013) A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Trans Power Syst 28(2):884–892

    Article  Google Scholar 

  36. Xia Youshen (2004) An extended projection neural network for constrained optimization. Neural Comput 16(4):863–883

    Article  MathSciNet  MATH  Google Scholar 

  37. Xia Y, Wang J (2000) On the stability of globally projected dynamical systems. J Optim Theory Appl 106(1):129–150

    Article  MathSciNet  MATH  Google Scholar 

  38. Friesz TL, Bernstein D, Mehta NJ, Tobin RL, Ganjalizadeh S (1994) Day-to-day dynamic network disequilibria and idealized traveler information systems. Oper Res 42(6):1120–1136

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by Fundamental Research Funds for the Central Universities (Grant no. XDJK2016B017, XDJK2016E032), Natural Science Foundation of China (Grant nos: 61403313, 61374078), and also supported by Natural Science Foundation Project of Chongqing CSTC (Grant no. cstc2014jcyjA40014). This publication was made possible by NPRP Grant No. NPRP 7-1482-1-278 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Yao Yao & Xing He

  2. Department of Mathematics, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  3. School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC, 3000, Australia

    Chaojie Li

  4. School of Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Dawen Xia

Authors
  1. Yao Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tingwen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaojie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dawen Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xing He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, Y., He, X., Huang, T. et al. A projection neural network for optimal demand response in smart grid environment. Neural Comput & Applic 29, 259–267 (2018). https://doi.org/10.1007/s00521-016-2532-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2532-0

Keywords

Search

Navigation