Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları

Yıl 2024, Cilt: 7 Sayı: 2, 153 - 171

Mert Ökten

https://doi.org/10.51764/smutgd.1542508

Öz

Optimizasyon, tüm olası alternatifler arasından bir problemin en optimal çözümünü belirleme sürecidir. Enerji sistemlerinde metasezgisel optimizasyon algoritmaları, karmaşık enerji problemlerini çözmede önemli bir rol oynamaktadır. Metasezgisel optimizasyon algoritmaları, genetik algoritmalar, parçacık sürü optimizasyonu, simüle edilen tavlama, karınca kolonisi optimizasyonu gibi doğal süreçlerden esinlenerek geliştirilen ve genellikle bilgisayar tabanlı modellerle kullanılan özel optimizasyon yöntemleridir. Metasezgisel optimizasyon algoritmaları, büyük veri setleriyle çalışabilir ve farklı kısıtlamalar altında optimize edilmesi gereken çok sayıda değişkeni ele alabilirler. Bu nedenle enerji sektöründe sürdürülebilirlik, verimlilik ve karlılık açısından büyük öneme sahiptirler. Bu algoritmalar, enerji verimliliğini artırmak, enerji maliyetini azaltmak, enerji üretimi, dağıtımı, tüketimi ve depolanması gibi enerji sistemlerinin farklı bileşenlerini optimize etmek için, yenilenebilir enerji kaynaklarını entegre etmek ve enerji sistemlerinin karbon ayak izini azaltmak gibi çeşitli hedeflere ulaşmak için kullanılmaktadırlar. Bu çalışmada, enerji sistemleri uygulamalarında metasezgisel optimizasyon algoritmalarının kullanımı örnekler üzerinden incelenmiştir. İncelenen 2532 makale dikkate alındığında en çok genetik algoritma (%37.4) ile parçacık sürü optimizasyonunun (%25.5) kullanıldığı görülmüştür. Bu algoritmaların kullanımı ile karmaşık problemlerin çözümlerinin daha kolaya indirgendiği görülmüştür.

Anahtar Kelimeler

Enerji sistemleri, Enerji verimliliği, Metasezgisel optimizasyon algoritmaları, Sürdürülebilirlik

Metaheuristic Optimization Techniques in Energy Systems: Innovative Algorithms and Application Areas

Öz

Optimization is the process of determining the most optimal solution to a problem from among all possible alternatives. In energy systems, metaheuristic optimization algorithms play a significant role in solving complex energy problems. Metaheuristic optimization algorithms are specialized optimization methods developed by drawing inspiration from natural processes, such as genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms are typically used in conjunction with computer-based models. Metaheuristic optimization algorithms can work with large datasets and handle a multitude of variables that need to be optimized under different constraints. Therefore, they are of great importance in the energy sector in terms of sustainability, efficiency, and profitability. These algorithms are used to achieve various objectives in the energy sector, including increasing energy efficiency, reducing energy costs, optimizing different components of energy systems such as energy production, distribution, consumption, and storage, integrating renewable energy sources, and reducing the carbon footprint of energy systems. In this study, the use of metaheuristic optimization algorithms in energy system applications has been examined through examples. Considering the 2532 articles analyzed, it was seen that genetic algorithm (37.4%) and particle swarm optimization (%25.5) were used the most. It has been observed that the use of these algorithms simplifies the solutions to complex problems in various energy-related contexts.

Anahtar Kelimeler

Energy systems, Energy efficiency, Metaheuristic optimization algorithms, Sustainability

Kaynakça

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