Cost Optimization for Sustainable Economy with Heuristic Algorithms in Power System

Öz

Economically transmitting the energy obtained from power generation units through transmission and distribution lines is critical for environmentally friendly and sustainable energy management. The component that plays an important role in delivering electrical energy from production units to distribution and consumption units is the transmission/distribution network. At this stage, economic sustainability of the generated active and reactive power is possible by keeping operating costs and loss expenses under control. Insufficient power generation units or increased losses increase the operating costs in power systems. Capacity excess and cost increase affect stability by reducing system reliability. These negativities can cause problems in power systems and negatively affect consumers by making the power transmission network unusable. Developing technology and increasing energy demands bring quality problems in power systems. The operating costs of existing power generation units, which will provide the increasing demand power with the most appropriate cost and power generation, need to be revised with optimization techniques. Thus, the efficiency of power systems can be increased. If power systems are inadequate, new and renewable power generation units should be included in the power system. In this study, power system operation and cost optimizations were carried out with Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms that use swarm intelligence on IEEE 30-bus test systems. Significant differences in the results were observed when the number of population and re-runs were selected as 20, 30 and 50 for the PSO and GWO algorithms, respectively. When the results for three different situations are compared on the basis of algorithms; In the simulation tests conducted for the third case, where 50 population and re-run values of the PSO algorithm were used, the optimal operating cost value of 800.47 $/h was reached. As a result of the study, it was seen that the PSO and GWO algorithms used in the power system brought the total operating cost closer to minimum values and made power production more sustainable by increasing the number of population and re-runs.

Anahtar Kelimeler

• Power Systems
• Sustainable Energy
• Cost Optimization
• PSO
• GWO.

Güç Sisteminde Sezgisel Algoritmalarla Sürdürülebilir Ekonomi Amaçlı Maliyet Optimizasyonu

Öz

Güç üretim birimlerinden elde edilen enerjinin iletim ve dağıtım hatları üzerinden ekonomik bir şekilde iletilmesi, çevre dostu ve sürdürülebilir enerji yönetimi açısından kritiktir. Elektrik enerjisinin üretim birimlerinden dağıtım ve tüketim birimlerine ulaştırılmasında önemli bir rol oynayan bileşen, iletim/dağıtım şebekesidir. Bu aşamada, üretilen aktif ve reaktif gücün ekonomik bir şekilde sürdürülebilirliği, işletme maliyetleri ve kayıp giderlerinin kontrol altında tutulması ile mümkündür. Güç üretim birimlerinin yetersiz olması veya kayıpların artması, güç sistemlerinde işletme maliyetini artırır. Kapasite aşımı ve maliyet artışı ise sistem güvenilirliğini azaltarak kararlılığı etkiler. Bu olumsuzluklar, güç sistemlerinde sorunlara yol açabilir ve güç iletim şebekesini kullanılamaz hale getirerek tüketicileri olumsuz etkiler. Gelişen teknoloji ve artan enerji talepleri, güç sistemlerinde kalite sorunlarını beraberinde getirmektedir. Artan talep gücünü en uygun maliyet ve güç üretimiyle sağlayacak mevcut güç üretim ünitelerinin işletme maliyetlerinin optimizasyon teknikleri ile revize edilmesi gerekmektedir. Böylelikle güç sistemlerinin verimliliği artırılabilir. Güç sistemlerinin yetersiz kalması durumunda yeni ve yenilenebilir güç üretim üniteleri güç sistemine dahil edilmelidir. Bu çalışmada, IEEE 30-bara test sistemleri üzerinde sürü zekasını kullanan Parçacık Sürü Optimizasyonu (PSO) ve Gri Kurt Optimizasyonu (GKO) algoritmaları ile güç sistemi işletme ve maliyet optimizasyonları gerçekleştirilmiştir. PSO ve GKO algoritmaları için popülasyon ve tekrar çalıştırma sayısı sırasıyla 20, 30 ve 50 değerlerinde seçildiğinde sonuçlarda önemli farklılıklar gözlemlenmiştir. Üç farklı durum için oluşan sonuçların algoritmalar bazında karşılaştırılması yapıldığında; PSO algoritmasının 50 popülasyon ve yeniden çalıştırma değerlerinin kullanıldığı üçüncü durum için yapılan simülasyon testlerinde en uygun işletme maliyet değeri olan 800,47 $/Saat’e ulaşılmıştır. Çalışma sonucunda güç sisteminde kullanılan PSO ve GKO algoritmalarının popülasyon ve tekrar çalıştırma sayısının artışıyla toplam işletme maliyetini asgari değerlere yaklaştırdığı ve güç üretimini daha sürdürülebilir hale getirdiği görülmüştür.

Anahtar Kelimeler

• Güç Sistemleri
• Sürdürülebilir Enerji
• Maliyet Optimizasyonu
• PSO
• GKO

Kaynakça

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