Skip to main content

Advertisement

SpringerLink
Log in

Data-driven approach to form energy-resilient microgrids with identification of vulnerable nodes in active electrical distribution network

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

With the commitment to climate, globally many countries started reducing brownfield energy production and strongly opting towards green energy resources. However, the optimal allocation of distributed energy resources (DERs) in electrical distribution systems still pertains as a challenging issue to attain the maximum benefits. It happens due to the system’s complex behaviour and inappropriate integration of DERs that adversely affects the distribution grid. In this work, we propose a methodology for the optimal allocation of DERs with vulnerable node identification in active electrical distribution networks. A failure or extreme event at the vulnerable node would interrupt the power flow in the distribution network. Also, the power variation in these vulnerable nodes would significantly affect the operation of other linked nodes. Thus, these nodes are found suitable for the optimal placement of DERs. We demonstrate the proposed data-driven approach on a standard IEEE-123 bus test feeder. Initially, we partitioned the distribution system into optimal microgrids using graph theory and graph neural network architecture. Further, using Granger causality analysis, we identified vulnerable nodes in the partitioned microgrid, suitable for DERs integration. The placement of DERs on the vulnerable nodes enhanced network resilience. Improvement in resilience is validated by computing the percolation threshold for the microgrid networks. The results show a 37% improvement in the resilience of the system due to the optimal allocation of DERs.

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

Similar content being viewed by others

References

  1. Sanchez, R., Santiago, L., Shah, K.: Power returning to puerto rico after massive outage caused by fallen tree [online, cited 2.09.2022]

  2. Yuanyuan, Z., Yiran, A., Qian, A.: Research on size and location of distributed generation with vulnerable node identification in the active distribution network. IET Gen. Transm. Distrib. 8, 1801–1809 (2014)

    Article  Google Scholar 

  3. Dwivedi, D., Victor, S.M.B.K., Yemula, P.K., Chakraborty, P., Pal, M.: Evaluation of energy resilience and cost benefit in microgrid with peer-to-peer energy trading. arXiv preprint arXiv:2212.02318 (2022)

  4. Dwivedi, D., Yemula, P.K., Pal, M.: A methodology for identifying resiliency in renewable electrical distribution system using complex network. arXiv preprint arXiv:2208.11682 (2022)

  5. Dou, C., Hu, L., Yue, D., Zhang, Z., Ding, X., Li, Y.: Active power distribution network vulnerable node identification method which considers new energy impact (2020). https://patents.google.com/patent/WO2022134596A1/en

  6. Zhao, T., Xu, Y., Wang, Y., Lin, Z., Xu, W., Yang, Q.: On identifying vulnerable nodes for power systems in the presence of undetectable cyber-attacks. In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), pp. 1062–1067 (2016)

  7. Li, M., Li, S., Li, L., Jia, Y., Liu, X., Yang, Y.: Identifying vulnerable nodes of complex networks in cascading failures induced by node-based attacks. In: Mathematical Problems in Engineering. Hindawi Publishing Corporation (2013)

  8. Huang, B., Li, J., Wang, J.: An evidential reasoning based approach to building node selection criterion for network reduction (2020). arXiv preprint arXiv: 2012.13684

  9. Karatepe, E., Ugranlı, F., Hiyama, T.: Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios. Renew. Sustain. Energy Rev. 48, 317–327 (2015)

    Article  Google Scholar 

  10. Dulău, L.I., Abrudean, M., Bică, D.: Effects of distributed generation on electric power systems. In: Procedia Technology, the 7th International Conference Interdisciplinarity in Engineering, INTER-ENG 2013, 10–11 October 2013, vol. 12, pp. 681–686. Petru Maior University of Tirgu Mures, Romania (2014)

  11. Burtt, D., Dargusch, P.: The cost-effectiveness of household photovoltaic systems in reducing greenhouse gas emissions in Australia: linking subsidies with emission reductions. Appl. Energy 148, 439–448 (2015)

    Article  Google Scholar 

  12. Saad, O., Abdeljebbar, C.: Historical literature review of optimal placement of electrical devices in power systems: critical analysis of renewable distributed generation efforts. IEEE Syst. J. 15(3), 3820–3831 (2021)

    Article  Google Scholar 

  13. Taha, H.A., Alham, M.H., Youssef, H.K.M.: Multi-objective optimization for optimal allocation and coordination of wind and solar DGs, BESSs and capacitors in presence of demand response. IEEE Access 10, 16225–16241 (2022)

    Article  Google Scholar 

  14. Singh, P., Meena, N.K., Slowik, A., Bishnoi, S.K.: Modified African buffalo optimization for strategic integration of battery energy storage in distribution networks. IEEE Access 8, 14289–14301 (2020)

    Article  Google Scholar 

  15. Purlu, M., Turkay, B.E.: Optimal allocation of renewable distributed generations using heuristic methods to minimize annual energy losses and voltage deviation index. IEEE Access 10, 21455–21474 (2022)

    Article  Google Scholar 

  16. Zheng, Y., Song, Y., Huang, A., Hill, D.J.: Hierarchical optimal allocation of battery energy storage systems for multiple services in distribution systems. IEEE Trans. Sustain. Energy 11(3), 1911–1921 (2020)

    Article  Google Scholar 

  17. Fang, F., Zhu, Z., Jin, S., Hu, S.: Two-layer game theoretic microgrid capacity optimization considering uncertainty of renewable energy. IEEE Syst. J. 15(3), 4260–4271 (2021)

    Article  Google Scholar 

  18. Huang, B., Wang, J.: Adaptive static equivalences for active distribution networks with massive renewable energy integration: a distributed deep reinforcement learning approach. IEEE Trans. Netw. Sci. Eng. 1–13 (2023)

  19. Huang, B., Wang, J.: Deep-reinforcement-learning-based capacity scheduling for pv-battery storage system. IEEE Trans. Smart Grid 12(3), 2272–2283 (2021)

    Article  Google Scholar 

  20. Lee, S.H., Park, J.-W.: Optimal placement and sizing of multiple DGS in a practical distribution system by considering power loss. IEEE Trans. Ind. Appl. 49(5), 2262–2270 (2013)

    Article  Google Scholar 

  21. Kizito, R., Li, X., Sun, K., Li, S.: Optimal distributed generator placement in utility-based microgrids during a large-scale grid disturbance. IEEE Access 8, 21333–21344 (2020)

    Article  Google Scholar 

  22. Vugrin, E., Andrea, C.D.R., Silva-Monroy, C.A.: Resilience metrics for the electric power system: a performance-based approach, United States, p. 2017 (2017)

  23. Phillips, T., McJunkin, T., Rieger, C., Gardner, J., Mehrpouyan, H.: An operational resilience metric for modern power distribution systems. In: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 334–342 (2020)

  24. Chanda, S., Srivastava, A.K., Mohanpurkar, M.U., Hovsapian, R.: Quantifying power distribution system resiliency using code-based metric. IEEE Trans. Ind. Appl. 54(4), 3676–3686 (2018)

    Article  Google Scholar 

  25. Poudel, S., Dubey, A., Bose, A.: Risk-based probabilistic quantification of power distribution system operational resilience. IEEE Syst. J. 14(3), 3506–3517 (2020)

    Article  Google Scholar 

  26. Kandaperumal, G., Pandey, S., Srivastava, A.: AWR: anticipate, withstand, and recover resilience metric for operational and planning decision support in electric distribution system. IEEE Trans. Smart Grid 13(1), 179–190 (2022)

    Article  Google Scholar 

  27. Mohamed, M.A., Chen, T., Su, W., Jin, T.: Proactive resilience of power systems against natural disasters: a literature review. IEEE Access 7, 163778–163795 (2019)

    Article  Google Scholar 

  28. Blagojević, N., Hefti, F., Henken, J., Didier, M., Stojadinović, B.: Quantifying disaster resilience of a community with interdependent civil infrastructure systems. Struct. Infrastruct. Eng. 0(0), 1–15 (2022)

    Article  Google Scholar 

  29. Newman, M.: Networks: An Introduction. Oxford University Press (2010)

    Book  MATH  Google Scholar 

  30. Stockmayer, W.H.: Theory of molecular size distribution and gel formation in branched polymers II. General cross linking. J. Chem. Phys. 12, 125–131 (1944)

    Article  Google Scholar 

  31. Hammersley, J.M., Handscomb, D.C.: Percolation Processes, pp. 134–141. Springer, Dordrecht (1964)

    Google Scholar 

  32. Blanc, R.: Introduction to Percolation Theory, pp. 425–478. Springer, Dordrecht (1986)

    Google Scholar 

  33. Granger, C.W.J.: Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Volume 2 of Econometric Society Monographs, pp. 31–47. Cambridge University Press (2001)

  34. Lin, F.-H., Hara, K., Solo, V., Vangel, M., Belliveau, J., Stufflebeam, S., Hamalainen, M.: Dynamic Granger–Geweke causality modeling with application to interictal spike propagation. Neuroimage 47, S169 (2009). (organization for Human Brain Mapping 2009 Annual Meeting)

    Article  Google Scholar 

  35. Yuan, Q., Jiang, T.: Brain efficient connectivity analysis of attention based on the granger causality method. J. Biomed. Eng. 33, 56–60 (2016)

    Google Scholar 

  36. Rodriguez-Rivero, J., Ramirez, J., Martínez-Murcia, F., Segovia, F., Ortiz, A., Salas, D., Castillo-Barnes, D., Illan, I., Puntonet, C., Jimenez-Mesa, C., Leiva, F., Carillo, S., Suckling, J., Gorriz, J.: Granger causality-based information fusion applied to electrical measurements from power transformers. Inform. Fusion 57, 59–70 (2020)

    Article  Google Scholar 

  37. Guo, J., Hug, G., Tonguz, O.K.: Intelligent partitioning in distributed optimization of electric power systems. IEEE Trans. Smart Grid 7(3), 1249–1258 (2016)

    Article  Google Scholar 

  38. Sánchez-García, R.J., Fennelly, M., Norris, S., Wright, N., Niblo, G., Brodzki, J., Bialek, J.W.: Hierarchical spectral clustering of power grids. IEEE Trans. Power Syst. 29(5), 2229–2237 (2014)

    Article  Google Scholar 

  39. Cotilla-Sanchez, E., Hines, P.D.H., Barrows, C., Blumsack, S., Patel, M.: Multi-attribute partitioning of power networks based on electrical distance. IEEE Trans. Power Syst. 28(4), 4979–4987 (2013)

    Article  Google Scholar 

  40. Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)

    Article  MATH  Google Scholar 

  41. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)

    Article  Google Scholar 

  42. Chen, K., Hu, J., Zhang, Y., Yu, Z., He, J.: Fault location in power distribution systems via deep graph convolutional networks. IEEE J. Sel. Areas Commun. 38(1), 119–131 (2020)

    Article  Google Scholar 

  43. He, J., Zhao, H.: Fault diagnosis and location based on graph neural network in telecom networks. Int. Conf. Network. Network Appl. 2020, 304–309 (2020)

    Google Scholar 

  44. Wu, H., Wang, M., Xu, Z., Jia, Y.: Graph attention enabled convolutional network for distribution system probabilistic power flow. IEEE Trans. Ind. Appl. 58(6), 7068–7078 (2022)

    Article  Google Scholar 

  45. Cavanaugh, J.E., Neath, A.A.: The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Comput. Stat. 11(3), e1460 (2019)

    Article  MathSciNet  Google Scholar 

  46. Profillidis, V., Botzoris, G.: Chapter 6—Trend projection and time series methods. In: Modeling of Transport Demand, pp. 225–270. Elsevier (2019)

  47. Mallala, B., Dwivedi, D., Venkata, G., Sowjan Kumar, K.: Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization. Int. J. Electr. Comput. Eng. 13, 1060 (2023)

    Google Scholar 

  48. Radicchi, F.: Predicting percolation thresholds in networks. Phys. Rev. E 91, 010801 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

Divyanshi Dwivedi and D. Maneesh Reddy would like to thank ABB Ability Innovation Centre, Hyderabad, for the financial support in research. The author Mayukha Pal would like to thank the ABB Ability Innovation Center, Hyderabad, for their support in this work.

Author information

Authors and Affiliations

  1. ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, Telangana, 500084, India

    D. Maneesh Reddy, Divyanshi Dwivedi & Mayukha Pal

  2. Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, Telangana, 502205, India

    Divyanshi Dwivedi & Pradeep Kumar Yemula

  3. Department of Mechanical and Aerospace Engineering, Indian Institute of Technology, Hyderabad, Telangana, 502205, India

    D. Maneesh Reddy

Authors
  1. D. Maneesh Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Divyanshi Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pradeep Kumar Yemula
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mayukha Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DMR helped in methodology, software, writing—original draft. DD was involved in methodology, software, data curation, writing—original draft. PKY contributed to supervision, writing—reviewing and editing. MP was involved in conceptualization, methodology, project administration, validation, supervision, writing—reviewing and editing.

Corresponding author

Correspondence to Mayukha Pal.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reddy, D.M., Dwivedi, D., Yemula, P.K. et al. Data-driven approach to form energy-resilient microgrids with identification of vulnerable nodes in active electrical distribution network. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00430-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-023-00430-8

Keywords

Search

Navigation