i-manager Publications

Optimal Allocation of Distribution Generation Units in Radial Distribution Systems using Nature Inspired Optimization Techniques

Y. Anil Kumar *, N. Prema Kumar **

* Research Scholar, Department of Electrical Engineering, Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India.

** Professor, Department of Electrical Engineering, Andhra University College of Engineering, Visakhapatnam, Andhra Pradesh, India.

Periodicity:November - January'2018
DOI : https://doi.org/10.26634/jps.5.4.14157

Abstract

Growth in energy demand is more than the energy production due to various reasons like industrial growth, rapid urbanization, increasing affordability of electric gadgets, etc. The integration of existing grids with renewable energy sources provides economical, sustainable and efficient power distribution, and this allows to control the greenhouse effect. The reduction in power loss and voltage profile improves the distribution system, which can be done by using some techniques, such as feeder or network reconfiguration, VAR compensation with capacitor banks, and Distributed Generation (DG). DG is a localized small scale generation installed in the distribution network capable of injecting active power and providing limited reactive power support, reduced distribution losses, improved voltage profile in the system, hence, improving the quality of the power. The significant process is to improve the power quality of the system and this system is used to find the size of the DG unit and their suitable locations of the system. In this paper, IPSO and a new gradient free, meta-heuristic, population based algorithm called BAT Inspired Algorithm, are used to evaluate the optimal size and location of DG units. Distributed load flow is carried out for 33-bus and 69-bus systems to obtain power losses and voltage at each bus. Optimization techniques like IPSO and BAT algorithms are considered for the optimal placement and optimal sizing of Distributed Generators in radial distribution system by multi-objective optimization approach has been discussed. The practical application and efficiency of this method is determined by using two test systems (33 and 69-bus). The proposed methods are carried out using MATLAB.

Keywords

Distributed Generation (DG), Power Flow, Placement and Sizing, Improved Particle Swarm Optimization (IPSO), Objective Function

How to Cite this Article?

Kumar, Y. A., and Kumar, N. P. (2018). Optimal Allocation of Distribution Generation Units in Radial Distribution Systems using Nature Inspired Optimization Techniques. i-manager’s Journal on Power Systems Engineering, 5(4), 15-23. https://doi.org/10.26634/jps.5.4.14157

References

[1]. Borges, C. L., & Falcao, D. M. (2006). Optimal distributed generation allocation for reliability, losses, and voltage improvement. International Journal of Electrical Power & Energy Systems, 28(6), 413-420.

[2]. Carpinelli, G., Celli, G., Mocci, S., Pilo, F., & Russo, A. (2005). Optimisation of embedded generation sizing and siting by using a double trade-off method. IEEE Proceedings-Generation, Transmission and Distribution, 152(4), 503-513.

[3]. Celli, G., Ghiani, E., Mocci, S., & Pilo, F. (2005). A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. IEEE Transactions on Power Systems, 20(2), 750-757.

[4]. El-Khattam, W., Bhattacharya, K., Hegazy, Y., & Salama, M. M. A. (2004). Optimal investment planning for distributed generation in a competitive electricity market. IEEE Transactions on Power Systems, 19(3), 1674-1684.

[5]. Griffin, T., Tomsovic, K., Secrest, D., & Law, A. (2000, January). Placement of dispersed generation systems for reduced losses. In System Sciences, 2000. Proceedings of the 33 Annual Hawaii International Conference on (pp.1-9). IEEE.

[6]. Harrison, G. P., & Wallace, A. R. (2005). Optimal power flow evaluation of distribution network capacity for the connection of distributed generation. IEE Proceedings- Generation, Transmission and Distribution, 152(1), 115- 122.

[7]. Keane, A., & O'Malley, M. (2005). Optimal allocation of embedded generation on distribution networks. IEEE Transactions on Power Systems, 20(3), 1640-1646.

[8]. Kim, K. H., Lee, Y. J., Rhee, S. B., Lee, S. K., & You, S. K. (2002, July). Dispersed generator placement using fuzzy- GA in distribution systems. In Power Engineering Society Summer Meeting, 2002 IEEE (Vol. 3, pp. 1148-1153). IEEE.

[9]. Kim, J. O., Nam, S. W., Park, S. K., & Singh, C. (1998). Dispersed generation planning using improved Hereford ranch algorithm. Electric Power Systems Research, 47(1), 47-55.

[10]. Nara, K., Hayashi, Y., Ikeda, K., & Ashizawa, T. (2001). Application of Tabu search to optimal placement of Distributed Generators. In Power Engineering Society Winter Meeting, 2001. IEEE (Vol. 2, pp. 918-923). IEEE.

[11]. Rau, N. S., & Wan, Y. H. (1994). Optimum location of resources in distributed planning. IEEE Transactions on Power Systems, 9(4), 2014-2020.

[12]. Suresh, M. C. V., & Edward, J. B. (2006). Optimal Placement of Distributed Generation in Distribution Systems by using Shuffled Frog Leaping Algorithm. ARPN Journal of Engineering and Applied Sciences, 12(3), 863- 868.

[13]. Wang, C., & Nehrir, M. H. (2004). Analytical approaches for optimal placement of Distributed Generation sources in power systems. IEEE Transactions on Power Systems, 19(4), 2068-2076.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.