A NOVEL PARTICLE SWARM OPTIMIZATION GUIDED GENETIC  TO THE DISTRIBUTION NETWORK RECONFIGURATION PROBLEM  WITH AN OBJECTIVE FUNCTION OF MINIMUM OPERATING  AND POWER OUTAGE COSTS

Nguyễn Tùng Linh; Phạm Vũ Long

doi:10.34238/tnu-jst.9246

A NOVEL PARTICLE SWARM OPTIMIZATION GUIDED GENETIC TO THE DISTRIBUTION NETWORK RECONFIGURATION PROBLEM WITH AN OBJECTIVE FUNCTION OF MINIMUM OPERATING AND POWER OUTAGE COSTS

About this article

Received: 16/11/23 Revised: 22/03/24 Published: 22/03/24

Authors

1. Nguyen Tung Linh , Electric Power University
2. Pham Vu Long, Institute of Energy

Abstract

In operating the distribution network, the problem of reconfiguration distribution network according to the change of load to reduce power loss has partly reduced the operation cost of the distribution network but it can impact the reliability of power supply to the load. Therefore, in this study, we propose a hybrid algorithm that integrates two well established methods, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm for the problem of reconfiguration distribution network with the objective function of the with the objective function of reducing power loss considering operating costs and power outage costs on the distribution network. To demonstrate the performance of the proposed PSO-GA Algorithm simulations have implemented through MATLAB 2019ª and PSS/ADEPT software. Utilizing the IEEE 33-bus distribution system for the experiment. The results show that the algorithm provides decision-makers with a range of equivalent options when addressing the challenge of distribution network reconfiguration.

Keywords

Genetic Algorithm; Particle Swarm Optimization; Distribution network ; Reconfiguration distribution network; Optimization

Full Text:

PDF

References

[1] Erav, “Demand Side Management/DSM,” 2023. [Online]. Available: https://www.erav.vn/tin-tuc/t985/chuong-trinh-quan-ly-nhu-cau-nhung-thuan-loi-va-kho-khan-tai-viet-nam.html. [Accessed Jun. 12, 2023].

[2] A. Merlin and H. Back, “Search for a minimal loss operating spanning tree configuration in an urban power distribution system,” Proceeding 5th power Syst. Comput. conf (PSCC), Cambridge, UK, vol.1. pp.1–18, 1975.

[3] S. Civanlar, J. J. Grainger, H. Yin, and S. S. H. Lee, “Distribution feeder reconfiguration for loss reductions,” IEEE Trans. Power Deliv., vol. 3, no. 3, pp. 1217–1223, 1988.

[4] M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Transactions on Power Delivery, vol. 4, no. 2. pp. 1401–1407, 1989.

[5] D. Shirmohammadi and H. W. Hong, “Reconfiguration of electric distribution networks for resistive line losses reduction,” IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 1492–1498, 1989.

[6] K. Nara, A. Shiose, M. Kitagawa, and T. Ishihara, “Implementation of genetic algorithm for distribution systems loss minimum re-configuration,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044–1051, 1992.

[7] B. Tomoiagǎ, M. Chindriş, A. Sumper, R. Villafafila-Robles, and A. Sudria-Andreu, “Distribution system reconfiguration using genetic algorithm based on connected graphs,” Electr. Power Syst. Res., vol. 104, pp. 216–225, 2013.

[8] H. Chitsaz, P. Zamani-Dehkordi, H. Zareipour, and P. P. Parikh, “Electricity Price Forecasting for Operational Scheduling of Behind-the-Meter Storage Systems,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6612–6622, Oct. 2018, doi: 10.1109/TSG.2017.2717282.

[9] N. Gupta, A. Swarnkar, K. R. Niazi, and R. C. Bansal, “Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework,” IET Gener. Transm. Distrib., vol. 4, no. 12, 2010, Art. no. 1288.

[10] D.-L. Duan, X.-D. Ling, X.-Y. Wu, and B. Zhong, “Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm,” Int. J. Electr. Power Energy Syst., vol. 64, pp. 88–95, 2015.

[11] W. C. Wu and M. S. Tsai, “Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1591–1599, 2011.

[12] T. T. Nguyen and A. V. Truong, “Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm,” Int. J. Electr. Power Energy Syst., vol. 68, pp. 233–242, 2015.

[13] R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, John Wiley & Sons, Inc., Hoboken, New Jersey, 2004.

[14] X. Hu, Y. Shi, and R. Eberhart, “Recent advances in particle swarm,” Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), Portland, OR, USA, 2004, pp. 90-97.

[15] C. F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34. no.2, pp. 997-1006, 2004.

[16] Y. T. Kao and E. Zahara, “A hybrid genetic algorithm and particle swarm optimization for multimodal functions,” Applied Soft Computing, vol. 8, no. 2, pp. 849-857, 2008.

[17] Y. Marinakis and M. Marinaki, “A hybrid genetic–Particle Swarm Optimization Algorithm for the vehicle routing problem,” Expert Systems with Applications, vol.37, no.2, pp.1446-1455, 2010.

[18] H. Duan, Q. Luo, Y. Shi, and G. Ma, “Hybrid particle swarm optimization and genetic algorithm for multi-uav formation reconfiguration,” IEEE Computational Intelligence Magazine, vol.8, no.3, pp. 16-27, 2013.

[19] R. J. Kuo and L. M. Lin, “Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering,” Decision Support Systems, vol. 49, no. 4, pp. 451-462, 2010.

[20] E. Mahboubi-Moghaddam, M. R. Narimani, M. H. Khooban, A. Azizivahed, and M. J. Sharifi, “Multi-Objective Distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations,” Int. J. Electr. Power Energy Syst., vol. 76, pp. 35–43, 2016.

DOI: https://doi.org/10.34238/tnu-jst.9246

Refbacks

There are currently no refbacks.



Remember me