Gerçek Bir Kampüs Şebekesinde Dağıtık Üretimin Eş Zamanlı Yerleşimi, Boyutlandırılması ve Miktar Optimizasyonu: GA ve GWO Üzerine Karşılaştırmalı Bir Çalışma

Öz

Dağıtık Üretim (DÜ) birimlerinin Radyal Dağıtım Şebekelerine (RDŞ) entegrasyonu, teknik kayıpları azaltmak ve gerilim kararlılığını artırmak açısından hayati önem taşımaktadır. Geleneksel çalışmalar, değişken DÜ sayısını dikkate almadan idealize edilmiş test sistemlerini kullanırken; bu çalışma, gerçek bir 17 baralı, 34,5/0,4 kV kampüs dağıtım şebekesinde (Trakya Üniversitesi Balkan Yerleşkesi) DÜ birimlerinin optimum yerini, kapasitesini ve adedini eşzamanlı olarak belirlemek için simülasyon tabanlı çok amaçlı bir optimizasyon yöntemi önermektedir. Aktif güç kaybını (Ploss), reaktif güç kaybını (Qloss) ve Gerilim Sapma İndeksini (VDI) optimize etmek için Genetik Algoritma (GA) ve Gri Kurt Optimizasyonu (GWO) yöntemlerini MATPOWER'ın analitik güç akışı denklemleriyle bütünleştirdik. Özgün bir dinamik kod çözme stratejisi mükerrer bara atamalarını önlerken, statik ceza fonksiyonları işletme kısıtlarını uygulamaktadır. Temel durum analizi, aşırı yüklenmiş transformatörler (>%80) nedeniyle ciddi gerilim kısıtları ortaya koymuştur. Hem GA hem de GWO, 4. ve 6. baralarda iki DÜ biriminin (toplam 4,82–4,84 MW) aynı optimum yerleşimine bağımsız olarak yakınsamıştır. Dengeli çok amaçlı koşullar altında, aktif kayıplar ~%15 oranında azalırken (0,0318 MW'den 0,0270 MW'ye), Qloss ve VDI'da eşzamanlı iyileşmeler elde edilmiştir. Buna karşın, yalnızca gerilim optimizasyonu yapılması >5,3 MW üretim fazlasına ve >%10 aktif kayıp artışına yol açmış; bu durum, dengeli çok amaçlı çalışmaların etkili bir RDŞ planlaması için şart olduğunu göstermiştir

Anahtar Kelimeler

• Dağıtık üretim
• Kampüs dağıtım şebekesi
• Genetik Algoritma
• Gri Kurt Optimizasyon Algoritması
• Çok amaçlı optimizasyon
• Güç kaybı azaltımı; Gerilim profili iyileştirme

Simultaneous Allocation, Sizing, and Quantity Optimization of Distributed Generation in a Real Campus Grid: A Comparative Study of GA and GWO

Öz

The integration of Distributed Generation (DG) into Radial Distribution Networks (RDNs) is crucial for reducing technical losses and enhancing voltage stability. Unlike traditional studies utilizing idealized test systems without variable DG counts, this study proposes a simulationbased multi-objective optimization method to simultaneously determine the optimal location, capacity, and quantity of DG units in an actual 17-bus, 34.5/0.4 kV campus distribution grid (Trakya University Balkan Campus). We couple a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO) with MATPOWER's analytical load-flow equations to optimize active power loss (Ploss), reactive power loss (Qloss), and the Voltage Deviation Index (VDI). A novel dynamic decoding strategy prevents duplicate bus allocations, while static penalty functions enforce operational constraints. Baseline analysis revealed severe voltage constraints due to overloaded transformers (>80%). Both GA and GWO independently converged to identical optimal placements of two DG units (totaling 4.82–4.84 MW) at Bus 4 and Bus 6. Under balanced multiobjective conditions, active losses decreased by ~15% (from 0.0318 MW to 0.0270 MW) with simultaneous improvements in Qloss and VDI. Conversely, single-objective voltage optimization yielded >5.3 MW over-generation and >10% increased active losses, demonstrating that balanced multi-objective frameworks are essential for effective RDN planning.

Anahtar Kelimeler

• Electro Discharge
• Distributed generation
• Campus distribution network
• Genetic Algorithm
• Grey Wolf Optimizer
• MATPOWER
• Multi-objective optimization
• Power loss reduction
• Voltage profile improvement

Kaynakça

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