OBPSO Kullanılarak Dağıtık Güneş Enerji Sistemlerinin Optimum Bağlantı Gücü ve Yerinin Belirlenmesi

Yıl 2022, Cilt: 10 Sayı: 2, 940 - 952, 30.04.2022

Mehmet Çeçen , Cenk Yavuz

https://doi.org/10.29130/dubited.953283

Cited By: 1

Öz

Bu çalışmada, dağıtık üretim (DÜ) sistemlerinin optimum şebeke entegrasyonu probleminin çözümü için zıtlık tabanlı parçacık sürü optimizasyonu (OBPSO) kullanımı önerilmektedir. Önerilen OBPSO yöntemi, DÜ’nün optimum yer ve büyüklük değerlerini bulurken çok amaçlı optimizasyon yaklaşımı kullanmaktadır. Ayrıca yük değişimlerine karşı duyarlılık analizi yöntemi ortaya konmuş ve yeni bir amaç fonksiyonu olarak da kullanılmıştır. Amaç fonksiyonları, aktif güç kaybı, gerilim değişimi ve duyarlılık analizi minimizasyonundan oluşmaktadır. DÜ kaynağı olarak fotovoltaik tabanlı güneş enerji sistemleri (DGES) esas alınmıştır. Birim güç faktörü ile işletilen 3 adet DGES eklendiği durumlar değerlendirilmiş ve amaç fonksiyonlarının değişimleri analiz edilmiştir. Önerilen metodun etkinliği standart test sistemlerinden IEEE 33 baralı dağıtım sistemi kullanılarak araştırılmıştır. Yük akışı analizi içi MATPOWER paket programı kullanılmıştır. Elde edilen sonuçlar literatürde bulunan diğer çalışmalarla karşılaştırılmıştır. Neticede, OBPSO yönteminin iyi sonuç verdiği ve karşılaştırılan diğer optimizasyon tekniklerine karşı üstünlükleri olduğu gözlenmiştir. Ayrıca, DGES’nin optimum değerler dikkate alınarak yapılan entegrasyonlarda amaç fonksiyonlarında belirgin iyileşmeye sağladığı gözlenmiştir.

Anahtar Kelimeler

OBPSO, Dağıtık güneş enerji sistemi, Aktif güç kaybı minimizasyonu, Gerilim değişimi, Duyarlılık analizi

Teşekkür

Değerli destek ve faydalı eleştirilerinden dolayı Sayın Dr. Talha Enes Gümüş'e teşekkür ederiz.

Determining the Optimum Size and Siting of Distributed Solar Energy Systems Using OBPSO

Öz

In this study, OBPSO is proposed to solve optimal grid integration of distributed generation (DG) systems. While the proposed OBPSO method finds the optimum location and size values of DG, three different singular objective functions are considered. Additionally vulnerability analysis method to load changes is proposed and used as a new objective function. Objective functions consist of active power loss, voltage variation and vulnerability analysis minimization. Photovoltaic solar energy systems (DPVG) are considered as a source of DG. The cases where 3 DGES are added with the unit power factor are considered and the changes of the objective functions are evaluated. The efficiency of the proposed method is achieved by using IEEE 33 bus distribution system, which is one of the standard test systems. MATPOWER package program is used for load flow analysis. Then, the effectiveness of the proposed method is compared with other studies in the literature. The results obtained showed that OBPSO is effective and give better results against other optimization techniques compared in the study. It has been observed that DGES systems provide significant improvement in the purpose functions in the integrations made by considering the optimum values.

Anahtar Kelimeler

OBPSO, DPVG, Real power loss minimization, Voltage deviation, Vulnerability analysis

Kaynakça

• [1] J. L. "Sawin et al., Renewables 2013. Global status report 2013. ; Renewable Energy Policy Network for the 21st Century - REN21, 15 rue de Milan, 75441 Paris Cedex 9 (France), 2013.
• [2] J. Von Appen, M. Braun, T. Stetz, K. Diwold, and D. Geibel, "Time in the sun: the challenge of high PV penetration in the German electric grid," IEEE Power and Energy magazine, vol. 11, no. 2, pp. 55-64, 2013.
• [3] G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer, "Distributed generation: definition, benefits and issues," Energy policy, vol. 33, no. 6, pp. 787-798, 2005.
• [4] Ö. Alkan, A. Öztürk, S. Tosun, "Rüzgar ve Güneş santrallerinde kisa dönem enerji üretim tahmini için matematiksel modellerin oluşturulmasi," Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 6, no. 1, pp. 188-195, 2018.
• [5] A. Peker, N. Yörükeren, and A. B. Arsoy, "Dağıtım Sisteminde Harmonik Analizi ve Etkilerinin Dağıtılmış Üretim Kullanılarak Azaltılması," Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 5, no. 1, pp. 23-33.
• [6] D. Q. Hung, N. Mithulananthan, and R. Bansal, "Analytical expressions for DG allocation in primary distribution networks," IEEE Transactions on energy conversion, vol. 25, no. 3, pp. 814-820, 2010.
• [7] J. Subrahmanyam and C. Radhakrishna, "Distributed generator placement and sizing in unbalanced radial distribution system," International Journal of Electrical Power and Energy Systems Engineering, vol. 2, no. 4, pp. 232-239, 2009.
• [8] N. Acharya, P. Mahat, and N. Mithulananthan, "An analytical approach for DG allocation in primary distribution network," International Journal of Electrical Power & Energy Systems, vol. 28, no. 10, pp. 669-678, 2006.
• [9] T. Gözel and M. H. Hocaoglu, "An analytical method for the sizing and siting of distributed generators in radial systems," Electric power systems research, vol. 79, no. 6, pp. 912-918, 2009.
• [10] I. Pisica, C. Bulac, and M. Eremia, "Optimal distributed generation location and sizing using genetic algorithms," in 2009 15th International Conference on Intelligent System Applications to Power Systems, 2009: IEEE, pp. 1-6.
• [11] M. KN and J. EA, "Optimal integration of distributed generation (DG) resources in unbalanced distribution system considering uncertainty modelling," International Transactions on Electrical Energy Systems, vol. 27, no. 1, p. e2248, 2017.
• [12] A. Mohanty and P. Modi, "Optimal location and sizing of distributed generation in a power distribution system," Cogeneration and Distributed Generation Journal, vol. 25, no. 4, pp. 20- 39, 2010.
• [13] S. Mahajan and S. Vadhera, "Optimal location and sizing of distributed generation unit using human opinion dynamics optimization technique," Distributed Generation & Alternative Energy Journal, vol. 33, no. 2, pp. 38-57, 2018.
• [14] A. Y. Abdelaziz, Y. G. Hegazy, W. El-Khattam, and M. M. Othman, "Optimal planning of distributed generators in distribution networks using modified firefly method," Electric Power Components and Systems, vol. 43, no. 3, pp. 320-333, 2015.
• [15] M. Pesaran, A. A. Mohd Zin, A. Khairuddin, and O. Shariati, "Optimal sizing and siting of distributed generators by a weighted exhaustive search," Electric Power Components and Systems, vol. 42, no. 11, pp. 1131-1142, 2014.
• [16] G. Chen, L. Liu, Y. Guo, and S. Huang, "Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems," COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2016.
• [17] E. Ali, S. Abd Elazim, and A. Abdelaziz, "Ant lion optimization algorithm for renewable distributed generations," Energy, vol. 116, pp. 445-458, 2016.
• [18] A. Y. Abdelaziz, R. A. Osama, and S. M. Elkhodary, "Distribution systems reconfiguration using ant colony optimization and harmony search algorithms," Electric Power Components and Systems, vol. 41, no. 5, pp. 537-554, 2013.
• [19] E. Haesen, J. Driesen, and R. Belmans, "Robust planning methodology for integration of stochastic generators in distribution grids," IET Renewable power generation, vol. 1, no. 1, pp. 25-32, 2007.
• [20] M. H. Moradi and M. Abedini, "A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems," International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66-74, 2012.
• [21] K. H. Kim, K. B. Song, S. K. Joo, Y. J. Lee, and J. O. Kim, "Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm," European Transactions on Electrical Power, vol. 18, no. 3, pp. 217-230, 2008.
• [22] V. Matlab, "7.10. 0 (R2010a)," The MathWorks Inc., Natick, Massachusetts, 2010.
• [23] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, "MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education," IEEE Transactions on power systems, vol. 26, no. 1, pp. 12-19, 2010.
• [24] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95- international conference on neural networks, 1995, vol. 4: IEEE, pp. 1942-1948.
• [25]H. R. Tizhoosh, "Opposition-based learning: a new scheme for machine intelligence," in International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06), 2005, vol. 1: IEEE, pp. 695-701.
• [26]S. Sultana and P. K. Roy, "Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems," International Journal of Electrical Power & Energy Systems, vol. 63, pp. 534-545, 2014.