Document Type : Review Article

Authors

Tabriz University

Abstract

In this paper, we applied the Data Envelopment Analysis (DEA) ranking method to have efficient placement of Distributed Generation (DG) in distribution network. In this regard first an analytical method to find the optimal size of DG in the network is used to reach the lowest possible losses. In this paper, benchmarks such as improvement of voltage profile, reducing energy not supplied value (as an index of reliability), reducing environmental pollution, and values related to the purchase and installation costs of DG equipment in each busses for selecting the appropriate DG location are considered, in addition to the network loss reduction. This method has been used because the loss reduction of whole the network will not be a complete criteria for selecting the best location to install DG, The necessarily node which has the highest reduction in power losses cannot be considered suitable node for the installation of DG. Therefore we used DEA to determine the most effective location for DG placement. The proposed method is implementation over the network of 33 buses and the results are presented. GAMS software is used for the simulation results extraction. 

Keywords

[1] J., Federico, J., Gonzalez and V., Lyra, , “learning classifiers shape reactive power to decrease losses in power distribution networks”, Power Engineering Society General Meeting IEEE; pp.557-562, 2005.
[2] L., Yingchen, G., Deqiang, C., Xingying, L., Gang, C.,Hui and K., Kun, “Optimal Power Flow of Receiving Power Network Considering Distributed Generation and Environment Pollution”, Power and Energy Engineering Conference (APPEEC) Asia-Pacific IEEE; pp.1-6, 2010.
[3] Y.M., Atwa, E.F., El-Saadany, “Reliability Evaluation for Distribution System with Renewable Distributed Generation during Islanded Mode of Operation”, IEEE Transactions on Power Systems; Vol.24, No.2, pp.572-581, 2009.
[4] L., Lezama, L., Maria, J., Contreras and A., Padilha-Feltrin , “Location and contract pricing of distributed generation using a genetic algorithm”, International Journal of Electrical Power and Energy Systems; Vol.36 , No.1, pp.117-126, 2012.
[5] K., Nara, Y., Hayashi, K., Ikeda and T., Ashizawa “Application of tabu search to optimal placement of distributed generators”, In Power Engineering Society Winter Meeting IEEE , pp.918-923, 2001.
[6] A., Tareq, K., Tapan Saha and N., Mithulananthan, “Distributed generators placement for loadability enhancement based on reactive power margin”, Conference Proceedings IEEE ,pp. 740-745,. 2010.
[7] C., Gianni, E., Ghiani, S., Mocci and F.A., Pilo, “multiobjective evolutionary algorithm for the sizing and siting of distributed generation”, IEEE Transactions on Power Systems; Vol.20, No.2, pp.750-757, 2005.
[8] W., Caisheng, M.H.,Nehrir, “Analytical approaches for optimal placement of distributed generation sources in power systems”, IEEE Transactions on Power Systems; Vol.19, No.4, pp.2068-2076, 2004.
[9] K., Lee, Y.J., Rhee, “Dispersed generator placement using fuzzy-GA in distribution systems”, Proc. IEEE Power Engineering Society Summer Meeting Chicago , pp.1148–1153, 2002.
[10] K., Nara, Y., Hayashi, “Application of tabu search to optimal placement of distributed generators”, Proc IEEE Power Engineering Society Winter Meeting, Columbus ,Vol.1, No.2, pp.918–923, 2001.
[11] H.L., Willis, “Analytical methods and rules of thumb for modeling DG-distribution interaction”, Proc IEEE Power Engineering Society Summer Meeting Seattle ,pp.1643–1644, 2000.
[12] G., Harrison, A., Wallace , “Optimal power flow evaluation of distribution network capacity for the connection of distributed generation”, IEE Proceedings Generation, Transmission and Distribution , Vol. 152, No.1, pp.115–122, 2005.
[13] A., Keane, M., O’Malley, “Optimal allocation of embedded generation on distribution networks”, IEEE Transactions on Power Systems; Vol.20, No.3, pp.1640–1646, 2005.
[14] D.H., Popovic, J.A., Greatbanks and M., Begovic, “Placement of distributed generators and reclosers for distribution network security and reliability”, International Journal of Electrical Power & Energy Systems ; Vol.27, No.6, pp.398–408, 2005.
[15] G., Celli, E., Ghaiani and S., Mocci, “A multiobjective evolutionary algorithm for the sizing and sitting of distributed generation”, IEEE Transactions on Power Systems ; Vol.20, No.2, pp.750–757, 2005.
[16] W., El-Khattam, K., Bhattacharya and Y., Hegazy, “Optimal investment planning for distributed generation in a competitive electricity market”, IEEE Transactions on Power Systems; Vol.19, No.3,pp.1674–1684, 2004.
[17] C.L.T., Borges, D.M., Falcao , “Optimal distributed generation allocation for reliability, losses and voltage improvement”, International Journal of Electrical Power and Energy Systems, Vol.28, No.6, pp.413–420, 2006.
[18] P., Mahat, N., Mithulananthan, “An analytical approach for DG allocation in primary distribution network”, International Journal of Electrical Power & Energy Systems; Vol.28, No.10, pp.669–678, 2006.
[19] D., Qu D, Z.H., Shen, “Application o f data envelopment analysis in evaluating higher education staff ratio ”, Journal of Tongji University; Vol.3l , No.11, pp.1369-1373, 2003.
[20] J., Lassila, S., Viljainen and S., Honkapuro ,“Investments in the benchmarking of the distribution network companies, IEEE International Conference on Electric Utility Deregulation”, Restructuring and Power Technologies (DRPT2004); Vol. 1, No.2, pp.445-450, 2004.
[21] Y., Chyan, W.M., Lu , “Assessing the performance and finding the benchmarks of the electricity distribution districts of Taiwan Power Company”, IEEE Transaction on Power Systems;Vol.21, No.2, pp.853-861, 2006.
[22] T.F., Zhang, J.S., Yuan, J.T., Wang and G., Wei ,“Utilization availability of distribution lines based on data envelopment analysis”, Power System Technology; Vol.30, No.4, pp.97-102, 2006.