Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm
Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
[1] A. Merlin, H. Back, "Search for a minimal-loss operating spanning tree
configuration in an urban power distribution system" Proceedings of 5th
Power System Computation Conference (PSCC), Cambridge, UK, 1975,
pp. 1-18.
[2] S. Civanlar, J. Grainger, H. Yin, and S. Lee, "Distribution feeder
reconfiguration for loss reduction," IEEE Trans. Power Del., vol. 3, no.
3, pp. 1217-1223, Jul. 1988.
[3] D. Shirmohammadi and H.W. Hong, Reconfiguration of electric
distribution networks for resistive line losses reduction," IEEE Trans.
Power Del., vol. 4, no. 2, pp. 1492-1498, Apr. 1989.
[4] T. P. Wagner, A. Y. Chikhani, and R. Hackam, "Feeder reconfiguration
for loss reduction: an application of distribution automation," IEEE
Trans. Power Del., vol. 6, no. 4, pp. 1922-1931, Oct. 1991.
[5] S. Goswami and S. Basu, "A new for the reconfiguration of distribution
feeders for loss minimization," IEEE Trans. Power Del., vol. 7, no. 3,
pp. 1484-1491, Jul. 1992.
[6] H. C. Cheng and C. C. Kou, "Network reconfiguration in distribution
systems using simulated annealing," Elect. Power Syst. Res., vol. 29, pp.
227-238, May 1994.
[7] H. D. Chiang and J. J. Rene, "Optimal network reconfiguration in
distribution systems: part 1: a new formulation and a solution
methodology," IEEE Trans. Power Del., vol. 5, no. 4, pp. 1902-1908,
Oct. 1990.
[8] H. D. Chiang and J. J. Rene, "Optimal network reconfiguration in
distribution systems: part 2: solution algorithms and numerical results,"
IEEE Trans. Power Del., vol. 5, no. 3, pp. 1568-1574, Jul. 1992.
[9] K. Nara, A. Shiose, M. Kitagawoa, and T. Ishihara, "Implementation of
genetic algorithm for distribution systems loss minimum
reconfiguration," IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044-1051,
Aug. 1992.
[10] D. Das, "A fuzzy multi-objective approach for network reconfiguration
of distribution systems," IEEE Trans. Power Del., vol. 21, no. 1, pp.
202-209, Jan. 2006.
[11] C. T. Su and C. S. Lee, "Network reconfiguration of distribution systems
using improved mixed-integer hybrid differential evolution", IEEE
Trans. on Power Delivery, Vol. 18, No. 3, July 2003.
[12] Y. C. Huang, "Enhanced genetic algorithm-based fuzzy multi-objective
approach to distribution network reconfiguration," Proc. Inst. Elect.
Eng., vol. 149, no. 5, pp. 615-620, 2002.
[13] I. Z. Zhu, "Optimal reconfiguration of electrical distribution network
using the refined genetic algorithm," Elect. Power Syst. Res., vol. 62, pp.
37-42, 2002.
[14] Y. Y. Hong and S. Y. Ho, "Determination of network configuration
considering multi-objective in distribution systems using genetic
algorithms," IEEE Trans. Power Syst., vol. 20, no. 2, pp. 1062-1069,
May 2005.
[15] K. Prasad, R. Ranjan, N. C. Sahoo, and A. Chaturvedi, "Optimal
reconfiguration of radial distribution systems using a fuzzy mutated
genetic algorithm," IEEE Trans. Power Del., vol. 20, no. 2, pp. 1211-
1213, Apr. 2005.
[16] J. Z. Zhu, "Optimal reconfiguration of electrical distribution network
using the refined genetic algorithm," Elect. Power Syst. Res., vol. 62, no.
1, pp. 37-42, May 2002.
[17] M. E. Baran and F.Wu, "Network reconfiguration in distribution system
for loss reduction and load balancing," IEEE Trans. Power Del., vol. 4,
no. 2, pp. 1401-1407, Apr. 1989.
[18] Y. Mishima, K. Nara, T. Satoh, T. Ito, "Method for minimum-loss
reconfiguration of distribution system by tabu search", Electrical Engg.
Japan, Vol. 152, No. 2, July 2005.
[19] D. Zhang, Z. Fu, L. Zhang, "An improved TS algorithm for lossminimum
reconfiguration in large-scale distribution systems", Electric
Power Systems Research, Vol. 77, pp. 685-694, 2007.
[20] Chun Wang and Hao Zhong Cheng, "Optimization of Network
Configuration in Large Distribution Systems Using Plant Growth
Simulation Algorithm," IEEE Transactions on Power Systems, VOL. 23,
NO. 1, February 2008.
[21] D. Karaboga, "An idea based on honey bee swarm for numerical
optimization," Technical Report TR06, Computer Engineering
Department, Erciyes University, Turkey, 2005.
[22] B. Basturk, D. Karaboga, "An artificial bee colony (ABC) algorithm for
numeric function optimization," IEEE Swarm Intelligence Symposium
2006, May 12-14, Indianapolis, IN, USA, 2006.
[23] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm",
Journal of Global Optimization, Vol. 39, pp. 459-471, 2007.
[24] D. Karaboga, B. Basturk, "On the performance of artificial bee colony
(ABC) algorithm", Applied Soft Computing Vol. 8 pp. 687-697, 2008.
[1] A. Merlin, H. Back, "Search for a minimal-loss operating spanning tree
configuration in an urban power distribution system" Proceedings of 5th
Power System Computation Conference (PSCC), Cambridge, UK, 1975,
pp. 1-18.
[2] S. Civanlar, J. Grainger, H. Yin, and S. Lee, "Distribution feeder
reconfiguration for loss reduction," IEEE Trans. Power Del., vol. 3, no.
3, pp. 1217-1223, Jul. 1988.
[3] D. Shirmohammadi and H.W. Hong, Reconfiguration of electric
distribution networks for resistive line losses reduction," IEEE Trans.
Power Del., vol. 4, no. 2, pp. 1492-1498, Apr. 1989.
[4] T. P. Wagner, A. Y. Chikhani, and R. Hackam, "Feeder reconfiguration
for loss reduction: an application of distribution automation," IEEE
Trans. Power Del., vol. 6, no. 4, pp. 1922-1931, Oct. 1991.
[5] S. Goswami and S. Basu, "A new for the reconfiguration of distribution
feeders for loss minimization," IEEE Trans. Power Del., vol. 7, no. 3,
pp. 1484-1491, Jul. 1992.
[6] H. C. Cheng and C. C. Kou, "Network reconfiguration in distribution
systems using simulated annealing," Elect. Power Syst. Res., vol. 29, pp.
227-238, May 1994.
[7] H. D. Chiang and J. J. Rene, "Optimal network reconfiguration in
distribution systems: part 1: a new formulation and a solution
methodology," IEEE Trans. Power Del., vol. 5, no. 4, pp. 1902-1908,
Oct. 1990.
[8] H. D. Chiang and J. J. Rene, "Optimal network reconfiguration in
distribution systems: part 2: solution algorithms and numerical results,"
IEEE Trans. Power Del., vol. 5, no. 3, pp. 1568-1574, Jul. 1992.
[9] K. Nara, A. Shiose, M. Kitagawoa, and T. Ishihara, "Implementation of
genetic algorithm for distribution systems loss minimum
reconfiguration," IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044-1051,
Aug. 1992.
[10] D. Das, "A fuzzy multi-objective approach for network reconfiguration
of distribution systems," IEEE Trans. Power Del., vol. 21, no. 1, pp.
202-209, Jan. 2006.
[11] C. T. Su and C. S. Lee, "Network reconfiguration of distribution systems
using improved mixed-integer hybrid differential evolution", IEEE
Trans. on Power Delivery, Vol. 18, No. 3, July 2003.
[12] Y. C. Huang, "Enhanced genetic algorithm-based fuzzy multi-objective
approach to distribution network reconfiguration," Proc. Inst. Elect.
Eng., vol. 149, no. 5, pp. 615-620, 2002.
[13] I. Z. Zhu, "Optimal reconfiguration of electrical distribution network
using the refined genetic algorithm," Elect. Power Syst. Res., vol. 62, pp.
37-42, 2002.
[14] Y. Y. Hong and S. Y. Ho, "Determination of network configuration
considering multi-objective in distribution systems using genetic
algorithms," IEEE Trans. Power Syst., vol. 20, no. 2, pp. 1062-1069,
May 2005.
[15] K. Prasad, R. Ranjan, N. C. Sahoo, and A. Chaturvedi, "Optimal
reconfiguration of radial distribution systems using a fuzzy mutated
genetic algorithm," IEEE Trans. Power Del., vol. 20, no. 2, pp. 1211-
1213, Apr. 2005.
[16] J. Z. Zhu, "Optimal reconfiguration of electrical distribution network
using the refined genetic algorithm," Elect. Power Syst. Res., vol. 62, no.
1, pp. 37-42, May 2002.
[17] M. E. Baran and F.Wu, "Network reconfiguration in distribution system
for loss reduction and load balancing," IEEE Trans. Power Del., vol. 4,
no. 2, pp. 1401-1407, Apr. 1989.
[18] Y. Mishima, K. Nara, T. Satoh, T. Ito, "Method for minimum-loss
reconfiguration of distribution system by tabu search", Electrical Engg.
Japan, Vol. 152, No. 2, July 2005.
[19] D. Zhang, Z. Fu, L. Zhang, "An improved TS algorithm for lossminimum
reconfiguration in large-scale distribution systems", Electric
Power Systems Research, Vol. 77, pp. 685-694, 2007.
[20] Chun Wang and Hao Zhong Cheng, "Optimization of Network
Configuration in Large Distribution Systems Using Plant Growth
Simulation Algorithm," IEEE Transactions on Power Systems, VOL. 23,
NO. 1, February 2008.
[21] D. Karaboga, "An idea based on honey bee swarm for numerical
optimization," Technical Report TR06, Computer Engineering
Department, Erciyes University, Turkey, 2005.
[22] B. Basturk, D. Karaboga, "An artificial bee colony (ABC) algorithm for
numeric function optimization," IEEE Swarm Intelligence Symposium
2006, May 12-14, Indianapolis, IN, USA, 2006.
[23] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm",
Journal of Global Optimization, Vol. 39, pp. 459-471, 2007.
[24] D. Karaboga, B. Basturk, "On the performance of artificial bee colony
(ABC) algorithm", Applied Soft Computing Vol. 8 pp. 687-697, 2008.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51761", author = "R. Srinivasa Rao and S.V.L. Narasimham and M. Ramalingaraju", title = "Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm", abstract = "Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
", keywords = "Distribution system, Network reconfiguration, Loss
reduction, Artificial Bee Colony Algorithm.", volume = "2", number = "9", pages = "1799-7", }
