Identification of Preferable Distributed Generators Locations for Congestion Relief in Multi-Bus Power Network


Installation of Distributed Generator (DG) is a well accepted method to improve power system operation from the point of reducing congestion and improving voltage profiles. For best results, Distributed Generators should be placed at strategic locations to exploit maximum benefits out of them. The (N-1) contingency criterion has been taken into account in this work. Most congested lines of the grids are ranked by congestion Index and considered to study the impact of DG penetration on congestion. The present paper proposes contribution factors of Distributed Generators for the placement of DG to keep the line flow within the capacity of each transmission line of the network. The results obtained from IEEE 30-bus test system indicate that the proposed methods are capable of identifying desirable DG location and its maximum allowable size. The influence of DG on bus voltage profile has also been demonstrated in this paper.

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Sarkar, B. , Chakrabarti, A. and De, A. (2014) Identification of Preferable Distributed Generators Locations for Congestion Relief in Multi-Bus Power Network. Energy and Power Engineering, 6, 161-173. doi: 10.4236/epe.2014.67015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Ng, W.Y. (1981) Generalized Generation Distribution Factors for Power System Security Evaluation. IEEE Transactions on Power Apparatus and Systems, PAS-100, 1001-1005.
[2] Lin, C.E., Chen, S.T. and Huang, C.L. (1992) A Direct Newton-Raphson Economic Dispatch. IEEE Transactions on Power Apparatus and Systems, 7, 1149-1153.
[3] Medicherla, T.K.P., Billinton, R. and Sachdev, M.S. (1979) Generation Rescheduling and Load Shedding to Alleviate Line Overload-Analysis. IEEE Transactions on Power Apparatus and Systems, 98, 1876-1884.
[4] Hogan, W.W. (1992) Contract Networks for Electric Power Transmission. Journal of Regulatory Economics, 4, 211-242.
[5] Chan, S.M. and Schweppe, F.C. (1979) A Generation Reallocation and Load Shedding Algorithm. IEEE Transactions on Power Apparatus and Systems, 90, 26-34.
[6] Sinha, A.K. and Hazarika, D (2001) A Fast Algorithm for Line Overload Alleviation in Power System. IE (I) J EL, 81, 64-71.
[7] Pandey, S.N., Tapaswi, S. and Srivastava, L. (2009) Growing RBFNN-Based Soft Computing Approach for Congestion Management. Neural Computation & Application, 18, 945-955.
[8] Yassami, H. (2011) Optimal Distributed Generation Planning Considering Reliability, Cost of Energy and Power Loss. Scientific Research and Essays, 6, 1963-1976.
[9] Alderfer, B., Eldridge, M. and Starrs, T. (2002) Distributed Generation in Liberalized Electric Markets. International Energy Agency, Paris.
[10] Joos, G., Ooi, B.T., McGillis, D., Galiana, F.D. and Marceau, R. (2000) The poteNtial of Distributed Generation to Provide Ancillary Services. Power Engineering Society, IEEE Summer Meeting, 3, 1762-1767.
[11] Gautam, D. and Nadarajah, M. (2010) Influence of Distributed Generation and LMP in Competitive Electricity Market. International Journal of Electrical Engineering, 4, 8.
[12] Liu, J., Salama, M. and Mansour, R. (2005) Identify the Impact of Distributed Resources on Congestion Management. IEEE Trans Power Delivery, 20, 1998-2005.
[13] Afkousi, M. and Rashidinejad, M. (2010) Transmission Congestion Management Using Distributed Generation Considering Load Uncertainty. Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific, Chengdu, 28-31 March 2010, 1-4.
[14] Macken, K.J.P., Bollen, M.H.J. and Belmans, R.J.M. (2004) Mitigation of Voltage Dips through Distributed Generation Systems. IEEE Transactions on Industry Applications, 40, 1686-1693.
[15] Ayres, H.M., Freitas, W., De Almeida, M.C. and Da Silva, L.C.P. (2010) Method for Determining the Maximum Allowable Penetration Level of Distributed Generation without Steady-State Voltage Violations. IET Generation, Transmission Distribution, 4, 495-508.
[16] Varikuti, R. and Damodar Reddy, M. (2009) Optimal Placement of DG Units Using Fuzzy and Real Coded Genetic Algorithm. Journal of Theoretical and Applied Information Technology, 7, 145-151.
[17] Ameli, M.T., Shokri, V. and Shokri, S. (2010) Using Fuzzy Logic & Full Search for Distributed Generation Allocation to Reduce Losses and Improve Voltage Profile. International Conference on Computer Information Systems and Industrial Management Applications (CISIM), Krackow, 8-10 October 2010, 626-630.
[18] Wang, C. and Nehrir, M.H. (2004) Analytical Approaches for Optimal Placement of Distributed Generation Sources in Power Systems. IEEE Transactions on Power Systems, 19, 2068-2076.
[19] Celli, G. and Pilo, F. (2001) MV Network Planning under Uncertainty on Distributed Generation Penetration. Proc. IEEE PES Summer Meeting, 1, 485-490.
[20] Carpinelli, G., Celli, G., Pilo, F. and Russo, A. (2001) Distributed Generation Sitting and Sizing under Uncertainty. Power Tech Proceedings, 2001 IEEE Porto, 4.
[21] Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A. and Seethapathy, R. (2010) Optimal Renewable Resources Mix Distribution System Energy Loss Minimization. IEEE Transactions on Power Systems, 25, 360-370.
[22] Rahim, S.R.A., Rahman, T.K.A., Musirin, I., Azmi, S.A., Mohammed, M.F., Hussain, M.H. and Faridun, M. (2008) Comparing the Network Performance between the Installation of DG and Compensating Capacitor Using EP. International Journal of Power, Energy and Artificial Intelligence, 1, 14-20.
[23] Du, K.-L. (2010) Clustering: A Neural Network Approach. Neural Networks, 23, 89-107.
[24] Wang, M.-H. and Chang, H.-C. (1994) Novel Clustering Method for Coherency Identification Using an Artificial Neural Network. IEEE Transaction on Power System, 9, 2056-2062.
[25] Ghosh, S. and Chowdhury, B.H. (1996) Design of Artificial Neural Network for Fast Line Flow Contingency Ranking. Electrical Power Energy, 18, 271-277.
[26] De, A. and Chatterjee, N. (2002) Recognition of Impulse Fault Patterns in Transformer Using Kohonen’s Self Organizing Feature Map. IEEE Transactions on Power Delivery, 17, 489-494.

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