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Evaluating complex network indices for vulnerability analysis of a territorial power grid

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Abstract

In order to meet power demands in a sustainable way, power grids are gradually being adjusted to fit into a smart grid paradigm. A common problem in this kind of transition is to identify locations where it is most beneficial to invest in distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid. We apply optimization strategies on power flows, and verify the current degree of self-sufficiency of the network, with special reference to the effect of natural variations in wind-based production. We propose a method to assess collateral damage to the network resulting from a localized failure, and proceed to perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, with the purpose of attempting to determine which model is best suited to predict features of our network.

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References

  • Albert R, Nakarado GL (2004) Structural vulnerability of the North American power grid. Phys Rev E 69(2):025103. doi:10.1103/PhysRevE.69.025103

    Article  Google Scholar 

  • Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177. doi:10.1080/0022250X.2001.9990249

    Article  MATH  Google Scholar 

  • Brown R (2008) Impact of smart grid on distribution system design. In:(2008) IEEE Power and Energy Society General Meeting—conversion and delivery of electrical energy in the 21st century, pp 1–4. doi:10.1109/PES.2008.4596843

  • Chen CS, Lin CH, Hsieh SC, Hsieh WL (2013) Development of smart distribution grid. In:(2013) IEEE international symposium on next-generation electronics (ISNE), pp 4–6. doi:10.1109/ISNE.2013.6512272

  • Chen G, Zhao J, Dong ZY, Weller SR (2012) Complex network theory based power grid vulnerability assessment from past to future. In: 9th IET international conference on advances in power system control, operation and management (APSCOM 2012), pp 1–6. doi:10.1049/cp.2012.2165

  • Clements S, Kirkham H (2010) Cyber-security considerations for the smart grid. In:(2010) IEEE Power and Energy Society General Meeting, pp 1–5. doi:10.1109/PES.2010.5589829

  • Cotilla-Sanchez E, Hines P, Barrows C, Blumsack S (2012) Comparing the topological and electrical structure of the North American electric power infrastructure. IEEE Syst J 6(4):616–626. doi:10.1109/JSYST.2012.2183033

    Article  Google Scholar 

  • Dimeas A, Hatziargyriou N (2007) Agent based control of virtual power plants. In: International conference on intelligent systems applications to power systems, 2007. ISAP 2007, pp 1–6, doi:10.1109/ISAP.2007.4441671

  • Dwivedi A, Yu X, Sokolowski P (2009) Identifying vulnerable lines in a power network using complex network theory. In: IEEE international symposium on industrial electronics. ISIE 2009, pp 18–23, doi:10.1109/ISIE.2009.5214082

  • Grasberg L, Osterlund L (2001) SCADA EMS DMS—a part of the corporate IT system. In: 22nd IEEE Power Engineering Society international conference on power industry computer applications. PICA 2001. Innovative computing for power—electric energy meets the market, pp 141–147. doi:10.1109/PICA.2001.932337

  • Hashmi M, Hanninen S, Maki K (2011) Survey of smart grid concepts, architectures, and technological demonstrations worldwide. In: 2011 IEEE PES conference on innovative smart grid technologies (ISGT Latin America), pp 1–7. doi:10.1109/ISGT-LA.2011.6083192

  • Hines P, Cotilla-Sanchez E, Blumsack S (2011) Topological models and critical slowing down: two approaches to power system blackout risk analysis. In: 2011 44th Hawaii international conference on system sciences (HICSS), pp 1–10. doi:10.1109/HICSS.2011.444

  • Jaramillo Garcia PA, Lopera Gonzalez LI, Amft O (2014) Using implicit user feedback to balance energy consumption and user comfort of proximity-controlled computer screens. J Ambient Intell Humaniz Comput. doi:10.1007/s12652-014-0222-2

  • Kok K, Karnouskos S, Nestle D, Dimeas A, Weidlich A, Warmer C, Strauss P, Buchholz B, Drenkard S, Hatziargyriou N, Lioliou V (2009) Smart houses for a smart grid. In: 20th international conference and exhibition on electricity distribution—part 1. CIRED 2009, pp 1–4

  • Nguyen TA, Raspitzu A, Aiello M (2014) Ontology-based office activity recognition with applications for energy savings. J Ambient Intell Humaniz Comput 5(5):667–681. doi:10.1007/s12652-013-0206-7

    Article  Google Scholar 

  • Overbye TJ (2004) Power system simulation: understanding small- and large-system operations. IEEE Power Energy Mag 2(1):20–30. doi:10.1109/MPAE.2004.1263413

    Article  Google Scholar 

  • Pagani GA, Aiello M (2013) The power grid as a complex network: a survey. Phys A Stat Mech Appl 392(11):2688–2700. doi:10.1016/j.physa.2013.01.023

    Article  MathSciNet  Google Scholar 

  • Petinrin J, Shaaban M (2012) Smart power grid: technologies and applications. In: 2012 IEEE international conference on power and energy (PECon), pp 892–897. doi:10.1109/PECon.2012.6450343

  • Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation. In: Power systems conference and exposition. PSCE ’09. IEEE/PES, pp 1–8. doi:10.1109/PSCE.2009.4840087

  • Skogdalen JE, Vinnem JE (2012) Quantitative risk analysis of oil and gas drilling, using deepwater horizon as case study. Reliab Eng Syst Saf 100:58–66. doi:10.1016/j.ress.2011.12.002

    Article  Google Scholar 

  • Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics (Oxford, England) 27(3):431–432. doi:10.1093/bioinformatics/btq675 PMID: 21149340

    Article  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442. doi:10.1038/30918

    Article  Google Scholar 

  • Zhou X, Lukic S, Bhattacharya S, Huang A (2009) Design and control of grid-connected converter in bi-directional battery charger for plug-in hybrid electric vehicle application. In: IEEE vehicle power and propulsion conference, 2009. VPPC ’09, pp 1716–1721. doi:10.1109/VPPC.2009.5289691

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Acknowledgments

Pier Luigi Pau gratefully acknowledges Sardinia Regional Government for the financial support of his Ph.D. scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2007–2013—Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.).

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Correspondence to Gianni Fenu.

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Fenu, G., Pau, P.L. Evaluating complex network indices for vulnerability analysis of a territorial power grid. J Ambient Intell Human Comput 6, 297–306 (2015). https://doi.org/10.1007/s12652-015-0264-0

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  • DOI: https://doi.org/10.1007/s12652-015-0264-0

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