Skip to main content
SpringerLink
Log in

Predicting Smart Grid Stability with Optimized Deep Models

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In a smart grid, consumer demand information is collected, centrally evaluated against current supply conditions and the resulting proposed price information is sent back to customers for them to decide about usage. As the whole process is time dependent, dynamically estimating grid stability becomes not only a concern but a major requirement. Decentral Smart Grid Control (DSGC) systems monitor one particular property of the grid—its frequency. So, it ties the electricity price to the grid frequency so that it is available to all participants, i.e., all energy consumers and producers. DSGC has some assumptions to infer the behavior of participants. DSGC system is described with differential equations. In this paper, we study on optimized deep learning (DL) models to solve fixed inputs (variables of the equations) and equality issues in DSGC system. Therefore, measuring the grid frequency at the premise of each customer would suffice to provide the network administrator with all required information about the current network power balance, so that it can price its energy offering—and inform consumers—accordingly. To predict smart grid stability, we use different optimized DL models to analyze the DSGC system for many diverse input values, removing those restrictive assumptions on input values. In our tests, DL model accuracy has reached up to 99.62%. We demonstrate that DL models indeed give way to new insights into the simulated system. We have learned that fast adaptation generally improves system stability.

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

Similar content being viewed by others

References

  1. Eltigani D, Masri S. Challenges of integrating renewable energy sources to smart grids: a review. Renew Sustain Energy Rev. 2015;52:770–80.

    Article  Google Scholar 

  2. Singh AK, et al. Stability analysis of networked control in smart grids. IEEE Trans Smart Grid. 2014;6(1):381–90.

    Article  Google Scholar 

  3. Schäfer B, et al. Taming instabilities in power grid networks by decentralized control. Eur Phys J Spec Top. 2016;225(3):569–82.

    Article  Google Scholar 

  4. Arzamasov V, et al. Towards concise models of grid stability. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2018, pp. 1–6.

  5. Sinopoli B, et al. Optimal linear lqg control over lossy networks without packet acknowledgment. In: Proceedings of the 45th IEEE Conference on Decision and Control, IEEE, 2006, pp. 392–397.

  6. Ayar M, et al. A distributed control approach for enhancing smart grid transient stability and resilience. IEEE Trans Smart Grid. 2017;8(6):3035–44.

    Article  Google Scholar 

  7. Farraj A, et al. A cyber-physical control framework for transient stability in smart grids. IEEE Trans Smart Grid. 2016;9(2):1205–15.

    Article  Google Scholar 

  8. Bejestani AK, et al. A hierarchical transactive control architecture for renewables integration in smart grids: analytical modeling and stability. IEEE Trans Smart Grid. 2014;5(4):2054–65.

    Article  Google Scholar 

  9. Shamisa A, et al. Sliding-window-based real-time model order reduction for stability prediction in smart grid. IEEE Trans Power Syst. 2018;34(1):326–37.

    Article  Google Scholar 

  10. Schäfer B, et al. Decentral smart grid control. New J Phys. 2015;17(1):015002.

    Article  Google Scholar 

  11. Abedinia O, Zareinejad M, Doranehgard MH, Fathi G, Ghadimi N. Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach. J Clean Prod. 2019;215:878–89.

    Article  Google Scholar 

  12. Saeedi M, Moradi M, Hosseini M, Emamifar A, Ghadimi N. Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl Therm Eng. 2019;148:1081–91.

    Article  Google Scholar 

  13. Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N. Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst. 2019;104:423–35.

    Article  Google Scholar 

  14. Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O. Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy. 2018;161:130–42.

    Article  Google Scholar 

  15. Khodaei H, Hajiali M, Darvishan A, Sepehr M, Ghadimi N. Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl Therm Eng. 2018;137:395–405.

    Article  Google Scholar 

  16. Bagal HA, Soltanabad YN, Dadjuo M, Wakil K, Ghadimi N. Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Solar Energy. 2018;169:343–52.

    Article  Google Scholar 

  17. Pedregosa F, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

    MathSciNet  MATH  Google Scholar 

  18. Agostinelli F, et al. Learning activation functions to improve deep neural networks. arXiv preprint:1412.6830, 2014.

  19. Ho Y, Wookey S. The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access. 2019;8:4806–13.

    Article  Google Scholar 

  20. Zhang Z. Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), IEEE, 2018, pp. 1–2.

  21. Cutkosky A, Orabona F. Momentum-based variance reduction in non-convex sgd. In: Advances in Neural Information Processing Systems, 2019, pp. 15236–15245.

  22. Jakovetić D, et al. Fast distributed gradient methods. IEEE Trans Autom Control. 2014;59(5):1131–46.

    Article  MathSciNet  Google Scholar 

  23. Chen M, Liu Q, Chen S, Liu Y, Zhang C-H, Liu R. Xgboost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access. 2019;7:13149–58.

    Article  Google Scholar 

  24. Zare H, Alinejad-Beromi Y, Yaghobi H. Intelligent prediction of out-of-step condition on synchronous generators because of transient instability crisis. Int Trans Electr Energy Syst. 2019;29(1):e2686.

    Article  Google Scholar 

  25. Mohammad R, Aghamohammadi F, Morteza A. Dt based intelligent predictor for out of step condition of generator by using pmu data. Int J Electr Power Energy Syst. 2018;99:95–106.

    Article  Google Scholar 

  26. Gupta A, Gurrala G, Sastry PS. An online power system stability monitoring system using convolutional neural networks. IEEE Trans Power Syst. 2018;34(2):864–72.

    Article  Google Scholar 

  27. James JQ, Hill David J, Lam Albert YS, Gu J, Li VOK. Intelligent time-adaptive transient stability assessment system. IEEE Trans Power Syst. 2017;33(1):1049–58.

    Google Scholar 

  28. Darbandi F, Jafari A, Karimipour H, Dehghantanha A, Derakhshan F, Choo KKR. Real-time stability assessment in smart cyber-physical grids: a deep learning approach. IET Smart Grid. 2020;3(4):454–61.

    Article  Google Scholar 

Download references

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Data Scientist, scIQant LLC, São Paulo, Brazil

    Paulo Breviglieri

  2. Department of Information Systems Engineering, Kocaeli University, 41001, İzmit, Turkey

    Türkücan Erdem & Süleyman Eken

Authors
  1. Paulo Breviglieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Türkücan Erdem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Süleyman Eken
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Süleyman Eken.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Cyber Security and Privacy in Communication Networks” guest edited by Rajiv Misra, R K Shyamsunder, Alexiei Dingli, Natalie Denk, Omer Rana, Alexander Pfeiffer, Ashok Patel and Nishtha Kesswani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Breviglieri, P., Erdem, T. & Eken, S. Predicting Smart Grid Stability with Optimized Deep Models. SN COMPUT. SCI. 2, 73 (2021). https://doi.org/10.1007/s42979-021-00463-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00463-5

Keywords

Search

Navigation