AN APPROACH FOR COMMUNICATION RELAIBILITY USING SELF-ADAPTIVE AUTONOMIC COMPUTING TECHNIQUES

Aleksandar Stanimirović, Miloš Bogdanović, Nikola Davidović, Aleksandar Dimov, Krasimir Baylov, Leonid Stoimenov

DOI Number
https://doi.org/10.22190/FUACR1901041S
First page
041
Last page
056

Abstract


Interdependency of electric power grids and information and communication technology is a rapidly growing topic. With the introduction of Smart Grid, handling dynamic load tracking, dynamic tariffs, clients that can consume but also produce electricity that can be delivered to the grid has become a part of everyday operational cycles within power supply companies. Hence, electricity distribution and power supply companies are in need for introduction of efficient mechanisms for the optimal tracking and use of available electric energy. In this paper, we describe the low voltage (LV) distribution network monitoring system developed for the Electric Power Industry of Serbia (EPS) electricity distribution company. The system we present is implemented in a way so that it provides abilities to measures, communicates and stores real-time data, translating it into actionable information needed by EPS to meet the described challenges regarding LV distribution networks. The implemented system is using self-adaptive autonomic computing techniques to provide a reliable data transfer from measurement devices deployed in different parts of the LV distribution network.

Keywords

self-adaptive, autonomic computing, low voltage network monitoring, communication reliability, smart grid

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References


S.M. Amin, B.F. Wollenberg, Toward a smart grid: power delivery for the 21st century. IEEE power and energy magazine, 3(5), pp.34-41, 2005. [Online]. Available: http://dx.doi.org/10.1109/MPAE.2005.1507024

J. D. Taft, A. S. Becker-Dippmann, The Emerging Interdependence of the Electric Power Grid & Information and Communication Technology (No. PNNL--24643). Pacific Northwest National Laboratory (PNNL), Richland, WA (United States), 2015.

P. Siano, Demand response and smart grids—A survey. Renewable and Sustainable Energy Reviews, 30, pp.461-478, 2014. [Online]. Available: https://doi.org/10.1016/j.rser.2013.10.022

B. Pfeiffer, P. Mulder, Explaining the diffusion of renewable energy technology in developing countries, Energy Economics, vol. 40, pp. 285-296, 2013. [Online]. Available: https://doi.org/10.1016/j.eneco.2013.07.005

D. Della Giustina, A. Dedè, G. Invernizzi, D. P. Valle, F. Franzoni, A. Pegoiani, L. Cremaschini, Smart Grid Automation Based on IEC 61850: An Experimental Characterization, IEEE Transactions on Instrumentation and Measurement, , vol.64, no.8, pp.2055,2063, 2015. [Online]. Available: https://doi.org/10.1109/TIM.2015.2415131

S. Jamali, A. Bahmanyar, E. Bompard, Fault location method for distribution networks using smart meters, Measurement, vol. 102, pp. 150-157, 2017. [Online]. Available: https://doi.org/10.1016/j.measurement.2017.02.008

J. Leiva, A. Palacios, J. A. Aguado, Smart metering trends, implications and necessities: A policy review, Renewable and Sustainable Energy Reviews, vol. 55, pp. 227-233, 2016. [Online]. Available: https://doi.org/10.1016/j.rser.2015.11.002

A. Barbato, A. Capone, Optimization models and methods for demand-side management of residential users: A survey, Energies, vol. 7, no. 9, pp. 5787–5824, 2014. [Online]. Available: https://doi.org/10.3390/en7095787

A. Grilo, L. Buttyan, J. Gonçalves, C. A. Fortunato, Wireless Sensor and Actuator Network for Improving the Electrical Power Grid Dependability, In Proceedings of the 8th EURO-NGI Conference on Next Generation Internet (NGI), Karlskrona, Sweden, 25–27 June 2012; pp. 71–78, 2012. [Online]. Available: https://doi.org/10.1109/NGI.2012.6252167

R. Leon, A. Vittal, G. Manimaran, Application of sensor network for secure electric energy infrastructure, IEEE Trans. Power Delivery vol. 22, pp. 1021–1028, 2007. [Online]. Available: https://doi.org/10.1109/TPWRD.2006.886797

N. Dahal, V. M. Mohan, S. S. Durbha, A. K. Srivastava, R. L. King, N. H. Younan, N. N. Schulz, Wide area monitoring using Common Information Model and Sensor Web, In Proceedings of the Power Systems Conference and Exposition, Seattle, WA, USA, 15–18 March 2009, pp. 1–7, 2009. [Online]. Available: http://dx.doi.org/10.1109/PSCE.2009.4840207

A. Biem, E. Bouillet, H. Feng, A. Ranganathan, A. Riabov, O. Verscheure, H. N. Koutsopoulos, M. Rahmani, B. Güç, Real-Time Traffic Information Management using Stream Computing, IEEE Data Eng. Bull., 33(2), pp.64-68, 2010.

L. Neumeyer, B. Robbins, A. Nair, A. Kesari, S4: Distributed stream computing platform, 2010 IEEE International Conference on Data Mining Workshops, pp. 170-177, IEEE, 2010. [Online]. Available: https://doi.org/10.1109/ICDMW.2010.172

P. D. Diamantoulakis, V. M. Kapinas, G. K. Karagiannidis, Big data analytics for dynamic energy management in smart grids. Big Data Research, 2(3), pp.94-101, 2015. [Online]. Available: https://doi.org/10.1016/j.bdr.2015.03.003

K. Sornalakshm, G. Vadivu, A Survey on Realtime Analytics Framework for Smart Grid Energy Management, In International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue 3, pp. 1054-1058, ISSN(Online) : 2319-8753, 2015.

M. Couceiro, R. Ferrando, D. Manzano, L. Lafuente, Stream analytics for utilities. Predicting power supply and demand in a smart grid, In Cognitive Information Processing (CIP), 2012 3rd International Workshop on (pp. 1-6). IEEE, 2012. [Online]. Available: https://doi.org/10.1109/CIP.2012.6232904

The Right Big Data Technology for Smart Grid – Distributed Stream Computing [Online], Available> https://www.accenture.com/us-en/blogs/blogs-the-right-big-data-technology-for-smart-grid-distributed-stream-computing , [Accessed on March 2019].

V. Braberman, N. D'Ippolito, J. Kramer, D. Sykes, S. Uchitel, Morph: A reference architecture for configuration and behaviour self-adaptation, In: Proceedings of the 1st International Workshop on Control Theory for Software Engineering. ACM, pp. 9-16, 2015. [Online]. Available: https://doi.org/10.1145/2804337.2804339

D. Garlan, S. W. Cheng, A. C. Huang, B. Schmerl, P. Steenkiste, Rainbow: Architecture-based self-adaptation with reusable infrastructure, Computer, 37(10), pp. 46-54, 2004. [Online]. Available: https://doi.org/10.1109/MC.2004.175

S. W. Cheng, D. Garlan, B. Schmerl, Evaluating the effectiveness of the rainbow self-adaptive system, In: Software Engineering for Adaptive and Self-Managing Systems, SEAMS'09. ICSE Workshop on. IEEE, pp. 132-141, 2009. [Online]. Available: https://doi.org/10.1109/SEAMS.2009.5069082

J. O. Kephart, D. M. Chess, The vision of autonomic computing, Computer 36(1), pp. 41-50, 2003. [Online]. Available: https://doi.org/10.1109/MC.2003.1160055

Y. Brun, G. D. Serugendo, C. Gacek, H. Giese, H. Kienle, M. Litoiu, H. Müller, M. Pezzè, M. Shaw, Engineering Self-Adaptive Systems through Feedback Loops, In Software Engineering for Self-Adaptive Systems, LNCS, Vol. 5525. Springer-Verlag, pp. 48-70, 2009. [Online]. Available: https://doi.org/10.1007/978-3-642-02161-9_3

L. Stoimenov, A. Stanimirovic, A. Krstic, N. Davidovic, M. Bogdanovic, D. Nikolic, GinisED Enterprise GIS – Framework for the utility of the future, 21st International Conference on Electricity Distribution, Frankfurt, June 6-9, paper 1223, 2011.

Netico NTPM 100/200 Energy Management Sensor. [Online]. Available: http://netico-group.com/NTPM-100200 , [Accessed on March 2019]

S. Gokhale, Architecture-Based Software Reliability Analysis: Overview and Limitations, IEEE Transactions on Dependable Security Computing, vol. 4, no. 1, pp. 32-40, 2007. [Online]. Available: https://doi.org/10.1109/TDSC.2007.4




DOI: https://doi.org/10.22190/FUACR1901041S

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