Optimal Sizing and Allocation of Renewable Sources in Northwest Anatolia Power System

Yıl 2023, , 644 - 653, 30.04.2023

Rukiye B.aymaz , Mehmet Ali Yalçın , Talha Enes Gümüş

https://doi.org/10.29130/dubited.1078418

Öz

In this study, the problem of determining the location and sizing of the distributed generation (DG) resources added to the power systems has been calculated using genetic algorithms (GA), one of the heuristic optimization methods. The optimization problem aims to minimize the active power losses and improve the system bus voltages as an objective function. Therefore, keeping the system bus voltages within the defined limits has been added to the optimization problem as a constraint. The proposed approach has been applied for two objective function to 114 bus North West Anatolia (KBA) power system. With this approach, it has been observed that the active power losses for each case have been reduced and the bus voltage profile has been improved when distributed generation resources are added without changing the network topology of the power system.

Anahtar Kelimeler

Genetic algorithms, Distributed generation, Optimization, Renewable energy sources

Kuzeybatı Anadolu Güç Sisteminde Yenilenebilir Enerji Kaynaklarının Optimal Boyutlandırılması ve Yerleşimi

Öz

Bu çalışmada güç sistemlerine eklenecek dağıtık üretim (DÜ) kaynaklarının bağlantı yeri ve güç değerlerinin belirlenmesi problemi, sezgisel optimizasyon yöntemlerinden genetik algoritma (GA) kullanılarak hesaplanmıştır. Optimizasyon probleminin çözümünde amaç fonksiyonu olarak aktif güç kayıplarının minimize edilmesi ve sistem bara gerilimlerin iyileştirilmesi amaçlanmıştır. Bu nedenle sistem bara gerilimlerinin belirli aralıkta tutulması kısıt olarak optimizasyon problemine eklenmiştir. Önerilen yaklaşım Kuzeybatı Anadolu (KBA) 114 baralı güç iletim sistemine 2 farklı amaç fonksiyonu için uygulanmıştır. Bu yaklaşımla güç sisteminin ağ topolojisini değiştirmeksizin dağıtık üretim kaynaklarının eklenmesi halinde her durum için aktif güç kayıplarının azaldığı ve bara gerilim profilinin iyileştiği gözlenmiştir.

Anahtar Kelimeler

Genetik algoritma, Dağıtık üretim, Optimizasyon, Yenilenebilir enerji kaynakları

Kaynakça

• [1]H. Bevrani, A. Ghosh, and G. Ledwich, “Renewable energy sources and frequency regulation: Survey and new perspectives,” IET Renewable Power Generation, vol. 4, no. 5, pp. 438–457, Sep. 2010.
• [2]N. Eghtedarpour and E. Farjah, “Distributed charge/discharge control of energy storages in a renewable-energy-based DC micro-grid,” IET Renewable Power Generation, vol. 8, no. 1, pp. 45–57, 2014.
• [3]V. Veera, V. Satya, N. Murty, and A. Kumar, “Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation; Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation”, IET Smart Grid, vol. 2, no. 2, pp. 192 – 202, 2019.
• [4](2021, 10 Aralık), “2021-Kuresel-Enerji-Raporu-Ozeti”, [Çevrimiçi]. Erişim: https://www.dunyaenerji.org.tr/2021-kuresel-enerji-raporu/
• [5]T. Ackermann, G. Ran Andersson, and L. Sö Der A, “Distributed generation: a definition,”, Smart Grid and Renewable Energy, Vol.8 No.7, July 18, 2017, doi:10.1016/S0378 7796(01)00101-8. [6]Y. Alinejad-Beromi, M. Sedighizadeh and M. Sadighi, "A particle swarm optimization for sitting and sizing of Distributed Generation in distribution network to improve voltage profile and reduce THD and losses," 2008 43rd International Universities Power Engineering Conference, 2008, pp. 1-5.
• [7]S. Chaitusaney and A. Yokoyama, “Prevention of reliability degradation from recloser-fuse miscoordination due to distributed generation,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 2545–2554, 2008.
• [8]V. V. S. N. Murty and A. Kumar, “Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth,” International Journal of Electrical Power and Energy Systems, vol. 69, pp. 246–256, 2015.
• [9]A. Ali, M. U. Keerio, and J. A. Laghari, “Optimal Site and size of Distributed Generation Allocation in Radial Distribution Network Using Multi-objective Optimization,” Journal of Modern Power Systems and Clean Energy, vol. 9, no. 2, pp. 404–415, Mar. 2021.
• [10]D. Q. Hung and N. Mithulananthan, “Multiple distributed generator placement in primary distribution networks for loss reduction,” IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1700–1708, 2013.
• [11]D. Q. Hung and N. Mithulananthan, “Loss reduction and loadability enhancement with DG: A dual-index analytical approach,” Applied Energy, vol. 115, pp. 233–241, Feb. 2014.
• [12]M. Pesaran, P. Dang Huy, and V. K. Ramachandaramurthy, “A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms,”, Renewable and Sustainable Energy Reviews, vol. 75, pp. 293-312, 2016.
• [13]D. H. Popovicápopovicá, J. A. Greatbanks, M. Begovic´c, B. Begovic´c, and A. Pregelj, “Placement of distributed generators and reclosers for distribution network security and reliability”, International Journal of Electrical Power & Energy Systems, vol. 27, pp. 398-408, 2005.
• [14]F. S. Abu-Mouti and M. E. El-Hawary, “Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm,” IEEE TRANSACTIONS ON POWER DELIVERY, vol. 26, no. 4, 2011.
• [15]K. Varesi, “Optimal Allocation of DG Units for Power Loss Reduction and Voltage Profile Improvement of Distribution Networks using PSO Algorithm,” 2011, Accessed: Nov. 03, 2021. [Online]. Available: https://www.researchgate.net/publication/275354162
• [16] 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 and Energy Systems, vol. 34, no. 1, pp. 66–74, Jan. 2012.
• [17]T. E. Gümüş, C. Aksoy Tirmikçi, C. Yavuz, M. A. Yalçin, and M. Turan, “Power loss minimization for distribution networks with load tap changing using genetic algorithm and environmental impact analysis,” Tehnicki Vjesnik, vol. 28, no. 6, pp. 1927–1935, Nov. 2021.
• [18]E. M. Nihat, “Nihat Pamuk, 380 ve 154 kV’luk Kuzeybatı Anadolu Şebekesi Güç Akışı Benzetimleri’, Yüksek lisans tezi, Elektrik Elektronik Mühendisliği Bölümü, Sakarya Üniversitesi, Sakarya, Türkiye, 2009.
• [19]M. Gandomkar, M. Vakilian, and M. Ehsan, “A combination of genetic algorithm and simulated annealing for optimal DG allocation in distribution networks,” in Canadian Conference on Electrical and Computer Engineering, 2005, vol. 2005, pp. 645–648.
• [20]Y. Alinejad-beromi, M. Sedighizadeh, M. R. Bayat, and M. E. Khodayar, “Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profıle.”, 42nd International Universities Power Engineering Conference (UPEC 2007), 2007, 4-6 september, brighton, united kingdom.
• [21]K. M. Muttaqi, A. D. T. Le, M. Negnevitsky, and G. Ledwich, “An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks,” IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1351–1360, 2014,.
• [22]R. Baños, F. Manzano-Agugliaro, F. G. Montoya, C. Gil, A. Alcayde, and J. Gómez, “Optimization methods applied to renewable and sustainable energy: A review,” Renewable and Sustainable Energy Reviews, vol. 15, no. 4, pp. 1753–1766, May 2011.
• [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, Feb. 2011.
• [24](2022, 10 Şubat). “2021 Yılı Elektrik Üretim-Tüketim Raporu.”. [Çevrimiçi]. Erişim: https://www.teias.gov.tr/tr-TR/aylik-elektrik-uretim-tuketim-raporlari