Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Customer Reliability Improvement and Power Loss Reduction in Distribution Systems Using Distributed Generations


Affiliations
1 Young Researchers club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran, Islamic Republic of
2 Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran, Islamic Republic of
 

Distributed Generations (DGs) because owning many advantages, exist in distribution systems and are installed by either the utilities or the customers. In this paper, a study on reliability of customers and power loss reduction as the two most important aspects of both customers and utilities will be studied. Problem formulation includes several and in contrast to each other individual objectives, hence an optimization algorithm, here dynamic adaptation of particle swarm optimization (DAPSO) was used to allocate multi-DG units in radial distribution systems. To verify the effectiveness of the proposed algorithm in finding best solutions, IEEE 33 bus standard system and a practical system of Tehran (Afsarie)-22 bus are selected as the test systems.

Keywords

Customer Reliability, Dynamic Adaptation of Particle Swarm Optimization, Distributed Generation, Power Loss, Radial Distribution System
User

  • Acharya N, Mahat P and Mithulananthan N (2006) An analytical approach for DG allocation in primary distribution network. Int. J. Elect. Power & Energy Systems. 28(10), 669-678.

  • Ackermann T, Andersson G and Soder L (2001) Distributed generation: a definition. Electric. Power Sys. Res.57 (3),195- 204.

  • AlHajri MF, AlRashidi MR and El-Hawary ME (2007) Hybrid particle swarm optimization approach for optimal distribution generation sizing and allocation in distribution systems. Proc. Canadian Confe. Electric & Comp. Engg. Vancouver, Canada. pp. 1290–1293.

  • Atwa YM, El-Saadany EF, Salama MMA and Seethapathy R (2010) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Sys. 25(1), 360-370.

  • Chung I, Liu W, Leng SM, Andrus DA, Cartes Steurer M and Schoder K (2009) Integration of a bi-directional DC-DC converter model intoa large-scale system simulation of a shipboard MVDC power system. Proc. IEEE ESTS , Apr. 20– 22, 318–325.

  • El-Khattam W, Hegazy YG and Salama MMA (2009) An integrated distributed with load models. IEEE Trans. Power Sys. 24(1), 427–436.

  • Falaghi H and Haghifam M (2004) Modeling and analyzing the impact of DGs on reliability of distributed generation. 19th Conf. Proc. Int. Power Sys. Conf. (PSC), Tehran, Iran.

  • Gandomkar M, Vakilian M and Ehsan M (2005) A combination of genetic algorithm and simulated annealing for optimal distributed DG allocation in distributed networks. Proc. IEEE Electric. Comp. Engg. Can. Conf . 645–648.

  • Jen-Hao Teng (2003) A direct approach for distribution system load flow solutions. IEEE Trans. Power Delivery . 18 (3).

  • Kashem M, Ganapathy AV, Jasmon GB and Buhari MI (2000) A novel method for loss minimization in distribution networks. IEEE Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies.Proc. 251-256.

  • Keane A and O’Malley M (2006) Optimal distributed generation plant mix with novel loss adjustment factors. IEEE Power Eng. Society General Meeting

  • Kennedy J and Eberhart R (1995) Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, Perth, Australia. 4, 1942–1948.

  • Marwali MN, Jung JW and Keyhani (2007) A stability analysis of load sharing control for distributed generation systems. IEEE Trans. Energy Conversion, 22(3), 737-745.

  • Navid Khalesi and Seyed Ali Mohammad Javadian (2011) Distribution system reliability with considering variation in DG and load consumption. Indian J.Sci.Technol. 4 (10), 1285-1289.

  • Ochoa LF, Padilha-Feltrin A and Harrison GP (2008) Evaluating distributed time-varying generation through a multiobjective index. IEEE Trans. Power Delivery. 23(2),1132-1138.

  • Robinson J and Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetics. IEEE Trans. Antennas Propag. 52 (2), 397– 407.

  • Seyed Ali Mohammad Javadian and Maryam Massaeli (2011a)An adaptive overcurrent protection scheme for distribution networks including DG using distribution automation system and its implementation on a real distribution network. Indian J.Sci.Technol. 4 (11), pp: 1438- 1445.

  • Seyed Ali Mohammad Javadian and Maryam Massaeli (2011b) A fault location method in distribution networks including DG. Indian J.Sci.Technol. 4 (11), 1446-1451.

  • Seyed Ali Mohammad Javadian and Maryam Massaeli (2011c) Calculation of maximum DG’s capacity according to their location for remaining the protection coordination in distribution networks. Indian J.Sci.Technol. 4 (11), 1452- 1457.

  • Singh D and Misra RK (2007) Effect of load models in distributed generation planning. IEEE Trans. Power Systems. 22 (4), 2204-2212.

  • Singh D and Verma KS (2009) Multiobjective optimization for DG planning with load models. IEEE. Trans. Power Systems. 24 (1), 427-436.

  • Thong VV, Driesen J and Belmans R (2007) Transmission system operation concerns with high penetration level of distributed generation. Proc. of Inter. Universities Power Engineering Conference, Brighton. pp. 867–871.


Abstract Views: 421

PDF Views: 158




  • Customer Reliability Improvement and Power Loss Reduction in Distribution Systems Using Distributed Generations

Abstract Views: 421  |  PDF Views: 158

Authors

P. Farhadi
Young Researchers club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran, Islamic Republic of
H. Shayeghi
Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran, Islamic Republic of
T. Sojoudi
Young Researchers club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran, Islamic Republic of
M. Karimi
Young Researchers club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran, Islamic Republic of

Abstract


Distributed Generations (DGs) because owning many advantages, exist in distribution systems and are installed by either the utilities or the customers. In this paper, a study on reliability of customers and power loss reduction as the two most important aspects of both customers and utilities will be studied. Problem formulation includes several and in contrast to each other individual objectives, hence an optimization algorithm, here dynamic adaptation of particle swarm optimization (DAPSO) was used to allocate multi-DG units in radial distribution systems. To verify the effectiveness of the proposed algorithm in finding best solutions, IEEE 33 bus standard system and a practical system of Tehran (Afsarie)-22 bus are selected as the test systems.

Keywords


Customer Reliability, Dynamic Adaptation of Particle Swarm Optimization, Distributed Generation, Power Loss, Radial Distribution System

References





DOI: https://doi.org/10.17485/ijst%2F2012%2Fv5i3%2F30383