Advertisement

Neural networks for power management optimal strategy in hybrid microgrid

  • 305 Accesses

  • 2 Citations

Abstract

This paper proposes a more reasonable objective function for combined economic emission dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to obtain optimal scheduling of a hybrid microgrid, which includes power generation resources, variable demands and energy storage system for energy storing and supplying. Combining variable neurons with Lagrange neurons, the LPNN aims to minimize the cost function and maximize the power generated by the renewable sources. The asymptotic stability condition of the neurodynamic model is analyzed, and simulation results show that optimal power of each component with certain time interval can be obtained. In addition, a new method by radial basis function neural network is proposed to predict the power values of renewable energy and load demand, which are used as the input values in the optimal process.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  1. 1.

    Wang J (1992) Recurrent neural network for solving quadratic programming problems with equality constraints. Electron Lett 28(14):1345–1347

  2. 2.

    Wang Y, Cheng L, Hou ZG, Junzhi Yu, Tan M (2016) Optimal formation of multi-robot systems based on a recurrent neural network. IEEE Trans Neural Netw Learn Syst 27(2):322–333

  3. 3.

    Cheng L, Hou ZG, Lin Y, Tan M, Zhang W, Wu F-X (2011) Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans Neural Netw 21(5):714–726

  4. 4.

    Gulin M, Vašak M, Perić N (2013) Dynamical optimal positioning of a photovoltaic panel in all weather conditions. Appl Energy 108:429–438

  5. 5.

    Manbachi M, Sadu A, Farhangi H et al (2016) Impact of EV penetration on Volt–VAR optimization of distribution networks using real-time co-simulation monitoring platform. Appl Energy 169:28–39

  6. 6.

    Moradi MH, Eskandari M, Mahdi HS (2015) Operational strategy optimization in an optimal sized smart microgrid. IEEE Trans Smart Grid 6(3):1087–1095

  7. 7.

    Manbachi M, Farhangi H, Palizban A et al (2016) Smart grid adaptive energy conservation and optimization engine utilizing particle swarm optimization and fuzzification. Appl Energy 174:69–79

  8. 8.

    Shi W, Xie X, Chu CC et al (2015) Distributed optimal energy management in microgrids. IEEE Trans Smart Grid 6(3):1137–1146

  9. 9.

    Kuznetsova E, Li YF, Ruiz C et al (2014) An integrated framework of agent-based modelling and robust optimization for microgrid energy management. Appl Energy 129:70–88

  10. 10.

    Hu J, Cao J, Guerrero JM et al (2017) Improving frequency stability based on distributed control of multiple load aggregators. IEEE Trans Smart Grid 8(4):1553–1567

  11. 11.

    Li C, Yu X, Yu W et al (2015) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inf 12(5):1775–1785

  12. 12.

    Liu N, Tang Q, Zhang J et al (2014) A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Appl Energy 129:336–345

  13. 13.

    Tang Y, Ju P, He H et al (2013) Optimized control of DFIG-based wind generation using sensitivity analysis and particle swarm optimization. IEEE Trans Smart Grid 4(1):509–520

  14. 14.

    Velik R, Nicolay P (2014) Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer. Appl Energy 130:384–395

  15. 15.

    Ikeda S, Ooka R (2015) Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system. Appl Energy 151:192–205

  16. 16.

    He X, Ho D, Huang T, Yu J, Abu-Rub H, Li C (2017) Second-order continuous-time algorithms for economic power dispatch in smart grids. IEEE Trans Syst Man Cybern Syst. doi:10.1109/TSMC.2017.2672205

  17. 17.

    Li C, Yu X, Huang T, He X (2017) Distributed optimal consensus over resource allocation network and its application to dynamical economic dispatch. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2017.2691760

  18. 18.

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

  19. 19.

    Osório GJ, Rodrigues EMG, Lujano-Rojas JM et al (2015) New control strategy for the weekly scheduling of insular power systems with a battery energy storage system. Appl Energy 154:459–470

  20. 20.

    Umeozor EC, Trifkovic M (2016) Operational scheduling of microgrids via parametric programming. Appl Energy 180:672–681

  21. 21.

    Lu Y, Li D, Xu Z et al (2014) Convergence analysis and digital implementation of a discrete-time neural network for model predictive control. IEEE Trans Ind Electron 61(12):7035–7045

  22. 22.

    Li C, Yu X, Yu W et al (2017) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 8(1):250–261

  23. 23.

    Huang HX, Li JC, Xiao CL (2015) A proposed iteration optimization approach integrating back propagation neural network with genetic algorithm. Expert Syst Appl 42(1):146–155

  24. 24.

    Matallanas E, Castillo-Cagigal M, Gutiérrez A et al (2012) Neural network controller for active demand-side management with PV energy in the residential sector. Appl Energy 91(1):90–97

  25. 25.

    Wang T, Gao H, Qiu J (2015) A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Netw Learn Syst 27(99):416–425

  26. 26.

    Patel O, Tiwari A, Patel V, et al (2015) Quantum based neural network classifier and its application for firewall to detect malicious web request. In: 2015 IEEE symposium on intelligence. IEEE

  27. 27.

    Zhang S, Constantinides AG (1992) Lagrange programming neural networks. IEEE Trans Circuits Syst II Analog Digital Signal Process 39(7):441–452

  28. 28.

    Wood AJ, Wollenberg BF (1996) Power generation, operation and control. Wiley, New York

  29. 29.

    Mohamed FA, Koivo HN (2010) System modelling and online optimal management of MicroGrid using mesh adaptive direct search. Int J Electr Power Energy Syst 32(5):398–407

  30. 30.

    Bagherian A, Tafreshi SMM (2009) A developed energy management system for a microgrid in the competitive electricity market. In: 2009 IEEE Bucharest PowerTech. IEEE, pp 1–6

  31. 31.

    Ceraolo M (2000) New dynamical models of lead-acid batteries. IEEE Trans Power Syst 15(4):1184–1190

  32. 32.

    Vahedi H, Noroozian R, Hosseini SH (2010) Optimal management of MicroGrid using differential evolution approach. In: 2010 7th international conference on the European energy market (EEM). IEEE, pp 1–6

  33. 33.

    Orero SO, Irving MR (1997) Large scale unit commitment using a hybrid genetic algorithm. Int J Electr Power Energy Syst 19(1):45–55

  34. 34.

    Talaq JH, El-Hawary F, El-Hawary ME (1994) A summary of environmental/economic dispatch algorithms. IEEE Trans Power Syst 9(3):1508–1516

  35. 35.

    Ranaweera DK, Hubele NF, Papalexopoulos AD (1995) Application of radial basis function neural network model for short-term load forecasting. In: IEE proceedings—IET generation, transmission and distribution, 1995, vol 142, no 1, pp 45–50

  36. 36.

    Yingwei L, Sundararajan N, Saratchandran P (1998) Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans Neural Netw 9(2):308–318

  37. 37.

    Ricalde LJ, Catzin GA, Alanis AY et al (2011) Higher order wavelet neural networks with Kalman learning for wind speed forecasting. In: 2011 IEEE symposium on computational intelligence applications in smart grid (CIASG). IEEE, pp 1–6

  38. 38.

    Li C, Liu C, Deng K et al (2017) Data-driven charging strategy of PEVs under transformer aging risk. IEEE Trans Control Syst Technol. doi:10.1109/TCST.2017.2713321

  39. 39.

    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge, 716 p

  40. 40.

    Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, Berlin, pp 760–766

Download references

Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 61403313, Grant 61773320, in part by the Fundamental Research Funds for the Central Universities under Grant XDJK2016B017, Grant XDJK2017D179, in part by the China Post-Doctoral Science Foundation under Grant 2016M600144. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Author information

Correspondence to Xing He.

Appendix

Appendix

To obtain the upper and lower restriction of battery bank, the algorithm in the following is given. \( {\text{SCB}} \) can take three values corresponding to three different operating modes. Readers can refer to [18] for detail method to calculate the value of restriction in Fig. 17).

Fig. 17
figure17

Flowchart of algorithm for the battery system

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, T., He, X. & Deng, T. Neural networks for power management optimal strategy in hybrid microgrid. Neural Comput & Applic 31, 2635–2647 (2019). https://doi.org/10.1007/s00521-017-3219-x

Download citation

Keywords

  • Hybrid microgrid
  • RBF neural network prediction
  • Quadratic optimization
  • Lagrange programming neural network
  • Renewable energy sources