APPLICATION OF FUZZY AND PSO FOR DG PLACEMENT FOR MINIMUM LOSS IN RADIAL DISTRIBUTION SYSTEM

This paper presents a new methodology using fuzzy and Particle Swarm Optimization (PSO) for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power losses and to improve the voltage profile. A two-stage methodology is used for the optimal DG placement. In the first stage, fuzzy approach is used to find the optimal DG locations and in the second stage, PSO is used to find the size of the DGs corresponding to maximum loss reduction. The proposed method is tested on standard IEEE 33 bus test system and the results are presented and compared with an existing method.

[1]  Fabrizio Giulio Luca Pilo,et al.  Optimal distributed generation allocation in MV distribution networks , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[2]  M. Mardaneh,et al.  Siting and sizing of DG units using GA and OPF based technique , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[3]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  P. A. Daly,et al.  Understanding the potential benefits of distributed generation on power delivery systems , 2001, 2001 Rural Electric Power Conference. Papers Presented at the 45th Annual Conference (Cat. No.01CH37214).

[5]  Jiuping Pan,et al.  Siting distributed generation to defer T&D expansion , 2001, 2001 IEEE/PES Transmission and Distribution Conference and Exposition. Developing New Perspectives (Cat. No.01CH37294).

[6]  F. Pilo,et al.  A multiobjective evolutionary algorithm for the sizing and siting of distributed generation , 2005, IEEE Transactions on Power Systems.

[7]  Unfccc.  Kyoto Protocol to the United Nations Framework Convention on Climate Change , 1997 .

[8]  A. Berizzi,et al.  Distributed generation planning using genetic algorithms , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).

[9]  R. Ramakumar,et al.  An approach to quantify the technical benefits of distributed generation , 2004, IEEE Transactions on Energy Conversion.

[10]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[11]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[13]  M. Lalitha,et al.  OPTIMAL DG PLACEMENT FOR MAXIMUM LOSS REDUCTION IN RADIAL DISTRIBUTION SYSTEM USING ABC ALGORITHM , 2010 .

[14]  Carmen L. T. Borges,et al.  Optimal distributed generation allocation for reliability, losses, and voltage improvement , 2006 .

[15]  E. Miguez,et al.  Application of evolutionary algorithms for the planning of urban distribution networks of medium voltage , 2002 .

[16]  Fabrizio Giulio Luca Pilo,et al.  Optimisation of embedded generation sizing and siting by using a double trade-off method , 2005 .

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.