Optimal Sizing Of Distributed Generations Using Heuristic Optimization Techniques

Mahmoud Abdallah Attia*, Marwa F. Fathi**, Ibrahim M. Diaa***
* Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
** Electrical Engineer, Sakr Factory (Arab Organization for Industrialization), Cairo, Egypt.
*** Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Periodicity:May - July'2017
DOI : https://doi.org/10.26634/jic.5.3.13678

Abstract

Nowadays Power system suffers from a lot of power flow problems. The main common problems are high power loss and overload of lines. The Distributed Generators (DGs) are considered as one of the efficient solutions for these problems. The DG can generate power at the location of load which helps in power loss problem. Also generation at load location will decrease the power flow in lines. This paper studies the DG sizing by new optimization techniques. Several cases studied in this work to select the suitable techno-economic strategy of DG sizing. Ranges of DG sizing in cases were studied are 40%, 60%, 80% and 100% of system loading. Studies consider trying to improve the voltage profile to certain limits by using the DGs. Finally, suggestion is concluded to which range of sizing is suitable and if the voltage limits consideration is economical or not in DG sizing.

Keywords

Distributed Generations, Harmony Search Algorithm, Teaching-Learning Based Optimization, Optimal Sizing, Distribution Systems

How to Cite this Article?

Attia, M.A., Fathi, M.F., and Diaa, I.M. (2017). Optimal Sizing of Distributed Generations using Heuristic Optimization Techniques. i-manager’s Journal on Instrumentation and Control Engineering, 5(3), 10-24. https://doi.org/10.26634/jic.5.3.13678

References

[1]. Abd, M. K., Cheng, S. J., & Sun, H. S. (2016, June). Optimal DG placement and sizing for power loss reduction in a radial distribution system using MPGSA and sensitivity index method. In Industrial Electronics and Applications (ICIEA), 2016 IEEE 11 Conference on (pp. 1579-1585). IEEE.
[2]. Ackermann, T., Andersson, G., & Söder, L. (2001). Distributed generation: A definition. Electric Power Systems Research, 57(3), 195-204.
[3]. Baran, M. E., & Wu, F. F. (1989). Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Transactions on Power Delivery, 4(2), 1401- 1407.
[4]. CIGRÉ Working Group. (1999). Impact of Increasing Contribution of Dispersed Generation on the Power System. CIGRÉ Final Rep, 137.
[5]. Diaa, I. M., Badra, N. M., & Attia, M. A. (2016). Harmony Search and Nonlinear Programming Based Hybrid Approach to Enhance Power System Performance with Wind Penetration. International Electrical Engineering Journal, 7(7), 2323-2330.
[6]. El-Zonkoly, A. M. (2013). Multistage expansion planning for distribution networks including unit commitment. IET Generation, Transmission & Distribution, 7(7), 766-778.
[7]. Falaghi, H., Singh, C., Haghifam, M. R., & Ramezani, M. (2011). DG integrated multistage distribution system expansion planning. International Journal of Electrical Power & Energy Systems, 33(8), 1489-1497.
[8]. Gan, C. K., Mancarella, P., Pudjianto, D., & Strbac, G. (2011). Statistical appraisal of economic design strategies of LV distribution networks. Electric Power Systems Research, 81(7), 1363-1372.
[9]. Garva, N., & Sanghi, A. (2016). Estimation of Optimal Location and Sizing of DG for Minimization of Loss in Radial Distribution System using Meta-Heuristic Technique. International Conference on Emerging Trends in Engineering and Management Sustainable Development.
[10]. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60-68.
[11]. Krueasuk, W., & Ongsakul, W. (2006, December). Optimal placement of distributed generation using Particle Swarm Optimization. In Proceedings of Power Engineering Conference in Australasian Universities, Australia.
[12]. Lotero, R. C., & Contreras, J. (2011). Distribution system planning with reliability. IEEE Transactions on Power Delivery, 26(4), 2552-2562.
[13]. Moreira, J. C., Miguez, E., Vilacha, C., & Otero, A. F. (2011). Large-scale network layout optimization for radial distribution networks by parallel computing. IEEE Transactions on Power Delivery, 26(3), 1946-1951.
[14]. Nadhir, K., Chabane, D., & Tarek, B. (2013). Distributed Generation location and size determination to reduce power losses of a distribution feeder by Firefly Algorithm. International Journal of Advanced Science and Technology, 56, 61-72.
[15]. Prabha, D. R., & Jayabarathi, T. (2014). Determining the optimal location and sizing of distributed generation unit using Plant Growth Simulation Algorithm in a radial distribution network. WSEAS Trans. Syst., 13, 543-550.
[16]. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
[17]. Rupa, J. M., & Ganesh, S. (2014). Power flow analysis for radial distribution system using backward/forward sweep method. International Journal of Electrical, Computer, Electronics and Communication Engineering, 8(10), 1540-1544.
[18]. Samper, M. E., & Vargas, A. (2013a). Investment decisions in distribution networks under uncertainty with distributed generation-Part I: Model formulation. IEEE Transactions on Power Systems, 28(3), 2331-2340.
[19]. Samper, M. E., & Vargas, A. (2013b). Investment decisions in distribution networks under uncertainty with distributed generation-Part II: Implementation and results. IEEE Transactions on Power Systems, 28(3), 2341-2351.
[20]. Uniyal, A., & Kumar, A. (2016, March). Comparison of optimal DG placement using CSA, GSA, PSO and GA for minimum real power loss in radial distribution system. In th Power Systems (ICPS), 2016 IEEE 6 International Conference on (pp. 1-6). IEEE.
[21]. Viral, R., & Khatod, D. K. (2012). Optimal planning of distributed generation systems in distribution system: A review. Renewable and Sustainable Energy Reviews, 16(7), 5146-5165.
[22]. Wang, D. T. C., Ochoa, L. F., & Harrison, G. P. (2011). Modified GA and data envelopment analysis for multistage distribution network expansion planning under uncertainty. IEEE Transactions on Power Systems, 26(2), 897-904.
[23]. Zahedi, A. (2011). A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid. Renewable and Sustainable Energy Reviews, 15(9), 4775-4779.
[24]. Ziari, I., Ledwich, G., Ghosh, A., & Platt, G. (2013). Optimal distribution network reinforcement considering load growth, line loss, and reliability. IEEE Transactions on Power Systems, 28(2), 587-597.
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