Smart Grid
Published December 4, 2017 | Version v1
Conference paper Open

Distribution Power Network Reconfiguration in the Smart Grid

Authors/Creators

  • 1. Salvador University - UNIFACS

Description

The power network reconfiguration algorithm with an "R" modeling approach evaluates its behavior in computing new reconfiguration topologies for the power grid in the context of the Smart Grid. The power distribution network modelling with the "R" language is used to represent the network and support computation of different algorithm configurations for the evaluation of new reconfiguration topologies. This work presents a reconfiguration solution of distribution networks, with a construction of an algorithm that receiving the network configuration data and the nodal measurements and from these data build a radial network, after this and using a branch exchange algorithm And verifying the best configuration of the network through artificial intelligence, so that there are no unnecessary changes during the operation, and applied an algorithm that analyses the load levels, to suggest changes in the network.

Notes

CIGRÉ-BRASIL - Brazilian National Committee

Files

2017 CIGRE Eonassis - Versão Final.pdf

Files (655.8 kB)

Name Size Download all
md5:110f7c68bb3602522afc8c95cb52f842
655.8 kB Preview Download

Additional details

References

  • Sen, P. Ghosh, V. Vittal, B. Yang. A New Min-Cut Problem with Application to Electric Power Network Partitioning, European Transactions on Electrical Power, V. 19, No 6, 2009, pp. 778-797.
  • Pradhan, K. H. Reddy, D. S. Roy and D. K. Mohanta "Intentional islanding of electric power systems in a grid computing framework: a graph-theoretic approach", Proceedings of the International Conference Recent Trends in Information Systems, 2011, pp.156 -160.
  • Ben Ubah and RPowerLABS Team. 2015. The RPowerLABS Project. [ONLINE] Available at: http://rpowerlabs.org/. [Accessed 04 July 15]
  • F. G. Calhau, A. Pezzutti, Bezerra, Romildo Martins da Silva, and Joberto S. B. Martins, "Hybrid Algorithm based on Genetic Algorithm and Tabu Search for the Reconfiguration Problem in Smart Grid Networks using 'R,'" in IV International Workshop on ADVANCEs in ICT Infrastructures and Services - ADVANCE 2015, Recife, 2015, pp. 1–10.
  • F. G. Calhau, A. Pezzutti, and Joberto S. B. Martins, "On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid," in Proceedings of the 5th International Workshop on ADVANCEs in ICT Infrastructure and Services (ADVANCE), Paris, 2017, pp. 1–7.
  • S. Civanlar, et al. Distribution reconfiguration for loss reduction. IEEE Transactions on Power Delivery, vol. 3, no. 3, p. 1217-1223, Jul. 1988.
  • G. Haughton, and T. Heydt. Smart distribution system design: Automatic reconfiguration for improved reliability. Proceedings of the IEEE Power and Energy Society General Meeting, 2010, pp. 1-8.
  • D. Deka, S. Backhaus and M. Chertkov. Structure learning and statistical estimation in distribution networks - Part II. arXiv preprint arXiv:1501.04131, 2015.
  • D. Deka, S. Backhaus and M. Chertkov, Structure learning and statistical estimation in distribution networks - Part I. arXiv preprint arXiv:1501.04131, 2015.
  • E.M. Carreno, R. Romero, and A.P. Feltrin. An Efficient Codification to Solve Distribution Network for Loss Reduction Problem. IEEE Transactions on Power Systems, vol. 23, no. 4, pp., 2008, 1542-1551.
  • F. Glover. Tabu Search - Part 1, ORSA Journal on Computing, Vol. 1, No. 3, pp. 190-206, 1989.
  • Kiran Kumar, Venkata Ramana, S. Kamakshaiah, and P. M. Nishanth. State of Art for Network Reconfiguration Methodologies of Distribution System. Journal of Theoretical and Applied Information Technology, V. 57, n 1, 2013, pp. 25-40.
  • V. J. Garcia, and P. M. Franca. Multiobjective Tabu Search for service restoration in electric distribution networks, Power Tech, 2005.
  • F. V. Gomes et al. A new heuristic reconfiguration algorithm for large distribution system. IEEE.
  • M. A. N. Guimaraes. Reconfiguração de redes de distribuição de energia elétrica utilizando algoritmos de busca tabu. Dissertação (Mestrado em Engenharia Elétrica) - Faculdade de Engenharia Elétrica e Computação da Universidade Estadual de Campinas - UNICAMP, Campinas SP, 2005.
  • J. Mendoza, R. Lopez, and D. Morales. Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: Real application, IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 948-54, 2006.
  • J. Wang, A. Luo, M. Qi, and M. Li. The improved clonal Genetic Algorithm and its application in reconfiguration of distribution networks, Proceedings of Power System Conference Expo., vol. 3, pp.1423 -1428, 2004.
  • N. Kagan, H. P. Schmidt, C. C. B. Oliveira, and H. Kagan. Problema da Reconfiguração da Rede em Situação de Contingência. In: Métodos de Otimização Aplicados a Sistemas Elétricos de Potência. Sao Paulo: Blucher, 2009, p. 119.
  • M. Merdan, W. Lepuschitz, T. Strasser, and F. Andren, Multi-Agent system for self-optimizing power distribution grids, IEEE International Conference on Automation, Robotics and Applications, 2001, pp. 312-317.
  • M.S. Thomas, S. Arora, and V. K. Chandna, Distribution Automation Leading to a Smarter Grid, IEEE Innovative Smart Grid Technologies, 2011, pp. 211- 216.
  • Y. Mao, and K. N. Miu. Switch Placement to Improve System Reliability for Radial Distribution Systems with Distributed Generation. IEEE Transactions on Power Systems, 4 November, 18(4), pp. 1346- 1352, 2003.
  • K. Nara, I. Satal, and M. Kitawana. Distribution system loss minimum reconfiguration genetic algorithm. Proc 3rd Symposium Expert System Application to Power System (ESAPS). Tokvo and Koje Japun, pp.724, 730, 1999.
  • Oldham, Kalil T. Swain. The doctrine of description: Gustav Kirchhoff, classical physics, and the purpose of all science in 19th-century Germany (Ph. D.). University of California, Berkeley. p. 52. 2008.
  • P. Guo, X. Wang and Y. Han. The enhanced genetic algorithms for the optimization design. 3rd International Conference on Biomedical Engineering and Informatics (BMEI), 2010, pp. 2990 - 2994.
  • Pereira. F. S. Reconfiguração Ótima de Sistema de Distribuição de Energia Elétrica Baseado no Comportamento de Colônia de Formigas. 2010. 104f. Tese de Doutorado em Engenharia Elétrica. USP - Universidade de São Paulo. Sao Carlos. 2010.
  • Pfitscher,L.L. Reconfiguração automática das redes de distribuição de energia elétrica com monitoramento em tempo real. Tese de Doutorado UFSM, 2013.
  • Q. Zhou, D. Shirmohammadi, and W.-H. E. Liu. Distribution feeder reconfiguration for service restoration and load balancing. IEEE Transactions on Power Systems, vol. 12, no. 2, pp. 724729, 1997.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2014. Available at: http://www.R-project.org
  • R. M. Ciric and D. S. Popovic, Multi-objective distribution network restoration using heuristic approach and mix integer programming method. International Journal of Electrical Power Energy Systems, vol. 22, no. 7, pp. 497505, 2000.
  • R.Cherkaoui, A.bart and A.J.Germond. Optimal configuration of electrical distribution networks using heuristic methods, Proc. 11th power system computation Conf. (PSCC) Avignon France 1993 pp.147 154.
  • Ramon A. Gallego, Alcir Jose Monticelli and Ruben Romero. Optimal Capacitor Placement in Radial Distribution Networks. IEEE Transactions on PWRS-16, No.4, Nov. 2001, pp. 630-637.
  • S. K. Chai and Arun Sekar. Graph theory application to deregulated power system. Proceeding of the 33rd IEEE Southeastern Symposium on System Theory, pp. 117-121, 2001.